Clever proposal for integrating transport, lowering energy costs. Adds complexity to bus capabilities.
Stanford Lab Envisions Delivery Drones That Save Energy by Taking the Bus
VentureBeat
By Khari Johnson in ACM
In an effort to redesign urban package delivery, researchers in Stanford University's Intelligent Systems Laboratory and Autonomous Systems Lab developed a methodology that allows delivery drones to access buses or trams, which could reduce traffic congestion and energy consumption while allowing the drones to travel farther. The artificial intelligence network underlying this system, which can accommodate up to 200 drones delivering up to 5,000 packages, was designed for cities with up to 8,000 stops. The approach is aimed at minimizing the time required to complete a delivery, according to the researchers, who added that it “can achieve significant commercial benefits and social impact.” ... '
Thursday, June 11, 2020
Rethinking Innovation
Thoughts on innovation:
It’s Time to Rethink How You Innovate via K@W:
Solutions for Leading Innovation
The fallout from the pandemic has created new opportunities that organic growth leaders are best able to capture. By investing more in their pool of innovation talent, giving these people the latitude to seize these opportunities, and encouraging prudent risks, these growth leaders will extend their lead further, write Wharton’s George S. Day and Gregory P. Shea in this opinion piece. Day, an emeritus professor of marketing, and Shea, an adjunct professor of management, base these recommendations on their research into what sets growth leaders apart from their slower-moving rivals.
The intense innovation activity ignited by the global pandemic shows that some elephants can dance when they must. Companies are moving faster and taking bigger risks than could have been imagined a few months ago. A further impetus to rethinking established and cumbersome innovation approaches is the acceleration of many trends that are already underway. The lock-down has brought forward a shift to on-line work practices and team-sharing platforms while creating new opportunities. For example, 3D printing is getting a boost by helping to replace faraway suppliers with nearby 3D printing contractors and make supply chains more resilient. To capitalize on this shift, HP accelerated their “3D as a service” business model innovation, where customers pay only for what they print. The digital transformation of industries did not pause for the crisis.
A looming question is how to avoid reverting to the cumbersome and cautious legacy practices and risk-averse decision-making that had hobbled innovation performance in many organizations. As uncertainty abates, there is a pressing need for guidance on which changes to innovation approaches to prioritize, and how to decide which opportunities to grasp.
Our research identifying the fundamental innovation drivers that distinguish organic growth leaders from the laggards gives tested guidance to harried leadership teams on where and how to place their emphasis. The three drivers will be familiar to innovation practitioners, but their effects magnify when given intense leadership attention. They serve as so-called simple rules – they avoid the confusion and dilution of effort from pulling too many organizational levers at a time, and they focus and prioritize the efforts of the leadership team to improve the work of innovation in the future. To identify the three highest-leverage drivers, we assessed the efforts of 18 highly touted innovation drivers on relative organic growth performance in a sample of 192 global companies. Three of these 18 were robust in setting organic growth leaders apart from laggards and average performers:... "
It’s Time to Rethink How You Innovate via K@W:
Solutions for Leading Innovation
The fallout from the pandemic has created new opportunities that organic growth leaders are best able to capture. By investing more in their pool of innovation talent, giving these people the latitude to seize these opportunities, and encouraging prudent risks, these growth leaders will extend their lead further, write Wharton’s George S. Day and Gregory P. Shea in this opinion piece. Day, an emeritus professor of marketing, and Shea, an adjunct professor of management, base these recommendations on their research into what sets growth leaders apart from their slower-moving rivals.
The intense innovation activity ignited by the global pandemic shows that some elephants can dance when they must. Companies are moving faster and taking bigger risks than could have been imagined a few months ago. A further impetus to rethinking established and cumbersome innovation approaches is the acceleration of many trends that are already underway. The lock-down has brought forward a shift to on-line work practices and team-sharing platforms while creating new opportunities. For example, 3D printing is getting a boost by helping to replace faraway suppliers with nearby 3D printing contractors and make supply chains more resilient. To capitalize on this shift, HP accelerated their “3D as a service” business model innovation, where customers pay only for what they print. The digital transformation of industries did not pause for the crisis.
A looming question is how to avoid reverting to the cumbersome and cautious legacy practices and risk-averse decision-making that had hobbled innovation performance in many organizations. As uncertainty abates, there is a pressing need for guidance on which changes to innovation approaches to prioritize, and how to decide which opportunities to grasp.
Our research identifying the fundamental innovation drivers that distinguish organic growth leaders from the laggards gives tested guidance to harried leadership teams on where and how to place their emphasis. The three drivers will be familiar to innovation practitioners, but their effects magnify when given intense leadership attention. They serve as so-called simple rules – they avoid the confusion and dilution of effort from pulling too many organizational levers at a time, and they focus and prioritize the efforts of the leadership team to improve the work of innovation in the future. To identify the three highest-leverage drivers, we assessed the efforts of 18 highly touted innovation drivers on relative organic growth performance in a sample of 192 global companies. Three of these 18 were robust in setting organic growth leaders apart from laggards and average performers:... "
Wednesday, June 10, 2020
Clarity Language for Smart Contracts
Brought to my attention as a part of a continuing look at smart contracts for supply chain and related analytic systems.
Clarity Language for Smart Contracts
Clarity Language
Clarity is a new language for smart contracts. Clarity is a decidable language, meaning you can know, with certainty, from the code itself what the program will do. Clarity is interpreted (not compiled) and the source code is published on the blockchain. Clarity gives developers a safe way to build complex smart contracts. The Clarity open-source project is supported by Blockstack and Algorand. ....
The future of Smart Contracts
Clarity recognizes the need for smart contract languages that are more safe, secure, and predictable. Our goal is to help the smart contract industry mature beyond its current state. Clarity is designed by scientists from Princeton and MIT, and the open-source project is initially supported by Blockstack and Algorand. We welcome other projects and developers to join this effort and help contribute! .... '
In Github: https://github.com/clarity-lang ....
Algorand and Blockstack join to adopt Clarity blockchain smart contract language By Kyt Dotson in SIliconAngle
Two blockchain industry leaders, Algorand Inc. and Blockstack PBC, today announced an independent open-source project to support Clarity, the blockchain distributed ledger smart contract language.
A smart contract is a programmable agreement that automates actions based on the completion of agreed-upon terms between multiple parties that have been encoded. The code of the smart contract and the agreements made exist within a blockchain, which acts as a tamperproof record of the contract, as well as assets under contract, which could be money, property, shares or anything else of value that can be represented digitally. .... '
Clarity Language for Smart Contracts
Clarity Language
Clarity is a new language for smart contracts. Clarity is a decidable language, meaning you can know, with certainty, from the code itself what the program will do. Clarity is interpreted (not compiled) and the source code is published on the blockchain. Clarity gives developers a safe way to build complex smart contracts. The Clarity open-source project is supported by Blockstack and Algorand. ....
The future of Smart Contracts
Clarity recognizes the need for smart contract languages that are more safe, secure, and predictable. Our goal is to help the smart contract industry mature beyond its current state. Clarity is designed by scientists from Princeton and MIT, and the open-source project is initially supported by Blockstack and Algorand. We welcome other projects and developers to join this effort and help contribute! .... '
In Github: https://github.com/clarity-lang ....
Algorand and Blockstack join to adopt Clarity blockchain smart contract language By Kyt Dotson in SIliconAngle
Two blockchain industry leaders, Algorand Inc. and Blockstack PBC, today announced an independent open-source project to support Clarity, the blockchain distributed ledger smart contract language.
A smart contract is a programmable agreement that automates actions based on the completion of agreed-upon terms between multiple parties that have been encoded. The code of the smart contract and the agreements made exist within a blockchain, which acts as a tamperproof record of the contract, as well as assets under contract, which could be money, property, shares or anything else of value that can be represented digitally. .... '
Reputation Manipulation with Collusion Networks
Had such a thing mentioned to me some time ago, and I just discovered this ACM study/paper on the topic. How can such 'collusion networks' be effectively detected? Facebook seems to be the target here.
Technical Perspective: Fake 'Likes' and Targeting Collusion Networks
By Geoffrey M. Voelker
Communications of the ACM, May 2020, Vol. 63 No. 5, Page 102 10.1145/3387722
The following scenario might sound like fiction. You and a million of your closest Facebook friends are going to band together to artificially improve your social networking reputation. You will willingly give a reputation manipulation service such as "official-liker.net" authorized access to your Facebook account. The manipulation service will cleverly exploit an authentication vulnerability in third-party Facebook apps to automate actions with your account. To use the service, you will view ads or pay explicit fees. The service will then use your account to "like" another Facebook account under their control—and that account will "like" yours back. You and others gain fake "likes," presumably improving your perceived online social standing, and the reputation service makes a profit.
But this scenario, and the problem it presents to Facebook and other successful online social networks, is both a very real and challenging problem: How to completely undermine this abusive activity without negatively impacting your users (who are knowingly and entirely complicit in the abuse) or changing how apps authenticate (because that would add friction to the app ecosystem).
The following paper presents a rigorous study that explores this reputation manipulation ecosystem, ultimately working with Facebook to examine ways to stop this kind of large-scale online social networking abuse. The manipulation services are called collusion networks since the users who knowingly participate collude with each other to generate fake actions. In their work, the authors describe how to use honeypot accounts to infiltrate the collusion networks and reveal how they operate. The authors detail how the collusion networks take advantage of an authentication vulnerability using leaked access tokens to perform their actions, and comprehensively measure the extent and activity of the collusion networks they find. Who would do this? Over a million Facebook users. How many apps are vulnerable? More than half of the top 100 third-party Facebook apps. How many services are exploring this unexpected business opportunity? More than 20 such services. Finally, can these collusion networks be safely and effectively shut down? Yes.
As a final effort, the authors performed a series of careful interventions with Facebook against these services. Consider the defensive perspective of the online social network. Companies know which accounts are using collusion networks, which apps are being exploited to perform collusion, and who the collusion networks are. But services cannot shutdown the user accounts: the users are legitimate, and services want them to continue to use the platform. They also cannot shutdown the apps, or how apps perform authentication: the apps have millions of legitimate users, and ease of app development relies upon the client-side token-based authentication. ... "
Technical Perspective: Fake 'Likes' and Targeting Collusion Networks
By Geoffrey M. Voelker
Communications of the ACM, May 2020, Vol. 63 No. 5, Page 102 10.1145/3387722
The following scenario might sound like fiction. You and a million of your closest Facebook friends are going to band together to artificially improve your social networking reputation. You will willingly give a reputation manipulation service such as "official-liker.net" authorized access to your Facebook account. The manipulation service will cleverly exploit an authentication vulnerability in third-party Facebook apps to automate actions with your account. To use the service, you will view ads or pay explicit fees. The service will then use your account to "like" another Facebook account under their control—and that account will "like" yours back. You and others gain fake "likes," presumably improving your perceived online social standing, and the reputation service makes a profit.
But this scenario, and the problem it presents to Facebook and other successful online social networks, is both a very real and challenging problem: How to completely undermine this abusive activity without negatively impacting your users (who are knowingly and entirely complicit in the abuse) or changing how apps authenticate (because that would add friction to the app ecosystem).
The following paper presents a rigorous study that explores this reputation manipulation ecosystem, ultimately working with Facebook to examine ways to stop this kind of large-scale online social networking abuse. The manipulation services are called collusion networks since the users who knowingly participate collude with each other to generate fake actions. In their work, the authors describe how to use honeypot accounts to infiltrate the collusion networks and reveal how they operate. The authors detail how the collusion networks take advantage of an authentication vulnerability using leaked access tokens to perform their actions, and comprehensively measure the extent and activity of the collusion networks they find. Who would do this? Over a million Facebook users. How many apps are vulnerable? More than half of the top 100 third-party Facebook apps. How many services are exploring this unexpected business opportunity? More than 20 such services. Finally, can these collusion networks be safely and effectively shut down? Yes.
As a final effort, the authors performed a series of careful interventions with Facebook against these services. Consider the defensive perspective of the online social network. Companies know which accounts are using collusion networks, which apps are being exploited to perform collusion, and who the collusion networks are. But services cannot shutdown the user accounts: the users are legitimate, and services want them to continue to use the platform. They also cannot shutdown the apps, or how apps perform authentication: the apps have millions of legitimate users, and ease of app development relies upon the client-side token-based authentication. ... "
Hiring from the Autism Spectrum
Fascinating piece, with some real world examples. I admit I did not know this was being done. With how tos and cautions.
Hiring from the Autism Spectrum By Esther Shein
Communications of the ACM, June 2020, Vol. 63 No. 6, Pages 17-19 10.1145/3392509
Years ago, Michael Field-house had a dinner party and friends attended with their young son Andrew, who is autistic, non-verbal, and low-functioning. At one point, Fieldhouse noticed Andrew, who was five- or six-years-old at the time, outside dropping pebbles into an urn in a Japanese garden.
"I was curious about that and I started timing him," recalls Fieldhouse. "I noted there were perfect intervals between every stone. He did that for at least an hour."
Fieldhouse had an epiphany that night that "changed my view on talent. We look at so many people's deficits and not their strengths; what they can't do versus what they can do."
That inspired Fieldhouse to begin conducting research around autism-at-work programs. Over the course of about a year, "I interviewed lots of people at companies that had done this, and all of their programs failed." He found three reasons for those failures, he says: not enough training for managers and coworkers; not enough effort put into changing the work culture; and a lack of focus on sustainable employment.
Those three elements became the backbone of a program Fieldhouse helped build in 2013 at Hewlett-Packard; he and the program moved in 2017 to DXC, which was the result of the spinoff of the Enterprise Service segment of what in 2015 became Hewlett-Packard Enterprise, and its merger with Computer Sciences Corporation. Today, he is social impact practice leader in DXC's Dandelion Program, which has approximately 120 employees in Australia and New Zealand, and a 92% retention rate.
"I went in with a business case and [upper management] liked the idea," he recalls. "I pushed it as a talent gain and didn't sell it as a disability program or some kind of inclusion initiative, but as capability uplift." As a tech company, there were vacancies in a lot of areas, and Fieldhouse focused on areas of need where autistic workers could fill a void, such as analytics, cybersecurity, software testing, and automation work. ... " ... '
Hiring from the Autism Spectrum By Esther Shein
Communications of the ACM, June 2020, Vol. 63 No. 6, Pages 17-19 10.1145/3392509
Years ago, Michael Field-house had a dinner party and friends attended with their young son Andrew, who is autistic, non-verbal, and low-functioning. At one point, Fieldhouse noticed Andrew, who was five- or six-years-old at the time, outside dropping pebbles into an urn in a Japanese garden.
"I was curious about that and I started timing him," recalls Fieldhouse. "I noted there were perfect intervals between every stone. He did that for at least an hour."
Fieldhouse had an epiphany that night that "changed my view on talent. We look at so many people's deficits and not their strengths; what they can't do versus what they can do."
That inspired Fieldhouse to begin conducting research around autism-at-work programs. Over the course of about a year, "I interviewed lots of people at companies that had done this, and all of their programs failed." He found three reasons for those failures, he says: not enough training for managers and coworkers; not enough effort put into changing the work culture; and a lack of focus on sustainable employment.
