Simple but powerful idea. Have gone through voice training on several assistants. This takes it further. Note the idea of separating components of a conversation.
Google AI can pick out voices in a crowd
It could boost audio quality for video chats and hearing aids.
Jon Fingas, @jonfingas in Engadget
4h ago in Personal
ComputingHumans are usually good at isolating a single voice in a crowd, but computers? Not so much -- just ask anyone trying to talk to a smart speaker at a house party. Google may have a surprisingly straightforward solution, however. Its researchers have developed a deep learning system that can pick out specific voices by looking at people's faces when they're speaking. The team trained its neural network model to recognize individual people speaking by themselves, and then created virtual "parties" (complete with background noise) to teach the AI how to isolate multiple voices into distinct audio tracks. .... " Contains video and audio example.
Friday, April 13, 2018
More on Location Analysis
They may differ on the value, but its almost always done and reviewed closely. Sure there are differences in its use among different kinds of retailers. Consider at very least the capital investment involved.
Retailers Differ on Value of Location Analysis
by Brian Kilcourse in Retailwire.
Through a special arrangement, what follows is a summary of an article from Retail Paradox, RSR Research’s weekly analysis on emerging issues facing retailers, presented here for discussion.
RSR’s first benchmark on location analytics in retail showed there are significant differences in focus depending on what kind of products a retailer sells, and some of the differences are counter-intuitive.
Retailers across the different verticals, performance and size all agree that “more targeted marketing” is essential to their future well-being.
But why, for example, would almost twice as many fashion retailers as fast-moving-consumer-goods (FMCG) and general merchandisers (GM) rank “improved merchandising plan localization” as a high-priority? Fashion retailers introduce new collections every season and typically execute relatively few replenishments after the initial allocation is delivered to the stores. Grocers, drug stores and big box merchants, on the other hand, have many more SKUs in their standardized assortments and replenish very frequently. The payback from assortment localization based on customer-centered analytics are far greater. ... "
Retailers Differ on Value of Location Analysis
by Brian Kilcourse in Retailwire.
Through a special arrangement, what follows is a summary of an article from Retail Paradox, RSR Research’s weekly analysis on emerging issues facing retailers, presented here for discussion.
RSR’s first benchmark on location analytics in retail showed there are significant differences in focus depending on what kind of products a retailer sells, and some of the differences are counter-intuitive.
Retailers across the different verticals, performance and size all agree that “more targeted marketing” is essential to their future well-being.
But why, for example, would almost twice as many fashion retailers as fast-moving-consumer-goods (FMCG) and general merchandisers (GM) rank “improved merchandising plan localization” as a high-priority? Fashion retailers introduce new collections every season and typically execute relatively few replenishments after the initial allocation is delivered to the stores. Grocers, drug stores and big box merchants, on the other hand, have many more SKUs in their standardized assortments and replenish very frequently. The payback from assortment localization based on customer-centered analytics are far greater. ... "
Thursday, April 12, 2018
Pizza and a Game?
Pizza and game? There have always been attempts to find multiple value contexts to deliver to a customer. Loyalty, and delivering it via the internet, is one. But too easily duplicated. Is this another? Is the reward the game? How much reward engagement will it build? With more expert Retail opinion at the link.
Will a mobile game and free pizza combo deliver sales for Domino’s? by Matthew Stern in Retailwire
Pizza is widely regarded as the video game fanatic’s cuisine of choice. And for members of Domino’s loyalty program, mobile gaming skills will now be one of the keys to unlocking free pizza.
Domino’s is launching a mobile game called “Piece of the Pie Pursuit,” according to a press release. Customers who are able to beat all six pizza-themed levels of the game will be granted 10 loyalty points. Members can redeem 60 points for a free medium two-topping pizza. The game play, as seen in a YouTube video, consists of navigating Domino’s-branded tokens, marbles and other objects through a complex, three-dimensional Rube Goldberg-type machine. ...."
Will a mobile game and free pizza combo deliver sales for Domino’s? by Matthew Stern in Retailwire
Pizza is widely regarded as the video game fanatic’s cuisine of choice. And for members of Domino’s loyalty program, mobile gaming skills will now be one of the keys to unlocking free pizza.
Domino’s is launching a mobile game called “Piece of the Pie Pursuit,” according to a press release. Customers who are able to beat all six pizza-themed levels of the game will be granted 10 loyalty points. Members can redeem 60 points for a free medium two-topping pizza. The game play, as seen in a YouTube video, consists of navigating Domino’s-branded tokens, marbles and other objects through a complex, three-dimensional Rube Goldberg-type machine. ...."
Siemens and Process Mining
Note always the potential connection between process mining and business process modeling, to set the stage for more value, and to guide future process mining.
Siemens and Process Mining By Lindsay Clark in Computer Week
German industrial conglomerate Siemens has turned to process mining to reveal inefficiencies in the way it pays suppliers, organises logistics and runs order-to-cash processes
When global manufacturing giant Siemens created a central data lake, it had no idea it would generate such positive business benefits. Compliance was the main reason it started to collect data from its disparate ERP and finance systems and put them in one place, from 2008 using an Oracle database, and then from 2012 using SAP’s Hana technology.
But from 2014, it began to realise the data could do more than help the organisation mitigate risk and provide financial reporting. Using a technique called process mining, the company began to reveal inefficiencies in the way it pays its suppliers, organises logistics and runs its order-to-cash process, creating savings for the business in the 10s of millions of Euros. .... "
Siemens and Process Mining By Lindsay Clark in Computer Week
German industrial conglomerate Siemens has turned to process mining to reveal inefficiencies in the way it pays suppliers, organises logistics and runs order-to-cash processes
When global manufacturing giant Siemens created a central data lake, it had no idea it would generate such positive business benefits. Compliance was the main reason it started to collect data from its disparate ERP and finance systems and put them in one place, from 2008 using an Oracle database, and then from 2012 using SAP’s Hana technology.
But from 2014, it began to realise the data could do more than help the organisation mitigate risk and provide financial reporting. Using a technique called process mining, the company began to reveal inefficiencies in the way it pays its suppliers, organises logistics and runs its order-to-cash process, creating savings for the business in the 10s of millions of Euros. .... "
Data as Feature
Good approach, start with and feature the data as it exists in value producing context.
“Data as a feature” is coming. Are product managers ready?
By packaging and delivering actionable data in applications, product managers can help users achieve their goals. ...By Alice LaPlante in O'Reilly
More and more, apps are capable of delivering more than a service, such as access to a bank account or the ability to order a pizza. Apps can offer users data—and not just a dump of data that has little value, but data specifically designed to be of value to the user.
This is called “data as a feature”—it is the act and process of treating data as a core feature of a software product in a way that delivers value to the user. Taking this definition a step further, a product with data as a feature delivers that data in a way that helps the user meet a goal. ... "
“Data as a feature” is coming. Are product managers ready?
By packaging and delivering actionable data in applications, product managers can help users achieve their goals. ...By Alice LaPlante in O'Reilly
More and more, apps are capable of delivering more than a service, such as access to a bank account or the ability to order a pizza. Apps can offer users data—and not just a dump of data that has little value, but data specifically designed to be of value to the user.
This is called “data as a feature”—it is the act and process of treating data as a core feature of a software product in a way that delivers value to the user. Taking this definition a step further, a product with data as a feature delivers that data in a way that helps the user meet a goal. ... "
Jupyter and Data Science
Agree, good overview, your analytics people should be there. To discover methods and ook towards reproducibility, aid with maintainability. Even as a manager, you should be looking on.
Jupyter is where humans and data science intersect
Discover how data-driven organizations are using Jupyter to analyze data, share insights, and foster practices for dynamic, reproducible data science.
By Paco Nathan .... in O'Reilly
Jupyter is where humans and data science intersect
Discover how data-driven organizations are using Jupyter to analyze data, share insights, and foster practices for dynamic, reproducible data science.
By Paco Nathan .... in O'Reilly
Reinforcement Learning
Lengthy musings on the capabilities of Reinforcement Learning. Which in one way feels very attractive, but as a former classical optimization oriented analyst, I have my doubts. Still intriguing piece about the challenge. A considerable and technical piece.
Deep Reinforcement Learning Doesn't Work Yet
This mostly cites papers from Berkeley, Google Brain, DeepMind, and OpenAI from the past few years, because that work is most visible to me. I’m almost certainly missing stuff from older literature and other institutions, and for that I apologize - I’m just one guy, after all.
Introduction
Once, on Facebook, I made the following claim.
Whenever someone asks me if reinforcement learning can solve their problem, I tell them it can’t. I think this is right at least 70% of the time.
Deep reinforcement learning is surrounded by mountains and mountains of hype. And for good reasons! Reinforcement learning is an incredibly general paradigm, and in principle, a robust and performant RL system should be great at everything. Merging this paradigm with the empirical power of deep learning is an obvious fit. Deep RL is one of the closest things that looks anything like AGI, and that’s the kind of dream that fuels billions of dollars of funding.
Unfortunately, it doesn’t really work yet.
Now, I believe it can work. If I didn’t believe in reinforcement learning, I wouldn’t be working on it. But there are a lot of problems in the way, many of which feel fundamentally difficult. The beautiful demos of learned agents hide all the blood, sweat, and tears that go into creating them.
Several times now, I’ve seen people get lured by recent work. They try deep reinforcement learning for the first time, and without fail, they underestimate deep RL’s difficulties. Without fail, the “toy problem” is not as easy as it looks. And without fail, the field destroys them a few times, until they learn how to set realistic research expectations.
This isn’t the fault of anyone in particular. It’s more of a systemic problem. It’s easy to write a story around a positive result. It’s hard to do the same for negative ones. The problem is that the negative ones are the ones that researchers run into the most often. In some ways, the negative cases are actually more important than the positives.
In the rest of the post, I explain why deep RL doesn’t work, cases where it does work, and ways I can see it working more reliably in the future. I’m not doing this because I want people to stop working on deep RL. I’m doing this because I believe it’s easier to make progress on problems if there’s agreement on what those problems are, and it’s easier to build agreement if people actually talk about the problems, instead of independently re-discovering the same issues over and over again. .... "
Deep Reinforcement Learning Doesn't Work Yet
This mostly cites papers from Berkeley, Google Brain, DeepMind, and OpenAI from the past few years, because that work is most visible to me. I’m almost certainly missing stuff from older literature and other institutions, and for that I apologize - I’m just one guy, after all.
Introduction
Once, on Facebook, I made the following claim.
Whenever someone asks me if reinforcement learning can solve their problem, I tell them it can’t. I think this is right at least 70% of the time.
Deep reinforcement learning is surrounded by mountains and mountains of hype. And for good reasons! Reinforcement learning is an incredibly general paradigm, and in principle, a robust and performant RL system should be great at everything. Merging this paradigm with the empirical power of deep learning is an obvious fit. Deep RL is one of the closest things that looks anything like AGI, and that’s the kind of dream that fuels billions of dollars of funding.
Unfortunately, it doesn’t really work yet.
Now, I believe it can work. If I didn’t believe in reinforcement learning, I wouldn’t be working on it. But there are a lot of problems in the way, many of which feel fundamentally difficult. The beautiful demos of learned agents hide all the blood, sweat, and tears that go into creating them.
Several times now, I’ve seen people get lured by recent work. They try deep reinforcement learning for the first time, and without fail, they underestimate deep RL’s difficulties. Without fail, the “toy problem” is not as easy as it looks. And without fail, the field destroys them a few times, until they learn how to set realistic research expectations.
This isn’t the fault of anyone in particular. It’s more of a systemic problem. It’s easy to write a story around a positive result. It’s hard to do the same for negative ones. The problem is that the negative ones are the ones that researchers run into the most often. In some ways, the negative cases are actually more important than the positives.
In the rest of the post, I explain why deep RL doesn’t work, cases where it does work, and ways I can see it working more reliably in the future. I’m not doing this because I want people to stop working on deep RL. I’m doing this because I believe it’s easier to make progress on problems if there’s agreement on what those problems are, and it’s easier to build agreement if people actually talk about the problems, instead of independently re-discovering the same issues over and over again. .... "
Wednesday, April 11, 2018
Bots in the Twittersphere
A quite interesting view of Bots in the twittersphere. From PEW below. Useful definitions. Which points out that they are not close to mostly out to make mischief, but also a useful part of the infrastructure. Call them automated accounts if you like. Just like there are many, many automated systems out there, which query their environment and some action: assisting, adapting, counting, sensing ..... You could call it all an IOB (internet of Bots) Some human, some machine, some sensor ... simple and complex. Makes me think about about the detection of intent. Can we measure the intent of an automated bot? By its actions, developers?
A considerable piece of work below that made methink ... read it all:
Bots in the Twittersphere
An estimated two-thirds of tweeted links to popular websites are posted by automated accounts – not human beings .... "
... By Stefan Wojcik, Solomon, Messing AAron Smith, Lee Arainie and Paul Hitlin
A considerable piece of work below that made methink ... read it all:
Bots in the Twittersphere
An estimated two-thirds of tweeted links to popular websites are posted by automated accounts – not human beings .... "
... By Stefan Wojcik, Solomon, Messing AAron Smith, Lee Arainie and Paul Hitlin
Supercharging Simulations with GPUs
Makes lots of sense since machine learning utilizes lots of simulations to train, check and deliver results. So why not use the same hardware to simulate real complex process, systems or design? Or choices of parameter. Monte Carlo or not. Abductive design? Of course these may have different architecture than a neural network, so adaptation may be required. But still. the thought is good.
