AI Powered Contract Management

Another example in the legal space, here specifically looking at contracts,  and the management of related data.  Much more at the link.  Note machine learning training by lawyers.  Check out the tabs to find much more in the connection of analytics, contracts and decision process.  Also how regulations like CDPR relate to legal contracts.

Exigent launches AI-powered contract management system for speed to smart data in DSS Resources

With a machine learning engine trained by lawyers, Exigent's end-to-end CMS now auto-extracts contract data in milliseconds and runs contextual search with improved accuracy

CHICAGO, March 11, 2020 /PRNewswire/ -- Legal technology provider, Exigent, has released the latest update to its Contract Management Solution to include AI-based auto-extraction and contextual search for increased speed to data, accuracy and business impact. Exigent's AI auto-extraction tool, Scarlett, is powered by a machine learning engine set up and trained by legal professionals, financial experts, and data scientists, creating faster and more accurate outcomes.

Using Exigent's new CMS automates the once costly, slow and error-ridden manual extraction and input of contract data. The solution extracts smartly selected business and legal terms quickly, automatically placing them into a data lake and making key data accessible at all times in a universal format. All extracted information is then aggregated on dashboards or included in easily buildable reports in seconds. Scarlett has unlimited applications across many industries and departments: the tool can be used by legal, procurement, and other departments across an organization to auto-extract and structure data for powerful, customized analysis. Information is aggregated and used to review and enhance performance, as well as predict business outcomes. Scarlett's power is already being used by Exigent's clients for more efficient contract review, to tackle LIBOR transition and for compliance in general (GDPR, POPI, CCPA and any other clauses that need attention in a contract). ... "

See also, Exigent.

Scaling up Digital and Analytics Results in Consumer Goods

Thoughtful formulation here.

Solving the digital and analytics scale-up challenge in consumer goods in McKinsey

" ... Many consumer-goods companies have entered the digital and analytics race, but very few are scaling impact. Here’s what leaders are doing right.... '

sk any consumer-goods executive if his or her company has invested in digital and analytics, and you’ll almost certainly get an affirmative response. But ask whether those investments have yielded the desired results—and more than half of the time the answer will be no. Our research shows that only 40 percent of consumer-goods companies that have made digital and analytics investments are achieving returns above the cost of capital. The rest are stuck in “pilot purgatory,” eking out small wins but failing to make an enterprise-wide impact.

The value at stake isn’t trivial: our analysis suggests that a company’s aptitude at scaling up digital and analytics programs is correlated with its financial performance. In this article, we describe the most common pitfalls that companies encounter in their journey toward digital and analytics scale-up. We also explore an emerging recipe for sustained success.    ... " 

Reinforcement Learning for the Real World

Conceptually reinforcement learning should work well, but in practice it can be problematical.  Like that it is being considered for business applications.  Which are about making decisions.   But you do also have to formulate that decision in context and over time to make it workable.

Reinforcement learning for the real world
Edward Jezierski on the science of bringing creativity and curiosity together in a learning system.
By Jenn Webb

Edward Jezierski interview via O'Reilly

Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL). They discuss why RL’s role in AI is so important, challenges of applying RL in a business environment, and how to approach ethical and responsible use questions.

Here are some highlights from their conversation:

Reinforcement learning is different than simply trying to detect something in an image or extract something from a data set, Jezierski explains— it’s about making decisions. “That entails a whole set of concepts that are about exploring the unknown,” he says. “You have the notion of exploring versus exploiting, which is do the tried and true versus trying something new. You bring in high-level concepts like the notion of curiosity—how much should you buy as you try new things? The notion of creativity—how crazy are the things you’re willing to try out? Reinforcement learning is a science that studies how these things come together in a learning system. (00:18)   ... "

Arable: Data Driven Farming

Interesting development, maps with my background in this area.   Could have used this in forestry and vineyard applications. 

Arable launches a new generation of IoT tools for data-driven farming  By  Dean Takahashi

Arable is moving agriculture tech forward today with a new generation of internet of things (IoT) tools that enable farmers to take advantage of advanced sensors, wireless networks, and machine learning recommendations to improve crop growth.

The company has come up with a new Mark 2 sensor, a new mobile app, a sensor-integrating bridge device, and Arable Open, a customizable applications programming interface that partners can use.  ... "

See their blog for a number of good examples.

Goggle Assistant Adds Support for Passive Sensors

As I read this quite interesting, but the Dash buttons of Amazon, now discontinued, did a similar thing.    But this appears to do things with more general sensors.   Look to examine this in more detail with my own Assistant set up.    Pointers to people using this?

Google Assistant Adds Support for Passive Sensors, Laying Foundation for More Responsive Smart Homes      In Voicebot.ai  by Eric Hal Schwartz 

Google Assistant has added native support for new sensors, such as smoke and carbon monoxide detectors, according to an Android Police report. The updated Smart Home Device Type list now includes sensor-only devices, bringing new potential options to the voice assistant’s smart home integration.

Action and Sensing
The new additions to the sensor list make it possible for third-party device makers to integrate Google Assistant into sensor-only devices that can measure air quality, humidity, light, motion, water softness, and other elements. This direct inclusion makes it possible to ask Google Assistant directly about each of those conditions individually. Smoke and carbon monoxide are now their own category of sensor and there is also an update to the security system support allowing the voice assistant to report on each sensor’s status separately.

Google Assistant is built around actions initiated by users. When it comes to smart home devices, people have to ask for changes in temperature, lighting, and other home systems. They can be scheduled out, but it all begins with a verbal request. The updated support list lays out a potential road to passive alerts. The voice assistant could theoretically adjust the blinds based on how much light is coming in, tell you to switch out a water filter, or open a window when a smoke or carbon monoxide sensor is triggered.

Google’s Smarter Homes
The quiet expansion of Google Assistant’s support fits with Google’s ongoing smart home ambitions. The company boosted security for Google Nest smart home products last month and started requiring a Google account when connecting Google Nest products to other smart home devices as part of its effort to bring every smart home device under one software roof. .... "

Acceptance of AI as a creative Inventor

In Forbes, more examples in the space, here using a zero-knowledge approach. 

Ernst & Young Doubles Down On Its Bet With Ethereum
Ben Jessel  Contributor
Crypto & Blockchain
I write about fintech and enterprise adoption of blockchain technology.

On December 19th 2019, the audit and consulting giant Ernst & Young announced the release of their “third-generation zero-knowledge proof blockchain technology” to the public domain as part of the firm’s effort to make public networks ready for enterprise adoption.

Ernst & Young’s advocacy of public blockchain network infrastructure, as opposed to permissioned and private networks (referred to as private networks going forward, for brevity), is notable given that the firm’s industry peers have either remained neutral on the public vs. private network debate or have voiced skepticism on the viability of public networks. ... "

AI and Legal Discovery

More developments for the textually and logic intense world of Law.  How much of legal tasks be replaced by systems? Following this.

Everlaw announces $62M Series C to continue modernizing legal discovery   By Ron Miller@ron_miller in TechCrunch

Everlaw is bringing modern data management, visualization and machine learning to eDiscovery, the process in which legal entities review large amounts of evidence to build a case. Today, the company announced a $62 million Series C investment.

CapitalG  (Alphabet’s growth equity investment fund) and Menlo Ventures led the round. Existing investors Andreessen Horowitz and K9 Ventures also participated. The startup has now raised $96 million, according to the company.

Everlaw  co-founder and CEO AJ Shankar says eDiscovery, which has been around for years, has become a classic big data problem. “We help legal professionals sift through huge volumes of evidence in lawsuits and investigations to find the smoking gun and the incriminating email,” Shankar told TechCrunch.

The software also helps teams of legal professionals work together and collaborate around this evidence. “Turns out that the law is incredibly collaborative, and we help these teams create a work product, and communicate and collaborate with each other in a system specifically built for the practice of law,” he explained.

He says this coordination is often done manually in spreadsheets with communication taking place via email, and even companies using legacy eDiscovery software are using systems designed in a time of lower data volumes.

The company offers a variety of tools to help humans locate the information they need to build a case. There is a search feature, of course, and data visualization tools including a timeline tool that helps pinpoint when key events happened. This can help lawyers direct researchers to find evidence within that critical period, greatly narrowing the focus of the search.