Those three elements became the backbone of a program Fieldhouse helped build in 2013 at Hewlett-Packard; he and the program moved in 2017 to DXC, which was the result of the spinoff of the Enterprise Service segment of what in 2015 became Hewlett-Packard Enterprise, and its merger with Computer Sciences Corporation. Today, he is social impact practice leader in DXC's Dandelion Program, which has approximately 120 employees in Australia and New Zealand, and a 92% retention rate.
"I went in with a business case and [upper management] liked the idea," he recalls. "I pushed it as a talent gain and didn't sell it as a disability program or some kind of inclusion initiative, but as capability uplift." As a tech company, there were vacancies in a lot of areas, and Fieldhouse focused on areas of need where autistic workers could fill a void, such as analytics, cybersecurity, software testing, and automation work. ... " ... '
What is the Pent up Shopping Demand?
To adjust demand in supply chain models.
How Much Pent up Shopping Demand?
by George Anderson in Retailwire with further expert comment at the link.
When retail stores across the country began closing to customers back in March, the hope was that Americans would come back and make up for lost time and sales when states began to let merchants reopen. Based on at least some initial reports, many merchants are pleasantly surprised to find out that there was more pent up demand for goods than they expected.
American Eagle Outfitters, Kohl’s and Macy’s are three chains that have reported that, despite fewer hours, limited shopper capacity and concerns about the spread of the novel coronavirus, people are showing up to shop.
American Eagle reported last week that the 556 stores it has reopened are generating about 95 percent of the sales the retailer reported last year. At the same time, digital sales which picked up significantly while stores were closed, have remained strong. COO Michael Rampell told analysts on the retailer’s first quarter earnings call that digital sales for the company’s namesake chain were up about 50 percent on a quarter-to-date basis and that Aerie’s online business was up more than 100 percent. .... "
How Much Pent up Shopping Demand?
by George Anderson in Retailwire with further expert comment at the link.
When retail stores across the country began closing to customers back in March, the hope was that Americans would come back and make up for lost time and sales when states began to let merchants reopen. Based on at least some initial reports, many merchants are pleasantly surprised to find out that there was more pent up demand for goods than they expected.
American Eagle Outfitters, Kohl’s and Macy’s are three chains that have reported that, despite fewer hours, limited shopper capacity and concerns about the spread of the novel coronavirus, people are showing up to shop.
American Eagle reported last week that the 556 stores it has reopened are generating about 95 percent of the sales the retailer reported last year. At the same time, digital sales which picked up significantly while stores were closed, have remained strong. COO Michael Rampell told analysts on the retailer’s first quarter earnings call that digital sales for the company’s namesake chain were up about 50 percent on a quarter-to-date basis and that Aerie’s online business was up more than 100 percent. .... "
Looking for Ocean Bed Riches
We were involved in early regional estimates of such efforts. This gets better information about the specifics vs high costs. Data for further predictive 'mining'. Have heard of Konigsberg Maritime.
Underwater Drones Join Hunt for Trillions in Mineral Riches Trapped on Ocean's Floor
By CNBC
The nascent seabed mining industry is using underwater drones from companies like Kongsberg Maritime to map the ocean floor in the hunt for manganese and other precious minerals worth trillions.
Kongsberg's underwater automated vehicles can capture seabed images with a resolution superior to surface-ship sonar, and its latest model takes acoustic, laser, and photographic measurements.
Kongsberg's Richard Mills predicts next-generation drones will have more in-mission processing capability, including automated object detection for real-time quantification of manganese nodules.
Mining company DeepGreen Metals’ Gerard Barron said automated vehicles are the only solution for tapping the ocean floor's mineral wealth. He added that drones will be excellent tools for environmental studies of potential mining areas, for tracking and monitoring mining apparatus, and for compiling data on dust plumes that can threaten deep-sea life.
From CNBC
View Full Article
Underwater Drones Join Hunt for Trillions in Mineral Riches Trapped on Ocean's Floor
By CNBC
The nascent seabed mining industry is using underwater drones from companies like Kongsberg Maritime to map the ocean floor in the hunt for manganese and other precious minerals worth trillions.
Kongsberg's underwater automated vehicles can capture seabed images with a resolution superior to surface-ship sonar, and its latest model takes acoustic, laser, and photographic measurements.
Kongsberg's Richard Mills predicts next-generation drones will have more in-mission processing capability, including automated object detection for real-time quantification of manganese nodules.
Mining company DeepGreen Metals’ Gerard Barron said automated vehicles are the only solution for tapping the ocean floor's mineral wealth. He added that drones will be excellent tools for environmental studies of potential mining areas, for tracking and monitoring mining apparatus, and for compiling data on dust plumes that can threaten deep-sea life.
From CNBC
View Full Article
Alibaba Blockchain Domain Names
Note the similarity to the Cosmos Network. A useful direction.
Alibaba Claims Patented Cross-Chain System Is Better Than Cosmos
Jun 1, 2020 at 15:00 UTC
Alibaba says its “unified domain name scheme” would simplify and improve communication between blockchains.
In a patent granted by the U.S. Patent Office last Tuesday, the e-commerce giant said its domain name system could enable cross-chain communications that are more simple and seamless than on existing interoperability solutions, specifically naming blockchain interoperability project Cosmos.
Alibaba says that assigning universally recognizable domain names to blockchains, or parts of a blockchain, could enable better identification and interaction with others in a broader network, in much the same way that entities recognize and communicate with one another using domain names, such as “.com” or “.org .... "
See also: https://cosmos.network/
Alibaba Claims Patented Cross-Chain System Is Better Than Cosmos
Jun 1, 2020 at 15:00 UTC
Alibaba says its “unified domain name scheme” would simplify and improve communication between blockchains.
In a patent granted by the U.S. Patent Office last Tuesday, the e-commerce giant said its domain name system could enable cross-chain communications that are more simple and seamless than on existing interoperability solutions, specifically naming blockchain interoperability project Cosmos.
Alibaba says that assigning universally recognizable domain names to blockchains, or parts of a blockchain, could enable better identification and interaction with others in a broader network, in much the same way that entities recognize and communicate with one another using domain names, such as “.com” or “.org .... "
See also: https://cosmos.network/
Tuesday, June 09, 2020
Classification of Relational Data
A simplification by classification. Would this work in any context? Application to social networks is of interest.
Training Agents to Walk with Purpose By KAUST Discovery
The new classification algorithm that can dramatically simplify relational data.
Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a new classification algorithm for relational data.
Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a classification algorithm for relational data that is more accurate and orders of magnitude more efficient than previous methods.
The new algorithm represents a more robust approach to classifying relational data by introducing machine learning techniques.
Classifying relational data involves a search agent taking an exploratory "walk" following the connections among nodes.
The algorithm is a graph-based classification model that trains the agent using a reinforcement learning method, which achieves a better classification result.
The new method "is also generally applicable to any kind of graph-structured data, such as social-network recommendation systems and classification of biomolecules, as well as cybersecurity," says NortonLifeLock researcher Han Yufei.
From KAUST Discovery
View Full Article
Paper:
https://discovery.kaust.edu.sa/en/article/959/training-agents-to-walk-with-purpose%E2%80%8B
Akujuobi, U., Zhang, Q., Yufei, H. & Zhang, X. (2020). Recurrent attention walk for semi-supervised classification. Proceedings of the 13th International Conference on Web Search and Data Mining Houston TX USA, January 2020, 16-24.| article
Training Agents to Walk with Purpose By KAUST Discovery
The new classification algorithm that can dramatically simplify relational data.
Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a new classification algorithm for relational data.
Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a classification algorithm for relational data that is more accurate and orders of magnitude more efficient than previous methods.
The new algorithm represents a more robust approach to classifying relational data by introducing machine learning techniques.
Classifying relational data involves a search agent taking an exploratory "walk" following the connections among nodes.
The algorithm is a graph-based classification model that trains the agent using a reinforcement learning method, which achieves a better classification result.
The new method "is also generally applicable to any kind of graph-structured data, such as social-network recommendation systems and classification of biomolecules, as well as cybersecurity," says NortonLifeLock researcher Han Yufei.
From KAUST Discovery
View Full Article
Paper:
https://discovery.kaust.edu.sa/en/article/959/training-agents-to-walk-with-purpose%E2%80%8B
Akujuobi, U., Zhang, Q., Yufei, H. & Zhang, X. (2020). Recurrent attention walk for semi-supervised classification. Proceedings of the 13th International Conference on Web Search and Data Mining Houston TX USA, January 2020, 16-24.| article
Cosmos Network: Interoperable Blockchains
Intriguing direction: A network of Blockchains
Cosmos Network
The foundation for a new token economy
Join the most interoperable
ecosystem of connected blockchains
About
The Cosmos Network is a decentralized network of independent, scalable, and interoperable blockchains, creating the foundation for a new token economy.
Before the Cosmos Network, blockchains were siloed and unable to communicate with each other. They were hard to build and could only handle a small number of transactions per second. Cosmos solves some of the hardest blockchain problems of scalability, usability and interoperability.
The Cosmos SDK is a developer-friendly, modular framework, each powered by a Byzantine Fault-Tolerant consensus algorithm such as Tendermint BFT, allowing developers to fully customize their decentralized applications and focus on business logic.
At Cosmos, we’re building the "Internet of Blockchains." Join our growing network and plug into the most powerful blockchain ecosystem. ... '
Cosmos Network
The foundation for a new token economy
Join the most interoperable
ecosystem of connected blockchains
About
The Cosmos Network is a decentralized network of independent, scalable, and interoperable blockchains, creating the foundation for a new token economy.
Before the Cosmos Network, blockchains were siloed and unable to communicate with each other. They were hard to build and could only handle a small number of transactions per second. Cosmos solves some of the hardest blockchain problems of scalability, usability and interoperability.
The Cosmos SDK is a developer-friendly, modular framework, each powered by a Byzantine Fault-Tolerant consensus algorithm such as Tendermint BFT, allowing developers to fully customize their decentralized applications and focus on business logic.
At Cosmos, we’re building the "Internet of Blockchains." Join our growing network and plug into the most powerful blockchain ecosystem. ... '
Sewage as New Data Source
A long time promoter of finding new sources of data, especially where they can be predictive early warning indicators. An argument for experimenting with data from new sources. Here an example that shows the possibilities.
Cities are using sewer systems as COVID-19 early warning signs
New Haven, Connecticut and Carmel, Indiana both say sewage data is the canary in a coal mine By Nicole Wetsman in TheVerge
Each day, workers at the wastewater treatment plant in New Haven, Connecticut, siphon off a bit of sewage and put it in a cooler. Then, researchers from Yale University swing by to pick it up. In their hands, that pile of refuse is a key tool to predict the trajectory of the local COVID-19 outbreak.
Cities around the United States are dipping into their sewer systems to track the levels of the novel coronavirus circulating inside their populations. If someone is infected with the virus, it shows up in their feces, even before they might feel sick. Virus-flecked feces make their way through sewage systems — and checking in on all that sewage gives public health officials another layer of data on the extent of the outbreak.
“It gives us a better idea of what’s going on in the city,” says New Haven epidemiologist Brian Weeks, who uses the data collected by the Yale team. .... "
Cities are using sewer systems as COVID-19 early warning signs
New Haven, Connecticut and Carmel, Indiana both say sewage data is the canary in a coal mine By Nicole Wetsman in TheVerge
Each day, workers at the wastewater treatment plant in New Haven, Connecticut, siphon off a bit of sewage and put it in a cooler. Then, researchers from Yale University swing by to pick it up. In their hands, that pile of refuse is a key tool to predict the trajectory of the local COVID-19 outbreak.
Cities around the United States are dipping into their sewer systems to track the levels of the novel coronavirus circulating inside their populations. If someone is infected with the virus, it shows up in their feces, even before they might feel sick. Virus-flecked feces make their way through sewage systems — and checking in on all that sewage gives public health officials another layer of data on the extent of the outbreak.
“It gives us a better idea of what’s going on in the city,” says New Haven epidemiologist Brian Weeks, who uses the data collected by the Yale team. .... "
Memristor Chips Emerge for Local Intelligence
The idea has been around for a while. Getting closer to real synapses in real brains. But still a considerable way to go. As mentioned a means to produce IOT capabilities on local devices. Paper and techical detail pointed to. Requires considerable materials innovation.
Engineers put tens of thousands of artificial brain synapses on a single chip
The design could advance the development of small, portable AI devices.
Jennifer Chu | MIT News Office
June 8, 2020
MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses known as memristors — silicon-based components that mimic the information-transmitting synapses in the human brain.
The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.
Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices — electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, portable devices, and would carry out complex computational tasks that only today’s supercomputers can handle.
“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.” ... "
Engineers put tens of thousands of artificial brain synapses on a single chip
The design could advance the development of small, portable AI devices.
Jennifer Chu | MIT News Office
June 8, 2020
MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses known as memristors — silicon-based components that mimic the information-transmitting synapses in the human brain.
The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.
Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices — electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, portable devices, and would carry out complex computational tasks that only today’s supercomputers can handle.
“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.” ... "
Monday, June 08, 2020
Google Reports on Recent Advances in Translate
Google reports measure improvements in their Translate, but there are still contextual challenges. I would be tempted to try to construct a risk analysis regarding its context, but that too would be hard. Though impressed, I woud still be cautious about autonomous use.
Recent Advances in Google Translate
Monday, June 8, 2020
Posted by Isaac Caswell and Bowen Liang, Software Engineers, Google Research
Advances in machine learning (ML) have driven improvements to automated translation, including the GNMT neural translation model introduced in Translate in 2016, that have enabled great improvements to the quality of translation for over 100 languages. Nevertheless, state-of-the-art systems lag significantly behind human performance in all but the most specific translation tasks. And while the research community has developed techniques that are successful for high-resource languages like Spanish and German, for which there exist copious amounts of training data, performance on low-resource languages, like Yoruba or Malayalam, still leaves much to be desired. Many techniques have demonstrated significant gains for low-resource languages in controlled research settings (e.g., the WMT Evaluation Campaign), however these results on smaller, publicly available datasets may not easily transition to large, web-crawled datasets.
In this post, we share some recent progress we have made in translation quality for supported languages, especially for those that are low-resource, by synthesizing and expanding a variety of recent advances, and demonstrate how they can be applied at scale to noisy, web-mined data. These techniques span improvements to model architecture and training, improved treatment of noise in datasets, increased multilingual transfer learning through M4 modeling, and use of monolingual data. The quality improvements, which averaged +5 BLEU score over all 100+ languages, are visualized below. ... "
And see also, on issues with translation, from the ACM:
Automatic Translators are Not Really Capable of Learning
By Herbert Bruderer ... "
Recent Advances in Google Translate
Monday, June 8, 2020
Posted by Isaac Caswell and Bowen Liang, Software Engineers, Google Research
Advances in machine learning (ML) have driven improvements to automated translation, including the GNMT neural translation model introduced in Translate in 2016, that have enabled great improvements to the quality of translation for over 100 languages. Nevertheless, state-of-the-art systems lag significantly behind human performance in all but the most specific translation tasks. And while the research community has developed techniques that are successful for high-resource languages like Spanish and German, for which there exist copious amounts of training data, performance on low-resource languages, like Yoruba or Malayalam, still leaves much to be desired. Many techniques have demonstrated significant gains for low-resource languages in controlled research settings (e.g., the WMT Evaluation Campaign), however these results on smaller, publicly available datasets may not easily transition to large, web-crawled datasets.
