The thought came to mind as I walked retail yesterday, made me shudder as I counted the mask wearers in the aisle. Gathering data. There seems to be no end in sight. As I tried to smile at a helpful employee. Masks are becoming apparel, and they may be with us ... forever?
Will face masks be a lifeline for apparel retail?
with 16 Expert Comments ....
by Matthew Stern
Apparel continues to struggle amid the uncertainty of the novel coronavirus pandemic, but one brand has made the best out of the situation, pivoting to use materials already in its pipeline to create a pandemic-appropriate product.
Facing sales declines of 60 percent at the onset of the novel coronavirus outbreak, San Francisco menswear brand Blade + Blue began working with its manufacturing partner to use remnants from last season’s shirts as well as the upcoming summer season’s shirts to make masks, CNN Business reported. The brand’s founder, Peter Papas, said that the move not only saved the business, it also attracted new customers. Blade + Blue has donated some masks to healthcare workers and first responders in addition to selling them to the general public.
In addition to slowing sales and supply chain disruptions, backlogged inventory has been one of the biggest problems faced by apparel brands and retailers throughout the duration of the pandemic. Retailers with highly-seasonal stock are having to choose between holding over unreleased seasonal merchandise into the following year or selling at huge markdowns as stores slowly reopen.... "
Wednesday, May 20, 2020
Google Knowledge Graphs, Knowledge Panels
Want to build from these basics. an introduction:
A reintroduction to our Knowledge Graph and knowledge panels
Danny Sullivan In Google Blog
Public Liaison for Search
Sometimes Google Search will show special boxes with information about people, places and things. We call these knowledge panels. They’re designed to help you quickly understand more about a particular subject by surfacing key facts and to make it easier to explore a topic in more depth. Information within knowledge panels comes from our Knowledge Graph, which is like a giant virtual encyclopedia of facts. In this post, we’ll share more about how knowledge panels are automatically generated, how data for the Knowledge Graph is gathered and how we monitor and react to reports of incorrect information.
What’s a knowledge panel?
Knowledge panels are easily recognized by those who do desktop searching, appearing to the right of search results: ... "
A reintroduction to our Knowledge Graph and knowledge panels
Danny Sullivan In Google Blog
Public Liaison for Search
Sometimes Google Search will show special boxes with information about people, places and things. We call these knowledge panels. They’re designed to help you quickly understand more about a particular subject by surfacing key facts and to make it easier to explore a topic in more depth. Information within knowledge panels comes from our Knowledge Graph, which is like a giant virtual encyclopedia of facts. In this post, we’ll share more about how knowledge panels are automatically generated, how data for the Knowledge Graph is gathered and how we monitor and react to reports of incorrect information.
What’s a knowledge panel?
Knowledge panels are easily recognized by those who do desktop searching, appearing to the right of search results: ... "
Like People, AI will also Fail.
A very nicely done, non-technical and usefully skeptical view of AI. Making the case that even if your AI solves a problem today, it is likely to fail tomorrow, when time and context drift and shift. Just like human problem solvers can fail to find solutions to all problems. In our own progress in the space, we engaged many of these experiences. Sometimes we had to wait for decades to get better solutions. I say know the risk and understand it too is shifting. Build to solve useful problems. Check your data and recheck your results.
What to Do When AI Fails
By Andrew Burt and Patrick Hall in O'Reilly
These are unprecedented times, at least by information age standards. Much of the U.S. economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world. Moreover, things seem likely to keep changing until a vaccine or effective treatment for COVID-19 becomes available. All this change could wreak havoc on artificial intelligence (AI) systems. Garbage in, garbage out still holds in 2020. The most common types of AI systems are still only as good as their training data. If there’s no historical data that mirrors our current situation, we can expect our AI systems to falter, if not fail.
To date, at least 1,200 reports of AI incidents have been recorded in various public and research databases. That means that now is the time to start planning for AI incident response, or how organizations react when things go wrong with their AI systems. While incident response is a field that’s well developed in the traditional cybersecurity world, it has no clear analogue in the world of AI. What is an incident when it comes to an AI system? When does AI create liability that organizations need to respond to? This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. Our aim is to help explain when and why AI creates liability for the organizations that employ it, and to outline how organizations should react when their AI causes major problems. ... "
What to Do When AI Fails
By Andrew Burt and Patrick Hall in O'Reilly
These are unprecedented times, at least by information age standards. Much of the U.S. economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world. Moreover, things seem likely to keep changing until a vaccine or effective treatment for COVID-19 becomes available. All this change could wreak havoc on artificial intelligence (AI) systems. Garbage in, garbage out still holds in 2020. The most common types of AI systems are still only as good as their training data. If there’s no historical data that mirrors our current situation, we can expect our AI systems to falter, if not fail.
