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Thursday, March 22, 2018


Useful Informatica white paper on GDPR, brought to my attention.  Which happens come May 25.   Implications for Assistants?

In the WP: " ... The General Data Protection Regulation (GDPR) (EU) 2016/679 is a regulation in EU law on data protection and privacy for all individuals within the European Union. It addresses the export of personal data outside the EU. The GDPR aims primarily to give control back to citizens and residents over their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU.[1] When the GDPR takes effect, it will replace the 1995 Data Protection Directive (Directive 95/46/EC).[2] .... "

IBM Watson Assistant: Business Skills as Intelligence Architecture

Have now had a few month look at the Watson Assistant in Beta.   Also have had three years learning with the Amazon Echo and a year plus with the Google Home.    So the comparison is quite interesting.  Watson Assistant is very much a 'white label', a system designed  to be installed in other, more complex things like cars or hotel night stands or Hospital rooms.   Or even a tiny part of your IOT.   Not to say that the Echo and Home's have not also crept into other devices.  And both now have a considerable lead in implementation.

What Watson Assistant does now have is the ability to link to Watson meta skills that have already been built for Watson.   Conversation,  Discovery and  Personality detection are just a few of dozens.  In the future also Blockchain.  Some are arranged in industry functional groups:  Say Financial, Supply chain or Retail.   So you should be able to look up just the  intelligence 'skills' you need and apply it to your need, in API fashion.  Mix and match them like parts of a business brain.  And then you get the skill functions to augment business needs.

And these needs .... like understanding speech, speaking to you, linking to information on the Internet and communicating with the IOT, and performing typical business transactional interactions are all there. But how to attach them is still not clear.   For example the Discovery Watson Skill, which lets you ingest private information and then interact with it intelligently,  is still to be connected.  Similarly business capabilities, like Business process modeling , are also possible future available methods.

IBM has gotten closer to making useful business oriented capabilities useful as skills.  Better than Home or Echo.   Closer to having a true assistant.   So if developers and startups line up to produce meta-skills that will deliver business value, we may see great things.  It remains to be seen if IBM Watson has the architecture to make it the place to do that.   Or should the developers just write a business value from the ground up?   Looking for new examples.

 IBM’s Watson Assistant lets any company build Alexa-like voice interfaces

You get a voice assistant, and you get a voice assistant, and you  By James Vincent   @jjvincent  in TheVerge.

IBM is today launching Watson Assistant, a new service aimed at companies looking to build voice-activated virtual assistants for their own products. Want your hotel’s rooms to remember a guest’s preferences for air-con? Or your car’s dashboard to be controllable via voice interface? IBM’s message to companies is: we can help you build that.

It’s an interesting pitch, especially as voice assistants like Amazon’s Alexa are being integrated into new arenas. (See, for example, the Wynn Las Vegas’s decision to install Echoes in every room.) IBM says this shows the popularity of conversational interfaces, and believes companies should choose Watson Assistant over Alexa or Siri for a number of reasons — namely: branding, personalization, and privacy.

First, Watson Assistant is a white label product. There’s no Watson animated globe, or “OK Watson” wake-word — companies can add their own flair rather than ceding territory to Amazon or Apple. Second, clients can train their assistants using their own datasets, and IBM says it’s easier to add relevant actions and commands than with other assistant tech. And third, each integration of Watson Assistant keep its data to itself, meaning big tech companies aren’t pooling information on users’ activities across multiple domains. .... "

Machine Learning with Limited Data

Despite all the claims for all the data we have, this is often the case.  And the term 'mixed scale' is important,  you often have many different quantities of data by context.

Machine Learning With Limited Data
Government Computer News  By Matt Leonard

Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a Mixed-Scale Dense Convolution Neural Network, a system that requires fewer parameters and training images when working toward image-recognition technology. A typical neural network is comprised of layers, each of which performs a specific analysis--one layer informs the next layer, so relevant information must be copied and passed along. Standard practice involves looking at fine-scale information in the early layers and large-scale information in the later layers. However, the new system mixes different scales within each layer, says Berkeley Lab's Daniel Pelt. This means large-scale information is analyzed earlier along with fine-scale information, enabling the algorithm to focus on the relevant fine-grain details. In addition, the layers in the new system are densely connected, meaning information does not have to be copied repeatedly throughout the network, and earlier layers can communicate relevant information directly to layers later in the series.
 .... "

Wednesday, March 21, 2018

Defining Normal

Useful idea.  The example shows a very specific context  at what space or times scales?