In this post, we share some recent progress we have made in translation quality for supported languages, especially for those that are low-resource, by synthesizing and expanding a variety of recent advances, and demonstrate how they can be applied at scale to noisy, web-mined data. These techniques span improvements to model architecture and training, improved treatment of noise in datasets, increased multilingual transfer learning through M4 modeling, and use of monolingual data. The quality improvements, which averaged +5 BLEU score over all 100+ languages, are visualized below. ... "
And see also, on issues with translation, from the ACM:
Automatic Translators are Not Really Capable of Learning
By Herbert Bruderer ... "
Smart Meters not Influencing Electric Usage
Have always been interested in the influence of measurements, and the presentation of those measurements to influence behavior. Many of our smart home experiments have tried to include that aspect of delivery. Our home is fitted with a 'smart meter'. Yet in this case Smart Meters are not doing the job:
Smart meters have little impact on people's energy usage habits, research finds by Keele University in TechExplore
When the Smart Meter Rollout Programme launched a decade ago, it was touted as a way of helping consumers cut down on their energy usage, but new research has found that environmental concerns have little impact on reducing energy consumption.
Smart meters were introduced to allow customers to manage their energy use and it was hoped they would save money on bills and lessen their environmental impact by reducing energy demand, as well as changing their attitudes towards their energy use.
But new research by Keele University using focus groups has found that 10 years after the programme was rolled out, people's awareness around strategies for improving our energy efficiency is still only limited.
The findings also showed that environmental concerns are not a key driver in promoting energy reduction behaviours, not because participants don't care about the environment but because they felt a reduction in their energy consumption would have very little impact on the environment. ... "
Smart meters have little impact on people's energy usage habits, research finds by Keele University in TechExplore
When the Smart Meter Rollout Programme launched a decade ago, it was touted as a way of helping consumers cut down on their energy usage, but new research has found that environmental concerns have little impact on reducing energy consumption.
Smart meters were introduced to allow customers to manage their energy use and it was hoped they would save money on bills and lessen their environmental impact by reducing energy demand, as well as changing their attitudes towards their energy use.
But new research by Keele University using focus groups has found that 10 years after the programme was rolled out, people's awareness around strategies for improving our energy efficiency is still only limited.
The findings also showed that environmental concerns are not a key driver in promoting energy reduction behaviours, not because participants don't care about the environment but because they felt a reduction in their energy consumption would have very little impact on the environment. ... "
Ring Adds Power Buttons
More add ons to security infrastructure. World getting to be a more insecure place.
Ring adds 'panic' buttons to its home security alarm in Engadget
You can instantly get in contact with police, fire or medical responders.
Ring’s first home security alarm did its job, but wasn’t exactly the prettiest piece of hardware you’ll find on the shelf. Now, almost two years later, the company is back with a second-generation Ring Alarm that’s a lot smaller and sleeker than its predecessor. The big change, aside from the look, is that the keypad now has “panic” buttons that’ll call Medical, Fire or Police services if you so desire. ... "
Ring adds 'panic' buttons to its home security alarm in Engadget
You can instantly get in contact with police, fire or medical responders.
Ring’s first home security alarm did its job, but wasn’t exactly the prettiest piece of hardware you’ll find on the shelf. Now, almost two years later, the company is back with a second-generation Ring Alarm that’s a lot smaller and sleeker than its predecessor. The big change, aside from the look, is that the keypad now has “panic” buttons that’ll call Medical, Fire or Police services if you so desire. ... "
Smart Contracts and Demand Sensing
Good overview piece on the concept of a 'smart contract'.
How Smart Contracts Speed Up Demand Sensing and Fulfillment
Manish Grover & Rakesh Prasad, SCB Contributors
As consumer demand and buying patterns rapidly undergo changes in a digital economy, the supply chain that supports these emerging needs has to be both responsive and lean — and that’s a difficult balance to achieve.
The recent disruptions and demand-supply imbalances in healthcare due to the COVID-19 pandemic have further reinforced the need for a solution that allows all parties to be on the same page. The pandemic demonstrated that this need isn’t limited to retail alone, but is relevant to healthcare supply chains as well.
So where’s the most demand, what’s causing it, how can it be anticipated, and how can supplies be routed correctly?
The need for low-latency processes and information exchange is painfully apparent. Now throw in digital channel proliferation, with expectations of information and inventory being available on demand, and this quickly converts from a demand-supply problem to a real-time supply-chain visibility problem. The more we know, the more we want to know.
Emerging innovations in demand-sensing tools and frequent data sharing from retailers to suppliers and manufacturers have offered up new ways to meet these emerging demands. Now we need solutions that can bridge data islands across all sectors, to improve responsiveness while keeping costs of inventory down.
Can Smart Contracts and Blockchain Help?
Recently a new collaborative area of focus that emerged in the area of supply-chain provenance has promised the ability to track an item all the way from shelf to origin. A blockchain (a generic term we’ll use for distributed-ledger technology) based approach to provenance brings visibility to the end-to-end path that goods take. This capability was exactly what was needed during disasters such as the E-Coli outbreak in spinach, where the entire distribution was disrupted as the source of the problem was investigated. .... "
How Smart Contracts Speed Up Demand Sensing and Fulfillment
Manish Grover & Rakesh Prasad, SCB Contributors
As consumer demand and buying patterns rapidly undergo changes in a digital economy, the supply chain that supports these emerging needs has to be both responsive and lean — and that’s a difficult balance to achieve.
The recent disruptions and demand-supply imbalances in healthcare due to the COVID-19 pandemic have further reinforced the need for a solution that allows all parties to be on the same page. The pandemic demonstrated that this need isn’t limited to retail alone, but is relevant to healthcare supply chains as well.
So where’s the most demand, what’s causing it, how can it be anticipated, and how can supplies be routed correctly?
The need for low-latency processes and information exchange is painfully apparent. Now throw in digital channel proliferation, with expectations of information and inventory being available on demand, and this quickly converts from a demand-supply problem to a real-time supply-chain visibility problem. The more we know, the more we want to know.
Emerging innovations in demand-sensing tools and frequent data sharing from retailers to suppliers and manufacturers have offered up new ways to meet these emerging demands. Now we need solutions that can bridge data islands across all sectors, to improve responsiveness while keeping costs of inventory down.
Can Smart Contracts and Blockchain Help?
Recently a new collaborative area of focus that emerged in the area of supply-chain provenance has promised the ability to track an item all the way from shelf to origin. A blockchain (a generic term we’ll use for distributed-ledger technology) based approach to provenance brings visibility to the end-to-end path that goods take. This capability was exactly what was needed during disasters such as the E-Coli outbreak in spinach, where the entire distribution was disrupted as the source of the problem was investigated. .... "
Sunday, June 07, 2020
Golden: An Intelligent Knowledge Base
Brought to my attention: Golden, quite a considerable breadth of claims.
The intelligent knowledge base
Explore the world's first self-constructing knowledge database built by artificial and human intelligence.
Authoritative knowledge at your fingertips
Access a growing body of knowledge. Follow topics you're interested in. Explore new topics to create a personalized knowledge feed.
Query across the Golden Knowledge Base
The Golden Research Engine is a comprehensive knowledge tool to research and track information on specific topics including companies, investment funds, cryptocurrencies, crypto projects, people and more.
Request deep information on a topic
Click a button to trigger fast turnaround of full information surrounding a topic of interest. Trigger research requests on information around a query and our AI-enabled helpers will max out the data request.
Frictionless tools to compile knowledge
Golden is building an interface to compile and comprehend knowledge. Frictionless editing, enhanced fact validation systems, transparent version histories and topic tracking.
A new standard for evidence
Golden will compile deeper evidence to validate claims and verify knowledge. High-resolution citations and bibliometrics will allow users to determine the provenance of information and evaluate source credibility.
Human knowledge meets AI
Golden draws on the strengths of both humans and machines. Statistical models and heuristic algorithms will handle repetitive work and automate the process of gathering knowledge. ... "
The intelligent knowledge base
Explore the world's first self-constructing knowledge database built by artificial and human intelligence.
Authoritative knowledge at your fingertips
Access a growing body of knowledge. Follow topics you're interested in. Explore new topics to create a personalized knowledge feed.
Query across the Golden Knowledge Base
The Golden Research Engine is a comprehensive knowledge tool to research and track information on specific topics including companies, investment funds, cryptocurrencies, crypto projects, people and more.
Request deep information on a topic
Click a button to trigger fast turnaround of full information surrounding a topic of interest. Trigger research requests on information around a query and our AI-enabled helpers will max out the data request.
Frictionless tools to compile knowledge
Golden is building an interface to compile and comprehend knowledge. Frictionless editing, enhanced fact validation systems, transparent version histories and topic tracking.
A new standard for evidence
Golden will compile deeper evidence to validate claims and verify knowledge. High-resolution citations and bibliometrics will allow users to determine the provenance of information and evaluate source credibility.
Human knowledge meets AI
Golden draws on the strengths of both humans and machines. Statistical models and heuristic algorithms will handle repetitive work and automate the process of gathering knowledge. ... "
Connections from AI to the Pandemic
Increasingly deep applications, in AITrends
Updates on How AI Being Employed to Speed COVID-19 Treatments and Management
June 4, 2020 1109
Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for COVID-19. (GETTY IMAGES)
By AI Trends Staff
Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for the new COVID-19 coronavirus.
An early success story comes from BenevolentAI of London, which using tools developed to search through medical literature, identified rheumatoid arthritis drug baricitinib as a possible treatment for COVID-19.
In a pilot study at the end of March, 12 adults with moderate COVID-19 admitted to the hospital in either Alessandria or Prato, Italy, received a daily dose of baricitinib, along with an anti-HIV drug combination of lopinavir and ritonavir, for two weeks. Another study group of 12 received just lopinavir and ritonavir.
After their two-week treatment, the patients who received baricitinib had mostly recovered, according to a recent account in The Scientist. Their coughs and fevers were gone; they were no longer short of breath. Seven of the 12 had been discharged from the hospital. In contrast, the group who didn’t get baricitinib still had elevated temperatures, nine were coughing, and eight remained short of breath. Just one patient from the lopinavir-ritonavir–only group had been discharged.
Researchers at Benevolent AI, along with collaborator Justin Stebbing, an oncologist at Imperial College London, published a letter to The Lancet on February 4, describing how they used AI to identify baricitinib’s potential to treat COVID-19.
AI “makes higher-order correlations that a human wouldn’t be capable of making, even with all the time in the world. It links datasets that a human wouldn’t be able to link,” stated Stebbing.
Benevolent researchers used the company’s knowledge graph—a digital storehouse of biomedical information and connections inferred and enhanced by machine learning—to identify two human protein targets to focus on: AP2-associated protein kinase 1 (AAK1) and cyclin g-associated kinase (GAK). ... "
Updates on How AI Being Employed to Speed COVID-19 Treatments and Management
June 4, 2020 1109
Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for COVID-19. (GETTY IMAGES)
By AI Trends Staff
Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for the new COVID-19 coronavirus.
An early success story comes from BenevolentAI of London, which using tools developed to search through medical literature, identified rheumatoid arthritis drug baricitinib as a possible treatment for COVID-19.
In a pilot study at the end of March, 12 adults with moderate COVID-19 admitted to the hospital in either Alessandria or Prato, Italy, received a daily dose of baricitinib, along with an anti-HIV drug combination of lopinavir and ritonavir, for two weeks. Another study group of 12 received just lopinavir and ritonavir.
After their two-week treatment, the patients who received baricitinib had mostly recovered, according to a recent account in The Scientist. Their coughs and fevers were gone; they were no longer short of breath. Seven of the 12 had been discharged from the hospital. In contrast, the group who didn’t get baricitinib still had elevated temperatures, nine were coughing, and eight remained short of breath. Just one patient from the lopinavir-ritonavir–only group had been discharged.
Researchers at Benevolent AI, along with collaborator Justin Stebbing, an oncologist at Imperial College London, published a letter to The Lancet on February 4, describing how they used AI to identify baricitinib’s potential to treat COVID-19.
AI “makes higher-order correlations that a human wouldn’t be capable of making, even with all the time in the world. It links datasets that a human wouldn’t be able to link,” stated Stebbing.
Benevolent researchers used the company’s knowledge graph—a digital storehouse of biomedical information and connections inferred and enhanced by machine learning—to identify two human protein targets to focus on: AP2-associated protein kinase 1 (AAK1) and cyclin g-associated kinase (GAK). ... "
Gartner Looks at Data and Analytics Trends
Things that are useful,have the most business impact.
Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019
Augmented Analytics and Artificial Intelligence in the Spotlight at Gartner Data & Analytics Summit, February 18-19 in Sydney, Australia
Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc.
Speaking at the Gartner Data & Analytics Summit in Sydney today, Rita Sallam, research vice president at Gartner, said data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do. ... "
Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019
Augmented Analytics and Artificial Intelligence in the Spotlight at Gartner Data & Analytics Summit, February 18-19 in Sydney, Australia
Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc.
Speaking at the Gartner Data & Analytics Summit in Sydney today, Rita Sallam, research vice president at Gartner, said data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do. ... "
Saturday, June 06, 2020
Individualizing Mass Production
Seems a powerful idea, The data is all there, so to what degree and for what products can this be applied and scaled for manufacturing production? Proposed details at the link.
Mass production of individualized products
Fraunhofer Research News / June 02, 2020
How can mass production methods be applied to individualized products? One answer is to use a combination of digital manufacturing technologies, for example by integrating digital printing and laser processing into traditional manufacturing processes. This paves the way for in-line product customization. Six Fraunhofer institutes have pooled their expertise to take the new process to the next level.
The term mass production generally suggests large numbers of identical products rolling off an assembly line. However, the latest trends call for individualized products. The automotive industry is one example for this trend: Volkswagen, for example, produces only one or two identical Golf models a year. Yet this drive toward individualization is also pushing mass production techniques to their limits. The Fraunhofer Lighthouse Project Go Beyond 4.0 aims to meet this challenge by enabling the mass production of individualized products. It is a collaboration between four different Fraunhofer Groups and six Fraunhofer Institutes: the Fraunhofer Institute for Electronic Nano Systems ENAS, the Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM, the Fraunhofer Institute for Laser Technology ILT, the Fraunhofer Institute for Applied Optics and Precision Engineering IOF, the Fraunhofer Institute for Silicate Research ISC and the Fraunhofer Institute for Machine Tools and Forming Technology IWU. The project is managed by Fraunhofer ENAS in Chemnitz. ... "
Mass production of individualized products
Fraunhofer Research News / June 02, 2020
How can mass production methods be applied to individualized products? One answer is to use a combination of digital manufacturing technologies, for example by integrating digital printing and laser processing into traditional manufacturing processes. This paves the way for in-line product customization. Six Fraunhofer institutes have pooled their expertise to take the new process to the next level.