To date, at least 1,200 reports of AI incidents have been recorded in various public and research databases. That means that now is the time to start planning for AI incident response, or how organizations react when things go wrong with their AI systems. While incident response is a field that’s well developed in the traditional cybersecurity world, it has no clear analogue in the world of AI. What is an incident when it comes to an AI system? When does AI create liability that organizations need to respond to? This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. Our aim is to help explain when and why AI creates liability for the organizations that employ it, and to outline how organizations should react when their AI causes major problems. ... "
Tuesday, May 19, 2020
Forestry System for Growth Prediction
Another example of my previous work in forestry. Would have been useful to manage harvesting schedules.
Researcher to Receive Top Global Forestry Award From Swedish King
Oregon State University News
by Steve Ludeberg
Oregon State University professor emeritus Richard Waring will share this year's Marcus Wallenberg Prize, to be presented by Swedish King Carl Gustav XVI, for his role in developing a computer model to predict forest growth amid climate change. Waring and Australian colleagues and co-honorees Joe Landsberg and Nicholas Coops designed the Physiological Principles Predicting Growth model, which incorporates satellite imagery to show how different environmental conditions impact the world's forests. Said Waring, "By the late 1990s, enough was published that we thought a simplified model could be built that could help foresters as well as ecologists predict how stands of a composition of tree species might respond to changing environments." Waring and Landsberg first presented the model in 1997, and Coops' addition of satellite imagery in 1998 enabled large forest areas to be surveyed, and forest growth and carbon storage to be predicted on a larger scale. ... "
Researcher to Receive Top Global Forestry Award From Swedish King
Oregon State University News
by Steve Ludeberg
Oregon State University professor emeritus Richard Waring will share this year's Marcus Wallenberg Prize, to be presented by Swedish King Carl Gustav XVI, for his role in developing a computer model to predict forest growth amid climate change. Waring and Australian colleagues and co-honorees Joe Landsberg and Nicholas Coops designed the Physiological Principles Predicting Growth model, which incorporates satellite imagery to show how different environmental conditions impact the world's forests. Said Waring, "By the late 1990s, enough was published that we thought a simplified model could be built that could help foresters as well as ecologists predict how stands of a composition of tree species might respond to changing environments." Waring and Landsberg first presented the model in 1997, and Coops' addition of satellite imagery in 1998 enabled large forest areas to be surveyed, and forest growth and carbon storage to be predicted on a larger scale. ... "
Spot Herding Sheep and other Farm Tasks
Intrigued by the use of robotics for agricultural and other classic farming applications. Here the use of the Boston Dynamics Spot ... and a link to a video of it herding sheep in New Zealand. And doing other farming tasks. Even working with others in teams. So robots can be used for 'imprecise' tasks that require nimble action and reaction. At least at this time these would appear to be tele-operated as opposed to autonomous. For problems like herding animals, or even us?
Watch a Boston Dynamics robot herd sheep in New Zealand
Robotics company Rocos https://www.rocos.io/ shows how Spot might advance precision agriculture.
Christine Fisher, @cfisherwrites in Engadget
In addition to herding sheep, Spot robots might also harvest crops, inspect yields or create real-time maps, Rocos says. These capabilities are all possible now that Spot is more nimble, can handle rugged terrain and can carry infrared and LiDAR cameras. Rocos hopes to use its tech to remotely design and edit missions and collect sensor data. In other words, users might be able to herd sheep in New Zealand from anywhere in the world. .. "
Watch a Boston Dynamics robot herd sheep in New Zealand
Robotics company Rocos https://www.rocos.io/ shows how Spot might advance precision agriculture.
Christine Fisher, @cfisherwrites in Engadget
In addition to herding sheep, Spot robots might also harvest crops, inspect yields or create real-time maps, Rocos says. These capabilities are all possible now that Spot is more nimble, can handle rugged terrain and can carry infrared and LiDAR cameras. Rocos hopes to use its tech to remotely design and edit missions and collect sensor data. In other words, users might be able to herd sheep in New Zealand from anywhere in the world. .. "
Why We Need Bayesian
This link is first a reminder to myself that we need to continually promote means of risk and uncertainty awareness in models. Meta-reasoning is always important. Thinking about the context in which your models will be used. If you don't do that you have missed something important. Understanding the risks it will have in use. The article gets quite technical, but the intros are worthwhile to read. And there are links to good online courses, which also have good intros.
Bayesian meta-learning
This story introduces bayesian meta-learning approaches, which covers bayesian black-box meta-learning, bayesian optimization-based meta-learning, ensembles of MAMLs and probabilistic MAML. This a short summary of the course ‘Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 5 — Bayesian Meta-Learning’.