Researchers at Bethel University are studying how to teach computers to define "normal" data and then detect anomalies.

The team used mathematical models and real-world data to determine ways to detect needle-in-the-haystack anomalies and report them in real time, using far less computational power than conventional systems.

Their algorithm is based on recognizing a sudden increase of distance between vectors in a high-dimensional vector space.

The researchers tested the algorithm by installing a webcam in an office window to pick up a feed of outdoor foot traffic. Each quadrant in the field has its own anomaly detector attached to it, and if something enters into that box previously unseen by the system, an alert is sent, says Bethel's Brian Turnquist.  ... " 

Is the BlockChain Needed?

A critically contentious look at Blockchain.  Do we need it?  How is it different from a number of existing technical capabilities?  Worth thinking about it.

AI and Process Productivity

Nicely done, considerable case study.   Agree.   And suggest that a good way to ensure this is to make sure you know exactly where and how the AI is inserted in current or proposed process.  Then the needed training and skills of the employees involved should be apparent.

Why Artificial Intelligence Isn't a Sure Thing to Increase Productivity  in HBS Working Knowledge by Michael Blanding

As companies adopt artificial intelligence to increase efficiency, are their employees skilled enough to use those technologies effectively? Prithwiraj Choudhury looks to the US Patent and Trademark Office for a case study.  ... 

Will Amazon Own Your Customer?

Amazon Will Own Your Customer And What To Do About It
By James L. McQuivey   Vice President, Principal Analyst

 From twenty years of trying, I know this about covering Amazon: It’s tricky. Our report process can take months during which we comb through our extensive Technographics data to find patterns or we interview executives. Not to mention the time it takes to write, edit, and produce our reports. During which time, the moving target we call Amazon announces dozens of new things that you can’t go back and add to your report. So while I’m pleased to announce that my latest comprehensive review of Amazon’s long-term strategy is now ready for clients to read (see Amazon Will Own Your Customer In The Future), it will appear I have left a few things out. Except that I haven’t, because our read of Amazon’s strategy is so on-point that every one of these significant moves announced by the orange smile is accounted for in our model. For example, Amazon:   (See the full article at link) .... 

2018 Amazon Shopper Behavior Study

Looks to be most interesting, download it at the link:

CPC Strategy
The 2018  Amazon Shopper Behavior Study
How Shoppers Will Browse and Buy on Amazon in 2018

Get the Guide PDF

The Story: 2018 will be a pivotal year for retailers, and as usual, Amazon’s at the steering wheel. The only question is–where are they heading next, and more importantly, how will consumers react?

In our 2018 Amazon Shopper Behavior Study, we’ll reveal eye-opening statistical findings that drive Amazon shoppers to make a purchase and why consumers may not be as loyal to your brand as you thought.

The Study: In this year’s Amazon Shopper Study, we asked 1500 U.S. Amazon shoppers big questions including:

How far are Amazon shoppers willing to search beyond page one?
How often do you use Amazon to discover new products or brands?
Are you concerned about counterfeit products on Amazon?
What’s the biggest factor in your decision to buy a product?
And plenty more!   .... " 

Tuesday, March 20, 2018

Tiny, Disposable CPUs for the IOT

I like the idea that these CPUs will be embedded, even disposable.   In packaging for example.  Something we suggested in retail.    Bringing computing power closer to the edge.   Still not powerful by modern standards.

IBM’s latest computer is a blockchain-ready CPU smaller than a grain of salt  in DigitalTrends.

IBM kicked off its Think 2018 conference today with a bombshell announcement: It has made the world’s smallest computer, and it’s designed from the ground up to work with the blockchain. The computer itself is smaller than a single grain of salt, coming in at 1 millimeter by 1 millimeter and reportedly has about the same computing power as a 1990s era CPU.

“The world’s smallest computer is an IBM-designed edge device architecture and computing platform that is smaller than a grain of salt will cost less than ten cents to manufacture, and can monitor, analyze, communicate, and even act on data,” IBM claims. “It packs several hundred thousand transistors into a footprint barely visible to the human eye and can help verify that a product has been handled properly throughout its long journey. ... 

.... Essentially, these CPUs will be embedded in tags or product packaging, and they’ll log every movement the product makes, from shipment to delivery. They could also be used to ensure the authenticity of luxury goods. .... 

