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Monday, January 27, 2020

When can you Selectively look at Less than all of the Data?

I pass along Jason Brownlee's links from time to time, have found them very useful.   Subscribe to his stream, buy his books.  Here its about sampling and when you can look at less than all of the data. 

Jason @ ML Mastery jason@machinelearningmastery.com 
Thu, Jan 23, 1:12 PM (3 days ago)    to Franzdill

Hi, this week we have a tutorial on undersampling algorithms for imbalanced classification, a tutorial on combining oversampling and undersampling, and a tour of data sampling methods.

Discover how to delete examples from your dataset to improve performance:
>> Undersampling Algorithms for Imbalanced Classification

Discover specialized techniques that perform both oversampling and undersampling:
>> Combine Oversampling and Undersampling for Imbalanced Classification

Discover a suite of data sampling techniques available for imbalanced classification:
>> Tour of Data Sampling Methods for Imbalanced Classification

See his new book
Imbalanced Classification with Python
Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning  ... "

Sunday, January 26, 2020

Numba: Speeding up Python

Just brought to my attention.  Python use for numerical algorithms can be an issue when speed is required.  Technical.

Accelerate Python Functions
Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.

You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest.  .... " 

Networks in the Enterprise

Podcast and more about the use of internal networks.   Talking the podcast side we established some internal podcasts channels to talk to people about how emergent tech was being used.  Talked and experimented with automating the networking of insights among reports.

Internal Networks in the HBR
January 23, 2020

Do you wish you were more plugged-in at your organization? In this episode of HBR’s advice podcast, Dear HBR:, cohosts Alison Beard and Dan McGinn answer your questions with the help of Robin Abrahams, a research associate at Harvard Business School and the “Miss Conduct” columnist at Boston Globe Magazine. They talk through what to do when you want to network at a company retreat, your manager is bothered by your schmoozing with their peers, or you want to know about plum projects before they get assigned to someone else.

PODCAST  at the link

Listen to more episodes and find out how to subscribe on the Dear HBR: page. Email your questions about your workplace dilemmas to Dan and Alison at dearhbr@hbr.org.

From Alison and Dan’s reading list for this episode:

HBR: Learn to Love Networking by Tiziana Casciaro, Francesca Gino, and Maryam Kouchaki — “A mountain of research shows that professional networks lead to more job and business opportunities, broader and deeper knowledge, improved capacity to innovate, faster advancement, and greater status and authority. Building and nurturing professional relationships also improves the quality of work and increases job satisfaction.”

Boston Globe Magazine: Miss Conduct’s all-in-one career fix-it guide by Robin Abrahams — “Censor your snarky inner voice and have the courage to ask seemingly obvious questions or draw offbeat analogies. Networking is about creating possibilities. Giving people a safe space to explore and connect ideas is a great way to persuade them you are a uniquely insightful genius.”

HBR: The Best Way to Network in a New Job by Rob Cross and Peter Gray — “Anyone who hopes to hit the ground running in a new organization must first cultivate allies — a network of people who can provide the information, resources and support needed to succeed. But few onboarding programs offer concrete advice on how to build those all-important connections.” ... " 

Taiwan Improves Manufacturing with AI

Some good details here about what is going on in Taiwan, notably integration with big data and AI capabilities.

From plastic toys to Industry 4.0: How Taiwan is using science to upgrade its manufacturing
The island is turning to smart machinery and artificial intelligence to improve the quality and flexibility of the products it makes.

Animation showing production line of robotic arms producing electronics  ...  Image  Credit: Geoffroy de Crécy

In 2016, industrial engineer Chen-Fu Chien was asked to lead a university research centre in Taiwan that would develop new manufacturing technologies using artificial intelligence (AI).

Rather than aiming to publish academic papers, his brief was to produce ideas that could be quickly transferred into industrial settings, says Chien. His research at the National Tsing Hua University (NTHU) in Hsinchu City uses big-data analytics to make machines smarter through AI that lets them take decisions without human control. It is one of several approaches to creating ‘smart factories’ that use an interconnected, digital network of supply systems — part of Taiwan’s push to improve the flexibility, quality and efficiency of its manufacturing.  .... " 

Frontier in AI Training

We always need to consider the outlier case.      Its often in current and future training.... I like the idea of thinking about broadening the training to include 'nothing'.