And the company also offers both supervised and unsupervised machine learning algorithms to help the team find specific bits of information. He acknowledges it will take human ingenuity working with the tool to find what you need. “No one’s handing you anything on a silver platter. It absolutely requires some detective work. It’s iterative and we have tools to help you with that,” he said.

Shankar stumbled into this area of technology when he was still a graduate student in computer science and a law firm came to his department looking for a technical expert. He ended up working with the firm for a couple of years, saw the kinds of technical challenges it faced, and decided to build some tooling to help..... "

Cortana Going Away

At least as a standalone assistant for the home.  Now linking to their business systems, yet have yet to find it useful there.

Cortana, say goodbye
The digital assistant isn’t entirely going away, but we’re near the end of the road for another me-too technology from Microsoft that nobody really wanted. ... "

By Preston Gralla in Computerworld

Reducing Bias in AI by Forgetting

Intriguing thought.   When we built a learning system we included the notion of forgetting as an aspect of scheduling maintenance, re-running and retesting particular solutions.    But not in terms of changes in bias.  Which I would assume would be a re-test.

How to Reduce Bias in AI? Selective Amnesia.
USC Viterbi School of Engineering
Rishbha Bhagi
February 24, 2020

Artificial intelligence (AI) researchers at the University of Southern California (USC) Viterbi School of Engineering’s Information Sciences Institute have created a mechanism for inducing selective amnesia in computing models. This adversarial forgetting methodology could help reduce bias in AI by teaching deep learning models to ignore unwanted data factors. The mechanism is used to train a neural network to represent all underlying aspects of the data being analyzed, and then to forget specified biases, resulting in models that lack those biases when making decisions. Adversarial forgetting also could enhance content generation. USC's Greg Ver Steeg said, "For content generation to succeed, we need new ways to control and manipulate neural network representations and the forgetting mechanism could be a way of doing that."  ... "

Google Maps Restaurant Dishes with Images

Clever idea. Integration of crowdsourced images for search.  Recall when the idea of consumers providing images to businesses came out,  seems it has slipped in the last few years.  Now Google takes advantage of the idea again via their 'Lens'.

Google Maps borrows Lens tech to highlight popular restaurant dishes
A virtual overlay show plate descriptions and pricing.

By Steve Dent, @stevetdent

Google introduced big changes to Lens at its I/O conference last year by turning your phone's camera into a powerful search tool. With one of the key features, you could point your camera at a restaurant menu and it would highlight the most popular dishes and even translate menu items. Now, Google has made that feature much more useful in Google Maps by cleverly sourcing menu photos taken by restaurant customers, 9to5Google reported. .... " 

Quantum Tensorflow

Just released, support for testing quantum computing ideas.  Technical.

TensorFlow Quantum is a library for hybrid quantum-classical machine learning.

TensorFlow Quantum (TFQ) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google’s quantum computing frameworks, all from within TensorFlow.

TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It integrates quantum computing algorithms and logic designed in Cirq, and provides quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators. Read more in the TensorFlow Quantum white paper.  https://arxiv.org/abs/2003.02989  

Start with the overview:  https://www.tensorflow.org/quantum/overview, then run the notebook tutorials.

Also covered more generally in Venturebeat:

Google launches TensorFlow Quantum, a machine learning framework for training quantum models     By Khari Johnson

All Models are Wrong, some Useful? Numbers are Suspect Too.

Based on the classic statement, that I often gave to execs seeking my help.    Most often about the forecast, which they were too quick to accept.

All numbers are made up, some are useful
Keeping track of stuff is hard
By Vicki Boykis in NormCore Tech  .... '

Some good and considerable thoughts in the linked-to blog/newsletter about data, measurement, statistics and even about the coronavirus ....   Worth a look ....

BBC Reports Amazon is Offering Checkout Tech to Other Stores

Makes sense, Amazon has developed means for automatic checkout tech.  Which requires data and AI methods for its use,  Amazon and AWS is an expert at that.  In our own lab work with IBM and retail security system providers, we noted its similarity to product identification and tracking.

Amazon's Just Walk Out till-free tech offered to rivals  By Leo Kelion, BBC

Amazon opened its largest Go Grocery shop, last month, in Seattle
Amazon is offering its till-less technology to other High Street shops, just over two years after launching it via its own Go Grocery chain.

Go Grocery shoppers scan a smartphone app as they arrive, allowing them to pay via their main Amazon accounts.

It has now adapted its Just Walk Out system for other retailers so shoppers register a payment card on entry and are automatically billed as they leave.

But, unlike at Go Grocery, it will not use the data to track customer habits.

"We only collect the data needed to provide shoppers with an accurate receipt," Amazon's website says.

"Shoppers can think of this as similar to typical security camera footage."

The system involves fitting a shop with hundreds of cameras and depth-sensors, whose data is then remotely analysed on Amazon's computer servers.

The software can distinguish whether a shopper has picked up and kept a product for purchase or if they have only examined an item before replacing it back on a shelf.

Amazon says it can install the required equipment in "as little as a few weeks".  ...." 

Robot Arm for Frontline Medicine

Taking up front information, saving it in context, sterilizing equipment, could save lots of time when dealing with large groups.  Training in context, as it is feasible, could be useful as needs change.  Standardizing methods also very useful.

Robotic Arm Designed in China Could Help Save Lives on Medical Frontline
Reuters
by Martin Pollard

Researchers at Tsinghua University in China have developed a robot that can perform ultrasounds, take mouth swabs, and listen to sounds made by a patient's organs. The system, which consists of a robotic arm on wheels, could help save lives during the coronavirus outbreak. The researchers converted two mechanized robotic arms with the same technology used on space stations and lunar explorers. The robots were almost entirely automated, and could disinfect themselves after performing actions involving contact with patients. Doctors in China are currently training on the robots—one of which is at the team's lab at Tsinghua University and the other at the Wuhan Union Hospital. Said Tsinghua's Zheng Gangtie, “Doctors are all very brave. But this virus is just too contagious ... We can use robots to perform the most dangerous tasks."

Competing Pricing Algorithms

In HBSWK, interesting article on competing pricing algorithms, below the intro, reading:

Warring Algorithms Could be Driving Up Prices

Companies increasingly use software to conduct rapid price changes. Alexander MacKay explains why firms might benefit but consumers should be worried.

The widespread use of pricing algorithms is reshaping the nature of competition in online markets and potentially driving up the prices of retail goods, according to recent research.
These automated, price-adjusting software programs may also be catching the eye of government regulators and antitrust authorities, who fear they could ultimately harm consumers by raising prices above typical competitive levels.

It doesn’t seem too long ago when a price change was a major strategic decision for companies, requiring extensive data analysis, management consensus, coordination with advertising schedules, and other factors in an era when computers were helpful but not critical to pricing strategy. The result: A price change was more an annual or semiannual event. But these days, when companies can analyze consumer data and use technology to raise or lower prices in the blink of an eye, changes can be made not just once a year but multiple times daily.

“WHAT WE SHOW, THEORETICALLY, IS THAT (ALGORITHMIC COMPETITION) LEADS TO HIGHER PROFITS FOR BOTH FIRMS.”

Enter the rise of pricing algorithms, where software monitors prices posted by competitors and makes adjustments using parameters developed by the company’s marketers and strategists.

“They want to react to changing demand and supply conditions,” says study author Alexander J. MacKay, an assistant professor of business administration at Harvard Business School who studies competition, including pricing, demand, and market structure.  .... "

Amazon Advances Checkout-free

Reuters report on further work by Amazon Cashier-free applications.  The claim is that major problems with the approach have been resolved.

Amazon launches business selling automated checkout to retailers
By Jeffrey Dastin, Reuters

(Reuters) - Amazon.com Inc (AMZN.O) on Monday is set to announce a new business line selling the technology behind its cashier-less convenience stores to other retailers, the company told Reuters.
The world’s biggest web retailer said it has “several” signed deals with customers it would not name. A new website Monday will invite others to inquire about the service, dubbed Just Walk Out technology by Amazon.

The highly anticipated business reflects Amazon’s strategy of building out internal capabilities - such as warehouses to help with package delivery and cloud technology to support its website - and then turning those into lucrative services it offers others.....