How the University of Sheffield Uses GPUs to Supercharge Simulations
Networks Asia
Via Hannah Williams
Researchers at the University of Sheffield in the U.K. are increasingly relying on graphics-processing unit (GPU) technology to supercharge their work with complex system simulations. One of their projects used GPUs to improve the speed and accuracy of road micro-simulations by a maximum factor of 33. "We can scale these simulations up to represent human immune systems and run them fast enough and explore all of the different parameters around what type of interventions may produce emergent properties," says Sheffield's Paul Richmond, pointing to disease remission as an example of a "good patient outcome." Last year Sheffield bought a DGX-1 workstation and also teamed with Joint Academic Data Science Endeavor, Britain's biggest GPU facility, to advance its deep learning and artificial intelligence research. Richmond says about 25 percent of the time the workstation is used for functions such as high-performance computing simulations. .... "
How the University of Sheffield Uses GPUs to Supercharge Simulations
Networks Asia
Via Hannah Williams
Researchers at the University of Sheffield in the U.K. are increasingly relying on graphics-processing unit (GPU) technology to supercharge their work with complex system simulations. One of their projects used GPUs to improve the speed and accuracy of road micro-simulations by a maximum factor of 33. "We can scale these simulations up to represent human immune systems and run them fast enough and explore all of the different parameters around what type of interventions may produce emergent properties," says Sheffield's Paul Richmond, pointing to disease remission as an example of a "good patient outcome." Last year Sheffield bought a DGX-1 workstation and also teamed with Joint Academic Data Science Endeavor, Britain's biggest GPU facility, to advance its deep learning and artificial intelligence research. Richmond says about 25 percent of the time the workstation is used for functions such as high-performance computing simulations. .... "
Machine Learning and Blockchains
(This post will be updated with more information before the talk)
Tomorrow, April 11 2018, at 10:30 AM ET
Cognitive Systems Institute
Speaker: Awa Sun Yin
Talk: “Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning”
Slides.
Related article by author: https://medium.com/@awasunyin/are-icos-a-bubble-blockchain-ecosystem-1eae4f02e404
Abstract:
Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity's real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with z 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and Fl-score of z 0.75
Short bio:
Awa Sun Yin is a data scientist during the day at Chainalysis, where she builds programs that leverage machine learning to extract intelligence from blockchain data. During the night, she coordinates and leads research at the Interchain Foundation in areas such as (but not limited to) cryptography, artificial intelligence, and formal verification.
Chainalysis Inc https://www.chainalysis.com/
" ... Our products are targeted at spotting connections between entities on the Bitcoin blockchain. We help Bitcoin businesses detect fraud and prevent money laundering, we help banks put in place adequate controls to bank Bitcoin businesses. ... "
----------------------------
Cognitive Systems Institute Group Speaker Series Thursdays 10:30am US Eastern Time
Join the meetings by pointing your web browser to: https://zoom.us/j/7371462221 ; Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 ; International Numbers: https://zoom.us/zoomconference.
Join the CSIG LinkedIn Group to get reminders about talks and discuss them. Use twitter: #CSIGNews & #OpenTechAI
Replays before Dec 2015: Dial 877.471.6587 or 402.970.2667 and enter the call’s Replay ID when prompted for a program ID number. The Replay ID is listed in the Recording column of each date.
Tomorrow, April 11 2018, at 10:30 AM ET
Cognitive Systems Institute
Speaker: Awa Sun Yin
Talk: “Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning”
Slides.
Related article by author: https://medium.com/@awasunyin/are-icos-a-bubble-blockchain-ecosystem-1eae4f02e404
Abstract:
Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity's real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with z 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and Fl-score of z 0.75
Short bio:
Awa Sun Yin is a data scientist during the day at Chainalysis, where she builds programs that leverage machine learning to extract intelligence from blockchain data. During the night, she coordinates and leads research at the Interchain Foundation in areas such as (but not limited to) cryptography, artificial intelligence, and formal verification.
Chainalysis Inc https://www.chainalysis.com/
" ... Our products are targeted at spotting connections between entities on the Bitcoin blockchain. We help Bitcoin businesses detect fraud and prevent money laundering, we help banks put in place adequate controls to bank Bitcoin businesses. ... "
Cognitive Systems Institute Group Speaker Series Thursdays 10:30am US Eastern Time
Join the meetings by pointing your web browser to: https://zoom.us/j/7371462221 ; Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 ; International Numbers: https://zoom.us/zoomconference.
Join the CSIG LinkedIn Group to get reminders about talks and discuss them. Use twitter: #CSIGNews & #OpenTechAI
Replays before Dec 2015: Dial 877.471.6587 or 402.970.2667 and enter the call’s Replay ID when prompted for a program ID number. The Replay ID is listed in the Recording column of each date.
Update: Blockchain Applications Classification
Slides and recording of the talk given on April 5, 2018:
Excellent talk on effort that seeks to classify and track practical applications.
“Assessing the application landscape of blockchain applications”
SDA Bocconi School of Management By Nico Abbatemarco
In talks given to the Cognitive Systems Institute.
Presentation Slides
Recording.
Excellent talk on effort that seeks to classify and track practical applications.
“Assessing the application landscape of blockchain applications”
SDA Bocconi School of Management By Nico Abbatemarco
In talks given to the Cognitive Systems Institute.
Presentation Slides
Recording.
European Nations Agree in AI Deal
European nations agree AI deal In Innovators Mag.
A number of European nations today signed a declaration of cooperation on artificial intelligence (AI) as part of the continent’s Digital Day activities.
The 25 countries which have agreed to work more closely to overcome the challenges and harness the opportunities of AI.
“In Europe, any successful strategy dealing with AI needs to be cross-border. A large number of Member States agreed to work together on the opportunities and challenges brought by AI. That is excellent news. Cooperation will focus on reinforcing European AI research centres, creating synergies in R&D&I funding schemes across Europe, and exchanging views on the impact of AI on society and the economy. Member States will engage in a continuous dialogue with the Commission, which will act as a facilitator,”said Andrus Ansip, Vice-President for the Digital Single Market, and Mariya Gabriel, Commissioner for Digital Economy and Society on the European Commission website...... "
Talk to the European Commission.
A number of European nations today signed a declaration of cooperation on artificial intelligence (AI) as part of the continent’s Digital Day activities.
The 25 countries which have agreed to work more closely to overcome the challenges and harness the opportunities of AI.
“In Europe, any successful strategy dealing with AI needs to be cross-border. A large number of Member States agreed to work together on the opportunities and challenges brought by AI. That is excellent news. Cooperation will focus on reinforcing European AI research centres, creating synergies in R&D&I funding schemes across Europe, and exchanging views on the impact of AI on society and the economy. Member States will engage in a continuous dialogue with the Commission, which will act as a facilitator,”said Andrus Ansip, Vice-President for the Digital Single Market, and Mariya Gabriel, Commissioner for Digital Economy and Society on the European Commission website...... "
Talk to the European Commission.
Fear of Evolving AI Technology
In Think with Google. On the future role of marketing.
AI and machine learning have many wondering about marketers’ future role. GroupM’s Rob Norman says ‘fear not’
By Rob Norman Mar 2018 Emerging Technology, Search, Video
Rob Norman served as GroupM North America’s CEO, and more recently as the company’s global chief digital officer. He retired this year and shares why marketers shouldn’t fear for their survival as marketing moves toward automation and assistance.
After serving more than 30 years in the agency world, I’m retiring at a time when many aren’t clear what their careers might look like, even five years from now.
With marketing moving toward automation, there’s a lot of uncertainty about the future, and I’m often asked by peers about what’s next for marketers professionally. My advice: Fear not. The move toward automation will only create more need for the work marketers do to build relevant, assistive brands.
We’ve been here before
When I look back on my decades in the ad industry, I can think of several periods of similar uncertainty. And each one was brought on by a new innovation in tools and evolving technology.
There’s no need to fear. … The work marketers do is becoming more important, not less. .... "
AI and machine learning have many wondering about marketers’ future role. GroupM’s Rob Norman says ‘fear not’
By Rob Norman Mar 2018 Emerging Technology, Search, Video
Rob Norman served as GroupM North America’s CEO, and more recently as the company’s global chief digital officer. He retired this year and shares why marketers shouldn’t fear for their survival as marketing moves toward automation and assistance.
After serving more than 30 years in the agency world, I’m retiring at a time when many aren’t clear what their careers might look like, even five years from now.
With marketing moving toward automation, there’s a lot of uncertainty about the future, and I’m often asked by peers about what’s next for marketers professionally. My advice: Fear not. The move toward automation will only create more need for the work marketers do to build relevant, assistive brands.
We’ve been here before
When I look back on my decades in the ad industry, I can think of several periods of similar uncertainty. And each one was brought on by a new innovation in tools and evolving technology.
There’s no need to fear. … The work marketers do is becoming more important, not less. .... "
Advertising on Amazon
New to me: Digiday, with some interesting thoughts. Amazon owns some increasingly important channels, need to understand them
‘We are competing against them’: Some retailers are averse to advertising on Amazon
by Ilyse Liffreing in Digiday.
Although Amazon is relatively new to advertising, the Amazon ad platform is already seeing strong interest from new brands and ad spend from its largest partners increase. But not all retailers are willing to advertise on Amazon.
Retailers that see themselves as direct competitors to Amazon don’t want to give the e-commerce giant even a small fraction of their ad spends, no matter how dominant Amazon becomes.
“Why lend them a helping hand?” said one executive at a large retailer, requesting anonymity. “We are competing against them. The last thing we want to do is contribute to their success.”
‘We are competing against them’: Some retailers are averse to advertising on Amazon
by Ilyse Liffreing in Digiday.
Although Amazon is relatively new to advertising, the Amazon ad platform is already seeing strong interest from new brands and ad spend from its largest partners increase. But not all retailers are willing to advertise on Amazon.
Retailers that see themselves as direct competitors to Amazon don’t want to give the e-commerce giant even a small fraction of their ad spends, no matter how dominant Amazon becomes.
“Why lend them a helping hand?” said one executive at a large retailer, requesting anonymity. “We are competing against them. The last thing we want to do is contribute to their success.”
US, China Innovation
It has been about innovation for some time, saw it starting in our visits to China in the late 90s. We saw their innovation work and we presented ours. Impressive all around.
Why the U.S.-China ‘Trade War’ Is Really About the Future of Innovation In Knowledge@Wharton, by Wharton dean Geoffrey Garrett.
Recent trade skirmishes between China and the United States are less about steel and soybeans and more about which country will be the leader in global innovation in the 21st century, writes Wharton dean Geoffrey Garrett in this opinion piece.
The escalation of tariffs between China and the United States is haunting the financial markets. “Manageable, orchestrated trade skirmishes” is probably the right description. But “trade war” is so much better a headline.
Either way, what is really going on is not about trade; it is about who will lead global innovation in the 21st century. Think less steel, soybeans and solar panels, and more electric vehicles, self-driving cars and artificial intelligence. .... "
Why the U.S.-China ‘Trade War’ Is Really About the Future of Innovation In Knowledge@Wharton, by Wharton dean Geoffrey Garrett.
Recent trade skirmishes between China and the United States are less about steel and soybeans and more about which country will be the leader in global innovation in the 21st century, writes Wharton dean Geoffrey Garrett in this opinion piece.
The escalation of tariffs between China and the United States is haunting the financial markets. “Manageable, orchestrated trade skirmishes” is probably the right description. But “trade war” is so much better a headline.
Either way, what is really going on is not about trade; it is about who will lead global innovation in the 21st century. Think less steel, soybeans and solar panels, and more electric vehicles, self-driving cars and artificial intelligence. .... "
Tuesday, April 10, 2018
HPe Sponsors a Science Fiction Writers Look at Robotics, Assistants
Eminds me of an era past. Yes, the predictions look similar some are starting to work out.
My friend, the robot,
Hugo- and Nebula-winning science fiction author David Gerrold was spot-on in his 1999 predictions about smartphones. Now he predicts computing’s future…again.
Back in 1999, I was asked to write a short article for Sm@rt Reseller magazine about the future of computing, because (allegedly) science fiction authors are in the business of contemplating the future..... "
My friend, the robot,
Hugo- and Nebula-winning science fiction author David Gerrold was spot-on in his 1999 predictions about smartphones. Now he predicts computing’s future…again.
Back in 1999, I was asked to write a short article for Sm@rt Reseller magazine about the future of computing, because (allegedly) science fiction authors are in the business of contemplating the future..... "
BioMimicry in Robotics
Like the fact that this is being looked at broadly.
Festo's New Bionic Robots Include Rolling Spider, Flying Fox
The latest in weird and potentially useful robots from German automation giant Festo By Evan Ackerman
We love Festo because every year they invest an entirely appropriate amount of time and money into bio-inspired robots that are totally cool and very functional but have limited usefulness. More often than not, it seems like Festo is able to take some of what it learns from designing and constructing these things and create practical new revenue-generating products. Which is good for them, and means they’ll keep making cool stuff. Over the last few years, we’ve met ants, butterflies, flying jellyfish and penguins, kangaroos, seagulls, and much more.
Festo has just announced its two newest bionic learning network robots—one is a very convincing flying fox, and the other is a walking, tumbling robot inspired by a Saharan spider. ... "
Festo's New Bionic Robots Include Rolling Spider, Flying Fox
The latest in weird and potentially useful robots from German automation giant Festo By Evan Ackerman
We love Festo because every year they invest an entirely appropriate amount of time and money into bio-inspired robots that are totally cool and very functional but have limited usefulness. More often than not, it seems like Festo is able to take some of what it learns from designing and constructing these things and create practical new revenue-generating products. Which is good for them, and means they’ll keep making cool stuff. Over the last few years, we’ve met ants, butterflies, flying jellyfish and penguins, kangaroos, seagulls, and much more.