The Next Frontier in AI: Nothing
How an overlooked feature of deep learning networks can turn into a major breakthrough for AI
By Max Versace in IEEE

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.

At an early age, as we take our first steps into the world of math and numbers, we learn that one apple plus another apple equals two apples. We learn to count real things. Only later are we introduced to a weird concept: zero… or the number of apples in an empty box.

The concept of “zero” revolutionized math after Hindu-Arabic scholars and then the Italian mathematician Fibonacci introduced it into our modern numbering system. While today we comfortably use zero in all our mathematical operations, the concept of “nothing” has yet to enter the realm of artificial intelligence.

In a sense, AI and deep learning still need to learn how to recognize and reason with nothing.

Is it an apple or a banana? Neither!
Traditionally, deep learning algorithms such as deep neural networks (DNNs) are trained in a supervised fashion to recognize specific classes of things.

In a typical task, a DNN might be trained to visually recognize a certain number of classes, say pictures of apples and bananas. Deep learning algorithms, when fed a good quantity and quality of data, are really good at coming up with precise, low error, confident classifications.

The problem arises when a third, unknown object appears in front of the DNN. If an unknown object that was not present in the training set is introduced, such as an orange, then the network will be forced to “guess” and classify the orange as the closest class that captures the unknown object—an apple!

Basically, the world for a DNN trained on apples and bananas is completely made of apples and bananas. It can’t conceive the whole fruit basket.....

Enter the world of nothing

While its usefulness is not immediately clear in all applications, the idea of “nothing” or a “class zero” is extremely useful in several ways when training and deploying a DNN.

During the training process, if a DNN has the ability to classify items as “apple,” “banana,” or “nothing,” the algorithm’s developers can determine if it hasn’t effectively learned to recognize a particular class. That said, if pictures of fruit continue to yield “nothing” responses, perhaps the developers need to add another “class” of fruit to identify, such as oranges.  .... "

Saturday, January 25, 2020

Sim Swapping Insecurity

Had read about this.   The details here are that those responsible for the most minimal security are not taking it seriously. Poor security is not the right term,  nonexistent was too often the case.

SIM Swapping, Poor Web Security Put Millions at Risk
New Scientist
Chris Stokel-Walker
January 22, 2020

Researchers at Princeton University have found that two-factor authentication (2FA)—a security measure recommended by many websites and apps—is easily hackable and could put millions of people at risk. If a bad actor can compromise a user's phone, that will give them access to that user's online accounts. "SIM swapping" attacks allow hackers to port phone numbers to new SIM cards. Mobile phone networks should have security measures in place to prevent this, but the Princeton researchers found that five major U.S. networks do not have sufficient protections in place. Once hackers have control of a phone, they can reset passwords to online accounts by redirecting the 2FA confirmation texts. The team also analyzed 140 websites for their vulnerability to SIM swapping, and found that 17 major websites were "doubly insecure," meaning they did not ever require a user to insert their password to gain access to accounts, asking only for a telephone number..... '

Electronic Skin

 Note the long history of this kind of effort.

Integrate Microchips for Electronic Skin
Leibniz Institute for Solid State and Materials Research (Germany)
January 22, 2020

German and Japanese researchers have developed an active-matrix magnetic sensor system in a step toward the creation of electronic skin. The system is comprised of a series of magnetic sensors, an organic bootstrap shift register as a control mechanism, and organic signal amplifiers. All the electronic elements are based on organic thin-film transistors and integrated within a single platform; the device exhibits high magnetic sensitivity and resilience against mechanical deformation, and can facilitate two-dimensional magnetic field distribution in real time. Said researchers Oliver G. Schmidt and Daniil Karnaushenko, "[The] ultra-compliant and flexible nature of these devices is [an] indispensable feature for modern and future applications such as soft-robotics, implants, and prosthetics.”

Friday, January 24, 2020

Putting Humans in the AI Loop

I argue that they are always in the loop.  Just how effectively are they placed there. Always a very useful question. Starting with existing process helps.