The term mass production generally suggests large numbers of identical products rolling off an assembly line. However, the latest trends call for individualized products. The automotive industry is one example for this trend: Volkswagen, for example, produces only one or two identical Golf models a year. Yet this drive toward individualization is also pushing mass production techniques to their limits. The Fraunhofer Lighthouse Project Go Beyond 4.0 aims to meet this challenge by enabling the mass production of individualized products. It is a collaboration between four different Fraunhofer Groups and six Fraunhofer Institutes: the Fraunhofer Institute for Electronic Nano Systems ENAS, the Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM, the Fraunhofer Institute for Laser Technology ILT, the Fraunhofer Institute for Applied Optics and Precision Engineering IOF, the Fraunhofer Institute for Silicate Research ISC and the Fraunhofer Institute for Machine Tools and Forming Technology IWU. The project is managed by Fraunhofer ENAS in Chemnitz. ... "
Its about Common Sense
Have repeated this many times, it was clear in the late 80s when we built systems that could solve a problem, but not implement it among decision makers. For general AI, as well as installation of any system that interacts with humans. Both directly or indirectly through results. Looking for more out of the Allen Institute for AI. Here a short introduction to the problem, again:
ACM NEWS
Giving AI Common Sense By Bennie Mols
Senior Research Manager Yejin Choi says the Allen Institute for AI is teaching neural networks representations of common-sense knowledge and reasoning.
At the Allen Institute for AI: https://allenai.org/ in Seattle, computer scientist Yejin Choi is leading project Mosaic, which aims to teach machines common-sense knowledge and reasoning, one of the hardest and longest-standing challenges in the field of artificial intelligence (AI).
Choi, senior research manager, leads the project, which started in 2018 and recently delivered its first results. Choi is also an associate professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington in Seattle.
What is your definition of common sense?
Common sense is about the basic level of practical knowledge and reasoning that concerns everyday situations and events. This is knowledge that is commonly shared among most people. Most 10-year-old kids possess it, but it is very hard for machines. For example: don't leave the door of the fridge open too long, because the food will go bad. Or: if I drop my mug of coffee on the floor, the floor will get wet and the mug might break.
Common sense knowledge is not just about the physical world, but also about the social world. "If Kate smiles, she is probably happy." .... '
ACM NEWS
Giving AI Common Sense By Bennie Mols
Senior Research Manager Yejin Choi says the Allen Institute for AI is teaching neural networks representations of common-sense knowledge and reasoning.
At the Allen Institute for AI: https://allenai.org/ in Seattle, computer scientist Yejin Choi is leading project Mosaic, which aims to teach machines common-sense knowledge and reasoning, one of the hardest and longest-standing challenges in the field of artificial intelligence (AI).
Choi, senior research manager, leads the project, which started in 2018 and recently delivered its first results. Choi is also an associate professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington in Seattle.
What is your definition of common sense?
Common sense is about the basic level of practical knowledge and reasoning that concerns everyday situations and events. This is knowledge that is commonly shared among most people. Most 10-year-old kids possess it, but it is very hard for machines. For example: don't leave the door of the fridge open too long, because the food will go bad. Or: if I drop my mug of coffee on the floor, the floor will get wet and the mug might break.
Common sense knowledge is not just about the physical world, but also about the social world. "If Kate smiles, she is probably happy." .... '
Reinforcement Learning for Skill Discovery
Can skills be dsicovered. That is, a means to find better behavior that leads to prescribed real-world goals? Here in the Google Research blog, addressing the use of unsupervised reinforcement learning (RL). Note the determination and inclusion of constraints. Like in classic optimization problems. Largely technical, but thoughtful positioning. Considerable links in the article below.
DADS: Unsupervised Reinforcement Learning for Skill Discovery
Friday, May 29, 2020
Posted by Archit Sharma, AI Resident, Google Research
Recent research has demonstrated that supervised reinforcement learning (RL) is capable of going beyond simulation scenarios to synthesize complex behaviors in the real world, such as grasping arbitrary objects or learning agile locomotion. However, the limitations of teaching an agent to perform complex behaviors using well-designed task-specific reward functions are also becoming apparent. Designing reward functions can require significant engineering effort, which becomes untenable for a large number of tasks. For many practical scenarios, designing a reward function can be complicated, for example, requiring additional instrumentation for the environment (e.g., sensors to detect the orientation of doors) or manual-labelling of “goal” states. Considering that the ability to generate complex behaviors is limited by this form of reward-engineering, unsupervised learning presents itself as an interesting direction for RL.
In supervised RL, the extrinsic reward function from the environment guides the agent towards the desired behaviors, reinforcing the actions which bring the desired changes in the environment. With unsupervised RL, the agent uses an intrinsic reward function (such as curiosity to try different things in the environment) to generate its own training signals to acquire a broad set of task-agnostic behaviors. The intrinsic reward functions can bypass the problems of the engineering extrinsic reward functions, while being generic and broadly applicable to several agents and problems without any additional design. While much research has recently focused on different approaches to unsupervised reinforcement learning, it is still a severely under-constrained problem — without the guidance of rewards from the environment, it can be hard to learn behaviors which will be useful. Are there meaningful properties of the agent-environment interaction that can help discover better behaviors (“skills”) for the agents? ... " ... '
DADS: Unsupervised Reinforcement Learning for Skill Discovery
Friday, May 29, 2020
Posted by Archit Sharma, AI Resident, Google Research
Recent research has demonstrated that supervised reinforcement learning (RL) is capable of going beyond simulation scenarios to synthesize complex behaviors in the real world, such as grasping arbitrary objects or learning agile locomotion. However, the limitations of teaching an agent to perform complex behaviors using well-designed task-specific reward functions are also becoming apparent. Designing reward functions can require significant engineering effort, which becomes untenable for a large number of tasks. For many practical scenarios, designing a reward function can be complicated, for example, requiring additional instrumentation for the environment (e.g., sensors to detect the orientation of doors) or manual-labelling of “goal” states. Considering that the ability to generate complex behaviors is limited by this form of reward-engineering, unsupervised learning presents itself as an interesting direction for RL.
In supervised RL, the extrinsic reward function from the environment guides the agent towards the desired behaviors, reinforcing the actions which bring the desired changes in the environment. With unsupervised RL, the agent uses an intrinsic reward function (such as curiosity to try different things in the environment) to generate its own training signals to acquire a broad set of task-agnostic behaviors. The intrinsic reward functions can bypass the problems of the engineering extrinsic reward functions, while being generic and broadly applicable to several agents and problems without any additional design. While much research has recently focused on different approaches to unsupervised reinforcement learning, it is still a severely under-constrained problem — without the guidance of rewards from the environment, it can be hard to learn behaviors which will be useful. Are there meaningful properties of the agent-environment interaction that can help discover better behaviors (“skills”) for the agents? ... " ... '
Building A More Resilient Data-Driven Economy
Building a better economy:
Irving Wladawsky-Berger's Blog
A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects. ...
Building a More Resilient, Data-Driven Economy
“With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and government,” wrote MIT professor Alex “Sandy Pentland in the first chapter of Building the New Economy. Pentland cites two major historical examples. In the early 20th century, the rise of mass manufacturing led to the creation of regulations governing working conditions and pay, health rules for mass-produced foods, and laws to prevent monopolies that stifled competition. Then in mid-century, the decades following WWII saw greater access to higher education, racial and gender progress, and increased support for scientific research and medical advances. ... "
Irving Wladawsky-Berger's Blog
A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects. ...
Building a More Resilient, Data-Driven Economy
“With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and government,” wrote MIT professor Alex “Sandy Pentland in the first chapter of Building the New Economy. Pentland cites two major historical examples. In the early 20th century, the rise of mass manufacturing led to the creation of regulations governing working conditions and pay, health rules for mass-produced foods, and laws to prevent monopolies that stifled competition. Then in mid-century, the decades following WWII saw greater access to higher education, racial and gender progress, and increased support for scientific research and medical advances. ... "
Pandemic Status Checker App Asks for Physical Symptoms?
Trial implies actually blocking campus access based on stored information. Predictive? Seems some of the information is based on simple query. Implications of false positives and negatives?
U.S. University to Trial Covid-19 Checker App Linked to Campus Access
Financial Times
Dave Lee
June 4, 2020
About 1,000 University of Kansas graduate students and staff will trial a smartphone application designed to check for Covid-19 symptoms before permitting them to access the university campus. The CVKey system will ask users about their physical condition, and potential coronavirus symptoms, before generating a QR code that will permit access to six key buildings on campus. The non-profit CVKey organization said the app’s underlying code will be open source, and will store health data locally on each user's phone. Former U.S. Secretary of Health and Human Services Kathleen Sebelius, a proponent of the system, observed efforts like this face opposition from data privacy advocates. She said, "We will see about the trust element of [CVKey], and whether or not we can engage a population in using it." ... "
U.S. University to Trial Covid-19 Checker App Linked to Campus Access
Financial Times
Dave Lee
June 4, 2020
About 1,000 University of Kansas graduate students and staff will trial a smartphone application designed to check for Covid-19 symptoms before permitting them to access the university campus. The CVKey system will ask users about their physical condition, and potential coronavirus symptoms, before generating a QR code that will permit access to six key buildings on campus. The non-profit CVKey organization said the app’s underlying code will be open source, and will store health data locally on each user's phone. Former U.S. Secretary of Health and Human Services Kathleen Sebelius, a proponent of the system, observed efforts like this face opposition from data privacy advocates. She said, "We will see about the trust element of [CVKey], and whether or not we can engage a population in using it." ... "
Friday, June 05, 2020
South Korea Testing an Elderly Assistant
Hard to follow the depth and breadth of this precisely, but seems to be an assistant oriented to interact with lonely elderly. Prompted by recent pandemic pressure. With some aim to sense memory and cognitive functions and adapt to that status. Similar in form to 'intelligent' interacting speakers.
In Virus-Hit South Korea, AI Monitors Lonely Elders
Associated Press
Kim Tong-Hyung
May 30, 2020
South Korean telecommunications provider SK Telecom operates an experimental artificial intelligence (AI)-powered network of voice-enabled smart speakers to remotely monitor thousands of isolated seniors during the coronavirus pandemic. The speakers feature the "Aria" AI component, and a lamp that turns blue when processing voice commands for news, music, and online searches. The devices also can assess users' memory and cognitive functions with quizzes, which would be potentially helpful for advising treatments. However, it is difficult for SK Telecom customers to use this information without clear legal guidelines for managing health data on private networks. Seoul National University's Haksoo Ko said, "An appropriate control system needs to be baked into the process, to make decisions on data access based on necessity and sensitivity, and restrict access to information that isn't really needed." ... '
In Virus-Hit South Korea, AI Monitors Lonely Elders
Associated Press
Kim Tong-Hyung
May 30, 2020
South Korean telecommunications provider SK Telecom operates an experimental artificial intelligence (AI)-powered network of voice-enabled smart speakers to remotely monitor thousands of isolated seniors during the coronavirus pandemic. The speakers feature the "Aria" AI component, and a lamp that turns blue when processing voice commands for news, music, and online searches. The devices also can assess users' memory and cognitive functions with quizzes, which would be potentially helpful for advising treatments. However, it is difficult for SK Telecom customers to use this information without clear legal guidelines for managing health data on private networks. Seoul National University's Haksoo Ko said, "An appropriate control system needs to be baked into the process, to make decisions on data access based on necessity and sensitivity, and restrict access to information that isn't really needed." ... '
UF Acquires NVidia DGX System
Notable former connection working with new Nvidia System.
UF becomes first U.S. university to acquire cutting-edge NVIDIA DGX A100 system, advancing its artificial intelligence initiative - News - University of Florida
Moving forward on its sweeping vision to transform the future of education through artificial intelligence (AI), the University of Florida is collaborating with technology company NVIDIA to acquire the world’s most advanced AI system to boost the performance of UF’s powerful supercomputer.
UF today announced it will be the country’s first higher education institution to acquire the new NVIDIA DGX™ A100 — the world’s most advanced AI system.
The new systems are scheduled to arrive at UF at the end of May and mark a significant step in the university’s bold initiative to become a national leader in the application of AI, an expansive plan that will elevate UF in research, teaching, and economic development. The initiative includes a commitment from UF to hire 100 faculty members specifically focused on AI, in addition to the 500 new faculty hired across disciplines -- many of whom will integrate AI into their teaching and research. UF is also embarking on a unique plan to infuse AI across academic majors, creating a next generation AI-enabled workforce and democratizing a technology that has the potential to solve some of the globe's most formidable challenges. ... "
UF becomes first U.S. university to acquire cutting-edge NVIDIA DGX A100 system, advancing its artificial intelligence initiative - News - University of Florida
Moving forward on its sweeping vision to transform the future of education through artificial intelligence (AI), the University of Florida is collaborating with technology company NVIDIA to acquire the world’s most advanced AI system to boost the performance of UF’s powerful supercomputer.
UF today announced it will be the country’s first higher education institution to acquire the new NVIDIA DGX™ A100 — the world’s most advanced AI system.
The new systems are scheduled to arrive at UF at the end of May and mark a significant step in the university’s bold initiative to become a national leader in the application of AI, an expansive plan that will elevate UF in research, teaching, and economic development. The initiative includes a commitment from UF to hire 100 faculty members specifically focused on AI, in addition to the 500 new faculty hired across disciplines -- many of whom will integrate AI into their teaching and research. UF is also embarking on a unique plan to infuse AI across academic majors, creating a next generation AI-enabled workforce and democratizing a technology that has the potential to solve some of the globe's most formidable challenges. ... "
Privacy Threats in Intimate and Personal Relationships
Fascinating thoughts on the topic, true is too little discussed. Though I would think divorce lawyers have seen it all.
Privacy threats in intimate relationships
Karen Levy and Bruce Schneier
Department of Information Science, Cornell University; 2Cornell Law School, Ithaca, NY, USA; 3 Berkman Klein
Center for Internet and Society, Harvard University; and 4 Belfer Center for Science and International Affairs, Harvard Kennedy School, Cambridge, MA, USA
Email: karen.levy@cornell.edu
Received 11 December 2018; revised 10 March 2020; accepted 8 April 2020
Abstract
This article provides an overview of intimate threats: a class of privacy threats that can arise within our families, romantic partnerships, close friendships, and caregiving relationships. Many common assumptions about privacy are upended in the context of these relationships, and many otherwise effective protective measures fail when applied to intimate threats. Those closest to us know the answers to our secret questions, have access to our devices, and can exercise coercive power over us. We survey a range of intimate relationships and describe their common features. Based on these features, we explore implications for both technical privacy design and policy, and offer design recommendations for ameliorating intimate privacy risks.
Keywords: intimacy; family; abuse; children; relationships; privacy .... "
See further in Schneier's blog with discussion.