Festo has just announced its two newest bionic learning network robots—one is a very convincing flying fox, and the other is a walking, tumbling robot inspired by a Saharan spider. ... "
Barnes & Noble Crowdsources Reading with Browsery
Been a while that I have heard much from B&N, so this is interesting, can it recreate the bookstore virtually? Can this alter the nature of reading, book selection? With come expert comment:
Barnes & Noble’s crowdsourcing app engages readers and earns solid reviews By Tom Ryan
Barnes & Noble has launched Browsery, an app that uses crowdsourcing to help readers discover new books.
The app basically features a bunch of questions that support “browsing, community, and conversation.” Customers can “like”, comment on or contribute answers to questions about books posted by the Browsery community. They may also like or comment on the answers of others or ask questions of their own.
For example, the Biography & Memoir section includes questions such as:
“Favorite memoirs that include recipes?”
“What are some interesting biographies for a film-lover?”
“What celebrity made you laugh the hardest?”
Launched in late March, one question has already drawn 84 responses. Clicking through enables users to “agree” with a suggestion and offer the reason why. The authors with the most “agrees” to the “laugh the hardest” question included Tina Fey, Amy Poehler and Trevor Noah. .... "
Barnes & Noble’s crowdsourcing app engages readers and earns solid reviews By Tom Ryan
Barnes & Noble has launched Browsery, an app that uses crowdsourcing to help readers discover new books.
The app basically features a bunch of questions that support “browsing, community, and conversation.” Customers can “like”, comment on or contribute answers to questions about books posted by the Browsery community. They may also like or comment on the answers of others or ask questions of their own.
For example, the Biography & Memoir section includes questions such as:
“Favorite memoirs that include recipes?”
“What are some interesting biographies for a film-lover?”
“What celebrity made you laugh the hardest?”
Launched in late March, one question has already drawn 84 responses. Clicking through enables users to “agree” with a suggestion and offer the reason why. The authors with the most “agrees” to the “laugh the hardest” question included Tina Fey, Amy Poehler and Trevor Noah. .... "
False Choice Between Automation and Jobs
On Automation vs Jobs. We are seeing the need for some adaptation. Well put, in the HBR. What we need to consider:
The False Choice Between Automation and Jobs
By: James Manyikais the San Francisco-based director of the McKinsey Global Institute (MGI), the business and economics research arm of McKinsey & Company. and
Michael Spence a Nobel laureate in economics, is Professor of Economics at NYU’s Stern School of Business. ...
" ... First, a much sharper focus on skills and training. That means reversing the trend of declining government spending on training that is apparent in many OECD countries. It also means a stepped-up role for companies, which will be on the front line of automation adoption and will know better and faster which skills are required.
Second, we should take another look at making the labor market more fluid, including by more active use of digital technologies for job matching and for stimulating the rise of independent work. In fact, the dynamism of labor markets is waning: in the United States, for example, the job reallocation rate dropped by 25% between 1990 and 2013, and the share of workers relocating across state lines annually has fallen by half, to close to 1.5%.
Government, businesses, educational institutions, and labor organizations need to collaborate to ensure that incumbents and new entrants to the labor market have accurate forward-looking knowledge of the evolving mix of skill and experience requirements.
The third priority should be a reevaluation of income and transition support to help displaced workers or those struggling with transitions to new occupations. Germany set an example here by revamping its labor agency and putting an emphasis on acquiring skills. Its labor participation rate has risen by 10 percentage points since reunification, to above the U.S. level. ... "
The False Choice Between Automation and Jobs
By: James Manyikais the San Francisco-based director of the McKinsey Global Institute (MGI), the business and economics research arm of McKinsey & Company. and
Michael Spence a Nobel laureate in economics, is Professor of Economics at NYU’s Stern School of Business. ...
" ... First, a much sharper focus on skills and training. That means reversing the trend of declining government spending on training that is apparent in many OECD countries. It also means a stepped-up role for companies, which will be on the front line of automation adoption and will know better and faster which skills are required.
Second, we should take another look at making the labor market more fluid, including by more active use of digital technologies for job matching and for stimulating the rise of independent work. In fact, the dynamism of labor markets is waning: in the United States, for example, the job reallocation rate dropped by 25% between 1990 and 2013, and the share of workers relocating across state lines annually has fallen by half, to close to 1.5%.
Government, businesses, educational institutions, and labor organizations need to collaborate to ensure that incumbents and new entrants to the labor market have accurate forward-looking knowledge of the evolving mix of skill and experience requirements.
The third priority should be a reevaluation of income and transition support to help displaced workers or those struggling with transitions to new occupations. Germany set an example here by revamping its labor agency and putting an emphasis on acquiring skills. Its labor participation rate has risen by 10 percentage points since reunification, to above the U.S. level. ... "
Stricter Regs for Small Drone Operators
Just completed a book that looks at how tech like drones are used to interact with process. Changing regulations will change the longer term value of their use.
Alphabet, other companies want strict drone rules to apply to everyone
Even small drone operators could be subject to FAA regulations
By Zac Estrada @zacestrada
Alphabet, other companies want strict drone rules to apply to everyone
Even small drone operators could be subject to FAA regulations
By Zac Estrada @zacestrada
Monday, April 09, 2018
Zanzibar Scans Objects and Brings them into Virtual Reality
For games and toys, certainly, but also for design of objects and how they live in spaces. And more,
Microsoft’s object-detecting playmat brings toys to life
Project Zanzibar looks like more than a kids toy. ....
David Lumb, @OutOnALumb in Engadget
"...Zanzibar is a 'sensing platform' that combines capacitive sensing to track finger movements with Near Field Communication (NFC) to detect objects. It's also flexible, both literally and systematically: Just hook it up to any tablet or screen and unroll the mat, and you're ready to go. It can sense objects simply by placing an NFC sticker on their bottoms, which tracks them spatially and even vertically, identifying distinct items stacked on top of each other. .."
Youtube video demonstration.
Not yet available, a UK MS Research project. Paper on its use and development.
Wal-Mart Adding Pickup Towers
Makes sense, See also comments in Retailwire. The in-store location of the towers are criticized.
Walmart Is Adding Hundreds More Pickup Towers to Its Stores By Jonathan Vanian in Fortune.
Walmart is adding hundreds of more of its Pickup Towers at more of its brick-and-mortar stores all over the U.S.
After the retail giant sets up 500 more Pickup Towers at its U.S. stores, it will have a total of 700 of its over-sized delivery kiosks ready for use by the end of the year.
Walmart (WMT, +0.61%) first debuted the Pickup Towers last fall and pitched the 16-foot tall orange receptacles as an easier way for people to order toothpaste and other goods online, and then retrieve them from the self-serve machines.
The company said that since its debut, Walmart customers have placed over 500,000 orders. The kiosks large size makes them able to hold items as big as televisions, Walmart said. .... "
Walmart Is Adding Hundreds More Pickup Towers to Its Stores By Jonathan Vanian in Fortune.
Walmart is adding hundreds of more of its Pickup Towers at more of its brick-and-mortar stores all over the U.S.
After the retail giant sets up 500 more Pickup Towers at its U.S. stores, it will have a total of 700 of its over-sized delivery kiosks ready for use by the end of the year.
Walmart (WMT, +0.61%) first debuted the Pickup Towers last fall and pitched the 16-foot tall orange receptacles as an easier way for people to order toothpaste and other goods online, and then retrieve them from the self-serve machines.
The company said that since its debut, Walmart customers have placed over 500,000 orders. The kiosks large size makes them able to hold items as big as televisions, Walmart said. .... "
Free Talk on How Small are our Big Data
The 27th Annual E. Leonard Arnoff/Milton J. Schloss Memorial Lecture on the Practice of Business Analytics
Wednesday 04/18, 2018, 6:00-6:45PM
Kingsgate Marriott Conference Center (Ballroom on the 2nd Floor)
University of Cincinnati
Free & Open to the Public
“How Small Are Our Big Data: Turning the 2016 Surprise into a 2020 Vision”
Xiao-Li Meng, Harvard University
Whipple V. N. Jones Professor of Statistics
Dean of the Graduate School of Arts and Sciences
Xiao-Li Meng is well known for his depth and breadth in research, his innovation and passion in pedagogy, and his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng has received numerous awards and honors for the more than 150 publications; he has delivered more than 400 research presentations and public speeches. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, frequentist, and fiducial perspectives) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies).
Abstract: The term “Big Data” emphasizes data quantity, not quality. However, much of the current measures of statistical uncertainties and errors are adequate only when the data are of perfect quality. We show that once we take into account the data quality, the effective sample size of a Big Data set can be vanishingly small. Without understanding this phenomenon, Big Data can do more harm than good due to drastically inflated precision assessments that cause gross overconfidence. This overconfidence leads us to be caught by surprise when reality unfolds, as we all experienced during the 2016 US Presidential election. Data from the Cooperative Congressional Election Study (conducted by Stephen Ansolabehere, Douglas River and others, and analyzed by Shiro Kuriwaki), are used to assess the data quality in 2016 US election polls, with the aim of gaining a clearer vision for the 2020 election and beyond.
Sponsor: Department of Operations, Business Analytics, and Information Systems, Lindner College of Business, University of Cincinnati More.
Wednesday 04/18, 2018, 6:00-6:45PM
Kingsgate Marriott Conference Center (Ballroom on the 2nd Floor)
University of Cincinnati
Free & Open to the Public
“How Small Are Our Big Data: Turning the 2016 Surprise into a 2020 Vision”
Xiao-Li Meng, Harvard University
Whipple V. N. Jones Professor of Statistics
Dean of the Graduate School of Arts and Sciences
Xiao-Li Meng is well known for his depth and breadth in research, his innovation and passion in pedagogy, and his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng has received numerous awards and honors for the more than 150 publications; he has delivered more than 400 research presentations and public speeches. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, frequentist, and fiducial perspectives) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies).
Abstract: The term “Big Data” emphasizes data quantity, not quality. However, much of the current measures of statistical uncertainties and errors are adequate only when the data are of perfect quality. We show that once we take into account the data quality, the effective sample size of a Big Data set can be vanishingly small. Without understanding this phenomenon, Big Data can do more harm than good due to drastically inflated precision assessments that cause gross overconfidence. This overconfidence leads us to be caught by surprise when reality unfolds, as we all experienced during the 2016 US Presidential election. Data from the Cooperative Congressional Election Study (conducted by Stephen Ansolabehere, Douglas River and others, and analyzed by Shiro Kuriwaki), are used to assess the data quality in 2016 US election polls, with the aim of gaining a clearer vision for the 2020 election and beyond.
Sponsor: Department of Operations, Business Analytics, and Information Systems, Lindner College of Business, University of Cincinnati More.
Marketing Mix Optimization Using Bayesian Networks
A long time challenge in the enterprise. I note this seminar on the topic. I am about to review. Remember very well the causal assumptions applied and challenged.. Please do follow up if you have any comments, especially regarding practical use.
Stefan Conrady
Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning
Marketing Mix Optimization with Bayesian Networks
With over 130 participants, today's event was our most popular webinar yet. No wonder, marketing mix modeling remains a mystery to many! And, neither big data nor clever statistical techniques can solve this problem for us. One way or another, it's about the causal assumptions one needs to make (and be able to justify). However, Bayesian networks make this process much more transparent, and they allow for some brilliant shortcuts! Check out the webinar recording and see for yourself!
https://bayesia.wistia.com/medias/fhqe74avqp
Stefan Conrady
Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning
Marketing Mix Optimization with Bayesian Networks
With over 130 participants, today's event was our most popular webinar yet. No wonder, marketing mix modeling remains a mystery to many! And, neither big data nor clever statistical techniques can solve this problem for us. One way or another, it's about the causal assumptions one needs to make (and be able to justify). However, Bayesian networks make this process much more transparent, and they allow for some brilliant shortcuts! Check out the webinar recording and see for yourself!
https://bayesia.wistia.com/medias/fhqe74avqp
Datasets for Teaching Data Science
Useful to have for testing and teaching concepts. With sample code.
Some datasets for teaching data science
By Rafael Irizarry 2018/01/22
In this post I describe the dslabs package, which contains some datasets that I use in my data science courses.
A much discussed topic in stats education is that computing should play a more prominent role in the curriculum. I strongly agree, but I think the main improvement will come from bringing applications to the forefront and mimicking, as best as possible, the challenges applied statisticians face in real life. I therefore try to avoid using widely used toy examples, such as the mtcars dataset, when I teach data science. However, my experience has been that finding examples that are both realistic, interesting, and appropriate for beginners is not easy. After a few years of teaching I have collected a few datasets that I think fit this criteria. To facilitate their use in introductory classes, I include them in the dslabs package: .... "
Some datasets for teaching data science
By Rafael Irizarry 2018/01/22
In this post I describe the dslabs package, which contains some datasets that I use in my data science courses.
A much discussed topic in stats education is that computing should play a more prominent role in the curriculum. I strongly agree, but I think the main improvement will come from bringing applications to the forefront and mimicking, as best as possible, the challenges applied statisticians face in real life. I therefore try to avoid using widely used toy examples, such as the mtcars dataset, when I teach data science. However, my experience has been that finding examples that are both realistic, interesting, and appropriate for beginners is not easy. After a few years of teaching I have collected a few datasets that I think fit this criteria. To facilitate their use in introductory classes, I include them in the dslabs package: .... "
New Kinds of Brain Maps
Unique tagging and looking for patterns to determine interaction among the brain cells. Other ways to use this method?