“ ... These technologies pave the way for new solutions that tackle food safety, authenticity of manufactured components, genetically modified products, identification of counterfeit objects, and provenance of luxury goods,” Krishna continues. .... " 

On Algorithms and Reading

How to Think for Yourself When Algorithms Control What You Read   By Marc Zao-Sanders in HBR

With the flick of a switch, a handful of tech giants can change the nature and extent of mankind’s ingestion of information. In 2013, Google took a step towards understanding the intent of their users with the Hummingbird algorithm. Twitter replaced most-recent with most-important tweets when they introduced their algorithmic timeline in 2016. Facebook claimed they’ll be replacing clickbait with more meaningful interactions on their feeds earlier this year.  These changes are almost always met with public uproar for a few weeks, soon after which humanity acquiesces. The ability for an elite to instantly alter the thoughts and behavior of billions of people is unprecedented.

This is all possible because of algorithms. The personalized, curated news, information and learning feeds we consume several times a day have all been through a process of collaborative filtering. This is the principle that if I like X, and you and I are similar in some algorithmically determined sense, then you’ll probably like X too. Everyone gets their own, mass-personalized feed, rationed by the machines. ... "

IBM Delivers a Watson Voice-Powered Assistant

Been looking at this in Beta for some time.  More detail to follow.

IBM delivers Watson-powered voice assistant for consumer brands
Alexa and Google Assistant have taken residence in people's homes. IBM aims to give companies a way to deliver their own branded AI voice assistants

IBM has launched Watson Assistant, an artificial intelligence (AI) powered voice assistant for businesses.

Organisations showcasing the Watson Assistant include speaker maker Harman, retail bank Royal Bank of Scotland, Autodesk, Munich Airport and Motel One.  ... "

Behavioral Implications of Grab and Go Retailing

Some interesting behavioral observations of early use of the lack of checkouts in Amazon's Grab and Go tests.   We interviewed and watched consumers in our laboratory stores to learn how they felt and reacted to similar approaches.  Will this cause fewer visits, change the nature of visits and purchases?  How will it interact with online visits?    Will this be an ultimate expectation of physical stores?   Amazon is in position to learn much here.

Amazon Go customers are still adjusting to the grab-and-go model in DigitalTrends

Apparently, our parents have taught us well. While Amazon’s new cashless grocery store, Amazon Go, has encouraged folks to just walk out the door without paying, it would seem that folks aren’t quite on board with that model yet. According to Gianna Puerini, vice president of Amazon Go, it has taken shoppers a bit of time to get used to the fact that walking out of a store without stopping by a cash register is not, in fact, immoral or illegal.

At Shoptalk, a retail industry event in Las Vegas, Puerini noted that she has been struck by the number of customers who have second-guessed their ability to take advantage of the cashless convenience offered by Amazon Go. ‘‘What we didn’t necessarily expect was how many people would stop at the end on their first trip or two and ask, ‘Is it really OK if I just leave?’’’ Puerini said of the new-age store that opened in January in Amazon’s hometown of Seattle. .... " 

Monday, March 19, 2018

Optimizing Health Policies with Bayesian Networks

 Another excellent, mostly nontechnical presentation on the topic.   Interesting is the decision model itself, and the topic of health decisions.  Unlike most modeling methods, this approach embeds the details of the model into the decision process being modeled.  So you can visually see the details of what is being modeled and discuss it with decision makers.  Also, it directly models uncertainty involved, based on real known data.   We used these methods actively,  I but find them rarely applied in business.   Consider it.  ....

Presentation link and slides below: 

By Stefan Conrady

Managing Partner at Bayesia USA & Singapore: Bayesian Networks for Research, Analytics, and Reasoning

Optimizing Health Policies with Bayesian Networks

In case you missed today's webinar, here is the recording. Today's program was about developing a reasoning framework for health policies in developing nations with Bayesian networks. The specific study question was whether to implement a "test & treat" policy versus a presumptive treatment approach for malaria and bacterial pneumonia. https://bayesia.wistia.com/medias/16vb2vljlt

HBR: Getting Value from Machine Learning

Makes a very obvious case.    That has existed since the beginning of computing.  Yet still a good one to repeat.  Systems must be easy enough to use.  And then actually used, to make them valuable.  Of course when you add in some level of autonomy, with a clearly measurable value, that helps.   One way to do that is to plug them into a known and measurable business process.   You can show the value of it being better, faster, or cheaper, directly.  Augmentation of people and processes is best.  Making the method automatically considered and even applied.  We did it many times.  Good example of project management below. 

Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use    By Ben Schreck, Max Kanter, Kalyan Veeramachaneni, Sanjeev Vohra, Rajendra Prasad  in the HBR

Machine learning can drive tangible business value for a wide range of industries — but only if it is actually put to use. Despite the many machine learning discoveries being made by academics, new research papers showing what is possible, and an increasing amount of data available, companies are struggling to deploy machine learning to solve real business problems. In short, the gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.

How can companies close this execution gap? In a recent project we illustrated the principles of how to do it. We used machine learning to augment the power of seasoned professionals — in this case, project managers — by allowing them to make data-driven business decisions well in advance. And in doing so, we demonstrated that getting value from machine learning is less about cutting-edge models, and more about making deployment easier.  .... " 

Inferring Emotion and Cognitive Changes

The OBAIS department at the Lindner College of Business, University of Cincinnati, invites you to attend a research seminar:

Date and time: Wednesday, March 28th, 2018, 11:00AM-12:00PM

Location: Lindner Hall 608
Speaker: Prof. Joe Valacich, Eller Professor in MIS, University of Arizona

Title: Inferring Emotion and Cognitive Changes through Human-Computer Interaction Devices: From Basic Research to Communalization

Best wishes,

Yichen Qin, Assistant Professor
Department of Operations, Business Analytics, and Information Systems
Lindner College of Business, University of Cincinnati
Website: http://business.uc.edu/academics/departments/obais/faculty/qinyn.html
Email: qinyn@ucmail.uc.edu

Measuring Results

How Accurate Is Your AI? 
from Kyoto University

A researcher at Kyoto University in Japan has developed a new technique that evaluates artificial intelligence's (AI) performance based solely on the input data. In typical AI development, a performance evaluation is trusted if there is an equal number of positive and negative results, and data biased toward either value means the current system of evaluation will distort the system's ability. "The novelty of this technique is that it doesn't depend on any one type of AI technology, such as deep learning," says Kyoto's J.B. Brown. "It can help develop new evaluation metrics by looking at how a metric interplays with the balance in predicted data. We can then tell if the resulting metrics could be biased." Brown's work breaks down the AI utilization and analyzes the nature of the statistics used for reporting an AI's ability, while also producing a probability of the performance level, given evaluation data. .... " 

Testing and Automation of Assistant Skills

Like paying attention to the process of creating and delivering skills: 

Building Engaging Alexa Skills: Why Testing and Automation Matter

By Paul Cutsinger  In Amazon Developer

By Editor’s Note: Skill testing is one of the most important things you can do to build high-quality voice experiences. Today we welcome a community expert in testing tools for voice—John Kelvie, founder and CEO of Bespoken—to share some best practices.

Developing for Alexa can be a lot of fun. There are so many opportunities to create innovative user experiences. The cutting edge is constantly evolving. And the reachable audience is immense, and always expanding.

When building skills, it is incredibly important to build high-quality experiences for users. These users will not come back if a skill does not open or fails quietly halfway through. And we may not be aware of any problems until a user writes a one-star review. This is not the ideal way to identify and fix bugs; there must be a better one.

And there is. Testing and automation are the solution. They help us deliver reliable skills for customers and a great user experience. Through testing and automation, we can offer consistently great experiences to our users. This blog will outline how to do this at a high level and also offer some practical steps to implement it. .... " 

Online Grocery to Reach $100 Billion

Online grocery sales could reach $100 billion by 2022, researchers say    By Andrea Miller   ABC

Walmart plans on expanding grocery delivery to 100 metropolitan areas

Instead of taking a trip to a local grocery store, more and more consumers are opting to order essentials such as cereal, toothpaste or even apples from online retail giants.

Walmart, for one, announced Wednesday that it is bolstering its grocery-delivery service to reach even more cities. .... " 

Macy's Using Virtual Reality for Furniture Sales

Really a pretty old idea, was one of the first ideas we examined for demonstration and sales.    I encountered IKEAs approach in-store  just a few days ago, well done, but not enough AR to understand how your choices would fit in.   Also drove home the point that for store and online experiences the consumer needs to be able to use the system quickly.  Its different in research.   We experimented with it to understand how product would exist on shelves with other products.   See also approaches that mix VR, AR and physical digital displays, such as John Milby's Full Scale Virtual Research (FSVR).