Privacy threats in intimate relationships
Karen Levy and Bruce Schneier
Department of Information Science, Cornell University; 2Cornell Law School, Ithaca, NY, USA; 3 Berkman Klein
Center for Internet and Society, Harvard University; and 4 Belfer Center for Science and International Affairs, Harvard Kennedy School, Cambridge, MA, USA
Email: karen.levy@cornell.edu
Received 11 December 2018; revised 10 March 2020; accepted 8 April 2020
Abstract
This article provides an overview of intimate threats: a class of privacy threats that can arise within our families, romantic partnerships, close friendships, and caregiving relationships. Many common assumptions about privacy are upended in the context of these relationships, and many otherwise effective protective measures fail when applied to intimate threats. Those closest to us know the answers to our secret questions, have access to our devices, and can exercise coercive power over us. We survey a range of intimate relationships and describe their common features. Based on these features, we explore implications for both technical privacy design and policy, and offer design recommendations for ameliorating intimate privacy risks.
Keywords: intimacy; family; abuse; children; relationships; privacy .... "
See further in Schneier's blog with discussion.
ACM Bytecast: Donald Knuth on Computing
From my earliest days doing coding, I was reading Donald Knuth's legendary texts. Now here is a free and open podcast interview with him. Will be following.
" ... In the latest episode of ACM ByteCast, a new podcast series at the intersection of computing research and practice, host Rashmi Mohan interviews legendary computer scientist Donald Knuth. Knuth is the 1974 ACM A.M. Turing Laureate and author of the hugely popular textbook series, "The Art of Computer Programming." They discuss what led him to discover his love of computing as well as writing about computer programming, his outlook on how people learn technical skills, how his mentorship has helped him write “human oriented” programs, how his dissatisfaction with early digital typesetting led him to develop TeX, and the problems he is still working to solve.
The podcast is available in the ACM Learning Center https://learning.acm.org/bytecast , where you can also subscribe to an RSS feed as well as download a full transcript of each episode, and on popular podcast platforms including Apple Podcasts, Google Podcasts, Spotify, and Stitcher. ...
" ... In the latest episode of ACM ByteCast, a new podcast series at the intersection of computing research and practice, host Rashmi Mohan interviews legendary computer scientist Donald Knuth. Knuth is the 1974 ACM A.M. Turing Laureate and author of the hugely popular textbook series, "The Art of Computer Programming." They discuss what led him to discover his love of computing as well as writing about computer programming, his outlook on how people learn technical skills, how his mentorship has helped him write “human oriented” programs, how his dissatisfaction with early digital typesetting led him to develop TeX, and the problems he is still working to solve.
The podcast is available in the ACM Learning Center https://learning.acm.org/bytecast , where you can also subscribe to an RSS feed as well as download a full transcript of each episode, and on popular podcast platforms including Apple Podcasts, Google Podcasts, Spotify, and Stitcher. ...
UN: Global Computer Simulation Tool
Quite a considerable simulation is proposed. Would be difficult to do, depending on the breadth and context involved. Perhaps a realm where agent based modeling might be applied to simplify some aspects., which we aimed to do in regional models. Various predictive models could be used to drive aspects of the model. Investments are mentioned as drivers. Legal system as constraints. Risk could be included to understand aspects of events like pandemics. But all this is still hard to do accurately.
UN: Computer Simulation Tool Could Boost Global Development
Technology Review in CACM
May 29, 2020
The United Nations is backing a new computer simulation tool that could help governments boost sustainable development and address global challenges. The Policy Priority Inference (PPI) software employs agent-based modeling to predict and simulate what would happen if policymakers invested in one project rather than another, informing the simulation with economics, behavioral science, and network theory. PPI allocates funding to "bureaucrats" who spend their apportioned money on different projects, then applies data about government budgets, the historical impact of spending on past policies, the effectiveness of a country's legal system, and estimated losses due to known inefficiencies. The software then suggests which policies are most worthy of investment. The goal is to help policymakers understand the wider ramifications of their decisions. ... "
UN: Computer Simulation Tool Could Boost Global Development
Technology Review in CACM
May 29, 2020
The United Nations is backing a new computer simulation tool that could help governments boost sustainable development and address global challenges. The Policy Priority Inference (PPI) software employs agent-based modeling to predict and simulate what would happen if policymakers invested in one project rather than another, informing the simulation with economics, behavioral science, and network theory. PPI allocates funding to "bureaucrats" who spend their apportioned money on different projects, then applies data about government budgets, the historical impact of spending on past policies, the effectiveness of a country's legal system, and estimated losses due to known inefficiencies. The software then suggests which policies are most worthy of investment. The goal is to help policymakers understand the wider ramifications of their decisions. ... "
Thursday, June 04, 2020
Anaconda and IBM Watson Team to Simplify Enterprise AI
This was needed to provide easier to use capabilities in the enterprise.
Anaconda and IBM Watson Team to Simplify Enterprise Adoption of AI Open-Source Technologies
Anaconda, Inc., provider of the leading Python data science platform, and IBM Watson (NYSE: IBM) today announced a new collaboration to help simplify enterprise adoption of AI open-source technologies. ....
- Anaconda will feature IBM’s Watson Studio no-charge plan to its community of 20 million data scientists
AUSTIN, Texas, and ARMONK, New York, June 04, 2020 (GLOBE NEWSWIRE) -- Anaconda, Inc., provider of the leading Python data science platform, and IBM Watson (NYSE: IBM) today announced a new collaboration to help simplify enterprise adoption of AI open-source technologies. By working together, the two companies plan to help fuel innovation and address the AI and data science skills gap that many enterprises face today.
Today, we see that data scientists rely on open-source technologies for innovation and to tap into a wealth of AI skills and talent. However, using open source in the enterprise could be complex to adopt and manage from both a technical and operational standpoint.
To help enterprises overcome this challenge, Anaconda Team Edition repository will be integrated with IBM Watson Studio on IBM Cloud Pak for Data, enabling organizations to better govern and speed the deployment of AI open-source technologies across any cloud. Additionally, Anaconda’s community of 20 million users will now be able to access a no-charge lite plan of Watson Studio, offering the opportunity to manage their data science projects within IBM’s enterprise-grade environment. ..... '
Anaconda and IBM Watson Team to Simplify Enterprise Adoption of AI Open-Source Technologies
Anaconda, Inc., provider of the leading Python data science platform, and IBM Watson (NYSE: IBM) today announced a new collaboration to help simplify enterprise adoption of AI open-source technologies. ....
- Anaconda will feature IBM’s Watson Studio no-charge plan to its community of 20 million data scientists
AUSTIN, Texas, and ARMONK, New York, June 04, 2020 (GLOBE NEWSWIRE) -- Anaconda, Inc., provider of the leading Python data science platform, and IBM Watson (NYSE: IBM) today announced a new collaboration to help simplify enterprise adoption of AI open-source technologies. By working together, the two companies plan to help fuel innovation and address the AI and data science skills gap that many enterprises face today.
Today, we see that data scientists rely on open-source technologies for innovation and to tap into a wealth of AI skills and talent. However, using open source in the enterprise could be complex to adopt and manage from both a technical and operational standpoint.
To help enterprises overcome this challenge, Anaconda Team Edition repository will be integrated with IBM Watson Studio on IBM Cloud Pak for Data, enabling organizations to better govern and speed the deployment of AI open-source technologies across any cloud. Additionally, Anaconda’s community of 20 million users will now be able to access a no-charge lite plan of Watson Studio, offering the opportunity to manage their data science projects within IBM’s enterprise-grade environment. ..... '
Customer Thermal Imaging as Security
I was asked last week to explore the efficacy of using thermal imagery to determine if someone had increased body temperature and could that be used as indicative of possible flu infection. Now being installed widely. Even with wide liklihood of false negatives and positives. Possible applications in retail. Considerable discussion at the link and beyond.
Here in Bruce Schneier's Blog:
Thermal Imaging as Security Theater could that be used as anindicator
Seems like thermal imaging is the security theater technology of today.
These features are so tempting that thermal cameras are being installed at an increasing pace. They're used in airports and other public transportation centers to screen travelers, increasingly used by companies to screen employees and by businesses to screen customers, and even used in health care facilities to screen patients. Despite their prevalence, thermal cameras have many fatal limitations when used to screen for the coronavirus. ... "
Here in Bruce Schneier's Blog:
Thermal Imaging as Security Theater could that be used as anindicator
Seems like thermal imaging is the security theater technology of today.
These features are so tempting that thermal cameras are being installed at an increasing pace. They're used in airports and other public transportation centers to screen travelers, increasingly used by companies to screen employees and by businesses to screen customers, and even used in health care facilities to screen patients. Despite their prevalence, thermal cameras have many fatal limitations when used to screen for the coronavirus. ... "
Very Small Robots
Small task specific robotics are a long time interest. With clear application for pharma delivery and other healthcare sensor and related work.
Introducing the World’s smallest Microelectronic robot
This breakthrough invention would pave the way for the use of autonomous microbots in targeted medicinal delivery
By Faisal Khan
Tiny robots are nothing new to the world of technology, but this recent invention claims to be the smallest of them all. Nanobots are hypothesized solutions of the future, which can deliver targeted medication within a human body. If this idea eventually gets materialized, it would be the first such step in that direction. The team of international researchers working on this novel concept was led by Prof. Dr. Oliver G. Schmidt, Chair of the Professorship of Material Systems for Nanoelectronics at Chemnitz University of Technology.
Researchers on the team claim they have created the smallest Microelectronic robot of the world — more importantly, one which is propelled by “jet engines.” Dr. Schmidt, a pioneer in the field of micro-robotics and micrometers, along with his three colleagues conceived the idea of the smallest man-made jet engine in 2010. He received the Guinness World Record for his amazing invention.
Building on the decade-old research, Schmidt along with this fellow researchers at the Technical University of Dresden and the Chinese Academy of Sciences Changchun have now come up with a microelectronic robot which is 0.8 mm long, 0.8 mm wide and 0.14 mm tall — for context, a one-cent coin has a diameter of around 16mm.
“We construct the microbot initially in such a way that it swims in circles if no heat is applied at all. If some heat is applied, the turning is compensated and the microbot swims in a straight line. If more heat is applied, the microrobot turns in the other direction.”
~Dr. Oliver G. Schmidt, Lead Researcher ... "
Introducing the World’s smallest Microelectronic robot
This breakthrough invention would pave the way for the use of autonomous microbots in targeted medicinal delivery
By Faisal Khan
Tiny robots are nothing new to the world of technology, but this recent invention claims to be the smallest of them all. Nanobots are hypothesized solutions of the future, which can deliver targeted medication within a human body. If this idea eventually gets materialized, it would be the first such step in that direction. The team of international researchers working on this novel concept was led by Prof. Dr. Oliver G. Schmidt, Chair of the Professorship of Material Systems for Nanoelectronics at Chemnitz University of Technology.
Researchers on the team claim they have created the smallest Microelectronic robot of the world — more importantly, one which is propelled by “jet engines.” Dr. Schmidt, a pioneer in the field of micro-robotics and micrometers, along with his three colleagues conceived the idea of the smallest man-made jet engine in 2010. He received the Guinness World Record for his amazing invention.
Building on the decade-old research, Schmidt along with this fellow researchers at the Technical University of Dresden and the Chinese Academy of Sciences Changchun have now come up with a microelectronic robot which is 0.8 mm long, 0.8 mm wide and 0.14 mm tall — for context, a one-cent coin has a diameter of around 16mm.
“We construct the microbot initially in such a way that it swims in circles if no heat is applied at all. If some heat is applied, the turning is compensated and the microbot swims in a straight line. If more heat is applied, the microrobot turns in the other direction.”
~Dr. Oliver G. Schmidt, Lead Researcher ... "
Multifunction E-Glasses Track the Brain, Eyes, and More
Claim to considerable advances in sensor interfaces through a 'glasses style' interface. Consder how combining brain and vision sensor data would provide adaptive data about attention. More at the link.
Multifunction E-Glasses Track the Brain, Eyes, and More By New Atlas in CACM
The prototype electronic glasses.
Researchers at Korea University's KU-KIST Graduate School of Converging Science and Technology have developed multifunctional e-glasses.
Researchers at Korea University's KU-KIST Graduate School of Converging Science and Technology have developed prototype multifunctional e-glasses equipped with flexible electrodes near the ears and eyes.
Those electrodes monitor electrical activity in the brain and track eye movements, and transmit that information wirelessly for processing.
Meanwhile, an ultraviolet (UV) light sensor on the side of one arm quantifies the intensity of incoming UV rays, and can activate a gel within the lenses to temporarily darken the eyewear into sunglasses.
An accelerometer also tracks the wearer's posture and gait and detects falls, with potential applications in virtual reality and accident-alert systems for seniors.
From New Atlas
Multifunction E-Glasses Track the Brain, Eyes, and More By New Atlas in CACM
The prototype electronic glasses.
Researchers at Korea University's KU-KIST Graduate School of Converging Science and Technology have developed multifunctional e-glasses.
Researchers at Korea University's KU-KIST Graduate School of Converging Science and Technology have developed prototype multifunctional e-glasses equipped with flexible electrodes near the ears and eyes.
Those electrodes monitor electrical activity in the brain and track eye movements, and transmit that information wirelessly for processing.
Meanwhile, an ultraviolet (UV) light sensor on the side of one arm quantifies the intensity of incoming UV rays, and can activate a gel within the lenses to temporarily darken the eyewear into sunglasses.
An accelerometer also tracks the wearer's posture and gait and detects falls, with potential applications in virtual reality and accident-alert systems for seniors.
From New Atlas
Validating Clinical Usefulness of Wearable Tech
Ultimately measurement of value.
Verifying, Validating the Clinical Usefulness of Wearable Technology
By Duke University Pratt School of Engineering
May 28, 2020
Duke University assistant professor Jessilyn Dunn (right) watches as Dunn's Ph.d. student and co-author Brinnae Bent prepares to download information from a wearable health monitoring device.
An international team of biomedical engineers and researchers has created a framework for the assessment and documentation of the clinical usefulness of wearable computing devices.
An international team of biomedical engineers and researchers has created a framework to assess and document the clinical usefulness of wearable devices.
The proposed V3 framework would first verify the utility of Biometric Monitoring technologies (BioMeTs) by having hardware manufacturers test sample-level sensor outputs.
The next step, analytical validation, involves having engineers, data scientists, and physiologists evaluate algorithms that produce physiological metrics from sensor data.
The third and final step entails clinical validation, in which a technology vendor or clinical expert demonstrates that a BioMeT can acceptably identify, quantify, or predict the clinical, biological, physical, or functional experience it was intended to record, in the target population of users.
Duke's Will Wang said, "If such information is not clinically validated, users and researchers alike will be led to unjustified conclusions."
From Duke University Pratt School of Engineering .... " (Full text available at link)
Verifying, Validating the Clinical Usefulness of Wearable Technology
By Duke University Pratt School of Engineering
May 28, 2020
Duke University assistant professor Jessilyn Dunn (right) watches as Dunn's Ph.d. student and co-author Brinnae Bent prepares to download information from a wearable health monitoring device.
An international team of biomedical engineers and researchers has created a framework for the assessment and documentation of the clinical usefulness of wearable computing devices.
An international team of biomedical engineers and researchers has created a framework to assess and document the clinical usefulness of wearable devices.
The proposed V3 framework would first verify the utility of Biometric Monitoring technologies (BioMeTs) by having hardware manufacturers test sample-level sensor outputs.