Can AI Put Humans Back in the Loop?
ZDNet
Tiernan Ray

Scientists at Germany's Technische Universitat Darmstadt have developed a process for having a human domain expert review an artificial intelligence model's inner mechanisms during training, in order to catch simple problems and correct errors. Such an expert would check the reasoning offered by a neural network, with the overall goal of building more trust in machine learning. The experimental explanatory interactive learning procedure involves a convolutional neural network classifying the phenotype of a plant as healthy or diseased by analyzing leaf images. The researchers visualize the features the network is using, then a plant biology specialist fixes any network errors. ... 

Show Devices Can Now Recognize

Just brought to my attention, Alexa 'Show' devices, that is,  those that have an embedded camera,  can now be asked to recognize things.   Apparently only in the domain of' 'pantry items'.  Designed as an assist for the blind or those that need vision assistance.    This might raise some privacy issues despite that the action is requested.

Alexa can now recognize objects
A new Show and Tell feature on Echo Show devices can recognize some objects.   By Molly Price in CNet

If facial recognition concerns were on your radar, get ready to worry about soup cans, too. Amazon today announced a new feature for its Echo Show devices. The feature, called Show and Tell, is focused on recognizing household pantry items when you hold them in front of the camera.... "

Google Dataset Search

Worth another, closer look. In Flowingdata.  

Over a year ago, Google released Dataset Search in public beta. The goal was to index datasets across the internets to make them easier to find. It came out of beta:

Based on what we’ve learned from the early adopters of Dataset Search, we’ve added new features. You can now filter the results based on the types of dataset that you want (e.g., tables, images, text), or whether the dataset is available for free from the provider. If a dataset is about a geographic area, you can see the map. Plus, the product is now available on mobile and we’ve significantly improved the quality of dataset descriptions. One thing hasn’t changed however: anybody who publishes data can make their datasets discoverable in Dataset Search by using an open standard (schema.org) to describe the properties of their dataset on their own web page.

I haven’t tried it in a while, but the last time I did, there weren’t that many sources yet, because the indexing partially relies on others to use a standard to provide metadata. Kicking the tires on it now, it still kind of feels like an index of other dataset aggregators, but I’m interested.  .... " 

Update on Samsung Bixby

Took early looks at Bixby, was not impressed.  Has huge appliance exposure, IOT connections.  What's new and interesting

Bixby was quiet in 2019, but don't sleep on Samsung's assistant
It's not all bad news for Bixby.

Chris Velazco, @chrisvelazco in Engadget

Decades of science fiction assured us all that, yes, one day we'd be able to control the immensely complex gadgetry around us with just our voices. It was right, mostly. The rise of the virtual assistant, built atop still other developments in cloud computing and machine learning, means we can wonder out loud what the weather is like, or how far away the moon is, or hail a car and expect a response from a carefully crafted voice in moments. And now, those disembodied voices have taken up residence in our homes .... "

Thursday, January 23, 2020

Why is Wal-Mart Embracing AI

Certainly they are, but the motivation not too clear from this. 

Why Walmart is Embracing AI and Robotics

One of the most famous bonehead quotes from a chief executive who brushed off technological disruption came from Blockbuster CEO Jim Keyes in 2008. He said, “Neither RedBox nor Netflix are even on the radar screen in terms of competition.” Two years later, Blockbuster filed for bankruptcy and Keyes got a job stocking DVDs in RedBox machines. We might have made up one of those two stories, but there’s no denying the fact that businesses that don’t evolve, don’t survive. The malls of the United States are emptying faster than your bowels after eating from one-too-many sketchy-looking taco stands in Mexico City, with vacancy rates expected to hit up to 25% in a couple of years.  ... "

Delta Airlines Develops Disruption Predictions

Example in the aviation industry, would seem to be much available data, consideration and implications  of inaccurate predictions?

Delta Develops Artificial Intelligence Tool to Address Weather Disruption, Improve Flight Operations    By Woodrow Bellamy III | January 8, 2020

Delta Air Lines CEO Ed Bastian used his keynote speech at the annual Consumer Electronics Show to discuss a new 2020s operational structure for the international carrier that will be driven by the use of a new artificial intelligence (AI) machine learning tool.