By Qiurui Chen in TowardsDataScience
For meta-learning algorithms, 3 algorithmic properties are important: expressive power, consistency, and uncertainty awareness. Expressive power is the ability for f to represent a range of learning procedures, it measures scalability and applicability to a range of domains. Consistency means learned learning procedure will solve tasks with enough data, this property reduces reliance on meta-training tasks, which leads to good out-of-distribution performance. Uncertainty awareness is the ability to reason about ambiguity during learning. It allows us to think about how we might explore new environments in a reinforcement learning context in order to reduce our uncertainty. It also thinks about if we are in safety-critical settings, we want to calibrate uncertainty estimates. It also allows us to think about, from the Bayesian perspective of Meta-learning, what sort of principle approaches can be derived from those graphical models?
This story covers 1. Why be Bayesian? 2. Bayesian meta-learning approaches 3. How to evaluate Bayesians ... " ...'
Bayesian meta-learning
This story introduces bayesian meta-learning approaches, which covers bayesian black-box meta-learning, bayesian optimization-based meta-learning, ensembles of MAMLs and probabilistic MAML. This a short summary of the course ‘Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 5 — Bayesian Meta-Learning’.
By Qiurui Chen in TowardsDataScience
For meta-learning algorithms, 3 algorithmic properties are important: expressive power, consistency, and uncertainty awareness. Expressive power is the ability for f to represent a range of learning procedures, it measures scalability and applicability to a range of domains. Consistency means learned learning procedure will solve tasks with enough data, this property reduces reliance on meta-training tasks, which leads to good out-of-distribution performance. Uncertainty awareness is the ability to reason about ambiguity during learning. It allows us to think about how we might explore new environments in a reinforcement learning context in order to reduce our uncertainty. It also thinks about if we are in safety-critical settings, we want to calibrate uncertainty estimates. It also allows us to think about, from the Bayesian perspective of Meta-learning, what sort of principle approaches can be derived from those graphical models?
This story covers 1. Why be Bayesian? 2. Bayesian meta-learning approaches 3. How to evaluate Bayesians ... " ...'
Augmented Paper as a New Media
Good illustrations at the link. Clever thought, we still will use paper (and other media) How can we effectively get important virtual content there too. Saw this presented one time as a means for advertising in AR spaces.
The Virtual Made Real—New Technology for the Media of the Future
ETH Zurich
Florian Meyer
Researchers at the Swiss Federal Institute of Technology, Zurich's Media Technology Center (MTC) are developing future media technologies to transform journalism. One project is "augmented paper" that would allow anyone wearing augmented reality (AR) glasses to see moving images on a page that would display correctly even when the pages bend. MTC is using Zurich's city center to test the concept's practical legibility, with wearers of AR glasses able to see local information like tram departure times, ads for shops, or related newspaper articles. MTC's Severin Klingler said, "Our tool means users no longer have to grapple with the difficult question of how to get their virtual content into the right real place." ... '
The Virtual Made Real—New Technology for the Media of the Future
ETH Zurich
Florian Meyer
Researchers at the Swiss Federal Institute of Technology, Zurich's Media Technology Center (MTC) are developing future media technologies to transform journalism. One project is "augmented paper" that would allow anyone wearing augmented reality (AR) glasses to see moving images on a page that would display correctly even when the pages bend. MTC is using Zurich's city center to test the concept's practical legibility, with wearers of AR glasses able to see local information like tram departure times, ads for shops, or related newspaper articles. MTC's Severin Klingler said, "Our tool means users no longer have to grapple with the difficult question of how to get their virtual content into the right real place." ... '
Apple, Google and Europe on Contact Tracing
Continuing to follow this. Since Europe has different ideas about security, some disagreement was inevitable.
Apple, Google Start to Win Over Europe to Their Virus-Tracking Technology
The Wall Street Journal
Sam Schechner; Jenny Strasburg
European nations including Germany, Italy, and the Netherlands are considering or have opted for technology developed by Apple and Google for smartphone contact-tracing applications to contain the coronavirus pandemic. The Apple/Google system is designed for phones running Bluetooth, which emit a unique, frequently changing identification number and record the ID numbers of any phone in proximity for more than a few minutes. In this decentralized model, an infected person's phone uploads data on all of the ID numbers it has broadcast over a set time period to a temporary server; other phones then check the server to see if any of the IDs are among those they recorded, and alert the user if a match is detected. ... "
Apple, Google Start to Win Over Europe to Their Virus-Tracking Technology
The Wall Street Journal
Sam Schechner; Jenny Strasburg
European nations including Germany, Italy, and the Netherlands are considering or have opted for technology developed by Apple and Google for smartphone contact-tracing applications to contain the coronavirus pandemic. The Apple/Google system is designed for phones running Bluetooth, which emit a unique, frequently changing identification number and record the ID numbers of any phone in proximity for more than a few minutes. In this decentralized model, an infected person's phone uploads data on all of the ID numbers it has broadcast over a set time period to a temporary server; other phones then check the server to see if any of the IDs are among those they recorded, and alert the user if a match is detected. ... "
Alternatives to Powerpoint
I used to take a stronger point on this, but now think that powerpoint works, is understood by many people, and I have lots of my previous presentations there I can work from. But there are alternatives, if you are looking for some new ideas:
PowerPoint Makes Us Stupid. Here Are 3 Smarter Alternatives
"People who know what they are talking about don’t need PowerPoint." -- Steve Jobs
By Geoffrey James ... in Inc.com
PowerPoint Makes Us Stupid. Here Are 3 Smarter Alternatives
"People who know what they are talking about don’t need PowerPoint." -- Steve Jobs
By Geoffrey James ... in Inc.com
Monday, May 18, 2020
Microwaves Sensing Your Health?