The next step, analytical validation, involves having engineers, data scientists, and physiologists evaluate algorithms that produce physiological metrics from sensor data.
The third and final step entails clinical validation, in which a technology vendor or clinical expert demonstrates that a BioMeT can acceptably identify, quantify, or predict the clinical, biological, physical, or functional experience it was intended to record, in the target population of users.
Duke's Will Wang said, "If such information is not clinically validated, users and researchers alike will be led to unjustified conclusions."
From Duke University Pratt School of Engineering .... " (Full text available at link)
Virtual Care Services
Experienced this recently, was nicely done. Though I would require at least some face to face and visual analysis as part of the care. Might this be done by using images and sharing these ahead of the appointment?
Forrester writes about the quicker adoption. Part 2 with links to the first part.
Virtual Care Is A Requirement — Not A “Nice-To-Have”
Arielle Trzcinski, Senior Analyst Forrester
Virtual Care Blog Series — Part 2
The pandemic has pushed virtual care technologies to make the leap across the chasm of adoption as even the pragmatists and conservatives have started deploying these services. Forrester made the call that 2020 would be the tipping point in virtual care adoption, and the pandemic accelerated this shift as barriers that inhibited faster adoption have been removed, such as lack of consumer awareness, cost and reimbursement hurdles, and the ability for patients to connect with their existing provider that they trust.
To better understand how adoption has played out in the market, what areas are seeing the greatest amount of growth, and to establish a baseline in virtual care, Forrester has been connecting with the supply side — the vendors in the virtual care space. These vendors have reported a significant rise in enrollment, adoption, and new implementations by healthcare organizations (HCOs). As part of our weekly series on virtual care, we are continuing to highlight how virtual care is transforming the future of healthcare. Missed the previous blog? Check it out here. .... "
Forrester writes about the quicker adoption. Part 2 with links to the first part.
Virtual Care Is A Requirement — Not A “Nice-To-Have”
Arielle Trzcinski, Senior Analyst Forrester
Virtual Care Blog Series — Part 2
The pandemic has pushed virtual care technologies to make the leap across the chasm of adoption as even the pragmatists and conservatives have started deploying these services. Forrester made the call that 2020 would be the tipping point in virtual care adoption, and the pandemic accelerated this shift as barriers that inhibited faster adoption have been removed, such as lack of consumer awareness, cost and reimbursement hurdles, and the ability for patients to connect with their existing provider that they trust.
To better understand how adoption has played out in the market, what areas are seeing the greatest amount of growth, and to establish a baseline in virtual care, Forrester has been connecting with the supply side — the vendors in the virtual care space. These vendors have reported a significant rise in enrollment, adoption, and new implementations by healthcare organizations (HCOs). As part of our weekly series on virtual care, we are continuing to highlight how virtual care is transforming the future of healthcare. Missed the previous blog? Check it out here. .... "
Virus UnCertainty
McKinsey piece on restart, watching local businesses closely.
Crushing coronavirus uncertainty: The big ‘unlock’ for our economies
To safeguard lives and livelihoods, we must restore confidence.
This article was a collaborative, global effort by Sven Smit and Martin Hirt, with Penny Dash, Audrey Lucas, Tom Latkovic, Matt Wilson, Ezra Greenberg, Kevin Buehler, and Klemens Hjartar, representing views from The McKinsey Global Institute, and Strategy and Corporate Finance, Healthcare Systems and Services, Public and Social Sector, and Risk practices.
Only eight weeks ago, we published “Safeguarding our lives and our livelihoods: The imperative of our time.” Back then, we worried about the supply of ventilators and critical-care capacity, the world’s ability to suppress the coronavirus, and how governments would respond to the pandemic’s economic fallout. So what has the world learned since?
We now know that we can curb the spread of the virus, can rapidly expand critical care, and are on our way to scaling the availability of testing. We have seen most governments and central banks rapidly move to implement stimulus and liquidity measures to cushion the economic impact. Unfortunately, we have also confirmed that lockdowns cause deep economic shocks: peak to trough, developed economies are likely to see GDPs decline by between 8 and 13 percent in the second quarter of 2020 (Exhibit 1). By the end of April, more than 20.5 million jobs have been lost in the United States since the start of the pandemic. Clearly, some of the initial uncertainty associated with the coronavirus has been reduced—but it remains high. ... "
Crushing coronavirus uncertainty: The big ‘unlock’ for our economies
To safeguard lives and livelihoods, we must restore confidence.
This article was a collaborative, global effort by Sven Smit and Martin Hirt, with Penny Dash, Audrey Lucas, Tom Latkovic, Matt Wilson, Ezra Greenberg, Kevin Buehler, and Klemens Hjartar, representing views from The McKinsey Global Institute, and Strategy and Corporate Finance, Healthcare Systems and Services, Public and Social Sector, and Risk practices.
Only eight weeks ago, we published “Safeguarding our lives and our livelihoods: The imperative of our time.” Back then, we worried about the supply of ventilators and critical-care capacity, the world’s ability to suppress the coronavirus, and how governments would respond to the pandemic’s economic fallout. So what has the world learned since?
We now know that we can curb the spread of the virus, can rapidly expand critical care, and are on our way to scaling the availability of testing. We have seen most governments and central banks rapidly move to implement stimulus and liquidity measures to cushion the economic impact. Unfortunately, we have also confirmed that lockdowns cause deep economic shocks: peak to trough, developed economies are likely to see GDPs decline by between 8 and 13 percent in the second quarter of 2020 (Exhibit 1). By the end of April, more than 20.5 million jobs have been lost in the United States since the start of the pandemic. Clearly, some of the initial uncertainty associated with the coronavirus has been reduced—but it remains high. ... "
Wednesday, June 03, 2020
Amazon Streamlines Building Smart Home Alexa Skills
Nice idea, have often found myself groping for some skill name. Should have been done sooner.
Amazon Streamlines Building Smart Home Alexa Skills
by Eric Hal Schwartz in Voicebot.ai
Alexa developers can now combine their apps with smart home devices. Amazon had been piloting the Multi-Capability Skills feature for some time, but the option is now generally available to developers.
MERGING VOICES
Until now, an Alexa developer would need one app to handle smart home capabilities of a device, and another with a different name for features that Alexa’s smart home API didn’t support. With Multi-Capability Skills, both sides are combined into a single voice app, handling both custom skills and the smart home skills built into Alexa. It basically makes an Alexa skill flexible enough to handle custom commands within the existing framework of the voice app. Most smart home devices have an on and off switch, for instance, but a command to change lighting colors is only useful for the relevant devices. With the new feature, both aspects can be included under one Alexa skill.
“With MCS, customers no longer need to search for or enable multiple skills to access all the features of their Alexa-connected device,” Amazon explained in its announcement. “MCS removes the friction of customers needing to remember different skill names, allowing customers to access all the expanded smart home features with a single invocation name. For example, by building a multi-capability skill, Dyson enabled its customers to interact more naturally with their Alexa-connected devices. Customers can control their Dyson fans with commands like “Alexa, set the fan speed to 5,” or “Alexa, set Oscillation to wide,” and set night modes and quiet modes in their daily routines, all features previously not available in a single skill experience.” .... '
Amazon Streamlines Building Smart Home Alexa Skills
by Eric Hal Schwartz in Voicebot.ai
Alexa developers can now combine their apps with smart home devices. Amazon had been piloting the Multi-Capability Skills feature for some time, but the option is now generally available to developers.
MERGING VOICES
Until now, an Alexa developer would need one app to handle smart home capabilities of a device, and another with a different name for features that Alexa’s smart home API didn’t support. With Multi-Capability Skills, both sides are combined into a single voice app, handling both custom skills and the smart home skills built into Alexa. It basically makes an Alexa skill flexible enough to handle custom commands within the existing framework of the voice app. Most smart home devices have an on and off switch, for instance, but a command to change lighting colors is only useful for the relevant devices. With the new feature, both aspects can be included under one Alexa skill.
“With MCS, customers no longer need to search for or enable multiple skills to access all the features of their Alexa-connected device,” Amazon explained in its announcement. “MCS removes the friction of customers needing to remember different skill names, allowing customers to access all the expanded smart home features with a single invocation name. For example, by building a multi-capability skill, Dyson enabled its customers to interact more naturally with their Alexa-connected devices. Customers can control their Dyson fans with commands like “Alexa, set the fan speed to 5,” or “Alexa, set Oscillation to wide,” and set night modes and quiet modes in their daily routines, all features previously not available in a single skill experience.” .... '
Lowe's Goes Virtual for Pro Home Improvement
Most intrigued about how the knowledge is being stored, delivered,utilized. There are different levels of expertise embedded in 'Pro', so will this context be included? Ultimately essentially.
Lowe’s ‘virtually’ goes on the job for home improvement pros by George Anderson in Retailwire
Lowe’s is introducing a new tool that will enable carpenters, electricians, plumbers and other construction professionals to meet with customers to discuss projects without having to go to their homes.
Lowe’s for Pros JobSIGHT makes use of video, computer vision and augmented reality tech to help pros evaluate projects so they can provide quotes to consumers on a wide variety of repair and home improvement projects. Pros using the tool chat directly with homeowners and are able to conduct tasks, such as determining product serial numbers and product details. They can use an on-screen laser pointer and augmented reality quick-draw tools to work through the consultation with homeowners. When the virtual meeting is complete, pros receive a one-page summary including video and audio, hi-res photos and notes for follow-up.
Lowe’s is making Pros JobSIGHT free to trade professionals through Oct. 31. Those who sign up for the program also save five percent on the chain’s everyday prices and are eligible for zero-interest purchases using their business accounts with Lowe’s. Extended payment terms are also available. ... '
Lowe’s ‘virtually’ goes on the job for home improvement pros by George Anderson in Retailwire
Lowe’s is introducing a new tool that will enable carpenters, electricians, plumbers and other construction professionals to meet with customers to discuss projects without having to go to their homes.
Lowe’s for Pros JobSIGHT makes use of video, computer vision and augmented reality tech to help pros evaluate projects so they can provide quotes to consumers on a wide variety of repair and home improvement projects. Pros using the tool chat directly with homeowners and are able to conduct tasks, such as determining product serial numbers and product details. They can use an on-screen laser pointer and augmented reality quick-draw tools to work through the consultation with homeowners. When the virtual meeting is complete, pros receive a one-page summary including video and audio, hi-res photos and notes for follow-up.
Lowe’s is making Pros JobSIGHT free to trade professionals through Oct. 31. Those who sign up for the program also save five percent on the chain’s everyday prices and are eligible for zero-interest purchases using their business accounts with Lowe’s. Extended payment terms are also available. ... '
AR and Improved Online Shopping
We spent some time examining this proposition, but did not find that AR provided significant results in engagement and sales, except in very narrow domains. Here new studies of interest with new tech.
AR Can Improve Online Shopping, Study Finds
Cornell Chronicle
E.C. Barrett
Researchers at Cornell University, Iowa State University, and Virginia Polytechnic Institute found that online shopping could be enhanced by allowing consumers to try on garments virtually via Augmented Reality (AR). The goal is to reduce the expense and carbon footprint of bracket shopping, in which shoppers order an item in multiple sizes and colors, and send back those they find unsuitable. The AR system requires a computer, telephone, or tablet screen reflecting the shopper and their physical backgrounds, with selected garments overlaid; study participants assessed the AR garments for size, fit, and performance, followed by physical try-ons. Evaluating the fit in AR was problematic, but shoppers' responses to the AR and actual garments were positive. Cornell's Fatma Baytar said, "We can expect that as these technologies evolve, people will trust online shopping more." ... '
AR Can Improve Online Shopping, Study Finds
Cornell Chronicle
E.C. Barrett
Researchers at Cornell University, Iowa State University, and Virginia Polytechnic Institute found that online shopping could be enhanced by allowing consumers to try on garments virtually via Augmented Reality (AR). The goal is to reduce the expense and carbon footprint of bracket shopping, in which shoppers order an item in multiple sizes and colors, and send back those they find unsuitable. The AR system requires a computer, telephone, or tablet screen reflecting the shopper and their physical backgrounds, with selected garments overlaid; study participants assessed the AR garments for size, fit, and performance, followed by physical try-ons. Evaluating the fit in AR was problematic, but shoppers' responses to the AR and actual garments were positive. Cornell's Fatma Baytar said, "We can expect that as these technologies evolve, people will trust online shopping more." ... '
CVS Health Testing Nuro Delivery
This particular Nuro delivery systems is being tested by a number of companies. See a number of images at the tag.
CVS Health Tests Self-Driving Vehicle Prescription Delivery
Associated Press
Tom Murphy
May 28, 2020
CVS Health will test prescription delivery via self-driving vehicles to customers in Houston beginning this month, in partnership with the Nuro robotics company. A spokesperson for the drugstore chain said prescriptions will be delivered within an hour of ordering from a Houston-area store; customers will have to confirm their identity in order to unlock the vehicle to obtain their delivery. Customers can select the Nuro delivery option when they fill their prescriptions online, and track the vehicle's progress through a Nuro portal. Federal regulators earlier this year granted Nuro temporary approval to operate autonomous delivery vehicles on public roads for the first time without human occupants. .... "
CVS Health Tests Self-Driving Vehicle Prescription Delivery
Associated Press
Tom Murphy
May 28, 2020
CVS Health will test prescription delivery via self-driving vehicles to customers in Houston beginning this month, in partnership with the Nuro robotics company. A spokesperson for the drugstore chain said prescriptions will be delivered within an hour of ordering from a Houston-area store; customers will have to confirm their identity in order to unlock the vehicle to obtain their delivery. Customers can select the Nuro delivery option when they fill their prescriptions online, and track the vehicle's progress through a Nuro portal. Federal regulators earlier this year granted Nuro temporary approval to operate autonomous delivery vehicles on public roads for the first time without human occupants. .... "
Systems of Insight, Analytics in Context
Precisely what I have been suggesting for some time. The results of analytics need to be plugged into business need. As the Computerworld Article states it: " .... Businesses want to use data to understand customers, but they can’t do that without harnessing insights and consistently turning data into effective action ... " . In order to do this you need to know where the insight plugs in. Which means it helps to know how your business operates to begin with, to understand its effect. Not always as simple as it may seem. One approach is to understand your business with a process model. That is rare in business today, and often rejected as requiring too much effort. The insight should be understood in process context. Taking this further, the logic in the process model can also be modeled, leading to a cognitive model.
Tuesday, June 02, 2020
Simulating the Market
Quite a claim, often mentioned in early AI 'tests'? Can it work? Is simulation sufficiently complex proxy for the market?
AI stock trading experiment beats market in simulation by Chinese Association of Automation in TechExplore
Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning—a discipline within artificial intelligence—to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica.
The University of Cagliari-based team set out to create an AI-managed "buy and hold" (B&H) strategy—a system of deciding whether to take one of three possible actions—a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance..... "
More information: Silvio Barra, Salvatore Mario Carta, Andrea Corriga, Alessandro Sebastian Podda and Diego Reforgiato Recupero, "Deep Learning and Time Series-to-Image Encoding for Financial Forecasting," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 683-692, May 2020. www.ieee-jas.org/en/article/do … 109/JAS.2020.1003132
AI stock trading experiment beats market in simulation by Chinese Association of Automation in TechExplore
Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning—a discipline within artificial intelligence—to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica.