Under development at Delta’s operations and customer center, Bastian did not provide a specific product name for the technology, but instead called it a proprietary tool that will mainly be focused on helping passengers and flight crews overcome weather occurrences that impact the routes they fly on a daily basis. The keynote speech is a familiar strategy across all of the divisions of Delta, including their maintenance team whose predictive maintenance leadership gave a speech on how the airline is shifting towards the adoption of AI at the 2019 AEEC/AMC annual conference.  .... "

On the End of Tracking Cookies?

Quite a considerable change?  We analytically examined the use of Cookies early on.  Is this the end, and what are the implications?

R.I.P. Cookies. Why customer experience matters more than ever
Jean Belanger in CustomerThink

Marketing in the Internet age has seen several assumptions cast in stone:

1. Cookies: targeting and attribution has been fuelled by cookies, which were invented 25 years ago in 1994.
2. Ads: total US digital ad spending in 2019 was $130B, versus $110 billion for traditional ads.
3. Data: it was cool for brands to take the approach that ‘we have your data, and we can do what we want with it, as and when we see fit.’

Brands can track you, they can follow you across the web, they can target you with ads, if they are relevant – awesome. Therefore it comes as no surprise that traditional channels of communications – TV, radio, newspapers – are all giving way in our increasingly to digital lifestyles.

Meanwhile the Internet and smartphones are making us pickier and more informed than ever. But the natives are restless. We do not want to be tracked like animals in the jungle. We value our privacy more than we ever have done. My data and personal profile is mine, not yours. Where are our private property rights when we need them?

Google dropped its “cookie apocalypse” on the marketing industry earlier this month, when it announced that they will be phasing out the use of cross-website cookies, which have underpinned digital advertising for 25 years. They will also “obsolete” third-party cookies that follow internet users from site to site, and can trace their browsing for months and months.

Google’s move will drastically curb the ability of brands to extract private and sensitive insights about us. While the advertising industry has known for some time that third-party cookies are being consigned to history, slowly eliminating the basic concept of an open web that has dominated marketing matters for decades, Google’s news will totally disrupt the global digital advertising supply chain. ... "

AB InBev Uses Machine Learning for Corruption

Have in the past few months seen several interesting examples of automated and semi-automated fraud detection, and some cases where it should be being used.  Here another somewhat unexpected example.

AB InBev Taps Machine Learning to Root Out Corruption
The Wall Street Journal
By Dylan Tokar

Brewer Anheuser-Busch InBev spent three years developing machine learning technology to spot corruption in its business partners. The BrewRight analytics platform harnesses data from operations in more than 50 countries to proactively track legal risks and deter violations, rather than investigating problems after they crop up. Companies have traditionally probe misconduct after it happens, but Harvard Business School's Eugene Soltes said, "Data analytics and what AB InBev has done changes that equation. They want to put much more on the front-end—on prevention and detection." The machine learning aspect allows the platform to become smarter and more effective over time. It already has cut hundreds of thousands of dollars in costs associated with investigating suspect payments.  ... '

AI Enhancing Maps and Goal Process

You could add to this the ability to add contextual process knowledge to a map.   Beyond just what is there, and how do I navigate to X.   Say in the goal context of maintaining a road,  I may want to be shown places where proactive analysis needs to be done, what equipment is needed, what would the cost would be,  how does that fit into budgets and schedules.   That defines a process,  some algorithms, AI pattern recognition to be involved.   Leading to a change in some process plan.   A kind of 'task and process navigation'  to achieve a management process.

Using artificial intelligence to enrich digital maps
Model tags road features based on satellite images, to improve GPS navigation in places with limited map data.

Rob Matheson | MIT News Office

A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation.  

Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.

But creating detailed maps is an expensive, time-consuming process done mostly by big companies, such as Google, which sends vehicles around with cameras strapped to their hoods to capture video and images of an area’s roads. Combining that with other data can create accurate, up-to-date maps. Because this process is expensive, however, some parts of the world are ignored.