Macy’s will use VR to sell furniture in 50 stores by summer
 By Jeremy Horowitz  @Horowitz  in Venturebeat

VR and online shopping are often portrayed as enemies of brick-and-mortar retail, but shopping mall anchor Macy’s plans to embrace both technologies in a bid to improve its sales, reports FurnitureToday. Speaking at the ShopTalk retail conference in Las Vegas, Macy’s CEO Jeff Gennette announced that he will bring VR furniture-selling tools to 50 stores by this summer and plans to offer the immersive shopping technology in “as many stores as possible.”

According to Gennette, the virtue of virtual reality is its ability to “sell more furniture with less, or even no, square footage devoted to displaying it.” Macy’s piloted a VR system that let customers use a tablet to add furniture to a room, move the pieces around until they seemed optimal, then experience the fully furnished room using VR. The system enabled customers to feel more comfortable about furniture fit and “significantly increased” both total transaction sizes and sales of items that Macy’s carries but didn’t keep on site.  .... " 

AI for Competitive Value

Everyone is still asking,  how much of this is hype?  There is an element of that, but clearly value as well.  How much should the enterprise invest?

How machine learning is changing the game for app marketers  in Thinking with Google    ... Jason Spero Nov 2017 Apps, Emerging Technology, Mobile, Data & Measurement

Artificial intelligence and machine learning technology have the potential to revolutionize marketing as much as mobile, the internet, and television did in the past.

Forward-thinking companies are using machine learning tools to supercharge their marketing. These early adopters take advantage of the technology’s ability to streamline data, unlock user insights, and engage users in highly relevant ways. In fact, 85% of executives believe AI will allow their companies to obtain or sustain a competitive advantage, according to the The Boston Consulting Group.  ... "

Smart Speakers Addictive?

In what sense?  Because they are frequently always on, perhaps, but because they use voice as assistants I find  myself using them less than smartphone in public or semi-public situations.

How addictive are smart speakers?  by Tom Ryan  in Retailwire.

According to the latest Smart Audio Report from NPR and Edison Research, 65 percent of voice-activated smart speaker owners “wouldn’t want to go back to life without” their Amazon Echo or Google Home.

That finding exceeded the 46 percent of Americans who told Pew Research Center in 2014 they “couldn’t live without” their smartphones.

One caveat from NPR’s survey last November of 1,800 consumers is the finding that only 16 percent of Americans own a smart speaker. But the survey still demonstrated how smart speakers are changing behaviors and causing owners to form new habits.

For instance, when smart speaker owners were asked what other devices they are spending less time with as they use they increase their smart speaker usage, the top answer was traditional radio, at 39 percent. That was followed in the top-five by smartphones, 34 percent; television, 30 percent; tablets, 27 percent; and computers, 26 percent.  .... "

Sunday, March 18, 2018

Driverless Pizzas to be Delivered before People

Inclined to generally agree, general driver less delivery should precede driver-less vehicles with passengers.   If only for the liability and legal issues involved.    Yet driver-less vehicles will come.  But agree less with the article that we will soon see many customers meeting the driver-less delivery vehicles out by the curb to eliminate the last 100 yards.   It is still extreme convenience that is leading this transition.   Thoughtful piece on business process profitability issues:

Why Self-Driving Vehicles Are Going to Deliver Pizzas Before People     By Bloomberg in Forbes

In the wait for self-driving technology, cell-phone toting tech bros may have to cede their spot in line to pizzas, Craigslist couches and the mounting ephemera of e-commerce.

The future—at least in the near-term—will not only be driverless, but sans passenger as well.

The early conversations around driverless cars have focused on robot taxis because taking the human driver out of a cab seemed like the quickest path to profitability. But an increasing number of companies—automakers, tech giants, startups, parcel services—are seeing autonomous delivery as the more lucrative venture.

“The revolution in commercial vehicles will come first, then the passenger cars” will follow, said Ashwani Gupta, senior vice president of Renault-Nissan’s light commercial vehicle business. “The moment business people start believing this is going to generate additional revenue and that this is going to be more efficient, then I think they’ll start working on it.”  ... ' 

Fujitsu Human Centric AI

Was impressed with Fujitsu's work in retail when we visited.