This struck me at first as being even more interesting than it actually seems to be: Detect what machines you are using. And derive some health measures from behavior. It turns out I had just placed a large long russet potato in the microwave, and hit the 'potato' button, and for the first ten minutes the display said 'sensing', then it changed to 'cooking' as it proceeded to create a soft potato. Was this something like that? Well, no it seems, but it made me think more along those lines. Can more derived from the sensing? No plans to crawl into the microwave.
" ... Sapple, a system developed at the MIT Computer Science and Artificial Intelligence Laboratory, analyzes in-home appliance usage to better understand health patterns, using just radio signals and a smart electricity meter.
What can your microwave tell you about your health?
An MIT system uses wireless signals to measure in-home appliance usage to better understand health tendencies. Rachel Gordon | MIT CSAIL
For many of us, our microwaves and dishwashers aren’t the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way.
But we may soon be rethinking that, thanks to new research from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called “Sapple,” analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter.
Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used.
For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns. These can include personal hygiene, dressing, eating, maintaining continence, and mobility.
“This system uses passive sensing data, and does not require people to change the way they live,” says MIT PhD student Chen-Yu Hsu, the lead author on a new paper about Sapple. “It has potential to improve things like energy saving and efficiency, give us a better understanding of the daily activities of seniors living alone, and provide insight into the behavioral analytics for smart environments.” ... ." ... '
" ... Sapple, a system developed at the MIT Computer Science and Artificial Intelligence Laboratory, analyzes in-home appliance usage to better understand health patterns, using just radio signals and a smart electricity meter.
What can your microwave tell you about your health?
An MIT system uses wireless signals to measure in-home appliance usage to better understand health tendencies. Rachel Gordon | MIT CSAIL
For many of us, our microwaves and dishwashers aren’t the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way.
But we may soon be rethinking that, thanks to new research from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called “Sapple,” analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter.
Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used.
For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns. These can include personal hygiene, dressing, eating, maintaining continence, and mobility.
“This system uses passive sensing data, and does not require people to change the way they live,” says MIT PhD student Chen-Yu Hsu, the lead author on a new paper about Sapple. “It has potential to improve things like energy saving and efficiency, give us a better understanding of the daily activities of seniors living alone, and provide insight into the behavioral analytics for smart environments.” ... ." ... '
Pandemic Disruptions of Education:
Had an idea that universities would change greatly. Was struck by the prediction below. My own was more process-systematic. That Universities would be replaced by individual teachers, or groups of professors, empowered by technologies, connected in an Agora-like system. The teachers would then vouch for the education transmitted. This would probably work better for technical education. Probably more efficient. Would likely be expensive and exclusive as well. Dissimilar than the below, but with similar results.
The Coming Disruption Scott Galloway predicts a handful of elite cyborg universities will soon monopolize higher education. By James D. Walsh in NYMag via Walter Riker.
In 2017, Scott Galloway anticipated Amazon’s $13.7 billion purchase of Whole Foods a month before it was announced. Last year, he called WeWork on its “seriously loco” $47 billion valuation a month before the company’s IPO imploded. Now, Galloway, a Silicon Valley runaway who teaches marketing at NYU Stern School of Business, believes the pandemic has greased the wheels for big tech’s entrée into higher education. The post-pandemic future, he says, will entail partnerships between the largest tech companies in the world and elite universities. MIT@Google. iStanford. HarvardxFacebook. According to Galloway, these partnerships will allow universities to expand enrollment dramatically by offering hybrid online-offline degrees, the affordability and value of which will seismically alter the landscape of higher education. Galloway, who also founded his own virtual classroom start-up, predicts hundreds, if not thousands, of brick-and-mortar universities will go out of business and those that remain will have student bodies composed primarily of the children of the one percent. ... "
The Coming Disruption Scott Galloway predicts a handful of elite cyborg universities will soon monopolize higher education. By James D. Walsh in NYMag via Walter Riker.