See also in Engadget:

Amazon's checkout-free tech is heading to other retailers
A number of partners have already signed up for the 'Just Walk Out' system.   By Rachel England .... '

Envision Glasses: AI Augmentation for Vision Impairment

Had been expecting smart glasses to address visual impairment problems, now finally an example.

Envision brings AI to Google Glass to help visually impaired users see
By Jeremy Horowitz in Venturebeat

Google Glass might not have made the best impression when it first came out years ago, but the concept of a glasses-sized computer with a small screen, camera, and speaker had promise, particularly for specific applications. Today, Envision is debuting Envision Glasses, an AI-powered augmentation of Google Glass that can help visually impaired users “see” their environments.

Envision Glasses are a complete solution, combining Google Glass Enterprise Edition 2 with OCR and computer vision software to identify what’s in the wearer’s environment, then speak it out loud using Glass’ built-in speaker. Instead of holding up a smartphone and using its camera and software to read signs or identify people — the experience in Envision’s Android and iOS apps — the company has made the same AI technologies accessible from lightweight glasses frames, dramatically improving the real-world recognition experience for blind and low-vision users. ... "

Geofencing Warrant Policing

Had not heard the term 'Geofence' in law before, passed this along to lawyers I know to get their opinion on the current uses of this idea.

Google location data led police to investigate an innocent cyclist
The incident shows the very real problems with geofence warrants.
Jon Fingas, @jonfingas   in  Engadget
1h ago  ... ' 

Trends in AI/ML

Fairly good broad view of trends in progress.    All worth understanding in more detail than is presented here.

3 important trends in AI/ML you might be missing   By Sudharsan Rangarajan in Venturebeat

According to a Gartner survey, 48% of global CIOs will deploy AI by the end of 2020. However, despite all the optimism around AI and ML, I continue to be a little skeptical. In the near future, I don’t foresee any real inventions that will lead to seismic shifts in productivity and the standard of living. Businesses waiting for major disruption in the AI/ML landscape will miss the smaller developments.

Here are some trends that may be going unnoticed at the moment but will have big long-term impacts:

Monitoring a Complex System Over time for Patterns

Brought to my close attention because this is in the astronomy space.  A long time academic interest.  But then it came to mind that any system be observed in this way over time.  Then examine the data for anomalies, patterns, trends.   Probably with different observable parameters, and goals and needs. Thinking that further.Will be attending for inspiration.

Via DSC:

The Vera C. Rubin Observatory, currently under construction in Chile, will conduct a vast astronomical survey of our dynamic Universe starting in 2022. They plan to collect 500 petabytes of image data by observing the skies continuously for 10 years and produce nearly instant alerts for objects that change in position or brightness every night. In addition to astronomical data, their dataset will include DevOps, IoT, and real-time monitoring data.

In this latest Data Science Central webinar, Dr. Angelo Fausti will demonstrate:

How a time-series database has the versatility to address their needs
How they created a solution to enhance visibility across their organization and improve actionable insights
How they pull software development and sensor data from their telescope, camera and observatory IoT devices

Speaker:
Dr. Angelo Fausti, Software Engineer -- Vera C. Rubin Observatory

Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central 

Title: 500 Petabytes of Data to Understand the Universe Better
Date: Wednesday, March 18th, 2020
Time: 9:00 AM - 10:00 AM PDT 

Space is limited so please register early:
Reserve your Webinar seat now 
After registering you will receive a confirmation email containing information about joining the Webinar.    .... '

MIT Center for Deployable Machine Learning

Was just reminded of CDML, recently established. Would further continue to like to see more about the practical deployment of related and integrated processes for practical decision making.

The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.

Our Mission

The impressive—often "super-human"—performance of state-of-the-art learning systems creates a major expectation that broad deployment of machine learning will revolutionize almost every aspect of our lives. However, fulfilling this expectation requires ML that is robust to a variety of random and adversarial corruptions, provides reliable decision-making, and is understandable and easy to work with for humans, even if they have no ML expertise. The goal of the MIT Center for Deployable Machine Learning (CDML) is to bring together the broad expertise and focused effort needed to build ML systems that are safe, robust, and reliable enough to be confidently and responsibly deployed in the real world.  .... 

Further an article in MIT News on this:

“Doing machine learning the right way”
Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.
By Rob Matheson  .... 

Are the Category Captains Still in Charge?

I remember the term as used in early days in CPG.  But is the decision  intelligence now migrating elsewhere?   I see it every day in my local Kroger.

Should grocers just say ‘no’ to big CPG brands when it comes to shelf decisions?   by Tom Ryan in Retailwire, pointing to a WSJ report.  With further expert comment.

With proprietary data and software that measure “walk rates” in stores, grocers are increasingly taking greater control over what goes on the shelf and where, according to a Wall Street Journal report.

In many cases, legacy brands are losing shelf space to either private labels, which offer grocers higher margins, or products from emerging brands that better address natural and healthy trends. Carrying more retailer-owned and niche brands helps stores to differentiate.

Perhaps the biggest news in the article is that the use of category captains has diminished over the last 18 months. More grocers are also becoming open to foregoing slotting fees with confidence their in-house insights are creating an optimal shelf mix.

In response, bigger brands, from General Mills to Clorox and Hershey, are stepping up their respective analytical capabilities to again prove they have deeper insights into categories in an effort to recapture some shelf influence, according to the Journal’s reporting. .... " 

Recidivism Prediction

An example of comparison between human and machine solution.

Algorithms are Better Than People in Predicting Recidivism, Study Says
Berkeley News
Edward Lempinen

A study by researchers at the University of California, Berkeley and Stanford University has found that algorithms can predict the likelihood that specific criminal defendants will be arrested for a new crime at some future point, with significantly greater accuracy than humans. In experiments, risk assessment algorithms were nearly 90% accurate in predicting which defendants might be arrested again, while human predictions were about 60% accurate. The researchers replicated previous research that evaluated recidivism likelihood based on a limited number of risk factors, but added other datasets to test the theory that real-world settings would make algorithmic assessment more effective than human evaluations. The results appear to support continued use and refinement of such algorithms. However, Stanford's Sharad Goel said, "Like any tools, risk assessment instruments must be coupled with sound policy and human oversight to support fair and effective criminal justice reform." ... 

Case for Open Data in Fight Against COVID-19

The case for open data for AI in the fight against COVID-19
Posted by ajit jaokar  in DSC ....

COVID-19
   2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE
This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL). .... 

Note also the comments ...

Leveraging with Sensitive Metadata

Its all about the metadata, that is the context, of information being gathered.

MIT News
Rob Matheson
February 26, 2020

Massachusetts Institute of Technology (MIT) researchers have developed a scalable metadata-protection scheme to shield the information of millions of users of communications networks against possible state-level surveillance. In the Crossroads (XRD) scheme, users send encrypted messages to multiple server chains, with each chain mathematically ensured to have at least one hacker-free server. Each server decrypts and randomly shuffles the messages before sending them to the next server down the line; the final server decrypts the last encryption layer and transmits the message to the target recipient. XRD also uses aggregate hybrid shuffle, a type of cryptographic proof that guarantees servers are properly receiving and shuffling messages to identify malicious activity. MIT's Albert Kwon said, "We want to get to the point where we're sending metadata-protected messages in near-real-time."

AI as Workplace Disrupter

Expected for jobs that require high skills, but these same jobs will morph to related tasks as well.  Being better skilled usually means you have the ability to re-train.   And today there are many more ways to re-train and become certified in new areas.

AI is the Next Workplace Disrupter, and It's Coming for High-Skilled Jobs
The Wall Street Journal
Eric Morath
February 23, 2020

A Brookings Institution study predicts that the next wave of artificial intelligence (AI)-driven automation will target white-collar professions like marketing specialists, financial advisers, and computer programmers. AI enables computers to analyze data, anticipate outcomes, and learn from experience by recognizing patterns and making decisions—jobs typically performed by highly skilled, well-educated employees. Stanford University's Michael Webb used AI to review more than 16,000 AI-related patents to ascertain capabilities like disease diagnosis or object recognition, while examining a U.S. Labor Department database of occupations to catalog specific tasks needed for jobs. By matching the frequency of overlap between these two factors, Webb found that holders of bachelor's degrees are five times more likely to be impacted by AI than those with high-school diplomas. AI may potentially automate certain tasks for some professionals without entirely replacing them, while others could find their jobs simplified and easily filled by less-educated workers. ... '

Generating Random Numbers

A new means I had not head of, how random, repeatable is the result?