New Brain Maps With Unmatched Detail May Change Neuroscience
in Quanta Magazine by Monique Brouillette
A technique based on genetic bar codes can easily map the connections of individual brain cells in unprecedented numbers. Unexpected complexity in the visual system is only the first secret it has revealed....."
New Brain Maps With Unmatched Detail May Change Neuroscience
in Quanta Magazine by Monique Brouillette
A technique based on genetic bar codes can easily map the connections of individual brain cells in unprecedented numbers. Unexpected complexity in the visual system is only the first secret it has revealed....."
Microsoft's Next Act
From McKinsey, a Podcast:
CEO Satya Nadella talks about innovation, disruption, and organizational change.
In 2014, Satya Nadella was appointed CEO of Microsoft, making him only the third leader in the software company’s 40-year history, following Bill Gates and Steve Ballmer. Since taking the top job, Nadella has doubled down on cloud computing, artificial intelligence (AI), and social networking while also pushing Microsoft to become more innovative, collaborative, and customer focused. In 2017, he published Hit Refresh: The Quest to Rediscover Microsoft’s Soul and Imagine a Better Future for Everyone, a book reflecting on his journey from a cricket-obsessed childhood in India to leadership of one of the world’s largest companies .... ."
CEO Satya Nadella talks about innovation, disruption, and organizational change.
In 2014, Satya Nadella was appointed CEO of Microsoft, making him only the third leader in the software company’s 40-year history, following Bill Gates and Steve Ballmer. Since taking the top job, Nadella has doubled down on cloud computing, artificial intelligence (AI), and social networking while also pushing Microsoft to become more innovative, collaborative, and customer focused. In 2017, he published Hit Refresh: The Quest to Rediscover Microsoft’s Soul and Imagine a Better Future for Everyone, a book reflecting on his journey from a cricket-obsessed childhood in India to leadership of one of the world’s largest companies .... ."
Sunday, April 08, 2018
How to Think about Machine Learning
Nicely and non-technically put:
How to Think About Machine Learning Intro: by Jason Brownlee
Machine learning is a large and interdisciplinary field of study.
You can achieve impressive results with machine learning and find solutions to very challenging problems. But this is only a small corner of the broader field of machine learning often called predictive modeling or predictive analytics.
In this post, you will discover how to change the way you think about machine learning in order to best serve you as a machine learning practitioner.
After reading this post, you will know:
What machine learning is and how it relates to artificial intelligence and statistics.
The corner of machine learning that you should focus on.
How to think about your problem and the machine learning solution to your problem.
Let’s get started. .... "
How to Think About Machine Learning Intro: by Jason Brownlee
Machine learning is a large and interdisciplinary field of study.
You can achieve impressive results with machine learning and find solutions to very challenging problems. But this is only a small corner of the broader field of machine learning often called predictive modeling or predictive analytics.
In this post, you will discover how to change the way you think about machine learning in order to best serve you as a machine learning practitioner.
After reading this post, you will know:
What machine learning is and how it relates to artificial intelligence and statistics.
The corner of machine learning that you should focus on.
How to think about your problem and the machine learning solution to your problem.
Let’s get started. .... "
Radiologist Case Study of AI and Professional Jobs
Useful thoughts about how professional expertise might or might not be replaced. Augmented, but not replaced? With good detail. We examined similar professional applications in healthcare in previous AI efforts.
AI Will Change Radiology, but It Won’t Replace Radiologists
Thomas H. Davenport, Keith J. Dreyer, DO in HBR.
Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings.
That said, deep learning is rapidly advancing, and it’s a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology. Radiological practice would certainly benefit from systems that can read and interpret multiple images quickly, because the number of images has increased much faster over the last decade than the number of radiologists. Hundreds of images can be taken for one patient’s disease or injury. Imaging and radiology are expensive, and any solution that could reduce human labor, lower costs, and improve diagnostic accuracy would benefit patients and physicians alike. ... "
What does this mean for radiologists? Some medical students have reportedly decided not to specialize in radiology because they fear the job will cease to exist. We’re confident, however, that the great majority of radiologists will continue to have jobs in the decades to come — jobs that will be altered and enhanced by AI. One of us (Keith) is a radiologist and artificial intelligence researcher, and the other (Thomas) has researched the impact of AI on jobs for several years. We see several reasons why radiologists won’t be disappearing from the labor force, which we describe below. We also believe that several of these factors will inhibit the large-scale automation of other jobs supposedly threatened by AI. ... "
AI Will Change Radiology, but It Won’t Replace Radiologists
Thomas H. Davenport, Keith J. Dreyer, DO in HBR.
Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings.
That said, deep learning is rapidly advancing, and it’s a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology. Radiological practice would certainly benefit from systems that can read and interpret multiple images quickly, because the number of images has increased much faster over the last decade than the number of radiologists. Hundreds of images can be taken for one patient’s disease or injury. Imaging and radiology are expensive, and any solution that could reduce human labor, lower costs, and improve diagnostic accuracy would benefit patients and physicians alike. ... "
What does this mean for radiologists? Some medical students have reportedly decided not to specialize in radiology because they fear the job will cease to exist. We’re confident, however, that the great majority of radiologists will continue to have jobs in the decades to come — jobs that will be altered and enhanced by AI. One of us (Keith) is a radiologist and artificial intelligence researcher, and the other (Thomas) has researched the impact of AI on jobs for several years. We see several reasons why radiologists won’t be disappearing from the labor force, which we describe below. We also believe that several of these factors will inhibit the large-scale automation of other jobs supposedly threatened by AI. ... "
Level K thinking in Auctions and Assistants
How we also model interaction in any kind of interaction. Adversarial or Collaborative
Level-K Thinking in Auctions (Page 11) By David Banks, Duke University ...
From Talk notes.
" ... A fourth solution concept is level-k thinking. If Daphne is a level-0 thinker, she bids non-strategically. If she is a level-1 thinker, she believes Apollo is a level-0 thinker, and makes her best response given her subjective assessment of the probabilities. If she is a level-2 thinker, she believes Apollo is a level-1 thinker, and so forth.
The “I think that you think that I think ...” reasoning becomes intricate. (Recall Vizzini’s analysis of the iocaine powder in The Princess Bride). An example will be more clear: Suppose Daphne is a level-2 thinker. She believes Apollo is a level-1 thinker who thus believes that she is non-strategic.... "
From talk described here.
Level-K Thinking in Auctions (Page 11) By David Banks, Duke University ...
From Talk notes.
" ... A fourth solution concept is level-k thinking. If Daphne is a level-0 thinker, she bids non-strategically. If she is a level-1 thinker, she believes Apollo is a level-0 thinker, and makes her best response given her subjective assessment of the probabilities. If she is a level-2 thinker, she believes Apollo is a level-1 thinker, and so forth.
The “I think that you think that I think ...” reasoning becomes intricate. (Recall Vizzini’s analysis of the iocaine powder in The Princess Bride). An example will be more clear: Suppose Daphne is a level-2 thinker. She believes Apollo is a level-1 thinker who thus believes that she is non-strategic.... "
From talk described here.
Designing and Testing AI Microservices
Thought ultimately this would be a key aspect of delivering intelligence applications. The architectural design and selection of what each service looked like, and how they can be implemented to interact. A reminder of our look at Object Oriented programming, This is only the start to make things workable, and most important: maintainable.
Architecturally Aligned Testing
By Stefan Friese in InfoQ
, reviewed by Shane Hastie
Key Takeaways
Microservices are typically owned by cross-functional and autonomous teams, responsible for implementing, testing, and releasing.
When implementing a microservices architecture there should be no separate testing phase.
Everybody is responsible for the quality of the product.The complexity of integrating microservices requires carefully deciding the appropriate integration test approach.
Continuous Testing means a shift left in testing to ensure that teams stay autonomous and a shift right in testing towards exploration and experimentation. ...
Architecture impacts heavily testing. The way you test a monolith is different than how you should test a loosely coupled system. Let’s look at microservices as a prominent exponent of loosely coupled systems. As we will see, they challenge our previous definition of testing.... "
Architecturally Aligned Testing
By Stefan Friese in InfoQ
, reviewed by Shane Hastie
Key Takeaways
Microservices are typically owned by cross-functional and autonomous teams, responsible for implementing, testing, and releasing.
When implementing a microservices architecture there should be no separate testing phase.
Everybody is responsible for the quality of the product.The complexity of integrating microservices requires carefully deciding the appropriate integration test approach.
Continuous Testing means a shift left in testing to ensure that teams stay autonomous and a shift right in testing towards exploration and experimentation. ...
Architecture impacts heavily testing. The way you test a monolith is different than how you should test a loosely coupled system. Let’s look at microservices as a prominent exponent of loosely coupled systems. As we will see, they challenge our previous definition of testing.... "
Update: Adversarial Risk Analysis Talk
Talk given this week by Dr David Banks of Duke University and sponsored by Yichen Qin, Assistant Professor, Department of Operations, Business Analytics, and Information Systems, Lindner College of Business, University of Cincinnati, on April 6, 2018
Adversarial Risk Analysis Talk, full announcement.
Speaker: Dr. David Banks, Duke University
Title: Adversarial Risk Analysis
Abstract: Adversarial Risk Analysis (ARA) is a Bayesian alternative to classical game theory. Rooted in decision theory, one builds a model for the decision-making of one's opponent, placing subjective distributions over all unknown quantities. Then one chooses the action that maximizes expected utility. This approach aligns with some perspectives in modern behavioral economics, and enables principled analysis of novel problems, such as a multiparty auction in which there is no common knowledge and different bidders have different opinion about each other. .... "
Here are the slides from the talk (Technical)
Good talk. In particular because it dealt with how people work in interactions. Either versus nature, or versus other humans, here in some sort of competition. The most common game example used was the Auction. In the cases described these were adversarial. Under the structure of this 'game' to win the auction. Humans in any interaction build a model of who they are interacting with. The methods proposed construct numerical methods to define the value of alternative strategies.
But it immediately came to mind that these methods don't need to be adversarial. When people converse, or ask for help from an adviser (Human or machine). they are looking to maximize the value of the interaction. In a chatbot, for example a person wants to solve a problem, the chatbot has defined knowledge. How should the interaction proceed? How should these advisory approaches be strategically designed? Under constraints and costs? How do we rate Assistants in an approach to provide value? Whats the structure of that game? Examining.
Speaker: Dr. David Banks, Duke University
Title: Adversarial Risk Analysis
Abstract: Adversarial Risk Analysis (ARA) is a Bayesian alternative to classical game theory. Rooted in decision theory, one builds a model for the decision-making of one's opponent, placing subjective distributions over all unknown quantities. Then one chooses the action that maximizes expected utility. This approach aligns with some perspectives in modern behavioral economics, and enables principled analysis of novel problems, such as a multiparty auction in which there is no common knowledge and different bidders have different opinion about each other. .... "
Here are the slides from the talk (Technical)
Good talk. In particular because it dealt with how people work in interactions. Either versus nature, or versus other humans, here in some sort of competition. The most common game example used was the Auction. In the cases described these were adversarial. Under the structure of this 'game' to win the auction. Humans in any interaction build a model of who they are interacting with. The methods proposed construct numerical methods to define the value of alternative strategies.
But it immediately came to mind that these methods don't need to be adversarial. When people converse, or ask for help from an adviser (Human or machine). they are looking to maximize the value of the interaction. In a chatbot, for example a person wants to solve a problem, the chatbot has defined knowledge. How should the interaction proceed? How should these advisory approaches be strategically designed? Under constraints and costs? How do we rate Assistants in an approach to provide value? Whats the structure of that game? Examining.
Saturday, April 07, 2018
Marketing Strategy
Better together: Why integrating data strategy, teams, and technology leads to marketing success in ThinkWithGoogle
Customer interactions with brands generate an abundance of digital information, but the right elements must be combined correctly to yield insights. New research from Econsultancy and Google shows how leading marketers find success by integrating data strategy, teams, and technology.
During the 21st-century consumer’s meandering path to purchase, a stream of data flows out of each brand interaction. But even a torrent of digital information is useless if you’re unable to extract insights from it.
In order to win each micro-moment of the journey, marketers need to deliver better, more relevant experiences. How do you make those experiences more relevant, though? In today’s digital world, it requires embracing a data strategy as a means to more deeply understand audiences and applying those insights to marketing and advertising. But that’s not always as easy as it sounds.
Earlier this year, we shared that marketers still reported dealing with significant data-related challenges in their analytics initiatives. Among other things, they struggle with accessing or integrating data, recruiting the right kind (and right amount) of analytics talent, and gaining executive buy-in and support. ..... "
Customer interactions with brands generate an abundance of digital information, but the right elements must be combined correctly to yield insights. New research from Econsultancy and Google shows how leading marketers find success by integrating data strategy, teams, and technology.
During the 21st-century consumer’s meandering path to purchase, a stream of data flows out of each brand interaction. But even a torrent of digital information is useless if you’re unable to extract insights from it.
In order to win each micro-moment of the journey, marketers need to deliver better, more relevant experiences. How do you make those experiences more relevant, though? In today’s digital world, it requires embracing a data strategy as a means to more deeply understand audiences and applying those insights to marketing and advertising. But that’s not always as easy as it sounds.