A solution is to unleash machine-learning models on satellite images — which are easier to obtain and updated fairly regularly — to automatically tag road features. But roads can be occluded by, say, trees and buildings, making it a challenging task. In a paper being presented at the Association for the Advancement of Artificial Intelligence conference, the MIT and QCRI researchers describe “RoadTagger,” which uses a combination of neural network architectures to automatically predict the number of lanes and road types (residential or highway) behind obstructions.  .... "

Wednesday, January 22, 2020

Microsoft Project Cortex: Optimizing the Enterprise ?

Was reminded of this, announced generally last year.    It actually has similarities to some things we discussed with Linkedin a number of years ago: To use a company's internal organization chart, enhanced by a 'knowledge graph', to provide more intelligent and efficient internal and external communications further driven by AI.   A more precisely semantic way to organize how cloud/data is linked to task/process?  To ultimately optimize how a company works?  Following.

Introducing Project Cortex

Project Cortex

Today, we’re pleased to introduce Project Cortex, the first new service in Microsoft 365 since the launch of Microsoft Teams. Project Cortex uses advanced AI to deliver insights and expertise in the apps you use every day, to harness collective knowledge and to empower people and teams to learn, upskill and innovate faster.

Project Cortex uses AI to reason over content across teams and systems, recognizing content types, extracting important information, and automatically organizing content into shared topics like projects, products, processes and customers. Cortex then creates a knowledge network based on relationships among topics, content, and people.

New topic pages and knowledge centers—created and updated by AI—enable experts to curate and share knowledge with wiki-like simplicity. And topic cards deliver knowledge just-in-time to people in Outlook, Microsoft Teams, and Office.  .... "

Towards very Powerful Exoskeletons

Been talked for some time, now finally here?  Ways to really extend/improve the capability of the individual human.  Will this be the interim move towards robots replacing many manually intense jobs, by extending the ability of humans?  Reminds me of my training in ergonomic analyses too, can we now have human limits programmed into a robotic extension?

Channel your inner Ripley with Sarcos Robotics’ powered exoskeleton
I certainly wish I'd had one of these when I was a shipfitter.
Jim Salter- 1/22/2020, 2:21 PM in ArsTechnica

he most interesting thing we saw at the Consumer Electronics Show this year was the back side of Delta Airlines' exhibit, where some Sarcos Robotics folks were putting the Guardian XO—a powered industrial exoskeleton—through its paces, and the adventurous (and patient) could wait for half an hour or so in line to operate one disembodied arm of the Guardian attached to a 50-pound suitcase.

Unfortunately, neither Sarcos nor Delta were about to let any journalists inside an actual Guardian XO. They had good reason, though—which became abundantly clear after we took a test run with a disembodied, statically mounted Guardian XO right arm. The suits aren't just designed to be incredibly strong—they're also designed for long-term, ergonomically correct operation that won't destroy backs and knees the way a career in the military or heavy industry tends to. That's great, if you're a trained professional trying not to injure yourself—not so great, if you're a random enthusiast suddenly given 20:1 muscular amplification in a densely-packed crowd of thousands.... "

Winning with AI

Reviewing this study for an upcoming analysis of proposed work:

Winning With AI
Pioneers Combine Strategy, Organizational
Behavior, and Technology

OCTOBER 2019 RESEARCH REPORT
By Sam Ransbotham, Shervin Khodabandeh, Ronny Fehling,
Burt LaFountain, and David Kiron

In collaboration with
RESEARCH REPORT WINNING WITH AI
Copyright © MIT, 2019. All rights reserved.
Get more on artificial intelligence from MIT Sloan Management Review:
Read the report online at https://sloanreview.mit.edu/ai2019
Visit our site at https://sloanreview.mit.edu/big-ideas/artificial-intelligence-business-strategy
Get the free AI, data, and machine learning enewsletter at
https://sloanreview.mit.edu/enews-artificial-intelligence-and-strategy

Combatting Technology Hype

Back to  measures and risks,  often economical.    And then their reasonable forward prediction.

Issues in Science and Technology
Combatting Tech Hype

VOL. XXXVI, NO. 2, WINTER 2020

By BRENT GOLDFARB, DAVID A. KIRSCH, CARLOTA PEREZ, MARTIN KENNEY ...