Scientists Use Crystals to Generate Random Numbers
Popular Mechanics
Courtney Linder
February 19, 2020

Computer scientists at the University of Glasgow in the U.K. have generated random numbers through an automated system that completes inorganic chemical reactions and grows crystals within a computer numerical control machine. The researchers outfitted a camera to the device to record images of the solidifying crystals, then used image-segmentation algorithms to examine the pixels corresponding to the crystals; a binarization algorithm converted the data into a series of 0s and 1s based on the crystalline geometry, repeating the process until the desired binary-code length for encryption was realized. Comparing this random-number generator with the Mersenne Twister pseudorandom number generator, the researchers found the new system decrypted messages faster. ... " 

Honeywell Introduces a Quantum Computer

Somewhat unexpected, but the announced major client makes it seem serious.  Trading strategies are a kind of process and decision making approach that will be good to follow to see ow Quantum will help.

Honeywell to Roll Out Quantum Computer
The Wall Street Journal
Sara Castellanos
March 3, 2020

Honeywell will introduce an early-stage quantum computer for commercial experiments within about three months, with JPMorgan Chase as the first public user. The new machine is expected to be the world's most powerful quantum computer, based on its expected quantum volume (a measure of the performance of a quantum system) of at least 64. Honeywell’s Tony Uttley anticipates the technology will be used by organizations interested in developing new materials or new trading strategies for financial services, or by those looking to speed up calculations. Marco Pistoia of JPMorgan Chase said he expects to use quantum computing to speed up computing-intensive calculations, including Monte Carlo simulations, which are commonly used to calculate the theoretical value of an option. Quantum computing could also be used in portfolio optimization.  ... "   .... '

Industrial AI Enabled Devices

Informative look at AI uses in Industry.Which brought to my attention a number of companies involved in manufacturing.  More detail at the link.

SparkCognition and Sight Machine Take Top Spots in ABI Research assessment

SINGAPORE, March 3, 2020 /PRNewswire/ -- The total installed base of AI-enabled devices in industrial manufacturing is expected to reach 15.4 million in 2024. As such, the demands for the deployment of Artificial Intelligence (AI) in manufacturing have led to the emergence of startups that work on AI algorithms to increase and optimize production processes in the manufacturing setting other than machine vision-based solutions. These AI algorithms can discover patterns, recognize conditions, and provide early warnings and explanations on current operation status and abnormalities. ABI Research, a global tech market advisory firm, finds SparkCognition and Sight Machine to be the leaders in industrial AI focusing on production process optimization.

The Industrial AI competitive assessment analyzed and ranked seven industrial AI vendors on overall efficiency enhancement, namely Falkonry, FogHorn, Maana, Presenso, Sight Machine, SparkCognition, and Uptake, using ABI Research's proven, unbiased innovation / implementation criteria framework. For this competitive assessment, innovation scores examined the technical capabilities of the vendor's software and implementation scores focused on the vendor's commercial ability to deliver their solution and integrate with existing solutions from the incumbents, such as GE, Siemens, ABB and Bosch, across a variety of manufacturing verticals.

"While there are many players offering industrial AI solutions, ranging from cloud service providers to system integrators, this competitive assessment focuses on pure-play industrial AI software vendors. All seven vendors listed in this competitive assessment have a tight focus on developing AI software for overall efficiency enhancement in industrial and manufacturing applications," explains Lian Jye Su, Principal Analyst at ABI Research. These vendors establish partnerships with various public cloud vendors, system integrators, chipset and industrial equipment manufacturers, allowing their solutions to be deployed on device, gateways, and on-premise servers..... "

Apparel Blockchain Tracking

The application here looks at tracking in the supply chain. Linking to RFID efforts is also interesting.

Nike, Macy’s Run Blockchain Trial With Auburn’s RFID Lab in Coindesk
Mar 6, 2020 at 06:00 UTC

Blockchain might help major apparel brands from Nike to Macy’s better share product data across the retail supply chain, according to a white paper Auburn University’s RFID Lab published Wednesday.

The study, named the “Chain Integration Project" (CHIP), saw those retailers and others run Hyperledger Fabric nodes on a slice of their mammoth supply chains. The study found blockchain to be a promising way to share serialized data after following tens of thousands of products including Nike Kids’ Air Force 1 shoes and Michael Kors parkas as they moved between distribution centers.

RFID Lab is one of the most prominent outposts for U.S. retailers’ experiments with emerging supply chain tech, but Blockchain Fellow Allan Gulley said it’s a relative newcomer to distributed ledger technology. And so CHIP, which began in 2018, became a blockchain trial by fire for the Auburn research institute.  ... " 

Responsible Virus Charting

Click through for an interesting links to data visualization of virus data:

Responsible coronavirus charts via FlowingData

Statistical Visualization  /  coronavirus, Datawrapper

Speaking of responsible visualization, Datawrapper provides 17 charts and maps you can use in your stories, without causing unnecessary panic.

Below is an embedded example:  ...

Coronavirus COVID-19 cases worldwide

Currently infected people (confirmed cases), already recovered people and people who died due to the coronavirus, worldwide. This chart gets updated once a day with data by Johns Hopkins. ... "

Show Your Robots How to do Chores

The ultimate desire.  Or perhaps have someone else show a robot how to do your chores better.  And archive the results for use.  Much like the inclusion of 'planning with uncertain specifications' ... the humanlike planning ability to simultaneously weigh many ambiguous — and potentially contradictory — requirements ... '

Showing robots how to do your chores
By observing humans, robots learn to perform complex tasks, such as setting a table.
Watch Video at this post.
Rob Matheson | MIT News Office

Training interactive robots may one day be an easy job for everyone, even those without programming expertise. Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. In the workplace, you could train robots like new employees, showing them how to perform many duties.

Making progress on that vision, MIT researchers have designed a system that lets these types of robots learn complicated tasks that would otherwise stymie them with too many confusing rules. One such task is setting a dinner table under certain conditions.  

At its core, the researchers’ “Planning with Uncertain Specifications” (PUnS) system gives robots the humanlike planning ability to simultaneously weigh many ambiguous — and potentially contradictory — requirements to reach an end goal. In doing so, the system always chooses the most likely action to take, based on a “belief” about some probable specifications for the task it is supposed to perform.   .... "

ACM Tech Talk: Michael Jordan on Decision Making

Of particular interest to me, how do we integrate decision making with machine learning?  Plan to attend.

March 25 Talk on Machine Learning and Decision Making with ACM Fellow Michael I. Jordan

Register now for the next free ACM TechTalk, "The Decision-Making Side of Machine Learning: Computational, Inferential, and Economic Perspectives," presented on March 25 at 2:00 PM ET/11:00 AM PT by Michael I. Jordan, University of California, Berkeley; ACM Fellow. A questions and answers session will follow the talk.

Leave your comments and questions with our speaker now and any time before the live event on ACM's Discourse Page. And check out the page after the webcast for extended discussion with your peers in the computing community, as well as further resources on machine learning and economics.

(If you'd like to attend but can't make it to the virtual event, you still need to register to receive a recording of the TechTalk when it becomes available.)

Note: You can stream this and all ACM TechTalks on your mobile device, including smartphones and tablets.

Much of the recent focus in machine learning has been on the pattern-recognition side of the field. I will focus instead on the decision-making side, where many fundamental challenges remain. Some are statistical in nature, including the challenges associated with multiple decision-making, and some are algorithmic, including the challenge of coordinated decision-making on distributed platforms. Finally, others are economic, involving learning systems that must cope with scarcity and competition. I will present recent progress on each of these fronts.

Duration: 60 minutes (including audience Q&A)

Presenter:
Michael I. Jordan, University of California, Berkeley; ACM Fellow
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences. Prof. Jordan is a member of the National Academy of Sciences and a member of the National Academy of Engineering. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics, and he has given a Plenary Lecture at the International Congress of Mathematicians. He received the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009.

Full archives of ACM talks

Transportation Updates

Bottom line, lots of work yet to do in completely autonomous cars.  The number of miles driven in CA seems impressive.

Some excerpts from Wired Transportation.  Worth subscribing to, links to full articles.