In 2017, Scott Galloway anticipated Amazon’s $13.7 billion purchase of Whole Foods a month before it was announced. Last year, he called WeWork on its “seriously loco” $47 billion valuation a month before the company’s IPO imploded. Now, Galloway, a Silicon Valley runaway who teaches marketing at NYU Stern School of Business, believes the pandemic has greased the wheels for big tech’s entrée into higher education. The post-pandemic future, he says, will entail partnerships between the largest tech companies in the world and elite universities. MIT@Google. iStanford. HarvardxFacebook. According to Galloway, these partnerships will allow universities to expand enrollment dramatically by offering hybrid online-offline degrees, the affordability and value of which will seismically alter the landscape of higher education. Galloway, who also founded his own virtual classroom start-up, predicts hundreds, if not thousands, of brick-and-mortar universities will go out of business and those that remain will have student bodies composed primarily of the children of the one percent. ... "
Embracing Responsible AI from Pilot to Production
A topic rarely addressed well, we discovered early on it had to be carefully done. Upcoming DSC webinar:
Embracing Responsible AI from Pilot to Production
Join us for the latest DSC Webinar on May 27th, 2020
register-now: https://dsc.news/2LFMEHz
On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization. In this latest Data Science Central webinar, we'll talk about the framework for scaling AI pilots to production with a focus on ethical responsibilities and bias mitigation at each step.
We'll look at:
The five-step AI development cycle
Ways to control for unwanted bias across data, models, and run time at the production layer
Explainability and why it is key for moving AI pilots to production that delivers core business value
Speakers:
Lukas Biewald, Founder & CEO -- Weights & Biases
Alyssa Simpson-Rochwerger, VP of AI & Data -- Appen
Hosted by: Sean Welch, Host and Producer -- Data Science Central
Title: Embracing Responsible AI from Pilot to Production
Date: Wednesday, May 27th, 2020
Time: 9 AM - 10 AM PDT
Space is limited so please register early:
https://dsc.news/2LFMEHz
Reserve your Webinar seat now
After registering you will receive a confirmation email containing information about joining the Webinar. ...
Embracing Responsible AI from Pilot to Production
Join us for the latest DSC Webinar on May 27th, 2020
register-now: https://dsc.news/2LFMEHz
On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization. In this latest Data Science Central webinar, we'll talk about the framework for scaling AI pilots to production with a focus on ethical responsibilities and bias mitigation at each step.
We'll look at:
The five-step AI development cycle
Ways to control for unwanted bias across data, models, and run time at the production layer
Explainability and why it is key for moving AI pilots to production that delivers core business value
Speakers:
Lukas Biewald, Founder & CEO -- Weights & Biases
Alyssa Simpson-Rochwerger, VP of AI & Data -- Appen
Hosted by: Sean Welch, Host and Producer -- Data Science Central
Title: Embracing Responsible AI from Pilot to Production
Date: Wednesday, May 27th, 2020
Time: 9 AM - 10 AM PDT
Space is limited so please register early:
https://dsc.news/2LFMEHz
Reserve your Webinar seat now
After registering you will receive a confirmation email containing information about joining the Webinar. ...
What is Artificial General Intelligence (AGI)
A good non technical look at AGI, why its not solved yet, while the narrow kind continues to spread. I would further suggest that the narrow kind is also context limited in many ways. Often in unexpected ways. Impact regulation is also increasing, often motivated by unintended consequences of applying intelligence. here is still much to be done to approach general intelligence (AGI).
What is artificial general intelligence (general AI/AGI)? By Ben Dickson in TechTalks
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
From ancient mythology to modern science fiction, humans have been dreaming of creating artificial intelligence for millennia. But the endeavor of synthesizing intelligence only began in earnest in the late 1950s, when a dozen scientists gathered in Dartmouth College, NH, for a two-month workshop to create machines that could “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
The workshop marked the official beginning of AI history. But as the two-month effort—and many others that followed—only proved that human intelligence is very complicated, and the complexity becomes more evident as you try to replicate it.
That is why, despite six decades of research and development, we still don’t have AI that rivals the cognitive abilities of a human child, let alone one that can think like an adult. What we do have, however, is a field of science that is split into two different categories: artificial narrow intelligence (ANI), what we have today, and artificial general intelligence (AGI), what we hope to achieve. .... "
What is artificial general intelligence (general AI/AGI)? By Ben Dickson in TechTalks
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
From ancient mythology to modern science fiction, humans have been dreaming of creating artificial intelligence for millennia. But the endeavor of synthesizing intelligence only began in earnest in the late 1950s, when a dozen scientists gathered in Dartmouth College, NH, for a two-month workshop to create machines that could “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
The workshop marked the official beginning of AI history. But as the two-month effort—and many others that followed—only proved that human intelligence is very complicated, and the complexity becomes more evident as you try to replicate it.