The University of Cagliari-based team set out to create an AI-managed "buy and hold" (B&H) strategy—a system of deciding whether to take one of three possible actions—a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance..... "
More information: Silvio Barra, Salvatore Mario Carta, Andrea Corriga, Alessandro Sebastian Podda and Diego Reforgiato Recupero, "Deep Learning and Time Series-to-Image Encoding for Financial Forecasting," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 683-692, May 2020. www.ieee-jas.org/en/article/do … 109/JAS.2020.1003132
Alexa now has an Everywhere Intercom
Been a long-time user of Alexa as an intercom, but you had to select a destination. Now this new capability is nice, specially useful if you have a large multi-floor house or connected outside space. Previously you could broadcast, but only one-way. I could also see this as a means of asking the opinion of people in the house, as is shown in the example, and recording them in a list. Maybe a business use? The latter is not done automatically now.
Now all your home’s Alexa devices work like an intercom in Engadget
Amazon's 'Drop In' feature now works across the entire house.
Amazon Alexa users can now use the “Drop In” feature to talk with all of their Echo devices at once, Amazon announced on its blog. Previously, Drop In messages could only be sent to one other Alexa-enabled device at a time -- a user with an Alexa device in the bedroom could “drop in” on a device in the kitchen and have a two-way conversation.
Now, you can use a device to send a message to all Echo devices in the house at once. This could be helpful with asking group questions like, “Does anyone want anything from the grocery store?” according to the Amazon blog. To start a group Drop In conversation, you can ask Alexa to “Drop In everywhere.” ... "
Update: Later I read that they have also added an 'Everywhere' option to the reminder feature, 'Set a reminder at 5 PM to dress for dinner' ... Everywhere'
Now all your home’s Alexa devices work like an intercom in Engadget
Amazon's 'Drop In' feature now works across the entire house.
Amazon Alexa users can now use the “Drop In” feature to talk with all of their Echo devices at once, Amazon announced on its blog. Previously, Drop In messages could only be sent to one other Alexa-enabled device at a time -- a user with an Alexa device in the bedroom could “drop in” on a device in the kitchen and have a two-way conversation.
Now, you can use a device to send a message to all Echo devices in the house at once. This could be helpful with asking group questions like, “Does anyone want anything from the grocery store?” according to the Amazon blog. To start a group Drop In conversation, you can ask Alexa to “Drop In everywhere.” ... "
Update: Later I read that they have also added an 'Everywhere' option to the reminder feature, 'Set a reminder at 5 PM to dress for dinner' ... Everywhere'
Leveraging Unlabeled Data
The first step in using data is to make sure we know what the data is. Surprisingly this can often be an issue. Seen it a number of times in the real world. How was the data gathered, protected, updated, maintained, shared, preprocessed .... ? If we don't know how it was precisely identified, we don't know what it is. Taking it further, what data do we need to make this data useful? What is the metadata, and how has it been found? Has it been usefully labeled?
This article takes it farther yet. Efforts are underway to construct synthetic data for further and future use. Examples of robot control and speech recognition and analysis, healthcare learning, explainability and causality data is brought up. Which made me think, all those efforts need to be carefully labelled too, to make use feasible.
Leveraging Unlabeled Data By Chris Edwards
Communications of the ACM, June 2020, Vol. 63 No. 6, Pages 13-14
10.1145/3392496
Despite the rapid advances it has made it over the past decade, deep learning presents many industrial users with problems when they try to implement the technology, issues that the Internet giants have worked around through brute force.
"The challenge that today's systems face is the amount of data they need for training," says Tim Ensor, head of artificial intelligence (AI) at U.K.-based technology company Cambridge Consultants. "On top of that, it needs to be structured data."
Most of the commercial applications and algorithm benchmarks used to test deep neural networks (DNNs) consume copious quantities of labeled data; for example, images or pieces of text that have already been tagged in some way by a human to indicate what the sample represents.
The Internet giants, who have collected the most data for use in training deep learning systems, have often resorted to crowdsourcing measures such as asking people to prove they are human during logins by identifying objects in a collection of images, or simply buying manual labor through services such as Amazon's Mechanical Turk. However, this is not an approach that works outside a few select domains, such as image recognition.
Holger Hoos, professor of machine learning at Leiden University in the Netherlands, says, "Often we don't know what the data is about. We have a lot of data that isn't labeled, and it can be very expensive to label. There is a long way to go before we can make good use of a lot of the data that we have."
To attack a wider range of applications beyond image classification and speech recognition and push deep learning into medicine, industrial control, and sensor analysis, users want to be able to use what Facebook's chief AI scientist Yann LeCun has tagged the "dark matter of AI": unlabeled data.
"The problem I see now is that supervising with high-level concepts like 'door' or 'airplane' before the computer even knows what an object is simply invites disaster."
In parallel with those working in academia, technology companies such as Cambridge Consultants have investigated a number of approaches to the problem. Ensor sees the use of synthetic data as fruitful, using as one example a system built by his company to design bridges and control robot arms that is trained using simulations of the real world, based on calculations made by the modeling software to identify strong and weak structures as the DNN makes design choices. .... "
This article takes it farther yet. Efforts are underway to construct synthetic data for further and future use. Examples of robot control and speech recognition and analysis, healthcare learning, explainability and causality data is brought up. Which made me think, all those efforts need to be carefully labelled too, to make use feasible.
Leveraging Unlabeled Data By Chris Edwards
Communications of the ACM, June 2020, Vol. 63 No. 6, Pages 13-14
10.1145/3392496
Despite the rapid advances it has made it over the past decade, deep learning presents many industrial users with problems when they try to implement the technology, issues that the Internet giants have worked around through brute force.
"The challenge that today's systems face is the amount of data they need for training," says Tim Ensor, head of artificial intelligence (AI) at U.K.-based technology company Cambridge Consultants. "On top of that, it needs to be structured data."
Most of the commercial applications and algorithm benchmarks used to test deep neural networks (DNNs) consume copious quantities of labeled data; for example, images or pieces of text that have already been tagged in some way by a human to indicate what the sample represents.
The Internet giants, who have collected the most data for use in training deep learning systems, have often resorted to crowdsourcing measures such as asking people to prove they are human during logins by identifying objects in a collection of images, or simply buying manual labor through services such as Amazon's Mechanical Turk. However, this is not an approach that works outside a few select domains, such as image recognition.
Holger Hoos, professor of machine learning at Leiden University in the Netherlands, says, "Often we don't know what the data is about. We have a lot of data that isn't labeled, and it can be very expensive to label. There is a long way to go before we can make good use of a lot of the data that we have."
To attack a wider range of applications beyond image classification and speech recognition and push deep learning into medicine, industrial control, and sensor analysis, users want to be able to use what Facebook's chief AI scientist Yann LeCun has tagged the "dark matter of AI": unlabeled data.
"The problem I see now is that supervising with high-level concepts like 'door' or 'airplane' before the computer even knows what an object is simply invites disaster."
In parallel with those working in academia, technology companies such as Cambridge Consultants have investigated a number of approaches to the problem. Ensor sees the use of synthetic data as fruitful, using as one example a system built by his company to design bridges and control robot arms that is trained using simulations of the real world, based on calculations made by the modeling software to identify strong and weak structures as the DNN makes design choices. .... "
Research on Human - AI Teaming
Good piece on the topic. Not too different from teaming with other methods, like analytics But here there may be higher expectations and hints of expected 'autonomy'. I would suggest and add that there should be more embedded risk analysis considered, mostly due to sometimes overblown expectations of such methods. Humans will necessarily always be in the loup.
Teaming Up with Artificial Intelligence By Bennie Mols in CACM
Daniel S. Weld of the University of Washington in Seattle was part of a team that analyzed 20 years of research on the interactions between people and artificial intelligences.
Creating a good artificial intelligence (AI) user experience is not easy. Everyone who uses autocorrect while writing knows that while the system usually does a pretty good job of acthing and correcting errors, it sometimes makes bizarre mistakes. The same is true for the autopilot in a Tesla, but unfortunately the stakes are much higher on the road than when sitting behind a computer.
Daniel S. Weld of the University of Washington in Seattle has done a lot of research on human-AI teams. Last year, he and a group of colleagues from Microsoft proposed 18 generally applicable design guidelines for human-AI interaction, which were validated through multiple rounds of evaluation.
Bennie Mols interviewed Weld about the challenges of building a human-AI dream team:
What makes a human-AI team different from a team of a human and a digital system without AI?
First of all, AI systems are probabilistic: sometimes they get it right, but sometimes they err. Unfortunately, their mistakes are often unpredictable. In contrast, classical computer programs, like spreadsheets, work in a much more predictable way.
Second, AI can behave differently in subtly different contexts. Sometimes the change in context isn't even clear to the human. Google Search might give different auto-suggest results to different people, based on their previous behavior, which was different.
The third important difference is that AI systems can change over time, for example through learning.
How did your research team arrive at the guidelines for human-AI-interaction?
We started by analyzing 20 years of research on human-AI interaction. We did a user evaluation with 20 AI products and 50 practitioners, and we also did expert reviews. This led to 18 guidelines divided over four phases of the human-AI interaction process: the initial phase, this is before the interaction has started; the phase during interaction; the phase after the interaction, in case the AI system made a mistake; and finally, over time. During the last phase, the system might get updates, while humans might evolve their interaction with the system. .... "
Teaming Up with Artificial Intelligence By Bennie Mols in CACM
Daniel S. Weld of the University of Washington in Seattle was part of a team that analyzed 20 years of research on the interactions between people and artificial intelligences.
Creating a good artificial intelligence (AI) user experience is not easy. Everyone who uses autocorrect while writing knows that while the system usually does a pretty good job of acthing and correcting errors, it sometimes makes bizarre mistakes. The same is true for the autopilot in a Tesla, but unfortunately the stakes are much higher on the road than when sitting behind a computer.
Daniel S. Weld of the University of Washington in Seattle has done a lot of research on human-AI teams. Last year, he and a group of colleagues from Microsoft proposed 18 generally applicable design guidelines for human-AI interaction, which were validated through multiple rounds of evaluation.
Bennie Mols interviewed Weld about the challenges of building a human-AI dream team:
What makes a human-AI team different from a team of a human and a digital system without AI?
First of all, AI systems are probabilistic: sometimes they get it right, but sometimes they err. Unfortunately, their mistakes are often unpredictable. In contrast, classical computer programs, like spreadsheets, work in a much more predictable way.
Second, AI can behave differently in subtly different contexts. Sometimes the change in context isn't even clear to the human. Google Search might give different auto-suggest results to different people, based on their previous behavior, which was different.
The third important difference is that AI systems can change over time, for example through learning.
How did your research team arrive at the guidelines for human-AI-interaction?
We started by analyzing 20 years of research on human-AI interaction. We did a user evaluation with 20 AI products and 50 practitioners, and we also did expert reviews. This led to 18 guidelines divided over four phases of the human-AI interaction process: the initial phase, this is before the interaction has started; the phase during interaction; the phase after the interaction, in case the AI system made a mistake; and finally, over time. During the last phase, the system might get updates, while humans might evolve their interaction with the system. .... "
Astronomy Methods for Business Analytics?
Brought to attention by some of my astro colleagues, the effort is considerable. Could businesses also construct such a 'survey' of how they operate? Which could lead to a determination of where data might be used, needed?
The Vera C. Rubin Observatory, currently under construction in Chile, will conduct a vast astronomical survey of our dynamic Universe starting in 2022. They plan to collect 500 petabytes of image data by observing the skies continuously for 10 years and produce nearly instant alerts for objects that change in position or brightness every night. In addition to astronomical data, their dataset will include DevOps, IoT, and real-time monitoring data.
In this latest Data Science Central webinar, Dr. Angelo Fausti will demonstrate:
● How a time-series database has the versatility to address their needs
● How they created a solution to enhance visibility across their organization and improve actionable insights
● How they pull software development and sensor data from their telescope, camera and observatory IoT devices
Speaker:
Dr. Angelo Fausti, Software Engineer - Vera C. Rubin Observatory
Hosted by:
Sean Welch, Host and Producer - Data Science Central
--- --------------------------------------------------------------------------
LSST Project Mission Statement
LSST’s mission is to build a well-understood system that provides a vast astronomical dataset for unprecedented discovery of the deep and dynamic universe. ....
The Vera C. Rubin Observatory, currently under construction in Chile, will conduct a vast astronomical survey of our dynamic Universe starting in 2022. They plan to collect 500 petabytes of image data by observing the skies continuously for 10 years and produce nearly instant alerts for objects that change in position or brightness every night. In addition to astronomical data, their dataset will include DevOps, IoT, and real-time monitoring data.
In this latest Data Science Central webinar, Dr. Angelo Fausti will demonstrate:
● How a time-series database has the versatility to address their needs
● How they created a solution to enhance visibility across their organization and improve actionable insights
● How they pull software development and sensor data from their telescope, camera and observatory IoT devices
Speaker:
Dr. Angelo Fausti, Software Engineer - Vera C. Rubin Observatory
Hosted by:
Sean Welch, Host and Producer - Data Science Central
--- --------------------------------------------------------------------------
LSST Project Mission Statement
LSST’s mission is to build a well-understood system that provides a vast astronomical dataset for unprecedented discovery of the deep and dynamic universe. ....
Monday, June 01, 2020
State of China AI
ACM OPINION
The Art of AI
By Project Syndicate
Kai-Fu Lee.
Sinovation CEO and chairman Kai-Fu Lee says the best role for artificial intelligence in the future is to free humans and resources for well-paying jobs in care-giving and creative fields.
As the world enters a new decade, research and development into artificial intelligence and its many applications are barreling forward, and nowhere more so than in China. Although popular narratives tend to focus on the threats posed by AI, the truth is that many of the technology's dangers have been overhyped, and its promises neglected.
A leading figure in the Chinese tech scene and in artificial-intelligence development globally, Kai-Fu Lee earned a Ph.D. in computer science from Carnegie Mellon University in 1988 before serving in executive roles at Apple, SGI, Microsoft, and Google, where he was president of Google China. Now the chairman and CEO of Sinovation Ventures in Beijing, he is the author of AI Superpowers: China, Silicon Valley, and the New World Order. Here, he discusses the global AI race, the current state of the field, and what may – and should – come next.
Project Syndicate: As someone who long worked for U.S. companies and now oversees a tech venture capital firm, you're deeply familiar with the world's two main settings for AI development and research. What are the trade-offs of each R&D environment? What advantages does China offer over the U.S., and what must policymakers change or improve to achieve China's goal of catching up to and surpassing the U.S.?
Kai-Fu Lee: There is now a clear U.S.-China AI duopoly. AI in China is rising rapidly, boosted by several structural advantages: huge data sets, a young army of technical talent, aggressive entrepreneurs, and strong and pragmatic government policy. The attitude in China can be summarized as pro-tech, pro-experimentation, and pro-speed, all of which puts the country on track to becoming a major AI power.