At some points three or so years ago, it looked like self-driving cars might appear at your or my door any moment. But now it’s clear the journey to robot cars will be more of a slog. Last week, we gained insight into some important steps necessary before the vehicles really hit the road. For one, the federal government needs to figure out its approach to regulating autonomous vehicles, and to the advanced driver-assistance tech that precedes it. A federal safety board criticized regulators for the approach they’re taking to Tesla Autopilot—even as those same regulators shut down passenger trips on a kind of autonomous shuttle. Companies also need to reach their own internal self-driving benchmarks. We got some insight into how they’re doing that.

Plus, the Air Force gets into flying cars, and Uber gets into ads. Let’s get you caught up.
Headlines .... 

Advertisers should love Uber’s newest plan to make money: placing billboards on top of its drivers’ cars.

Thanks to data released last week, we now know that robot cars in autonomous mode drove almost 2.9 million miles on public California roads last year.  .... "

SETI Shuts Down

SETI (Search for ExtraTerrestial Intelligence)  Have followed this effort for many years, even participated for a while in the 90s.  Perhaps sad, but I have not seen any verifiable results.     Also note folding@home, also mentioned below.

The grandfather of distributed computing projects, SETI@home, shuts down
While SETI may no longer be an option, Folding@home wants your cycles.  By John Timmer in Arstechnica

Over the weekend, the people who manage the SETI@home distributed-computing project announced it would be going on hiatus at the end of March. The project was one of the first efforts that successfully convinced home users to donate some of their free computing time to help with research, and its success spawned a large number of related projects.

While it's on hiatus, users with a fondness for distributed computing might take a look at Folding@home, which is trying to figure out the structures of proteins on the surface of the SARS-CoV-2 coronavirus.

SETI sunset
The SETI@home's project page  describes the reason for the shutdown simply. Over the years, home users have done so much processing that the team now has a large backlog of processed data to analyze. So, the researchers are de-prioritizing the management of the data distribution and focusing instead on looking at what has already been done in the hope of getting their analysis published in an academic journal. As a result, no more work units will be distributed after the end of March. .... " 

See also on the Wikipedia: https://en.wikipedia.org/wiki/Search_for_extraterrestrial_intelligence

North Korean Cryptocurrency Money Laundering

Considerable detail in recent Technology Review Article.   An indication of how carefully we need to examine and regulate these technologies.  Much more at the link.

This is how North Korea uses cutting-edge crypto money laundering to steal millions

Hackers working for Kim Jong-un have become experts at covering their tracks on the Bitcoin blockchain.   by Mike Orcutt in Technology Review

The US government has just come down hard on two Chinese nationals for allegedly conspiring with North Korean state-sponsored hackers to steal millions of dollars’ worth of digital money from cryptocurrency exchanges. In the process, it has provided a glimpse at the cutting edge of crypto money laundering.... " 

Legal Complaint .... 

AI Used to Support the US Tax Code

Makes perfect sense to look for patterns and predictions based on complex rules. 

AI Comes to the Tax Code   By The Wall Street Journal

The U.S. Internal Revenue Service is using artificial intelligence to map taxpayer relationships.
The Internal Revenue Service is using artificial intelligence to design machine-built graphs that map the relationships of participants in business deals.

Governments increasingly are using artificial intelligence (AI) and data analytics to detect tax evasion, respond to taxpayer questions, and boost efficiency.

The U.S. Internal Revenue Service is designing machine-built graphs to map relationships among participants in business deals, allowing auditors to analyze transactions for signs of tax avoidance.
The agency also is employing AI to study notes that workers take when fielding questions from taxpayers, and testing which mixtures of formal notices and  contacts will most likely encourage taxpayers who owe money to pay off debts.

However, experts see potential harm if audit-selection algorithms inadvertently exhibit bias against taxpayers by race or location.

Tax preparers and accounting firms also utilize AI to minimize tax bills and advise clients.

From The Wall Street Journal
View Full Article - May Require Paid Subscription   .... 

Autonomous Drone piloting

Could have used this for our forestry resource measurements, which required expensive and time consuming surveys by road or helicopter.  Further doing this to determine embedded tasks for resource analysis. 

Drones can now scan terrain and excavations without human intervention  by Aarhus University in Techexplore

Drone pilots may become superfluous in the future. New research from Aarhus University has allowed artificial intelligence to take over control of drones scanning and measuring terrain.

A research project at Aarhus University (AU) in collaboration with the Technical University of Denmark (DTU) aims to make measuring and documenting gravel and limestone quarries much faster, cheaper and easier in the future.

The project has allowed artificial intelligence to take over the human-controlled drones currently being used for the task.

"We've made the entire process completely automatic. We tell the drone where to start, and the width of the wall or rock face we want to photograph, and then it flies zig-zag all the way along and lands automatically," says Associate Professor Erdal Kayacan, an expert in artificial intelligence and drones at the Department of Engineering at Aarhus University.  ... " 

Physician Voice Assistant Suki

Have now seen several related system, it appears none of these have been successful.

Physician Voice Assistant Startup Suki Closes $20M Funding Round 
 Eric Hal Schwartz in Voicebot

Suki, the developer of an eponymous voice assistant for healthcare professionals, has raised $20 million in a Series B funding round led by Flare Capital Partners. The new funding just short of two years after the startup closed a previous $20 million round of venture capital investment for its technology.

SUKI EVOLUTION
Suki’s voice assistant is designed as a tool for doctors that applies artificial intelligence and machine learning to collecting and transcribing notes and patient conversations. The AI can automatically can fill in Electronic Health Records (EHR), with relevant information. The point is to take over the doctor’s data entry duties to give them more time to care for patients. Suki claims that using its voice assistant cuts the time a doctor spends on paperwork by three-quarters. That’s no small thing when too much administrative work has consistently been ranked as a leading cause of doctors burning out and quitting the profession.

Breyer Capital and Epsilon Health joined Flare in the new investment round, along with previous investors First Round Capital and Venrock, the leader of the last funding round. The new capital will be used for hiring new employees and developing additional features for the voice assistant. The company has already begun expanding its partnerships and leadership including integrating its technology into Rx.Health’s toolkit for detecting and limiting physician burnout.

“It’s clear that physician burnout caused by documentation and administrative burden is a crisis in medicine, which is why we’ve seen such enthusiastic adoption of our technology to date and will continue this momentum with the support of our investors,” Suki CEO Punit Soni said in a statement. “Suki not only delivers a better physician experience but also supports high-quality, coordinated care and improved coding and billing through its accurate, detailed medical notes.”  .... "

Kirk Borne on AI and Human Nature

Have met Kirk, he has an astrophysics background similar to mine.  Here a podcast with him by Byron Reese.

On Episode 108 of Voices in AI, Byron and Kirk Borne discuss the intersection between human nature and artificial intelligence.

Listen to this episode or read the full transcript at    www.VoicesinAI.com

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm, and I’m Byron Reese. Today my guest is Kirk Borne. He is Principal Data Scientist and executive advisor at Booz Allen Hamilton. He holds a BS in Physics from Louisiana State and a PhD in Astronomy from Caltech. His background covers all kinds of things relating to data and data science and artificial intelligence so it should be a great conversation. Welcome to the show, Kirk.

Kirk Borne: Thank you Byron. It’s great to be here.

So for the folks who aren’t familiar with you and your work, can you give us a little bit of a history about how did you get here, what was the path you took?

Well as you mentioned my background is Astrophysics and Astronomy. Starting in grad school about 40 years ago, I was always working with data for scientific discovery either through modeling and simulation or data analysis. So that’s sort of what I was doing as my avocation, which is research and astronomy, but my vocation became supporting NASA research scientists data systems — so the data systems from various satellites that NASA had for studying the space/astronomy domain. I worked on those systems and provided access to those data for scientists worldwide. I did that for about 20 years and so I was always working with data, and I would say data is my day job; data is my night job as an astronomer.

And so it was about 20 years ago that we were starting to notice the data volumes of the experiments we were working with, were just becoming more off scale than ever imagined. I mean just one single dataset I still remember 1997 — we were trying to work with this dataset that just by itself was more than double the size of the other 15,000 experiments we were working with combined. So that was like unheard of. And so at that point I started looking around at what can one do with data of this volume and I discovered machine learning and data mining. So I had never actually looked at data that way before. I just thought about analysis, not so much discovery from data from a machine learning perspective, and so that was 20 years ago and sort of fell in love with that whole mathematical process and the applications that come from that, which include AI. That’s what I’ve been doing for the last two decades.