That is why, despite six decades of research and development, we still don’t have AI that rivals the cognitive abilities of a human child, let alone one that can think like an adult. What we do have, however, is a field of science that is split into two different categories: artificial narrow intelligence (ANI), what we have today, and artificial general intelligence (AGI), what we hope to achieve. .... "
Future of Smart Fabrics
Also a space we examined. Especially about how these fabrics could be maintained. Good to see some thoughts about how this is evolving.
3 Questions: The rapidly unfolding future of smart fabrics
Soon, your clothes may be able to monitor your vital signs, analyze the results, and warn you of health risks.
By David L. Chandler | MIT News Office
May 8, 2020
In an opinion piece published in the journal Matter, members of the Fibers@MIT research group recently laid out a detailed vision for how the rapidly growing field of advanced fibers and fabrics could transform many aspects of our lives. For example, “smart clothing” might continuously monitor temperature, heart rate, and other vital signs, then analyze the data and give warnings of potential health conditions. Headed by Professor Yoel Fink, the group is developing fibers and fabrics with advanced computational properties. MIT News asked PhD student Gabriel Loke, who was the article’s lead author, along with Fink and six others, to elaborate on the team’s outlook. ... "
3 Questions: The rapidly unfolding future of smart fabrics
Soon, your clothes may be able to monitor your vital signs, analyze the results, and warn you of health risks.
By David L. Chandler | MIT News Office
May 8, 2020
In an opinion piece published in the journal Matter, members of the Fibers@MIT research group recently laid out a detailed vision for how the rapidly growing field of advanced fibers and fabrics could transform many aspects of our lives. For example, “smart clothing” might continuously monitor temperature, heart rate, and other vital signs, then analyze the data and give warnings of potential health conditions. Headed by Professor Yoel Fink, the group is developing fibers and fabrics with advanced computational properties. MIT News asked PhD student Gabriel Loke, who was the article’s lead author, along with Fink and six others, to elaborate on the team’s outlook. ... "
Combining Museum and a Lab
When we built our retail innovation spaces, which sought to help us understand what the future of these spaces when inserting innovations, we looked at several museum solutions. One approach would be to allow remote interactions by different kinds of visitors. Sometimes using the spaces as a display, sometimes as a lab, This description reminded me of the process and how it would work in each way.
Take a virtual tour of the Museo Galileo in Florence, Italy
The museum holds one of the world's major collections of scientific instruments.
By Jennifer Ouellette in Arstechnica ...
Take a virtual tour of the Museo Galileo in Florence, Italy
The museum holds one of the world's major collections of scientific instruments.
By Jennifer Ouellette in Arstechnica ...
Automating Complex 3D Modeling
Better complex 3D models, that require less adjustments, can lead to beter understanding and manipulation and thus use of the model.
Automating Complex 3D Modeling
Sandia Labs News
April 27, 2020
Researchers at Sandia National Laboratories, the University of Maryland, College Park, the University of Texas at Austin, and the University of California, Davis have developed software to automatically generate three-dimensional (3D) digital models, or meshes, of complex objects. The VoroCrust software employs 3D polyhedral Voronoi cells to produce meshes. Sandia's Mohamed Ebeida said VoroCrust is the first software that creates Voronoi-cell meshes that conform to complex models without requiring manual correction. Points or seeds are positioned around the boundaries of geometrical objects to become Voronoi-cell footholds, and then VoroCrust fills the interior with additional cells. Ebeida said, "Once you decompose the object into these well-shaped pieces ... you can mesh any model you want with confidence about the quality of the resulting mesh without any post-processing." ... '
Automating Complex 3D Modeling
Sandia Labs News
April 27, 2020
Researchers at Sandia National Laboratories, the University of Maryland, College Park, the University of Texas at Austin, and the University of California, Davis have developed software to automatically generate three-dimensional (3D) digital models, or meshes, of complex objects. The VoroCrust software employs 3D polyhedral Voronoi cells to produce meshes. Sandia's Mohamed Ebeida said VoroCrust is the first software that creates Voronoi-cell meshes that conform to complex models without requiring manual correction. Points or seeds are positioned around the boundaries of geometrical objects to become Voronoi-cell footholds, and then VoroCrust fills the interior with additional cells. Ebeida said, "Once you decompose the object into these well-shaped pieces ... you can mesh any model you want with confidence about the quality of the resulting mesh without any post-processing." ... '
Sunday, May 17, 2020
5G Rollout is Proceeding Well
What will the implications be for broad rollout of 5G?