We enjoyed Jeffrey Funk’s “What’s Behind Technological Hype?” (Issues, Fall 2019). However, as authors of a recent book on financial speculation arising from the commercialization of new technology, Bubbles and Crashes: The Boom and Bust of Technological Innovation (Stanford, 2019), we take issue with a few of Funk’s interpretations.

First, Funk points in several instances to the “lack of good economic analysis” as a critical factor leading to hype. However, what’s really needed is different economic analysis. Understanding technology calls for economic analysis that engages what Robert Shiller called “narrative economics.” Traditional economic approaches are not much help when confronting the fundamental uncertainty that arises from the introduction and potential adoption of a new technology or system. To understand choices at that margin, we need to be sensitive to the sources and impacts of narratives. Narratives are a double-edged sword: carefully deployed, they can coordinate collective action and funnel resources into risky but ultimately profitable ventures, but narratives can also lead to hype, speculation, and damaging bubbles. Unfortunately, in spite of Shiller’s call to action, most economists would not recognize the study of narratives as central to the study of booms and busts, so we need more than “good” economic analysis.

Second, once we accept the intractability of uncertainty, it is not realistic to expect to be able to entirely soften the blow of failure. Indeed, failure may be good, and not in some milquetoast, learning-from-failure way. Awful, terrible, value-destroying failure is good because it signals that our local instance of late-entrepreneurial capitalism is still capable of taking big risks. The implications of this logic are far-reaching: what if the risk that we stop failing (because we stop placing big, transformational bets) is more dangerous than the cost of a little too much hype? This is a hard question to answer, partly because the costs and benefits are incommensurable and partly because they accumulate across time in messy, discontinuous ways. From our perspective, the critical issue is not minimizing failure, but maximizing the categories and numbers of people who can afford to fail. Unfortunately, recent macroeconomic developments suggest that we are doing little to redress the “Lost Einsteins” problem, thereby losing even more of the bold, if risky, ideas that we need in order to support meaningful economic experimentation.

A more critical, narrative economic analysis would focus on how much of the imagined future builds on only imagined elements of the new technological system. Knowing how much is imagined might counteract hype that glosses over these elements.

Brent Goldfarb   Associate Professor
David A. Kirsch Associate Professor    Robert H. Smith School of Business   University of Maryland


Operationalizing Analytics

Ultimately its always about how analytics will be used (implemented and operationalized)  Have seen many cases where the results have been lost in implementing them.   Here a Podcast that covers some of the related topics I am reading.

Operationalizing Analytics (Podcast)
by Phil Bowermaster In B-Eye-Network.

In this podcast, Tapan Patel, Senior Manager of Product Marketing at SAS, discusses the challenges organizations face when deploying and managing analytical models.  He also provides best practices for analytics governance. The interview is conducted by Phil Bowermaster, an independent consultant and analyst who writes and speaks about emerging technologies and the future. To listen to this podcast, click here.

Phil Bowermaster
Phil Bowermaster is an independent analyst and consultant specializing in big data, business intelligence and analytics. Phil is the founder of Speculist Media, which produces blogs, podcasts, and other social and traditional media exploring the role of technology, particularly data technology, in shaping the future. He works with select clients in developing and executing content strategies related to big data. Phil can be reached at phil@speculist.com.
Recent articles by Phil Bowermaster

Other Podcasts:

Artificial Intelligence: Improving Consumer Marketing (Podcast)
Data Science is No Longer Just for Data Scientists (Podcast)
Data Science: Democratization, Self-Service and Risk (Podcast)
HTAP Redefines the Data Management Landscape  ... '

Carmakers Move From Cars to Building Cities at CES

Hardly a complete move, but handling transportation efficiently will be a major part of any smart city.

Carmakers Move From Cars to Building Cities at CES
Financial Times
Patrick McGee; Song Jung-a; Peter Campbell

Automakers announced future technologies at CES 2020, with Toyota CEO Akio Toyoda stating his company would construct a 175-acre hydrogen-powered smart city near Japan’s Mount Fuji as a “living laboratory” to see how up to 2,000 residents will live with next-generation technology. Toyoda said the project aims to keep Toyota abreast of society-transforming megatrends like urbanization, 5G wireless connectivity, and the role of artificial intelligence in evolving consumer devices. Meanwhile, Hyundai announced a partnership with ride-hailing company Uber under which it will build electrically-powered driverless air taxis. The companies said test flights are planned for this year, and commercial operations to begin within three years. The goal is to help Hyundai compete with rivals in emerging technologies.... ' 

AR Continues Struggle in Retail

An area we spent lots of early time in.  Good overview article of current work.