And so as a practitioner, what’s the sort of work you’re doing now?

Well for me personally it’s really about, as my company likes to say, thought leadership. I feel kind of nervous when I say that about myself but I do a lot of public speaking, I write a lot of blogs. My title includes ‘executive advisor’, so I’m advising both internally our business managers around AI machine learning and data science, but also our clients. But at the same time I’m also doing sort of tutoring and mentoring to some of our younger data scientists because after my 20 years at NASA, I spent 12 years at George Mason University as a professor. I was Professor of Astrophysics, but I really was teaching data science; and so it’s sort of in my blood I guess to be an educator, to teach, to train and so that’s pretty much what I’m doing. I’m promoting the field, having conversations with people, for developing new ideas and concepts; not so much coding anymore like I used to do back when I was younger at NASA. I let the smart young coders today do all that work but we have lots of interesting conversations about which algorithms to use or developing. So it’s really exploratory innovation at the frontier of all this stuff. ... " ... ' 

Adapting Policing from Your Doorbell

Continue to follow this.   As a user and a believer in using technical collaboration to make the world safer.  Some interesting adaptations to construct community watch capabilities via Ring.

Ring update gives you more control over police video requests
It also helps you limit the people and devices that access your account.

By Jon Fingas, @jonfingas

Ring is acting on its promises to improve privacy and security in 2020. The Amazon brand has introduced a Control Center in the Ring mobile app that aims to deliver more control over access and sharing. Most notably, there's a toggle to opt out of law enforcement video requests -- you don't have to wait to receive one before making a decision. Ring is unsurprisingly encouraging customers to leave it on (it has police partnerships to maintain) in the name of neighborhood security, but it's at least acknowledging that some users are uncomfortable with serving as de facto eyes for police officers. ... "

Responding to the Unexpecteced

A place where we often need assistance,   Often an important aspect of process design and optimization.   Technical.

Cognitive Work of Hypothesis Exploration During Anomaly Response
A look at how we respond to the unexpected
Marisa R. Grayson

Web-production software systems operate at an unprecedented scale today, requiring extensive automation to develop and maintain services. The systems are designed to adapt regularly to dynamic load to avoid the consequences of overloading portions of the network. As the software systems scale and complexity grows, it becomes more difficult to observe, model, and track how the systems function and malfunction. Anomalies inevitably arise, challenging incident responders to recognize and understand unusual behaviors as they plan and execute interventions to mitigate or resolve the threat of service outage. This is anomaly response.1

The cognitive work of anomaly response has been studied in energy systems, space systems, and anesthetic management during surgery.9,10 Recently, it has been recognized as an essential part of managing web-production software systems. Web operations also provide the potential for new insights because all data about an incident response in a purely digital system is available, in principle, to support detailed analysis. More importantly, the scale, autonomous capabilities, and complexity of web operations go well beyond the settings previously studied.7,8

Four incidents from web-based software companies reveal important aspects of anomaly response processes when incidents arise in web operations, two of which are discussed in this article. One particular cognitive function examined in detail is hypothesis generation and exploration, given the impact of obscure automation on engineers' development of coherent models of the systems they manage. Each case was analyzed using the techniques and concepts of cognitive systems engineering.9,10 The set of cases provides a window into the cognitive work "above the line" (see "Above the Line, Below the Line" by Richard Cook in this issue) in incident management of complex web-operation systems (cf. Grayson, 2018).   .... "

Boston Dynamics Handle Robot

With video of the robotic system being used in real warehouses.

Handle Teams Up With Mobile Robots on Warehouse Logistics
Boston Dynamics and OTTO Motors partner to show how a heterogeneous robot team can be faster and more efficient

By Evan Ackerman

Boston Dynamics' Handle robot and OTTO Motors

Boston Dynamics Enters Warehouse Robots Market, Acquires Kinema Systems
Boston Dynamics' Handle is a humanoid robot on wheels, and it's amazing.
Boston Dynamics Officially Unveils Its Wheel-Leg Robot: "Best of Both Worlds"

Clearpath's OTTO Robot Can Autonomously Haul a Ton of Stuff
Today, Boston Dynamics and OTTO Motors (a division of Clearpath Robotics) are announcing a partnership to “coordinate mobile robots in the warehouse” as part of “the future of warehouse automation.” It’s a collaboration between OTTO’s autonomous mobile robots and Boston Dynamics’s Handle, showing how a heterogeneous robot team can be faster and more efficient in a realistic warehouse environment..... "

Pay for Data You Use—Not Data You Store

Towards using/measuring the value of data you use.

Cloud Services Tool Lets You Pay for Data You Use—Not Data You Store
IEEE Spectrum
Charles Q. Choi

Computer scientists at George Mason University have developed a new caching technique that can support pay-per-use cloud storage service. The team tested the service, InifiniCache, on Amazon Web Services' (AWS) Lambda computing service, and found that the technique achieved at least a 100-fold improvement in latency compared to the Amazon S3 service in about 60% of requests for objects larger than 10 megabytes. InfiniCache performed comparably with the AWS ElastiCache cloud caching service, but when it worked with large objects, InfiniCache cost users about one-thirtieth to one-ninetieth as much as ElastiCache. InfiniCache utilizes a data backup mechanism in which cached objects synchronize with clones of themselves to minimize the chances that reclaiming memory causes data loss.  ... "

Machine Learning and Earthquake Data

Been following this thread for sometime.  Been testing the CA use of local earthquake warning systems.   Actual predictive methods seem to be lagging here.  But the basic idea is very good.  Here aimed at detecting geothermal resources.

Machine Learning Picks Out Hidden Vibrations From Earthquake Data
MIT News
Jennifer Chu

Massachusetts Institute of Technology (MIT) researchers trained a convolutional neural network (CNN) with machine learning to identify low-frequency seismic vibrations in earthquake data. The researchers trained the network using the Marmousi model, a two-dimensional geophysical simulation of geological seismic-wave propagation. When presented with high-frequency seismic waves produced from a new simulated earthquake, the CNN could mimic the physics of wave propagation and accurately calculate the quake's hidden low-frequency waves. MIT's Laurent Demanet said the goal is to be able to use those low-frequency waves to map the Earth’s internal structures "and be able to say, for instance, 'this is exactly what it looks like underneath Iceland, so now you know where to explore for geothermal sources.'"

Rush to Deploy Robots in China Amid Coronavirus Outbreak

Rush to implement robotic solutions.  Less potential infection, but are they skilled and perceptive enough to perform assistant functions needed?

A patrol robot checking temperature and disinfecting people The Rush to Deploy Robots in China Amid Coronavirus Outbreak
CNBC     By Rebecca Fannin

Chinese companies are scrambling to deploy robots and automation to take over for absent workers as the coronavirus spreads. An American Chamber of Commerce survey of 109 companies in Shanghai found that about 50% face staff shortages over the next few weeks, while 66% cited insufficient personnel to run full production lines. The Reshoring Institute's Rosemary Coates said the COVID-19 outbreak is a reckoning for both Chinese and U.S. supply chain and risk managers, and finding ways to maintain production for restarting factories could include more automation and robots as substitutes for humans. Emil Hauch Jensen at Mobile Industrial Robots in Shanghai said the coronavirus epidemic has put a "renewed urgency behind the trend towards increased automation and use of robotics in China."  .... '

Autonomous Vehicles Should Start Small, Go Slow

Somewhat obvious,  but well examined suggestions here

Autonomous Vehicles Should Start Small, Go Slow
Self-driving vehicles can already work well on campuses where traffic moves slowly  By Shaoshan Liu and Jean-Luc Gaudiot   in IEEE Spectrum

Many young urbanites don’t want to own a car, and unlike earlier generations, they don’t have to rely on mass transit. Instead they treat mobility as a service: When they need to travel significant distances, say, more than 5 miles (8 kilometers), they use their phones to summon an Uber (or a car from a similar ride-sharing company). If they have less than a mile or so to go, they either walk or use various “micromobility” services, such as the increasingly ubiquitous Lime and Bird scooters or, in some cities, bike sharing.