Qualcomm President: 5G Rollout Is Doing Just Fine
Qualcomm has spent three decades working on the next big thing in wireless. The company’s sales peaked at $26.5 billion in 2014, as fourth-generation, or 4G, phones came of age. Now the smartphone chip maker is waiting for a new, 5G peak. It’s coming. Qualcomm expects 175 million to 225 million 5G handsets to be sold worldwide this year, even amid the Covid-19 pandemic. Wall Street expects Qualcomm’s revenue to hit $28 billion in 2022.
Barron’s recently spoke with Qualcomm (ticker: QCOM) President Cristiano Amon, a 25-year veteran of the company, about the much-anticipated 5G rollout, global technology competition, and the coronavirus. ... "
Qualcomm President: 5G Rollout Is Doing Just Fine
Qualcomm has spent three decades working on the next big thing in wireless. The company’s sales peaked at $26.5 billion in 2014, as fourth-generation, or 4G, phones came of age. Now the smartphone chip maker is waiting for a new, 5G peak. It’s coming. Qualcomm expects 175 million to 225 million 5G handsets to be sold worldwide this year, even amid the Covid-19 pandemic. Wall Street expects Qualcomm’s revenue to hit $28 billion in 2022.
Barron’s recently spoke with Qualcomm (ticker: QCOM) President Cristiano Amon, a 25-year veteran of the company, about the much-anticipated 5G rollout, global technology competition, and the coronavirus. ... "
Humans and AI Work Better Together
We determined this early on, the challenge remains on how to pass along the 'work' in context and asess the results and aportion the
Microsoft chief scientist: Humans and AI work better together than alone
Khari Johnson in VentureBeat
Humans and AI systems work better when they tackle problems together. That’s according to research from Microsoft chief scientist Eric Horvitz, Microsoft Research principal research Ece Kamar, and Harvard University student and Microsoft Research intern Bryan Wilder. The paper appears to be one of the first published by Horvitz since Microsoft named him chief scientific officer in March, the first in company history. Horvitz came to Microsoft as a principal researcher in 1993 and led Microsoft Research operations from 2017 to 2020.
The paper released earlier this month studies the performance of human and AI teams working together on two computer vision tasks: Galaxy classification and breast cancer metastasis detection. With the proposed approach, the AI model determines which tasks are best for humans to perform and which are best handled by AI. ..."
The paper released May 1 on preprint repository: https://arxiv.org/abs/2005.00582
Kamar and Horvitz worked together on a paper published in 2012:
https://www.microsoft.com/en-us/research/publication/combining-human-and-machine-intelligence-in-large-scale-crowdsourcing/
Microsoft chief scientist: Humans and AI work better together than alone
Khari Johnson in VentureBeat
Humans and AI systems work better when they tackle problems together. That’s according to research from Microsoft chief scientist Eric Horvitz, Microsoft Research principal research Ece Kamar, and Harvard University student and Microsoft Research intern Bryan Wilder. The paper appears to be one of the first published by Horvitz since Microsoft named him chief scientific officer in March, the first in company history. Horvitz came to Microsoft as a principal researcher in 1993 and led Microsoft Research operations from 2017 to 2020.
The paper released earlier this month studies the performance of human and AI teams working together on two computer vision tasks: Galaxy classification and breast cancer metastasis detection. With the proposed approach, the AI model determines which tasks are best for humans to perform and which are best handled by AI. ..."
The paper released May 1 on preprint repository: https://arxiv.org/abs/2005.00582
Kamar and Horvitz worked together on a paper published in 2012:
https://www.microsoft.com/en-us/research/publication/combining-human-and-machine-intelligence-in-large-scale-crowdsourcing/
Data is the Most Important Thing
Or poor associated metadata.
Poor data quality is the leading cause of digital transformation failure. It’s time to prioritise data transformation! in 7wdata
In a digitally empowered age, businesses across the globe have grand ambitions of leveraging the power of AI, Big Data, and machine learning. Innovation is happening at a rapid pace. Companies are investing millions of dollars in building data lakes, moving to the cloud, hiring data scientists and chief data officers to run their Digital Transformation plans.
Yet, they fail. Spectacularly. Plenty of reports and surveys show that over 85 per cent of big data projects are failing with varying causes.
Enough has been written lately about how business cultures and unchecked ambitions lead to big data project failures. This piece will focus on how poor data quality is often overlooked and makes for one of the leading cause of digital transformation failure.