Earlier this year, we shared that marketers still reported dealing with significant data-related challenges in their analytics initiatives. Among other things, they struggle with accessing or integrating data, recruiting the right kind (and right amount) of analytics talent, and gaining executive buy-in and support. ..... "
Brain Computer Interfaces
Could these methods be used beyond the hands-free enhancement? Could they be used to replace or enhance human interaction when mentally disabled? Presented on the site as technical augmentation, for computing platforms, but why not more generally? The ultimate kind of augmented reality.
Extensive details at the Neurable site.
"...Neurable builds brain-computer interfaces for next-generation computing platforms ...
Imagine your mind in mixed reality. ...
Neurable’s vision is to create a world without limitations. Our revolutionary brain-computer interface allows people to control software and devices using only their brain activity.
Immersive computing requires a new approach to human-computer interaction. Neurable designs software and solutions that function as a natural extension of our brains, creating new possibilities for human empowerment.
Our approach is science-driven and cross disciplinary, incorporating elements of neuroscience, biology, statistics, machine learning and design to create the ultimate user interface. With Neurable, mixed reality can finally achieve its full potential. ...."
Extensive details at the Neurable site.
"...Neurable builds brain-computer interfaces for next-generation computing platforms ...
Imagine your mind in mixed reality. ...
Neurable’s vision is to create a world without limitations. Our revolutionary brain-computer interface allows people to control software and devices using only their brain activity.
Immersive computing requires a new approach to human-computer interaction. Neurable designs software and solutions that function as a natural extension of our brains, creating new possibilities for human empowerment.
Our approach is science-driven and cross disciplinary, incorporating elements of neuroscience, biology, statistics, machine learning and design to create the ultimate user interface. With Neurable, mixed reality can finally achieve its full potential. ...."
Evolving Process of Automobile Use and Security
A link to a Consumer Reports article on the recent evolution of automobile access, control and security technologies. Non-technical. Informative for a recent process examination. While this starts talking about the Tesla it covers other manufacturers, and alternative technologies. Also alternative solutions like the use of smartphone Apps, and alternative communications and location methods. Instructive from a design and selection direction.
" ... The new Tesla Model 3 has lots of eye-opening features that distinguish this fully-electric car from other hybrid and electric vehicles, including a single touch screen that replaces the traditional dashboard full of buttons and displays..... "
" ... The new Tesla Model 3 has lots of eye-opening features that distinguish this fully-electric car from other hybrid and electric vehicles, including a single touch screen that replaces the traditional dashboard full of buttons and displays..... "
Mind Reading, Hands-free Augmentation
A long term goal, hands-free interaction with the Internet. I got a demo if this kind of thing a decade ago, and it didn't really work. Is it here now? Ideal use to drive an assistant. The ultimate augmentation?
Mind-reading headset lets you Google just with your thoughts
A mind-reading device can answer questions in your head. It works by picking up signals sent from your brain when you think about saying something By Chelsea Whyte
SILENTLY think of a question and I will answer it. That might sound like a magic trick, but it is the promise of AlterEgo, a headset that lets you speak to a computer without ever uttering a sound. It’s not quite a mind reader, but it is close.
The device brings us a step closer to a world where we can interact seamlessly with machines using only our thoughts. AlterEgo’s creators believe that rather than embarrassingly saying things like “OK Google” or “Hey Siri” and then …"
And more on this ....
Mind-reading tech is here (and more useful than you think!) in Computerworld
Reading brain waves was useless until A.I. got involved. Now mind reading has real-world, practical applications. By Mike Elgan ..... "
See also the company Neurable for an example of potential implementations ...
Mind-reading headset lets you Google just with your thoughts
A mind-reading device can answer questions in your head. It works by picking up signals sent from your brain when you think about saying something By Chelsea Whyte
SILENTLY think of a question and I will answer it. That might sound like a magic trick, but it is the promise of AlterEgo, a headset that lets you speak to a computer without ever uttering a sound. It’s not quite a mind reader, but it is close.
The device brings us a step closer to a world where we can interact seamlessly with machines using only our thoughts. AlterEgo’s creators believe that rather than embarrassingly saying things like “OK Google” or “Hey Siri” and then …"
And more on this ....
Mind-reading tech is here (and more useful than you think!) in Computerworld
Reading brain waves was useless until A.I. got involved. Now mind reading has real-world, practical applications. By Mike Elgan ..... "
See also the company Neurable for an example of potential implementations ...
Friday, April 06, 2018
Deloitte on Humans and Automation.
Trends and forecasts.
AI, robotics, and automation
As AI and other advanced technologies permeate the workplace, skills such as critical thinking, creativity, and problem-solving gain in importance. Leading companies are recognizing that these technologies are most effective when they complement humans, not replace them.
Trend Analysis
Respondents have a clear understanding of the human skills needed to manage AI, robotics, and automation, such as complex problem-solving, cognitive abilities, and social skills. Yet many also said they do not have a plan to cultivate these skills.
Expected emphasis on workforce skills as AI/robotics are integrated in the enterprise ... "
AI, robotics, and automation
As AI and other advanced technologies permeate the workplace, skills such as critical thinking, creativity, and problem-solving gain in importance. Leading companies are recognizing that these technologies are most effective when they complement humans, not replace them.
Trend Analysis
Respondents have a clear understanding of the human skills needed to manage AI, robotics, and automation, such as complex problem-solving, cognitive abilities, and social skills. Yet many also said they do not have a plan to cultivate these skills.
Expected emphasis on workforce skills as AI/robotics are integrated in the enterprise ... "
Better Roomba Needs a Breakthrough
We spent quite sometime looking at home robotics, It has now been around for some time, and continues to mature. It will require some breakthrough in value to become common.
An iRoomba Review in Wired.
This bot features iRobot's best vacuuming tech, but at less than half the price, about 50% of the time. ..."
An iRoomba Review in Wired.
This bot features iRobot's best vacuuming tech, but at less than half the price, about 50% of the time. ..."
Smithsonian Looks at an AI Future
A non technical view ...
What Will Our Society Look Like When Artificial Intelligence Is Everywhere?
Will robots become self-aware? Will they have rights? Will they be in charge? Here are five scenarios from our future dominated by AI ....
By Stephan Talty, illustrations by Jules Julien...."
What Will Our Society Look Like When Artificial Intelligence Is Everywhere?
Will robots become self-aware? Will they have rights? Will they be in charge? Here are five scenarios from our future dominated by AI ....
By Stephan Talty, illustrations by Jules Julien...."
Whats a Smart Contract?
When I asked the question at a recent talk I got the answer that smart contracts are the embedded agreements that exist in all Blockchains. But they can refer to other unrelated external agreements as well. But how often is that latter approach used?
See also: https://www.ethereum.org/
Whats a Smart Contract?
With the advent of the Ethereum blockchain everyone started to talk about “smart contracts” as an absolutely new approach to regulating relations among people. Today we will discuss what a smart contract is in simple words.
Smart contracts are a form of automated agreements that use electronic algorithms or conditions to facilitate the execution of various transactions. Their goal is to increase the transparency of transactions with a reduction in fees and eliminate potential conflicts in case one of the parties to a contract fails to fulfill its obligations.
A smart contract helps to avoid risks when making deals and prevent prolonged judicial process in case of default, to reduce costs by eliminating intermediaries and to ensure the bilateral trust. .... "
See also: https://www.ethereum.org/
Whats a Smart Contract?
With the advent of the Ethereum blockchain everyone started to talk about “smart contracts” as an absolutely new approach to regulating relations among people. Today we will discuss what a smart contract is in simple words.
Smart contracts are a form of automated agreements that use electronic algorithms or conditions to facilitate the execution of various transactions. Their goal is to increase the transparency of transactions with a reduction in fees and eliminate potential conflicts in case one of the parties to a contract fails to fulfill its obligations.
A smart contract helps to avoid risks when making deals and prevent prolonged judicial process in case of default, to reduce costs by eliminating intermediaries and to ensure the bilateral trust. .... "
VR and Eye Tracking
We saw the need for this early on. Where you are looking implies intent, so why not track it?
Peter Rubin in Wired.
Eye Tracking Coming to VR Sooner that you think. What Now? ....
".... Eye-tracking has been a part of the VR conversation for years; a company called FOVE even crowdsourced its own eye-tracking headset in 2015. Yet, when the feature would actually arrive at scale has been an open-ended question. Even as multiple companies race to release all-in-one "standalone" headsets—Lenovo's Mirage Solo launches in May, with Oculus' $199 Go device rumored to be close behind, and HTC's Vive Focus will be arriving stateside this year after a China-only debut—the dream of our eyes making it into VR seemed farsighted at best. ..."
Peter Rubin in Wired.
Eye Tracking Coming to VR Sooner that you think. What Now? ....
".... Eye-tracking has been a part of the VR conversation for years; a company called FOVE even crowdsourced its own eye-tracking headset in 2015. Yet, when the feature would actually arrive at scale has been an open-ended question. Even as multiple companies race to release all-in-one "standalone" headsets—Lenovo's Mirage Solo launches in May, with Oculus' $199 Go device rumored to be close behind, and HTC's Vive Focus will be arriving stateside this year after a China-only debut—the dream of our eyes making it into VR seemed farsighted at best. ..."
AliBaba Carpools
Continued interesting things being done by Alibaba in China.
Alibaba’s Mapping Unit AutoNavi Declares War On Didi Via Launch Of Carpooling Business By Yimian Wu
Alibaba’s mapping unit, AutoNavi Holdings Ltd. also known as Gaode Map, has launched its own carpooling business, starting in Chengdu and Wuhan initially with plans to roll out nationally.
In a move that declares war to existing ride sharing leader Didi Chuxing, AutoNavi’s expansion into the shared mobility space further complicates a sector that was once viewed as "battle completed."
Even though AutoNavi is beginning with carpooling services first, it is reasonable to expect that its real ambition is to expand into the core business of Didi. ... "
Alibaba’s Mapping Unit AutoNavi Declares War On Didi Via Launch Of Carpooling Business By Yimian Wu
Alibaba’s mapping unit, AutoNavi Holdings Ltd. also known as Gaode Map, has launched its own carpooling business, starting in Chengdu and Wuhan initially with plans to roll out nationally.
In a move that declares war to existing ride sharing leader Didi Chuxing, AutoNavi’s expansion into the shared mobility space further complicates a sector that was once viewed as "battle completed."
Even though AutoNavi is beginning with carpooling services first, it is reasonable to expect that its real ambition is to expand into the core business of Didi. ... "
Thursday, April 05, 2018
Assistants and Chatbots in the Enterprise
A non detailed view, with some stats. Though I would have to ask 'which enterprise'? Cortana as I see it is still primitive in its interaction with business process. Automated or otherwise. And there is so much opportunity. Something as simple as knowledge-graph search search of email? Linked to corporate resources from Linkedin? And Docs from your colleagues. Alerting colleagues for comments? Just the beginning.
Report: AI assistants and chatbots gain traction in the enterprise
By Matthew Finnegan Senior Reporter, Computerworld
Microsoft’s Cortana AI assistant currently leads the charge in the workplace, thanks to Windows 10 integration.
Despite concerns over data privacy and immature technology, AI assistants and chatbots are gaining a foothold among businesses, according to a report from IT professional network Spiceworks.
The report, which surveyed 529 IT professionals from North America and Europe, showed that 29% of respondents have implemented one or more chatbots or intelligent assistants for work-related tasks, or plan to do so this year.
Adoption was higher for larger organizations, which have more resources to evaluate emerging technologies, with 24% using AI assistants or chatbots currently and 16% set to deploy the technology this year, for a total of 40%. ... "
Report: AI assistants and chatbots gain traction in the enterprise
By Matthew Finnegan Senior Reporter, Computerworld
Microsoft’s Cortana AI assistant currently leads the charge in the workplace, thanks to Windows 10 integration.
Despite concerns over data privacy and immature technology, AI assistants and chatbots are gaining a foothold among businesses, according to a report from IT professional network Spiceworks.
The report, which surveyed 529 IT professionals from North America and Europe, showed that 29% of respondents have implemented one or more chatbots or intelligent assistants for work-related tasks, or plan to do so this year.
Adoption was higher for larger organizations, which have more resources to evaluate emerging technologies, with 24% using AI assistants or chatbots currently and 16% set to deploy the technology this year, for a total of 40%. ... "
B2B Value
Never seen this adequately and numerically positioned before the fact, thus we don't often get reasonable ROIs.
The B2B Elements of Value
By Eric Almquist. Jamie Cleghorn. Lori Sherer in the HBR
".... Maybe those two calculations are not all that different. Her fleet decision obviously included objective criteria such as price, warranties, and service levels, but other, more subjective criteria figured in as well. For instance, the vehicles have to reflect the company’s brand. And their design and handling need to appeal to the employees who drive or ride in them, especially with the higher-end models for executives.
In reality, the differences between business-to-business and consumer decisions are not cut-and-dried. True, B2B sellers need to optimize prices, meet specifications, comply with regulations, and follow ethical practices. Procurement teams rigorously evaluate vendors and run total cost-of-ownership models to ensure that rational, quantifiable criteria around price and performance shape their analyses. .... "
The B2B Elements of Value
By Eric Almquist. Jamie Cleghorn. Lori Sherer in the HBR
".... Maybe those two calculations are not all that different. Her fleet decision obviously included objective criteria such as price, warranties, and service levels, but other, more subjective criteria figured in as well. For instance, the vehicles have to reflect the company’s brand. And their design and handling need to appeal to the employees who drive or ride in them, especially with the higher-end models for executives.