The problem is that today’s mobility-as-a-service ecosystem often doesn’t do a good job covering intermediate distances, say a few miles. Hiring an Uber or Lyft for such short trips proves frustratingly expensive, and riding a scooter or bike more than a mile or so can be taxing to many people. So getting yourself to a destination that is from 1 to 5 miles away can be a challenge. Yet such trips account for about half of the total passenger miles traveled.

Many of these intermediate-distance trips take place in environments with limited traffic, such as university campuses and industrial parks, where it is now both economically reasonable and technologically possible to deploy small, low-speed autonomous vehicles powered by electricity. We’ve been involved with a startup that intends to make this form of transportation popular. The company, PerceptIn, hasautonomous vehicles operating at tourist sites in Nara and Fukuoka, Japan; at an industrial park in Shenzhen, China; and is just now arranging for its vehicles to shuttle people around Fishers, Ind., the location of the company’s headquarters.

Because these diminutive autonomous vehicles never exceed 20 miles (32 kilometers) per hour and don’t mix with high-speed traffic, they don’t engender the same kind of safety concerns that arise with autonomous cars that travel on regular roads and highways. While autonomous driving is a complicated endeavor, the real challenge for PerceptIn was not about making a vehicle that can drive itself in such environments—the technology to do that is now well established—but rather about keeping costs down.

Given how expensive autonomous cars still are in the quantities that they are currently being produced—an experimental model can cost you in the neighborhood of US $300,000—you might not think it possible to sell a self-driving vehicle of any kind for much less. Our experience over the past few years shows that, in fact, it is possible today to produce a self-driving passenger vehicle much more economically: PerceptIn’s vehicles currently sell for about $70,000, and the price will surely drop in the future. Here’s how we and our colleagues at PerceptIn brought the cost of autonomous driving down to earth.  .... " 

Designing Services for Utility Ecosystems

Upcoming Talk:

ISSIP Service Design Speaker Series: Designing Services for the Future of Utility Service Ecosystem   on Wednesday, March 4th 2020,     11:00 AM - 11.45 AM US Eastern Standard Time

The utility service ecosystem dynamic changes at a rapid pace in recent years and monopolizing the utility industry is not the only business strategy in today’s dynamic market. The utility regulation of liberalized market allows the entrance of new players and together with technological change, it alters the innovation process in the utility industry. 

Triggered by the above challenges, this study implements a Service Design approach to understand the current context of utility service ecosystem and to design the future services for the ecosystems. The presenters will share their experience and results of their study with the biggest utility player in Portugal.

Speakers:
Nabila Asad, Dr. Nina Costa, Dr. Jorge Teixeira, Dr. Lia Patrício

About the speakers:
Nabila Asad is a researcher at INESC TEC and a PhD student at the University of Porto, Portugal. She is exploring new methods for designing service platforms for the service ecosystems while analyzing the dynamics of the utility and air transportation service ecosystem respectively.

Dr. Nina Costa is a researcher currently based in Aveiro University, an integrated member at the Research Institute of Design, Media and Culture (ID+). Currently working on the DesignOBS project, “For a Design Observatory in Portugal”.

Dr. Jorge Teixeira is a researcher and assistant professor of the Department of Industrial Engineering and Management at the University of Porto. His research interests are in the field of technology-enabled services, with a special focus on customer experience, service design and interaction design.

Dr. Lia Patrício is Associate Professor at the School of Engineering of the University of Porto, where she is director of the Master in Service Engineering and Management. She is the Principal Investigator of the Service Design for Innovation Marie Curie Network. Her research focuses on service design and customer experience, particularly the design of technology-enabled services and complex service systems and value networks.

Service Design Series Chaired by - Payal Vaidya and Daniela Sangiorgi

Zoom information provided on registration.

Register Here

Google Fairness Gym

A considerable effort reported on here to experiment with the broad idea of fairness in machine learning, via the notion of a 'gym' to exercise choices and results with varying data.    Article below has quite a bit of  detail on what this is trying to be.

ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems
Wednesday, February 5, 2020
Posted by Hansa Srinivasan, Software Engineer, Google Research

Machine learning systems have been increasingly deployed to aid in high-impact decision-making, such as determining criminal sentencing, child welfare assessments, who receives medical attention and many other settings. Understanding whether such systems are fair is crucial, and requires an understanding of models’ short- and long-term effects. Common methods for assessing the fairness of machine learning systems involve evaluating disparities in error metrics on static datasets for various inputs to the system. Indeed, many existing ML fairness toolkits (e.g., AIF360, fairlearn, fairness-indicators, fairness-comparison) provide tools for performing such error-metric based analysis on existing datasets. While this sort of analysis may work for systems in simple environments, there are cases (e.g., systems with active data collection or significant feedback loops) where the context in which the algorithm operates is critical for understanding its impact. In these cases, the fairness of algorithmic decisions ideally would be analyzed with greater consideration for the environmental and temporal context than error metric-based techniques allow. ....  " 

Alexa Answers Healthcare and Pharma Questions

Good aim at typical questions from the home.  Maintenance, regulation and liability may prove a prove an issue.

Amazon’s Alexa can now answer more questions about prescription drugs
By Kyle Wiggers in Venturebeat

Alexa can now answer questions about medication and other health care concerns via voice, thanks to a collaboration between Amazon and drug and medical knowledge provider First Databank (FDB). Content in both English and Spanish allows Alexa users to ask about drug interactions, side effects, precautions, and the drug’s class (all of which FDB says will be updated on a regular basis), complementing the health information sources Alexa already draws from, including the Mayo Clinic and WebMD.

“People lead busy lives, and voice provides a simple way to get helpful information about medications, including side effects and drug interactions, for themselves and the people they care for. And this information will complement advice from their medical and pharmacy teams,” said FDB president Bob Katter. “Ultimately, we believe that more informed consumers will lead to improved medication adherence, the reduction of adverse drug events, and better patient outcomes.” .... " 

Trick for Teaching AI the Meaning of Language

More advances in machine language learning and understanding.

Baidu Has Trick for Teaching AI the Meaning of Language
By MIT Technology Review

Baidu, Chinas closest equivalent to Google, recently outperformed Google and Microsoft in a competition known as the General Language Understanding Evaluation, or GLUE.

Chinese technology company Baidu outperformed Microsoft and Google in the General Language Understanding Evaluation, a benchmark for artificial intelligence (AI)'s comprehension of human language.

Researchers built the Enhanced Representation through kNowledge IntEgration (ERNIE) model specifically for the Chinese language, using a method that also makes the model better understand English.

The researchers built on the Bidirectional Encoder Representations from Transformers (BERT) model, which predicts and interprets the meaning of words by considering context before and after words simultaneously. BERT randomly hides 15% of words in a given passage of text and attempts to predict those words from the remaining ones, and Baidu modified this technique for ERNIE, hiding strings of characters rather than single characters; ERNIE also differentiates between meaningful and random strings to mask the right character combinations.

Baidu uses ERNIE to produce more applicable search results, eliminate duplicate stories in its news feed, and make AI-facilitated responses to requests more accurate.

From MIT Technology Review ... " 

Fixing 3D Print

Instructive example, other applications?

3D Print Jobs Are More Accurate With Machine Learning

USC engineers have developed AI that can make any 3D printer more precise
3D printing is already being used to produce electric bikes, chocolate bars, and even human skin. Now, a new AI algorithm that learns each printer’s imprecisions can tweak print jobs to ensure greater accuracy.

The engineers who developed the algorithm find it increases a 3D printer’s accuracy by up to 50 percent. That can make a big difference for high-precision industrial jobs, says Qiang Huang, an associate professor of industrial and systems engineering at the University of Southern California, who helped create it.

Industrial 3D printers and additive manufacturing devices often use expensive materials, so the cost of throwing out prints that aren’t quite right can add up. For instance, it can cost hundreds of dollars in materials to 3D print a single airplane part. And some printers require seven to 10 drafts per job in order to fabricate an object that is accurate in every dimension and curve.

Huang’s group’s program, PrintFixer, requires a given printer to only produce five to 10 draft objects during initial setup. Once those objects are 3D laser scanned and compared to the computer-aided design (CAD) files that generated them, the algorithm develops a neural net model of the printer’s inaccuracies. ... "

China Gives Citizens a Danger Code

Via ACM.   Is this a 'troubling precedent', or a reasonable approach for imminent danger?