Data transformation, the process of transforming raw data into a usable format is often, incorrectly, juxtaposed with digital transformation. Companies assume that because they are implementing data lakes, data centres or new ERPs, (which are all part of digital transformation), they are transforming their data. ... "
Poor data quality is the leading cause of digital transformation failure. It’s time to prioritise data transformation! in 7wdata
In a digitally empowered age, businesses across the globe have grand ambitions of leveraging the power of AI, Big Data, and machine learning. Innovation is happening at a rapid pace. Companies are investing millions of dollars in building data lakes, moving to the cloud, hiring data scientists and chief data officers to run their Digital Transformation plans.
Yet, they fail. Spectacularly. Plenty of reports and surveys show that over 85 per cent of big data projects are failing with varying causes.
Enough has been written lately about how business cultures and unchecked ambitions lead to big data project failures. This piece will focus on how poor data quality is often overlooked and makes for one of the leading cause of digital transformation failure.
Data transformation, the process of transforming raw data into a usable format is often, incorrectly, juxtaposed with digital transformation. Companies assume that because they are implementing data lakes, data centres or new ERPs, (which are all part of digital transformation), they are transforming their data. ... "
SAS on How AI Changes the Rules
Always liked SAS as a analytical problem solving company, here a new paper from them of interest, register for it at the link.
How AI Changes the Rules
New Imperatives for the Intelligent Organization
About this paper
Many leaders are excited about AI’s potential to profoundly transform organizations by making them more innovative and productive. But implementing AI will also lead to significant changes in how organizations are managed, according to our recent survey of more than 2,200 business leaders, managers and key contributors. Those survey respondents, representing organizations across the globe, expect that reaping the benefits of AI will require changes in workplace structures, technology strategies and technology governance.to manage the significant changes to software development and deployment processes that most respondents expect from AI.
AI will drive organizational change and ask more of top leaders. The majority of survey respondents expect that implementing AI will require more significant organizational change than other emerging technologies including cloud. AI demands more collaboration among people skilled in data management, data analytics, IT infrastructure, and systems development, as well as business and operational experts. This means that organizational leaders need to ensure that traditional silos don’t hinder advanced analytics efforts, and they must support the training required to build skills across their workforces.
AI will place new demands on the CIO and CTO. AI implementation will influence the choices CIOs and CTOs make in setting their broad technology agendas. They will need to prioritize developing foundational technology capabilities, from infrastructure and cybersecurity to data management and development processes — areas in which those with more advanced AI implementations are taking the lead compared with other respondents. CIOs will also need to manage the significant changes to software development and deployment processes that most respondents expect from AI. The survey also indicated that many CIOs will be charged with overseeing or supporting formal data governance efforts: CIOs and CTOs are more likely than other executives to be tasked with this.
AI will require an increased focus on risk management and ethics. The Global survey shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Those with more advanced AI practices are establishing processes and policies for data governance and risk management, including providing ways to explain how their algorithms deliver results. They point out that understanding how AI systems reach their conclusions is both an emerging best practice and a necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements.
The report that follows explores these findings in depth. Read on to learn more about the changes that leaders must prepare for to successfully implement trusted AI. ... "
How AI Changes the Rules
New Imperatives for the Intelligent Organization
About this paper
Many leaders are excited about AI’s potential to profoundly transform organizations by making them more innovative and productive. But implementing AI will also lead to significant changes in how organizations are managed, according to our recent survey of more than 2,200 business leaders, managers and key contributors. Those survey respondents, representing organizations across the globe, expect that reaping the benefits of AI will require changes in workplace structures, technology strategies and technology governance.to manage the significant changes to software development and deployment processes that most respondents expect from AI.
AI will drive organizational change and ask more of top leaders. The majority of survey respondents expect that implementing AI will require more significant organizational change than other emerging technologies including cloud. AI demands more collaboration among people skilled in data management, data analytics, IT infrastructure, and systems development, as well as business and operational experts. This means that organizational leaders need to ensure that traditional silos don’t hinder advanced analytics efforts, and they must support the training required to build skills across their workforces.
AI will place new demands on the CIO and CTO. AI implementation will influence the choices CIOs and CTOs make in setting their broad technology agendas. They will need to prioritize developing foundational technology capabilities, from infrastructure and cybersecurity to data management and development processes — areas in which those with more advanced AI implementations are taking the lead compared with other respondents. CIOs will also need to manage the significant changes to software development and deployment processes that most respondents expect from AI. The survey also indicated that many CIOs will be charged with overseeing or supporting formal data governance efforts: CIOs and CTOs are more likely than other executives to be tasked with this.
AI will require an increased focus on risk management and ethics. The Global survey shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Those with more advanced AI practices are establishing processes and policies for data governance and risk management, including providing ways to explain how their algorithms deliver results. They point out that understanding how AI systems reach their conclusions is both an emerging best practice and a necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements.
The report that follows explores these findings in depth. Read on to learn more about the changes that leaders must prepare for to successfully implement trusted AI. ... "
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