Despite advancements, AR struggles to take off in retail   By Anna Hensel in Modern Retail
This is the third part in a series by Modern Retail about the technologies that were going to change retail — and where they are now. See our previous stories, about the future of robotics, and the truth about RFID.

Three years ago, retailers began spending money on AR.

In 2017, Williams-Sonoma acquired Outward, an AR and 3D imaging startup for $112 million. The following year, Ulta Beauty also acquired an AR startup for an undisclosed amount, as did L’Oreal. Retailers like Walmart and Nike acquired other startups that specialized in virtual reality and computer vision, but also with the goal of using them to create experiences that incorporated some type of virtual reality component.

That ushered in a stampede of retailers eager to experiment with AR. Michael Kors and Warby Parker were some of the first companies to experiment with running AR in Facebook ads. Macy’s launched AR experiences for both furniture and beauty shoppers.

AR right now is big among two categories: beauty or furniture. Sephora, Ulta Beauty, Wayfair, Williams-Sonoma and Ikea have all launched virtual try-on experiences that use AR. Retailers in apparel have also experimented with adding these types of tools — the most recent being Asos — many of those efforts remain firmly in the experimentation phase. Because of the nature of the technology, it’s really those three industries where it can make a real difference — especially in fashion.

But even for the retailers who have invested heavily in AR by building their own virtual try on experiences, they haven’t been able to point definitively to how much their AR tools have resulted in a sales increase.

“There’s still some hyping [around AR], no doubt,” said Andrew Lispman, e-commerce analyst at eMarketer. “But I think the reality has set in when you see the initial use cases…are mostly edge cases. They may work well for their category, but it’s not a use case that extends between all or even most categories.”   ...." 

Rapid Expansion of Amazon Pharmacy

What seems a rapid expansion of the Amazon Pharma delivery to multiple countries.

From Engadget by Mariella Moon

Amazon may be expanding its prescription drug delivery business to other countries. The e-commerce titan has applied for a trademark on the name "Amazon Pharmacy" in Canada, the UK and Australia, according to CNBC. Amazon reportedly filed its applications on January 9th, in what could've been one of the earliest steps it took to start medicine delivery in countries other than the US. .... '

Tuesday, January 21, 2020

RFID Powering of Smart Balls and Tags

An approach utilizing BLE power transmission that we examined and I still follow.

RFID Powers Smart Balls, Luggage Tags  in RFIDJournal

Kookaburra's Smart Balls with SportCor technology using BLE transmissions, as well as British Airways' RFID-enabled luggage tags, are leveraging a wireless transmission solution from Powercast that powers devices, eliminating the need for USB-cabled recharging or, in some cases, batteries.
By Claire Swedberg   Tags: Aerospace, Asset Tracking, BLE, Sensors, Smart Products

Jan 21, 2020—Several technology companies are leveraging radio frequency identification not only to transmit data, but also to power their devices, thereby ensuring consistent performance from sensor-using systems designed to make it easier to find and manage products and assets. Smart sports ball company SportCor has sold its electronics to cricket ball manufacturer Kookaburra and is marketing the product for balls used in a variety of other sports around the world.  ... " 

IBM Builds Policy Lab for Regulation

Will read with interest what the meaning of 'precision' is here.    And transcripts available of the discussion.

IBM unveils Policy Lab, advocates ‘precision regulation’ of AI
Kyle Wiggers in VentureBeat

BM formally announced the IBM Policy Lab — an initiative aimed at providing policymakers with recommendations for emerging problems in technology — ahead of a panel discussion to be held tomorrow at the World Economic Forum. The panel will be hosted by IBM CEO Ginni Rometty, with Siemens CEO Joe Kaeser, White House Deputy Chief of Staff for Policy Coordination Chris Liddell, and OECD Secretary General Angel Gurria. IBM also outlined a set of priorities for AI regulation, including several aimed at compliance and explainability.   .... "