In reality, the differences between business-to-business and consumer decisions are not cut-and-dried. True, B2B sellers need to optimize prices, meet specifications, comply with regulations, and follow ethical practices. Procurement teams rigorously evaluate vendors and run total cost-of-ownership models to ensure that rational, quantifiable criteria around price and performance shape their analyses. .... "
Evolutionary Computing
A pretty broad question. We solved specific, narrow problems using this, essentially genetic methods. But if the solution has to be a body of derived code, the problem is much harder.
Evolutionary computation has been promising self-programming machines for 60 years – so where are they? By Graham Kendall in TheConversation
Professor of Computer Science and Provost/CEO/PVC, University of Nottingham
What if computers could program themselves? Instead of the laborious job of working out how a computer could solve a problem and then writing precise coded instructions, all you would have to do is tell it what you want and the computer would generate an algorithm that solves your problem.
Enter evolutionary computation, which can be seen as a type of artificial intelligence and a branch of machine learning. First suggested in the 1950s, evolutionary computation is the idea that a computer can evolve its own solutions to problems, rather than humans having to go through a series of possibly complex steps to write the computer program ourselves. In theory, this would mean computer programs that might take weeks to program manually could be ready in a matter of minutes. ... "
Evolutionary computation has been promising self-programming machines for 60 years – so where are they? By Graham Kendall in TheConversation
Professor of Computer Science and Provost/CEO/PVC, University of Nottingham
What if computers could program themselves? Instead of the laborious job of working out how a computer could solve a problem and then writing precise coded instructions, all you would have to do is tell it what you want and the computer would generate an algorithm that solves your problem.
Enter evolutionary computation, which can be seen as a type of artificial intelligence and a branch of machine learning. First suggested in the 1950s, evolutionary computation is the idea that a computer can evolve its own solutions to problems, rather than humans having to go through a series of possibly complex steps to write the computer program ourselves. In theory, this would mean computer programs that might take weeks to program manually could be ready in a matter of minutes. ... "
Assessing the Application Landscape of Blockchains
Via Karolyn Schalk, Manager
Executive and Technical Expertise, Cloud, AI and IT Operations
April 5, 10:30 AM ET Assessing the application landscape of blockchain applications
Join us when Nico Abbatemarco, Research Associate of Practice on Information Systems and Digital Transformation at SDA Bocconi School of Management presents.
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Meeting slides and recordings, posted after the talk.
Talk Abstract:
Blockchain is emerging as a game changing technology in many industries. Although it is increasingly capturing the business community's attention, a comprehensive overview of commercially available applications was lacking to date. Our research aimed at fill this gap. Firstly, a structured approach to assess the application landscape of blockchain technologies will be proposed. To build our framework, we relied on largely accepted classifications of blockchains, on the base of protocols, consensus mechanisms and ownership type, as well as on the most cited application areas emerging from the literature. Then, the results emerged from a market analysis performed on a database of 460 released blockchains will be presented.
Short Bio:
Nico Abbatemarco is a Research Associate of Practice on Information Systems and Digital Transformation at SDA Bocconi School of Management. At SDA Bocconi, he is part of the core team of the research Lab "Digital Enterprise Value and Organization" (DEVO Lab). His research activities focus on digital transformation and in particular on blockchain and distributed ledger technologies and their impact on the business community. Nico earned a Bachelor of Science in Finance and a Master of Science in Management both from Bocconi University in Milan. He has also been a visiting student at National Taiwan University in Taipei, Taiwan. .... "
Executive and Technical Expertise, Cloud, AI and IT Operations
April 5, 10:30 AM ET Assessing the application landscape of blockchain applications
Join us when Nico Abbatemarco, Research Associate of Practice on Information Systems and Digital Transformation at SDA Bocconi School of Management presents.
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Meeting slides and recordings, posted after the talk.
Talk Abstract:
Blockchain is emerging as a game changing technology in many industries. Although it is increasingly capturing the business community's attention, a comprehensive overview of commercially available applications was lacking to date. Our research aimed at fill this gap. Firstly, a structured approach to assess the application landscape of blockchain technologies will be proposed. To build our framework, we relied on largely accepted classifications of blockchains, on the base of protocols, consensus mechanisms and ownership type, as well as on the most cited application areas emerging from the literature. Then, the results emerged from a market analysis performed on a database of 460 released blockchains will be presented.
Short Bio:
Nico Abbatemarco is a Research Associate of Practice on Information Systems and Digital Transformation at SDA Bocconi School of Management. At SDA Bocconi, he is part of the core team of the research Lab "Digital Enterprise Value and Organization" (DEVO Lab). His research activities focus on digital transformation and in particular on blockchain and distributed ledger technologies and their impact on the business community. Nico earned a Bachelor of Science in Finance and a Master of Science in Management both from Bocconi University in Milan. He has also been a visiting student at National Taiwan University in Taipei, Taiwan. .... "
Blockchain for Truth
Interesting, but non technical view, of using Blockchain type technology to establish truth. There is no such guarantee that the chain could not be cracked, but the thought is interesting ...
Blockchain for a Society Based on Truth
On April 8, 2017, Steve Omohundro spo n the 4th LAST Festival in San Jose, California on “Blockchain for a society based on truth”. Here is the video .... "
Blockchain for a Society Based on Truth
On April 8, 2017, Steve Omohundro spo n the 4th LAST Festival in San Jose, California on “Blockchain for a society based on truth”. Here is the video .... "
Wednesday, April 04, 2018
Quick Review of 10 Machine Learning Algorithms
A quick, relatively non technical view of some of the most common machine learning algorithms. Not how to, but Why you should care - to. You should know these already, but this provides some tips and re-introductory thoughts. Nicely done. Via O'Reilly.
10 Machine Learning Algorithms You Should Know to Become a Data Scientist By Yves Mulkers
BI And Data Architect enjoying Family, Business Intelligence, Music and DJ-ing, #BBBT member, founder @7wData
10 Machine Learning Algorithms You Should Know to Become a Data Scientist By Yves Mulkers
BI And Data Architect enjoying Family, Business Intelligence, Music and DJ-ing, #BBBT member, founder @7wData
What People See in Robot Faces
A reminder of work we did using brand equity to represent assistants on the Web. We assumed that a well known ad character would an ideal way to engage, with an otherwise chatbot-style interaction. This could have been good input to the project. Much more at the links below. Be glad to chat.
What People See in 157 Robot Faces
in IEEE Spectrum By Evan Ackerman
University of Washington in Seattle researchers conducted a study of robot faces--157 in all--across 76 dimensions, to determine the distinct ways people experience them. Among the study's insights was that robots whose faces were rated less-friendly lacked a mouth and pupils, but had eyelids; eyebrows also were deemed to be indicators of intelligence. Although no robot faces were rated as significantly more likable than the baseline face, the robot that possessed irises was the most liked by survey participants overall. "I think our work helps to elucidate what kind of effect certain design choices have, and while these results are by no means definitive, I think they do help prime our thinking about design consequences," says the University of Washington's Alisa Kalegina. The work was presented in March at the ACM/IEEE International Conference on Human Robot Interaction (HRI 2018) in Chicago, IL. ... "
Modeling Product Cannibilization with Bayesian Nets
Currently investigating further applications for the use of Bayesian nets.
Another interesting example of the use of Bayesian networks, here to model Product cannibalization, under conditions of uncertainty.
Stefan Conrady, Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning
Quantifying Product Cannibalization with Bayesian Networks
The example uses new car model introduction as an example, and interestingly compares it to modern machine learning techniques using pattern recognition.
Here is the latest installment of our popular BayesiaLab webinar series. .... "
Another interesting example of the use of Bayesian networks, here to model Product cannibalization, under conditions of uncertainty.
Stefan Conrady, Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning
Quantifying Product Cannibalization with Bayesian Networks
The example uses new car model introduction as an example, and interestingly compares it to modern machine learning techniques using pattern recognition.
Here is the latest installment of our popular BayesiaLab webinar series. .... "
Executive Management Getting Most from AI
Background for upcoming webinar, to be mentioned .....
Five Management Strategies for Getting the Most From AI
Big Idea: Artificial Intelligence and Business Strategy Blog By Jacques Bughin and Eric Hazan
A global survey of C-level executives finds that AI is delivering real value to companies that use it across operations and within their core functions.
Artificial Intelligence
Fueled by the buzz around powerful applications of artificial intelligence (AI), many business leaders are contemplating whether to introduce AI into their organizations. While practitioners and academics have outlined some of the strategic challenges of implementing AI, many executives are still seeking good models for how to generate competitive advantage from its application.
To find out more about what contributes to successful AI adoption, we helped lead a survey by the McKinsey Global Institute of 3,000 C-level executives across 10 countries and 14 sectors. From that research, we identified five fundamental strategies for how to get the most out of AI’s potential. ... "
Five Management Strategies for Getting the Most From AI
Big Idea: Artificial Intelligence and Business Strategy Blog By Jacques Bughin and Eric Hazan
A global survey of C-level executives finds that AI is delivering real value to companies that use it across operations and within their core functions.
Artificial Intelligence
Fueled by the buzz around powerful applications of artificial intelligence (AI), many business leaders are contemplating whether to introduce AI into their organizations. While practitioners and academics have outlined some of the strategic challenges of implementing AI, many executives are still seeking good models for how to generate competitive advantage from its application.
To find out more about what contributes to successful AI adoption, we helped lead a survey by the McKinsey Global Institute of 3,000 C-level executives across 10 countries and 14 sectors. From that research, we identified five fundamental strategies for how to get the most out of AI’s potential. ... "
Market Newsletter on AI
Nicely done:
nathan.ai newsletter By Nathan Benaich
March 18 · Issue #24 · View online
A market intelligence newsletter covering AI in the technology industry, research lab and venture capital market. ..... "
nathan.ai newsletter By Nathan Benaich
March 18 · Issue #24 · View online
A market intelligence newsletter covering AI in the technology industry, research lab and venture capital market. ..... "
Tuesday, April 03, 2018
Retail Systems Research
Brought to my attention
Retail Systems Research (RSR)
The Candid Voice in Retail Technology: Objective Insights, Pragmatic Advice ....
The BOOT Methodology©
To support our goal of creating a business context for technology, RSR created and trademarked The BOOT Methodology©.
We use this methodology in all our benchmark research. Our process is deceptively simple, yet powerful. It helps us focus the industry not on technology for technology's sake, but as an outcome of a business strategy. The BOOT Methodology© is designed to reveal and prioritize the following: ... "
Retail Systems Research (RSR)
The Candid Voice in Retail Technology: Objective Insights, Pragmatic Advice ....
The BOOT Methodology©
To support our goal of creating a business context for technology, RSR created and trademarked The BOOT Methodology©.
We use this methodology in all our benchmark research. Our process is deceptively simple, yet powerful. It helps us focus the industry not on technology for technology's sake, but as an outcome of a business strategy. The BOOT Methodology© is designed to reveal and prioritize the following: ... "
Walmart Expanding Digital Focus
Its inevitable to allow them to continue compete. My impression is that they have always been good at utilizing emergent tech.
Walmart is focused on expanding its digital brand portfolio by James Tenser in Retailwire with further expert comment.
Walmart has been throwing its considerable weight into an impressive array of digital strategies, ranging from click-and-collect at its mainline stores to digital-first brands like Jet.com, Bonobos and the newly-launched Allswell.
Walmart is moving fast and making lots of news, but is the strategy making sense?
At the recent Shoptalk conference in Las Vegas, Jet.com president & CEO Mark Lore and Andy Dunn, founder and CEO of Bonobos, discussed some of the reasoning behind Walmart’s digital expansion..... "
Walmart is focused on expanding its digital brand portfolio by James Tenser in Retailwire with further expert comment.
Walmart has been throwing its considerable weight into an impressive array of digital strategies, ranging from click-and-collect at its mainline stores to digital-first brands like Jet.com, Bonobos and the newly-launched Allswell.
Walmart is moving fast and making lots of news, but is the strategy making sense?
At the recent Shoptalk conference in Las Vegas, Jet.com president & CEO Mark Lore and Andy Dunn, founder and CEO of Bonobos, discussed some of the reasoning behind Walmart’s digital expansion..... "
Conversation with David Brin on AI
Have had some conversations with Sci Fi author David Brin on AI and privacy. Look forward to seeing the contextual and speculative views of a science fiction writer.
Voices in AI – Episode 39: A Conversation with David Brin Interviewed by Byron Reese
In this episode Byron and David discuss intelligence, consciousness, Moore’s Law, and an AI crisis. ....
No he does not expect AI to become truly self aware ....
Byron Reese: This is Voices in AI brought to you by GigaOm, and I’m Byron Reese. Today our guest is David Brin. He is best-known for shining light—both plausibly and entertainingly—on technology, society, and countless challenges confronting our rambunctious civilization. His best-selling novels include The Postman, which was filmed in ’97, plus explorations of our near-future in Earth and Existence. Other novels of his are translated into over 25 languages. His short stories explore vividly speculative ideas. His non-fiction book The Transparent Society won the American Library Association’s Freedom of Speech Award for exploring 21st-century concerns about security, secrecy, accountability, and privacy. And as a scientist, a tech consultant, a world-renowned author, he speaks and advises, and writes widely on topics from national defense to homeland security to astronomy to space exploration to nanotechnology, creativity, philanthropy. He kind of covers the whole gambit. I’m so excited to have him on the show. Welcome, David Brin ..... "
Voices in AI – Episode 39: A Conversation with David Brin Interviewed by Byron Reese
In this episode Byron and David discuss intelligence, consciousness, Moore’s Law, and an AI crisis. ....
No he does not expect AI to become truly self aware ....