From Project Syndicate
View Full Article
The Art of AI
By Project Syndicate
Kai-Fu Lee.
Sinovation CEO and chairman Kai-Fu Lee says the best role for artificial intelligence in the future is to free humans and resources for well-paying jobs in care-giving and creative fields.
As the world enters a new decade, research and development into artificial intelligence and its many applications are barreling forward, and nowhere more so than in China. Although popular narratives tend to focus on the threats posed by AI, the truth is that many of the technology's dangers have been overhyped, and its promises neglected.
A leading figure in the Chinese tech scene and in artificial-intelligence development globally, Kai-Fu Lee earned a Ph.D. in computer science from Carnegie Mellon University in 1988 before serving in executive roles at Apple, SGI, Microsoft, and Google, where he was president of Google China. Now the chairman and CEO of Sinovation Ventures in Beijing, he is the author of AI Superpowers: China, Silicon Valley, and the New World Order. Here, he discusses the global AI race, the current state of the field, and what may – and should – come next.
Project Syndicate: As someone who long worked for U.S. companies and now oversees a tech venture capital firm, you're deeply familiar with the world's two main settings for AI development and research. What are the trade-offs of each R&D environment? What advantages does China offer over the U.S., and what must policymakers change or improve to achieve China's goal of catching up to and surpassing the U.S.?
Kai-Fu Lee: There is now a clear U.S.-China AI duopoly. AI in China is rising rapidly, boosted by several structural advantages: huge data sets, a young army of technical talent, aggressive entrepreneurs, and strong and pragmatic government policy. The attitude in China can be summarized as pro-tech, pro-experimentation, and pro-speed, all of which puts the country on track to becoming a major AI power.
From Project Syndicate
View Full Article
On Technology Adoption
On tech adoption, measurement and useful stage models of prediction diffusion.
Technology Adoption
By Peter J. Denning, Ted G. Lewis
Communications of the ACM, June 2020, Vol. 63 No. 6, Pages 27-29
10.1145/3396265
Technology adoption is accelerating. The telegraph was first adopted by the Great Western Railway for signaling between London Paddington station and West Drayton in July 1839, but took nearly 80 years to peak (1920).a The landline telephone took 60 years to reach 80% adoption, electric power 33 years, color television 15 years, and social media 12 years.b The time to adoption is rapidly decreasing with advances in technology.
When we develop new technology, we would dearly like to predict its future adoption. For most technologies, total adoptions follow an S curve that features exponential growth in number of adopters to an inflection point, and then exponential flattening to market saturation. Is there any way to predict the S curve, given initial data on sales?
Technology adoption means that people in a community commit to a new technology in their everyday practices. A companion term diffusion means that ideas and information about a new technology spread through a community, giving everyone the opportunity to adopt. Adoption and diffusion are not the same. Here we are interested in adoption as it is manifest in sales of technology. Adoption models attempt to estimate two quantities that affect business decisions whether to produce technology. One is the total addressable market N, the number of people who will ultimately adopt. The other is t*, the time of the inflection point of the S curve.
It would seem that to develop a model of the S curve we would need a model of the underlying process by which technology is produced and sold. Three process models are common:
Pipeline: an idea flows through the stages of invention, prototyping, development, marketing, and sales, finally being incorporated into the market-place as a product people buy.
Funnel: similar to pipeline but the pipeline begins with multiple ideas and each stage winnows the number passed to the next stage until finally one product emerges into the marketplace. This model aims to compensate for the high failure rate of ideas. If failure rate is 96% (a common estimate), the funnel-pipeline must be seeded with 25 ideas so that there will be one survivor to the final stage.
Diffusion-Adoption: ideas are treated as innovation proposals that spread through a social community, giving each person the opportunity to adopt it or not.
Unfortunately, there are important innovations that are not explained by some or all of these models. For example, spontaneous innovations do not follow the pipeline or funnel models, and many diffusions do not result in adoption. Moreover, the models are unreliable when used as ways to organize projects—they explain what happened in the past but offer little guidance on what to do in the immediate future. Many organizations manage their internal processes according to one of these models. People in these organizations frequently experience a "Fog of Uncertainty" when something unanticipated comes up in one of the stages and it is not obvious what to do. .... "
Technology Adoption
By Peter J. Denning, Ted G. Lewis
Communications of the ACM, June 2020, Vol. 63 No. 6, Pages 27-29
10.1145/3396265
Technology adoption is accelerating. The telegraph was first adopted by the Great Western Railway for signaling between London Paddington station and West Drayton in July 1839, but took nearly 80 years to peak (1920).a The landline telephone took 60 years to reach 80% adoption, electric power 33 years, color television 15 years, and social media 12 years.b The time to adoption is rapidly decreasing with advances in technology.
When we develop new technology, we would dearly like to predict its future adoption. For most technologies, total adoptions follow an S curve that features exponential growth in number of adopters to an inflection point, and then exponential flattening to market saturation. Is there any way to predict the S curve, given initial data on sales?
Technology adoption means that people in a community commit to a new technology in their everyday practices. A companion term diffusion means that ideas and information about a new technology spread through a community, giving everyone the opportunity to adopt. Adoption and diffusion are not the same. Here we are interested in adoption as it is manifest in sales of technology. Adoption models attempt to estimate two quantities that affect business decisions whether to produce technology. One is the total addressable market N, the number of people who will ultimately adopt. The other is t*, the time of the inflection point of the S curve.
It would seem that to develop a model of the S curve we would need a model of the underlying process by which technology is produced and sold. Three process models are common:
Pipeline: an idea flows through the stages of invention, prototyping, development, marketing, and sales, finally being incorporated into the market-place as a product people buy.
Funnel: similar to pipeline but the pipeline begins with multiple ideas and each stage winnows the number passed to the next stage until finally one product emerges into the marketplace. This model aims to compensate for the high failure rate of ideas. If failure rate is 96% (a common estimate), the funnel-pipeline must be seeded with 25 ideas so that there will be one survivor to the final stage.
Diffusion-Adoption: ideas are treated as innovation proposals that spread through a social community, giving each person the opportunity to adopt it or not.
Unfortunately, there are important innovations that are not explained by some or all of these models. For example, spontaneous innovations do not follow the pipeline or funnel models, and many diffusions do not result in adoption. Moreover, the models are unreliable when used as ways to organize projects—they explain what happened in the past but offer little guidance on what to do in the immediate future. Many organizations manage their internal processes according to one of these models. People in these organizations frequently experience a "Fog of Uncertainty" when something unanticipated comes up in one of the stages and it is not obvious what to do. .... "
Healthcare Medical Virtual Assistant
An example of assistants for healthcare.
Healthcare AI Startup Phelix.ai Raises $1M for Medical Virtual Assistant
By Eric Hal Schwartz in Voicebot.ai
Healthcare AI developer Phelix.ai has closed a $1 million seed round of funding led by Well Health Technologies. Phelix’s technology is used to run virtual assistants that can handle making appointments, filling out paperwork, and other administrative tasks on behalf of healthcare providers.
PHELIX HEALTH
Toronto-based Phelix.ai essentially provides a virtual assistant for clinics and other healthcare providers. The AI can run a virtual call center, engaging with patients through online chatbots, phone calls, text messages, and even faxes to answer questions, arrange appointments, and bill patients as needed. According to Phelix, up to three-quarters of the time taken up by paperwork and administrative tasks can be reclaimed by doctors using its technology. Well Health accounts for a quarter of the $1 million investment, although the startup has not revealed who else contributed. As part of the deal, Well Health can now also use and sublicense Phelix.ai’s technology in other areas of its business.
“We’re thrilled to receive an investment from WELL and are thoroughly impressed with WELL’s vision and commitment to digital health in Canada,” Phelix.ai CEO Hassaan Ahmed said in a statement. “We look forward to being a part of WELL’s expanding portfolio of OSCAR compatible apps that are designed to better connect and make doctors’ practices more efficient.” ...
Healthcare AI Startup Phelix.ai Raises $1M for Medical Virtual Assistant
By Eric Hal Schwartz in Voicebot.ai
Healthcare AI developer Phelix.ai has closed a $1 million seed round of funding led by Well Health Technologies. Phelix’s technology is used to run virtual assistants that can handle making appointments, filling out paperwork, and other administrative tasks on behalf of healthcare providers.
PHELIX HEALTH
Toronto-based Phelix.ai essentially provides a virtual assistant for clinics and other healthcare providers. The AI can run a virtual call center, engaging with patients through online chatbots, phone calls, text messages, and even faxes to answer questions, arrange appointments, and bill patients as needed. According to Phelix, up to three-quarters of the time taken up by paperwork and administrative tasks can be reclaimed by doctors using its technology. Well Health accounts for a quarter of the $1 million investment, although the startup has not revealed who else contributed. As part of the deal, Well Health can now also use and sublicense Phelix.ai’s technology in other areas of its business.
“We’re thrilled to receive an investment from WELL and are thoroughly impressed with WELL’s vision and commitment to digital health in Canada,” Phelix.ai CEO Hassaan Ahmed said in a statement. “We look forward to being a part of WELL’s expanding portfolio of OSCAR compatible apps that are designed to better connect and make doctors’ practices more efficient.” ...
AI and Machine Learning DevOps
Aiming at a higher level of automation for quicker and more reliable for development and delivery. Really not all that different from the use of emergent analytical technology.
How AI and Machine Learning are Evolving DevOps
Artificial intelligence and ML can help us take DevOps to the next level through identifying problems more quickly and further automating our processes.
The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. DevOps breeds efficiency through automating software delivery and allowing companies to push software to market faster while releasing a more reliable product. What is next for DevOps? We need to look no further than artificial intelligence and machine learning.
Most organizations quickly realize the promise of AI and machine learning, but often fail to understand how they can properly harness them to improve their systems. That isn’t the case with DevOps. DevOps has some natural deficiencies that are difficult to solve without the computing power of machine learning and artificial intelligence. They are key to advancing your digital transformation. Here are three areas where AI and machine learning are advancing DevOps. ... "
How AI and Machine Learning are Evolving DevOps
Artificial intelligence and ML can help us take DevOps to the next level through identifying problems more quickly and further automating our processes.
The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. DevOps breeds efficiency through automating software delivery and allowing companies to push software to market faster while releasing a more reliable product. What is next for DevOps? We need to look no further than artificial intelligence and machine learning.
Most organizations quickly realize the promise of AI and machine learning, but often fail to understand how they can properly harness them to improve their systems. That isn’t the case with DevOps. DevOps has some natural deficiencies that are difficult to solve without the computing power of machine learning and artificial intelligence. They are key to advancing your digital transformation. Here are three areas where AI and machine learning are advancing DevOps. ... "
IOTA Smart Contracts
Brought to my attention, the use of smart contracts as is foreseen in the IOTA system we have been examining for use. Most of the detail is at the link.
Introduction to IOTA Smart Contracts
I want to thank my colleagues in the IOTA Foundation who provided input and feedback for this article. In particular, Eric Hop, who headed the Qubic project and now joins IOTA Smart Contracts, and Jake Cahill, who is responsible for most of the wording.
IOTA Smart Contracts is an ongoing effort by the IOTA Foundation. The goal of this article is to inform the community about what we are doing and where we are heading with IOTA Smart Contracts. It also presents an opportunity for the community to begin contributing to the project with questions and feedback.
Background
Recently, Eric Hop presented the Qubic project in his article The State of Qubic, and explained our decision to focus exclusively on smart contracts for the time being. Naturally, these developments raised many questions from the community about how IOTA Smart Contracts relate to Qubic’s vision.
This article will answer those questions, providing some context and a technical introduction to IOTA Smart Contracts. Although many aspects of IOTA Smart Contracts were derived from the Qubic project, in many ways it is a standalone project in its own right. We believe the direction we are taking has lots of potential and we are now taking the steps necessary to prove this potential in practice.
What is a Contract?
Before we define a smart contract, it is important to first understand what a legal contract is.
Legal contracts are non-deterministic agreements that are subject to complex legal systems. The laws surrounding contracts vary depending on a number of factors, such as the country in which all parties entered into the contract. The most important word here is “non-deterministic”. This means that contracts are often ambiguous, and their subjective interpretation can lead to disputes. .... "
Introduction to IOTA Smart Contracts
I want to thank my colleagues in the IOTA Foundation who provided input and feedback for this article. In particular, Eric Hop, who headed the Qubic project and now joins IOTA Smart Contracts, and Jake Cahill, who is responsible for most of the wording.
IOTA Smart Contracts is an ongoing effort by the IOTA Foundation. The goal of this article is to inform the community about what we are doing and where we are heading with IOTA Smart Contracts. It also presents an opportunity for the community to begin contributing to the project with questions and feedback.
Background
Recently, Eric Hop presented the Qubic project in his article The State of Qubic, and explained our decision to focus exclusively on smart contracts for the time being. Naturally, these developments raised many questions from the community about how IOTA Smart Contracts relate to Qubic’s vision.
This article will answer those questions, providing some context and a technical introduction to IOTA Smart Contracts. Although many aspects of IOTA Smart Contracts were derived from the Qubic project, in many ways it is a standalone project in its own right. We believe the direction we are taking has lots of potential and we are now taking the steps necessary to prove this potential in practice.
What is a Contract?
Before we define a smart contract, it is important to first understand what a legal contract is.
Legal contracts are non-deterministic agreements that are subject to complex legal systems. The laws surrounding contracts vary depending on a number of factors, such as the country in which all parties entered into the contract. The most important word here is “non-deterministic”. This means that contracts are often ambiguous, and their subjective interpretation can lead to disputes. .... "
Gap Adding More Robots
Warehouse Robotics expands.
Gap Rushes in More Robots to Warehouses to Solve Virus Disruption By Reuters
Gap Inc. is deploying warehouse robots more quickly amid the coronavirus pandemic, which has resulted in more online orders and fewer staff to fulfill them due to social distancing rules.
The U.S. apparel chain had reached a deal to more than triple its number of warehouse robots to 106 by the fall, but it called on Kindred AI to deliver the robots earlier.
Kindred has deployed 10 of the eight-foot-tall robotic stations — each of which can handle the work of four people — to Gap’s warehouse near Nashville, TN and another 20 near Columbus, OH. Kindred will deliver the final robots to four of Gap's five U.S. facilities by July.
Gap and Kindred said the robots are meant to complement, not replace, human workers.
From Reuters
Gap Rushes in More Robots to Warehouses to Solve Virus Disruption By Reuters
Gap Inc. is deploying warehouse robots more quickly amid the coronavirus pandemic, which has resulted in more online orders and fewer staff to fulfill them due to social distancing rules.
The U.S. apparel chain had reached a deal to more than triple its number of warehouse robots to 106 by the fall, but it called on Kindred AI to deliver the robots earlier.
Kindred has deployed 10 of the eight-foot-tall robotic stations — each of which can handle the work of four people — to Gap’s warehouse near Nashville, TN and another 20 near Columbus, OH. Kindred will deliver the final robots to four of Gap's five U.S. facilities by July.
Gap and Kindred said the robots are meant to complement, not replace, human workers.
From Reuters
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