In Coronavirus Fight, China Gives Citizens a Color Code, with Red Flags
The New York Times
Paul Mozur; Raymond Zhong; Aaron Krolik
March 1, 2020

To manage the coronavirus epidemic, China is requiring citizens to use software on their smartphones to determine whether they should be quarantined or allowed into public spaces—but an analysis in The New York Times found that the underlying code seems to share information with the police. Citizens sign up via the Alipay digital wallet app, then receive a color code denoting their health status. An accompanying quick-response code allows or disallows users to travel freely, according to the color code. Once a user grants the software access to personal data, a program component transmits their location, city name, and an identifying code number to a server. The developers of the Alipay Health Code said the software uses big data to rate an individual's contagion risk, while critics warn the system establishes a troubling precedent for automated social control through mass surveillance. ... " 

Wal-Mart Fights Amazon Prime with Walmart+

Not so quiet really,  have seen several notes on this in the past week.  Details will be interesting.  Amazon has the really broad reach here with embedded channels.   And brand recognition too.  Wal-Mart needs to make a big splash to get our there.

Walmart is quietly working on an Amazon Prime competitor called Walmart+    Amazon Prime has devastated traditional retail. Walmart is about to fight back.

By Jason Del Rey@DelRey in Vox Recode

When Amazon launched a funky membership program called Amazon Prime in 2005, Walmart boasted larger profits than Amazon had revenue. Fifteen years later, though, Prime is the key reason for Amazon’s dominance over Walmart in online sales.

That pressure has pushed the traditional retailer to burn tens of billions of dollars to fight back while its executives have cycled through various stages of reaction to Prime’s ascent: denial, followed by meek competition, followed by a reversal that seemed to signal Walmart wanted to stick to a free, no-membership strategy.

But Recode has learned that over the past 18 months, the world’s largest brick-and-mortar retailer has explored creating its own paid membership program that would include perks that Amazon can’t replicate, in part to avoid a direct comparison to Prime. Amazon now accounts for nearly 40 percent of all online retail sales in the US, according to eMarketer, and Prime is a huge reason why. Walmart is a distant No. 2 with only a little more than 5 percent of the US e-commerce market. .... "

Emphasis on Remote Work

In my own work, ver the last few weeks this has been mentioned several times. Obvious reaction.  Seen also the effect on physically attending conferences.

As coronavirus worsens, companies renew focus on collaboration, remote work

While some companies and organizations are cancelling big events such as MWC and Facebook’s F8, others are looking to see what’s in their collaboration toolbox to keep workers healthy and productive. .... "

By Charlotte Trueman in ComputerWorld

Robots Learning

Impressed with what is going on behind the scenes at Google,  ultimately having systems that will learn about their contexts, both physical and logical.

Google algorithm lets robots teach themselves to walk
It's a milestone in making robots more useful.

By Rachel England, @rachel_england in Engadget

Crowdsourcing Ethical Machines

Qute a remarkable piece, ultimately technical but a good proposal for getting at the problem of exploring 'ethics' in machines.  Can machines be more or less ethical than people can?

Crowdsourcing Moral Machines
By Edmond Awad, Sohan Dsouza, Jean-François Bonnefon, Azim Shariff, Iyad Rahwan
Communications of the ACM, March 2020, Vol. 63 No. 3, Pages 48-55    10.1145/3339904

Robots and other artificial intelligence (AI) systems are transitioning from performing well-defined tasks in closed environments to becoming significant physical actors in the real world. No longer confined within the walls of factories, robots will permeate the urban environment, moving people and goods around, and performing tasks alongside humans. Perhaps the most striking example of this transition is the imminent rise of automated vehicles (AVs). AVs promise numerous social and economic advantages. They are expected to increase the efficiency of transportation, and free up millions of person-hours of productivity. Even more importantly, they promise to drastically reduce the number of deaths and injuries from traffic accidents.12,30 Indeed, AVs are arguably the first human-made artifact to make autonomous decisions with potential life-and-death consequences on a broad scale. This marks a qualitative shift in the consequences of design choices made by engineers.  ... " 

Transforming Organizations

My own observation is that this is very haphazardly done. This piece looks at key variables.  But how do you make sure these variables are addressed, measured and adapted?   You need a Model to guide you.

What does it take to transform corporate organizations? New research reveals the variables that matter most.  ...    from McKinsey

On Assistants in Healthcare

Much work to be done on voice solutions to health problems.  The infrastructure is growing.

Industry Voices—Is Dr. Alexa ready to see you now? in FierceHealthcare   by Robin Cavanaugh

Smart speakers have become a ubiquitous part of our lives. In fact, by 2022 analysts expect that voice technology will reach 55% of U.S. households. Whether it’s a gut check on the day’s forecast or it’s time to place a grocery order, consumers are accustomed to saying, “Hey, Alexa,” and receiving an answer in mere seconds. Now, with Amazon Alexa becoming HIPAA-compliant, voice technology’s intersection with modern healthcare is poised to explode.

The goal of a HIPPA-compliant Alexa is to give consumers the opportunity to ask a voice assistant questions about their health, refill a prescription or make an appointment with their provider—ushering in a whole new era of patient experience.

It’s no secret that the U.S. healthcare system today is confusing. The global lack of access to essential health services further exacerbates the situation, with at least 400 million people worldwide facing obstacles preventing them from receiving treatment. A harmless condition can become serious if a person doesn’t have access to the right resources, information or tools to help follow their treatment plan.

While the shift to voice-activated technology in healthcare can reduce common barriers to care, there are still significant challenges for tech titans to overcome. Security and compliance are still top concerns, but more practical limitations must be addressed too before voice technology can become mainstream and viable in the healthcare ecosystem.     .... " 

Causal Monitoring for Distributed Systems: IOT Solutions

Spent some time looking at monitoring and troubleshooting systems, with the obvious need to infer causation.   Here an intro to 'Pivot Tracing', a means of causal monitoring I had not heard of.    Would seem to be very useful for interconnected IOT.   Technical.

Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems
By Jonathan Mace, Ryan Roelke, Rodrigo Fonseca
Communications of the ACM, March 2020, Vol. 63 No. 3, Pages 94-102
10.1145/3378933

Monitoring and troubleshooting distributed systems are notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today—logs, counters, and metrics—have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This paper presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. Pivot Tracing does not correlate cross-component events using expensive global aggregations, nor does it perform offline analysis. Instead, Pivot Tracing directly correlates events as they happen by piggybacking metadata alongside requests as they execute. This gives Pivot Tracing low runtime overhead—less than 1% for many cross-component monitoring queries.  ... "

Nordic Countries get 900K Smart Speakers

It seems Amazon has only a small part of this market.  Google assistant has the advantage of much more translation and localization of language.  Important if these devices form the future basis of human assistance capabilities.

Smart Speaker Shipments in the Nordic Countries Reached 900k in 2019
In Voicebot By Bret Kinsella  

Data from Strategy Analytics show that smart speaker shipments in the Nordic countries of Denmark, Norway, and Sweden reached 900,000 in 2019. That figure is up sharply from shipments of only about 200,000 in 2018. These figures indicate impressive growth and smart speaker interest in countries that collectively claim only about 20 million in population. Strategy Analytics estimates the household installed base for smart speakers these Nordic countries is about 6%. Finland and Iceland were not included in the analysis since none of the leading smart speaker makers have offerings with language localization for these countries. ....  "

Ingestible Medical Devices

Abilities to ingest and then remove devices readily.

Ingestible medical devices can be broken down with light
New light-sensitive material could eliminate some of the endoscopic procedures needed to remove gastrointestinal devices.

By Anne Trafton | MIT News Office
January 17, 2020

A variety of medical devices can be inserted into the gastrointestinal tract to treat, diagnose, or monitor GI disorders. Many of these have to be removed by endoscopic surgery once their job is done. However, MIT engineers have now come up with a way to trigger such devices to break down inside the body when they are exposed to light from an ingestible LED.

The new approach is based on a light-sensitive hydrogel that the researchers designed. Incorporating this material into medical devices could avoid many endoscopic procedures and would give doctors a faster and easier way to remove devices when they are no longer needed or are not functioning properly, the researchers say. .... "