Byron Reese: This is Voices in AI brought to you by GigaOm, and I’m Byron Reese. Today our guest is David Brin. He is best-known for shining light—both plausibly and entertainingly—on technology, society, and countless challenges confronting our rambunctious civilization. His best-selling novels include The Postman, which was filmed in ’97, plus explorations of our near-future in Earth and Existence. Other novels of his are translated into over 25 languages. His short stories explore vividly speculative ideas. His non-fiction book The Transparent Society won the American Library Association’s Freedom of Speech Award for exploring 21st-century concerns about security, secrecy, accountability, and privacy. And as a scientist, a tech consultant, a world-renowned author, he speaks and advises, and writes widely on topics from national defense to homeland security to astronomy to space exploration to nanotechnology, creativity, philanthropy. He kind of covers the whole gambit. I’m so excited to have him on the show. Welcome, David Brin ..... "
Computing and Emotional Intelligence
We worked with MIT on related systems. Followed Rosalind Picard. Most recently looking at what Watson has done with related skills emotion and personality detection. Consider deriving personality as a means of understanding emotion. Obvious connections to current assistants. How can such systems both express and detect emotions? Good overview article on the challenge.
Artificial (Emotional) Intelligence By Marina Krakovsky
Communications of the ACM, Vol. 61 No. 4, Pages 18-19 10.1145/3185521
Anyone who has been frustrated asking questions of Siri or Alexa—and then annoyed at the digital assistant's tone-deaf responses—knows how dumb these supposedly intelligent assistants are, at least when it comes to emotional intelligence. "Even your dog knows when you're getting frustrated with it," says Rosalind Picard, director of Affective Computing Research at the Massachusetts Institute of Technology (MIT) Media Lab. "Siri doesn't yet have the intelligence of a dog," she says.
Yet developing that kind of intelligence—in particular, the ability to recognize human emotions and then respond appropriately—is essential to the true success of digital assistants and the many other artificial intelligences (AIs) we interact with every day. Whether we're giving voice commands to a GPS navigator, trying to get help from an automated phone support line, or working with a robot or chatbot, we need them to really understand us if we're to take these AIs seriously. "People won't see an AI as smart unless it can interact with them with some emotional savoir faire," says Picard, a pioneer in the field of affective computing.
One of the biggest obstacles has been the need for context: the fact that emotions can't be understood in isolation. "It's like in speech," says Pedro Domingos, a professor of computer science and engineering at the University of Washington and author of The Master Algorithm, a popular book about machine learning. "It's very hard to recognize speech from just the sounds, because they're too ambiguous," he points out. Without context, "ice cream" and "I scream" sound identical, "but from the context you can figure it out."... "
Artificial (Emotional) Intelligence By Marina Krakovsky
Communications of the ACM, Vol. 61 No. 4, Pages 18-19 10.1145/3185521
Anyone who has been frustrated asking questions of Siri or Alexa—and then annoyed at the digital assistant's tone-deaf responses—knows how dumb these supposedly intelligent assistants are, at least when it comes to emotional intelligence. "Even your dog knows when you're getting frustrated with it," says Rosalind Picard, director of Affective Computing Research at the Massachusetts Institute of Technology (MIT) Media Lab. "Siri doesn't yet have the intelligence of a dog," she says.
Yet developing that kind of intelligence—in particular, the ability to recognize human emotions and then respond appropriately—is essential to the true success of digital assistants and the many other artificial intelligences (AIs) we interact with every day. Whether we're giving voice commands to a GPS navigator, trying to get help from an automated phone support line, or working with a robot or chatbot, we need them to really understand us if we're to take these AIs seriously. "People won't see an AI as smart unless it can interact with them with some emotional savoir faire," says Picard, a pioneer in the field of affective computing.
One of the biggest obstacles has been the need for context: the fact that emotions can't be understood in isolation. "It's like in speech," says Pedro Domingos, a professor of computer science and engineering at the University of Washington and author of The Master Algorithm, a popular book about machine learning. "It's very hard to recognize speech from just the sounds, because they're too ambiguous," he points out. Without context, "ice cream" and "I scream" sound identical, "but from the context you can figure it out."... "
Likely Jobs Soon to be Automated
Not very surprising, these were mentioned in the last go-around of AI Tech. Perhaps only less mentioned before, jobs that had a high robotic component that required vision+manipulation. Vision was less solved back then.
Replaced by robots: 8 jobs that could be hit hard by the A.I. revolution in Digital Trends
There’s no doubt that artificial intelligence (A.I.) and other cutting edge technologies are going to change the face of employment as we know it. According to one famous study, 47 percent of currently existing jobs in America are at high risk of potential automation in the coming decades.
What are some of the professions that will suffer the effects of the A.I. revolution? And is there anything people can hope to do about it? Read on to find out. ... "
Replaced by robots: 8 jobs that could be hit hard by the A.I. revolution in Digital Trends
There’s no doubt that artificial intelligence (A.I.) and other cutting edge technologies are going to change the face of employment as we know it. According to one famous study, 47 percent of currently existing jobs in America are at high risk of potential automation in the coming decades.
What are some of the professions that will suffer the effects of the A.I. revolution? And is there anything people can hope to do about it? Read on to find out. ... "
Always Out of Balance: Looking for Equilibria
Nicepiece, which includes a short, easy to understand video that explains the basics of the Nash Equilibrium. We sought to use these methods to understand competitive behavior. Not very successfully I admit, but the approach did make us think of better models for the processes and integrated behaviors involved. Even the supposed rationality of decisions were an issue. It is what we called competitive modeled economics. What were the right counter behavior for competition?
Always Out of Balance By Neil Savage in
Communications of the ACM, Vol. 61 No. 4, Pages 12-14
"Computational Theorists show that there is no easy way to to find Nash Equilibria, so game theory will have to look in new directions"....
When John Nash won the Nobel Prize in economics in 1994 for his contribution to game theory, it was for an elegant theorem. Nash had shown that in any situation where two or more people were competing, there would always be an equilibrium state in which no player could do better than he was already doing. That theorem has since been used to model all sorts of competitive systems, from markets to nuclear strategy to living creatures competing for finite resources.
"In some sense, it started not just game theory, but also modern economics," says Christos Papadimitriou, a professor of computer science at Columbia University. Nash's idea gave economists the ability to create hypotheses about market design, for instance. They could now ask what happened when a market reached equilibrium.
Nash's theorem is also an essential component of game theory, which had first been developed by computing pioneer John von Neumann. "Games are a mathematical thought experiment and we study them just because we want to understand how strategic rational players would behave in situations of conflict," Papadimitriou says. "And that's important because all of society is full of such situations."
Though Nash proved that at least one such Nash equilibrium existed for all games, what he did not do was predict how an equilibrium might be reached in a given situation. Was there, scientists wanted to know, an algorithm that would show players how to efficiently reach an equilibrium? After more than 65 years of researchers' studying that question, the answer turns out to be no, there is not. That means economists had better start rethinking some of their models. .... "
Always Out of Balance By Neil Savage in
Communications of the ACM, Vol. 61 No. 4, Pages 12-14
"Computational Theorists show that there is no easy way to to find Nash Equilibria, so game theory will have to look in new directions"....
When John Nash won the Nobel Prize in economics in 1994 for his contribution to game theory, it was for an elegant theorem. Nash had shown that in any situation where two or more people were competing, there would always be an equilibrium state in which no player could do better than he was already doing. That theorem has since been used to model all sorts of competitive systems, from markets to nuclear strategy to living creatures competing for finite resources.
"In some sense, it started not just game theory, but also modern economics," says Christos Papadimitriou, a professor of computer science at Columbia University. Nash's idea gave economists the ability to create hypotheses about market design, for instance. They could now ask what happened when a market reached equilibrium.
Nash's theorem is also an essential component of game theory, which had first been developed by computing pioneer John von Neumann. "Games are a mathematical thought experiment and we study them just because we want to understand how strategic rational players would behave in situations of conflict," Papadimitriou says. "And that's important because all of society is full of such situations."
Though Nash proved that at least one such Nash equilibrium existed for all games, what he did not do was predict how an equilibrium might be reached in a given situation. Was there, scientists wanted to know, an algorithm that would show players how to efficiently reach an equilibrium? After more than 65 years of researchers' studying that question, the answer turns out to be no, there is not. That means economists had better start rethinking some of their models. .... "
Monday, April 02, 2018
Kroger and Embedded Machine Learning Analytics
Good to see how much work Kroger is doing in advanced analytics, congrats to my friends there, we worked with them in the innovation area, always following them for leading tech. This is a leading direction, good to see Kroger leading the way in making the business needs known and investing in solutions.
84.51° Builds a Machine Learning Machine for Kroger
By Tom Davenport , Forbes Contributor
Opinions expressed by Forbes Contributors are their own.
Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea (at least for “supervised learning,” by far the most common type in business) is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models.
One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention. You may have heard of dunnhumby, a UK-based analytics company that’s owned by the big retailer Tesco. dunnhumby had a US joint venture with Kroger named dunnhumbyUSA. In 2015 Kroger purchased dunnhumbyUSA and named it 84.51°. The name coincides with the location of its Cincinnati headquarters and is a tribute to the longitudinal analytics the company employs. But 84.51° also analyzes data like a finely-tuned machine. .... "
(read the rest for details of the embedding)
84.51° Builds a Machine Learning Machine for Kroger
By Tom Davenport , Forbes Contributor
Opinions expressed by Forbes Contributors are their own.
Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea (at least for “supervised learning,” by far the most common type in business) is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models.
One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention. You may have heard of dunnhumby, a UK-based analytics company that’s owned by the big retailer Tesco. dunnhumby had a US joint venture with Kroger named dunnhumbyUSA. In 2015 Kroger purchased dunnhumbyUSA and named it 84.51°. The name coincides with the location of its Cincinnati headquarters and is a tribute to the longitudinal analytics the company employs. But 84.51° also analyzes data like a finely-tuned machine. .... "
(read the rest for details of the embedding)
Talk: Adversarial Risk Analysis
Of interest in particular, due to the adversarial element. Risk analysis does not often enough consider the intelligent nature of agents that create risk. Competitors for example, and now even intelligent agents in the form of assistants or other machine driven systems.
The OBAIS department at the Lindner College of Business, University of Cincinnati, invites you to attend the Lindner Research Excellence Seminar and Professional Development Discussion:
Research Seminar:
Date and time: Friday, April 6, 2018, 10:30AM-12:00PM
Location: Lindner Hall 218
Speaker: Dr. David Banks, Duke University
Title: Adversarial Risk Analysis
Abstract: Adversarial Risk Analysis (ARA) is a Bayesian alternative to classical game theory. Rooted in decision theory, one builds a model for the decision-making of one's opponent, placing subjective distributions over all unknown quantities. Then one chooses the action that maximizes expected utility. This approach aligns with some perspectives in modern behavioral economics, and enables principled analysis of novel problems, such as a multiparty auction in which there is no common knowledge and different bidders have different opinion about each other.
This presentation is based on:
Rios Insua, D., Rios, J., Banks, D. L., “Adversarial Risk Analysis,” Journal of the American Statistical Association, 2009.
https://www.tandfonline.com/doi/abs/10.1198/jasa.2009.0155
Banks, D. L., Rios, J., Rios Insua, D., “Adversarial Risk Analysis,” Chapman and Hall/CRC, 2015, ISBN 9781498712392.
https://www.crcpress.com/Adversarial-Risk-Analysis/Banks-Aliaga-Insua/p/book/9781498712392
Best wishes,
Yichen Qin, Assistant Professor
Department of Operations, Business Analytics, and Information Systems
Lindner College of Business, University of Cincinnati
Website: http://business.uc.edu/academics/departments/obais/faculty/qinyn.html
Email: qinyn@ucmail.uc.edu
The OBAIS department at the Lindner College of Business, University of Cincinnati, invites you to attend the Lindner Research Excellence Seminar and Professional Development Discussion:
Research Seminar:
Date and time: Friday, April 6, 2018, 10:30AM-12:00PM
Location: Lindner Hall 218
Speaker: Dr. David Banks, Duke University
Title: Adversarial Risk Analysis
Abstract: Adversarial Risk Analysis (ARA) is a Bayesian alternative to classical game theory. Rooted in decision theory, one builds a model for the decision-making of one's opponent, placing subjective distributions over all unknown quantities. Then one chooses the action that maximizes expected utility. This approach aligns with some perspectives in modern behavioral economics, and enables principled analysis of novel problems, such as a multiparty auction in which there is no common knowledge and different bidders have different opinion about each other.
This presentation is based on:
Rios Insua, D., Rios, J., Banks, D. L., “Adversarial Risk Analysis,” Journal of the American Statistical Association, 2009.
https://www.tandfonline.com/doi/abs/10.1198/jasa.2009.0155
Banks, D. L., Rios, J., Rios Insua, D., “Adversarial Risk Analysis,” Chapman and Hall/CRC, 2015, ISBN 9781498712392.
https://www.crcpress.com/Adversarial-Risk-Analysis/Banks-Aliaga-Insua/p/book/9781498712392
Best wishes,
Yichen Qin, Assistant Professor
Department of Operations, Business Analytics, and Information Systems
Lindner College of Business, University of Cincinnati
Website: http://business.uc.edu/academics/departments/obais/faculty/qinyn.html
Email: qinyn@ucmail.uc.edu
Brainstorming Killing Creativity
Always thought there was a flaw in brainstorming, and was in many of them.
Brainstorming Is Killing Your Creativity
Our go-to method for problem solving is majorly flawed. Here’s how to flip it on its head.
By Stephanie Vozza in Fastcompany
In a typical brainstorming session, you attempt to solve a challenge by gathering a team to generate new ideas. While it sounds logical, this method is actually rife with problems, like “social loafing,” coasting on others’ contributions, and social anxiety about having your ideas judged. That can hinder original thinking and stifle introverted team members, according to an article published in Harvard Business Review.
Instead of looking for answers, you should be brainstorming for questions, says Hal Gregersen, executive director of the MIT Leadership Center and MIT Sloan professor. “The best leaders in the world ask better questions, creating conditions and situations that cause them to be wrong or uncomfortable and quiet,” he says. “When that happens, they end up being almost forced to ask things other people wouldn’t ask.” .... "
Brainstorming Is Killing Your Creativity
Our go-to method for problem solving is majorly flawed. Here’s how to flip it on its head.
By Stephanie Vozza in Fastcompany
In a typical brainstorming session, you attempt to solve a challenge by gathering a team to generate new ideas. While it sounds logical, this method is actually rife with problems, like “social loafing,” coasting on others’ contributions, and social anxiety about having your ideas judged. That can hinder original thinking and stifle introverted team members, according to an article published in Harvard Business Review.
Instead of looking for answers, you should be brainstorming for questions, says Hal Gregersen, executive director of the MIT Leadership Center and MIT Sloan professor. “The best leaders in the world ask better questions, creating conditions and situations that cause them to be wrong or uncomfortable and quiet,” he says. “When that happens, they end up being almost forced to ask things other people wouldn’t ask.” .... "
Alexa Capabilities in Japan
Continued advanced of global assistants. Because of Japan's work in eldercare, would not be surprised to see advances there.
Alexa Voice Service now generally available for device makers in Japan
By Kari Johnson @KhariJohnson in Venturebeat
Amazon has now made its Alexa Voice Service generally available for hardware device makers in Japan who want to put Alexa inside products. The Alexa Voice Service was made available for prototyping last fall around the time Echo smart speakers were first made available in Japan.
The Alexa Voice Service can be used for voice commands or to let a person speak with Amazon’s AI assistant — much like Alexa in an Echo smart speaker — to do things like create a calendar event, launch an Alexa skill, or check the weather. ... "
Alexa Voice Service now generally available for device makers in Japan
By Kari Johnson @KhariJohnson in Venturebeat
Amazon has now made its Alexa Voice Service generally available for hardware device makers in Japan who want to put Alexa inside products. The Alexa Voice Service was made available for prototyping last fall around the time Echo smart speakers were first made available in Japan.
The Alexa Voice Service can be used for voice commands or to let a person speak with Amazon’s AI assistant — much like Alexa in an Echo smart speaker — to do things like create a calendar event, launch an Alexa skill, or check the weather. ... "
Its all About having the Right Data and Metadata
Spent quite a lot of time figuring this out for analytics in the enterprise.
Data governance and the death of schema on read in O'Reily
Comcast’s system of storing schemas and metadata enables data scientists to find, understand, and join data of interest.
By Barbara Eckman
In the olden days of data science, one of the rallying cries was the democratization of data. No longer were data owners at the mercy of enterprise data warehouses (EDWs) and extract, transform, load (ETL) jobs, where data had to be transformed into a specific schema (“schema on write”) before it could be stored in the enterprise data warehouse and made available for use in reporting and analytics. This data was often most naturally expressed as nested structures (e.g., a base record with two array-typed attributes), but warehouses were usually based on the relational model. Thus, the data needed to be pulled apart and “normalized" into flat relational tables in first normal form. Once stored in the warehouse, recovering the data’s natural structure required several expensive relational joins. Or, for the most common or business-critical applications, the data was “de-normalized,” in which formerly nested structures were reunited, but in a flat relational form with a lot of redundancy.
This is the context in which big data and the data lake arose. No single schema was imposed. Anyone could store their data in the data lake, in any structure (or no consistent structure). Naturally nested data was no longer stripped apart into artificially flat structures. Data owners no longer had to wait for the IT department to write ETL jobs before they could access and query their data. In place of the tyranny of schema on write, schema on read was born. Users could store their data in any schema, which would be discovered at the time of reading the data. Data storage was no longer the exclusive provenance of the DBAs and the IT departments. Data from multiple previously siloed teams could be stored in the same repository. ..... " (More)
Data governance and the death of schema on read in O'Reily
Comcast’s system of storing schemas and metadata enables data scientists to find, understand, and join data of interest.
By Barbara Eckman
In the olden days of data science, one of the rallying cries was the democratization of data. No longer were data owners at the mercy of enterprise data warehouses (EDWs) and extract, transform, load (ETL) jobs, where data had to be transformed into a specific schema (“schema on write”) before it could be stored in the enterprise data warehouse and made available for use in reporting and analytics. This data was often most naturally expressed as nested structures (e.g., a base record with two array-typed attributes), but warehouses were usually based on the relational model. Thus, the data needed to be pulled apart and “normalized" into flat relational tables in first normal form. Once stored in the warehouse, recovering the data’s natural structure required several expensive relational joins. Or, for the most common or business-critical applications, the data was “de-normalized,” in which formerly nested structures were reunited, but in a flat relational form with a lot of redundancy.
This is the context in which big data and the data lake arose. No single schema was imposed. Anyone could store their data in the data lake, in any structure (or no consistent structure). Naturally nested data was no longer stripped apart into artificially flat structures. Data owners no longer had to wait for the IT department to write ETL jobs before they could access and query their data. In place of the tyranny of schema on write, schema on read was born. Users could store their data in any schema, which would be discovered at the time of reading the data. Data storage was no longer the exclusive provenance of the DBAs and the IT departments. Data from multiple previously siloed teams could be stored in the same repository. ..... " (More)
Animating Visualized Data
Interesting piece in Observable on Animation of visualized data. Which categorizes various kinds of visualization. There has been an increase in these kinds of things, apparently to enhance the attention that such visualizations get. I wish they would have gotten to the bottom line quicker. If the animation shows wrong results, or implies some incorrect changes or it confuses the observer, it should not be used. Despite any attention-getting value. Being memorable to match your narrative is not value.
The Space Between By anbnyc @anbnyc
Understanding animated transitions in data visualization
Animated transitions are a popular and powerful feature of digital data visualization. They can make a visualization more fun, engaging, and memorable. By revealing information in stages rather than all at once, transitions make visualizations less overwhelming and more informative.
This visual essay is inspired by the question: What is happening conceptually between the start and end of a transition? I look at reasons for using animated transitions (besides "it looks cool") and at the kinds of variables that can be transitioned. I conclude that we can think of animated transitions in two categories: those where the space between the start and end states consists of real/realistic data and grammatically valid states for that visualization, and those ... "
The Space Between By anbnyc @anbnyc
Understanding animated transitions in data visualization
Animated transitions are a popular and powerful feature of digital data visualization. They can make a visualization more fun, engaging, and memorable. By revealing information in stages rather than all at once, transitions make visualizations less overwhelming and more informative.
This visual essay is inspired by the question: What is happening conceptually between the start and end of a transition? I look at reasons for using animated transitions (besides "it looks cool") and at the kinds of variables that can be transitioned. I conclude that we can think of animated transitions in two categories: those where the space between the start and end states consists of real/realistic data and grammatically valid states for that visualization, and those ... "
Sunday, April 01, 2018
Attentive Assistant Makers Patent their Leverage
This article in yesterday's NYT discusses how companies who have attentive sytems, like Amazon and Google, have pending patents that describe how they can take advantage of what they hear. Yet the companies involved usually deny that what is heard is retained. What are the implications for privacy, and who is responsible?
Hey, Alexa, What Can You Hear? And What Will You Do With It?
By Sapna Maheshwari ... in the NYT on 3/31/2018
( May require registration)"
Hey, Alexa, What Can You Hear? And What Will You Do With It?
By Sapna Maheshwari ... in the NYT on 3/31/2018
( May require registration)"
Informal Review of McDonald's In-Store Ordering Kiosks
We did lots of work with kiosks, standard and novel, in our innovation centers. And I am always ready to try them as they appear. Most recently McDonald's has been experimenting. Here is a review in Business Insider. Lots of pictures. My own take was different, the kiosks gave too many options, were not easy enough to customize to your standard order. And as the article mentioned, there were always several employees there to help out, so much customer training is necessary until they save any labor. I am sure they are thinking of connecting them to customer apps. Probably the best way to make them efficient.
We ordered from McDonald's kiosks to see if they're better than real cashiers — and the winner is clear By Hollis Johnson
" .... So, which is better? I'm used to the kiosks, so I prefer them — they're faster, more accurate, and make things easier to customize. And since workers are there to help if you're confused, I fail to see how it takes away jobs as of now. ... "
We ordered from McDonald's kiosks to see if they're better than real cashiers — and the winner is clear By Hollis Johnson
" .... So, which is better? I'm used to the kiosks, so I prefer them — they're faster, more accurate, and make things easier to customize. And since workers are there to help if you're confused, I fail to see how it takes away jobs as of now. ... "
Seeking Interpret-ability and Explanation as Components of our Brain
Modeling the apparent structure and resulting operation of the brain is difficult .... We have lots of neurons gathering data, and interacting in ways we do not fully understand. And that results in high level cognitive concepts, like language or consciousness. This piece looks at these interactions and seeks to produce some interpretive models based on things we know how to model well enough today, interpret and tag visual scenes. But that is yet a small and simplistic portion of what the Brain does. What does it mean for deep intelligence? Like being given a mass of Lego blocks and being asked to model a city without a map. You can see the engineers starting to sweat.
The Building Blocks of Interpretability
Interpretability techniques are normally studied in isolation.
We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space. ....
Researchers from Google and CMU explore ...
Chris Olah, Google Brain
Arvind Satyanarayan. Google Brain ....
With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building confidence about how they will behave in the real-world, detecting model bias, and for scientific curiosity. In order to do so, we need to both construct deep abstractions and reify (or instantiate) them in rich interfaces [1] . With a few exceptions [2, 3, 4] , existing work on interpretability fails to do these in concert.
The machine learning community has primarily focused on developing powerful methods, such as feature visualization [5, 6, 7, 8, 9, 10] , attribution [7, 11, 12, 13, 14, 15, 16, 17] , and dimensionality reduction [18] , for reasoning about neural networks. However, these techniques have been studied as isolated threads of research, and the corresponding work of reifying them has been neglected. On the other hand, the human-computer interaction community has begun to explore rich user interfaces for neural networks [19, 20, 21] , but they have not yet engaged deeply with these abstractions. To the extent these abstractions have been used, it has been in fairly standard ways. As a result, we have been left with impoverished interfaces (e.g., saliency maps or correlating abstract neurons) that leave a lot of value on the table. Worse, many interpretability techniques have not been fully actualized into abstractions because there has not been pressure to make them generalizable or composable. ....
The Building Blocks of Interpretability
Interpretability techniques are normally studied in isolation.
We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space. ....
Researchers from Google and CMU explore ...
Chris Olah, Google Brain
Arvind Satyanarayan. Google Brain ....
With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building confidence about how they will behave in the real-world, detecting model bias, and for scientific curiosity. In order to do so, we need to both construct deep abstractions and reify (or instantiate) them in rich interfaces [1] . With a few exceptions [2, 3, 4] , existing work on interpretability fails to do these in concert.
The machine learning community has primarily focused on developing powerful methods, such as feature visualization [5, 6, 7, 8, 9, 10] , attribution [7, 11, 12, 13, 14, 15, 16, 17] , and dimensionality reduction [18] , for reasoning about neural networks. However, these techniques have been studied as isolated threads of research, and the corresponding work of reifying them has been neglected. On the other hand, the human-computer interaction community has begun to explore rich user interfaces for neural networks [19, 20, 21] , but they have not yet engaged deeply with these abstractions. To the extent these abstractions have been used, it has been in fairly standard ways. As a result, we have been left with impoverished interfaces (e.g., saliency maps or correlating abstract neurons) that leave a lot of value on the table. Worse, many interpretability techniques have not been fully actualized into abstractions because there has not been pressure to make them generalizable or composable. ....
Weaponization Moment for Computer Science
I worked at the Pentagon in early years, so understand the direction. Your 'competitors' are doing it, so you have to consider it also. Even then there were capabilities floated that demonstrated potential destruction. Brought home when I saw the virtual destruction of my birthplace in the Fulda Gap.
This Is The A-Bomb Moment For Computer Science
Physics made the atomic bomb and chemistry created nerve gas. Today, computer scientists need to confront the weaponization of their work. ....
By Bryor Snefjella
This Is The A-Bomb Moment For Computer Science
Physics made the atomic bomb and chemistry created nerve gas. Today, computer scientists need to confront the weaponization of their work. ....
By Bryor Snefjella
Microsoft Launches Org to Focus on AI
Another push on AI. So then its not Hype? Because a big company that knows what computing is, supports it? That gets back to what AI is. If its a new kind of analytics, along with new kinds and volumes of data, that will do things we could not do just a few decades ago? Yes its new. But words like 'Intelligence' are quite loaded. And AI is still not there yet, so we still have to be careful what we call progress.
Microsoft is launching a huge reorganization to focus on AI and the cloud
The company is creating two new engineering divisions that it says will accelerate innovation and better serve its customers. One team will focus on the cloud and AI, the other on what it calls “experiences and devices.” .... '
Microsoft is launching a huge reorganization to focus on AI and the cloud
The company is creating two new engineering divisions that it says will accelerate innovation and better serve its customers. One team will focus on the cloud and AI, the other on what it calls “experiences and devices.” .... '
Subscribe to:
Posts (Atom)