Fujitsu drives a human centric model

AI is a core technology which enables many complex processes to be conducted independently of human judgment. Now, deep learning is often featured in the media. But it is not the whole story of AI, just an important piece of the puzzle. Our human cognition is continuously generated from complex interactions between our sensory organs, nervous system, brain and external environments.

To achieve an AI, we have to replicate and bring together a range of cognitive capabilities: perceiving, reasoning, making choices, learning, communicating, and moving and manipulating.

Fujitsu is developing key technologies under a comprehensive framework (see diagram). We call it Human Centric AI, Zinrai. Fujitsu is incorporating component technology such as machine learning, deep learning and visual recognition, into its digital solutions and services. .... " 

Baidu's AI Mimicing Voice

Baidu’s new A.I. can mimic your voice after listening to it for just one minute
By Luke Dormehl in Digital Trends

" ... “From a technical perspective, this is an important breakthrough showing that a complicated generative modeling problem, namely speech synthesis, can be adapted to new cases by efficiently learning only from a few examples,” Leo Zou, a member of Baidu’s communications team, told Digital Trends. “Previously, it would take numerous examples for a model to learn. Now, it takes a fraction of what it used to.” .... " 

Samsung TVs Controlled with Bixby Assistant

Rumor out there that perhaps Samsung would tap Alexa and/or Google for voice control.  But it seems they are sticking with their own Bixby voice assistant for controls.  Up to now only on phones, that will likely soon change.  Looking to test.

Samsung TVs tap Bixby for voice, SmartThings for home control

The company improves its already excellent Smart TV system with an app for easy setup and smart home control, as well as the Bixby voice assistant.     By David Katzmaier ...  

Misinformation and the Wikipedia

Been a longterm Wikipedia fan.   And have also been directed to many, many examples of misinformation there.  But still use it daily.  So whats the solution?  Apparently Youtube planning to resource credibility with WP articles, among others.    Further curated?   Only as good as the curators.  Bias is all over the place.

Don't ask Wikipedia to Cure the Internet   by Louise Matsakis in Wired.

" .... On stage at the South by Southwest conference on Tuesday, YouTube CEO Susan Wojcicki announced that her company would begin adding "information cues" to conspiracy theory videos, text-based links intended to provide users with better information about what they are watching. One of the sites YouTube plans to use is Wikipedia. "We’re just going to be releasing this for the first time in a couple weeks, and our goal is to start with the list of internet conspiracies listed where there is a lot of active discussion on YouTube," Wojcicki said on stage..... " 

Saturday, March 17, 2018

Tags in this Blog

This blog contains tags at the end of each post which lead to related posts.   I do go back selectively and update these tags, especially as they relate to my current research, interests or work. The tags can't be complete,  in some cases the tag topic may not exist until much later.     For example a company that is later formed to address some new technology.  This blog is for my own and client reference,  but if you have any suggestions pass then along in a comment or email.  I am on Linkedin and will respond there too.   - FAD

(Updated) Optimization using Genetic Methods

In our earliest days,  addressing supply chain and blending type manufacturing problems, we were an optimization shop.  Using the math structure of difficult combinatorial problems to find best solutions based on known goals and constraints.    But if you couldn't glean enough low level structure, we tested genetic methods, described here.   In this era of faster machines and more contextual information even more useful to try today.  Also for certain kinds of structure, also consider Dynamic Programming.  Happen to be examining that again today.

In KDNuggets  By Ahmed Gad, KDnuggets Contributor 

This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.

Selection of the optimal parameters values for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.


Suppose that a data scientist has an image dataset divided into a number of classes and an image classifier is to be created. After the data scientist investigated the dataset, the K-nearest neighbor (KNN) seems to be a good option. To use the KNN algorithm, there is an important parameter to use which is K. Suppose that an initial value of 3 is selected. The scientist starts the learning process of the KNN algorithm with the selected K=3. The trained model generated reached a classification accuracy of 85%. Is that percent acceptable? In another way, can we get a better classification accuracy than what we currently reached? We cannot say that 85% is the best accuracy to reach until conducting different experiments. But to do another experiment, we definitely must change something in the experiment such as changing the K value used in the KNN algorithm. We cannot definitely say 3 is the best value to use in this experiment unless trying to apply different values for K and noticing how the classification accuracy varies. The question is “how to find the best value for K that maximizes the classification performance?” This is what is called optimization.

In optimization, we start with some kind of initial values for the variables used in the experiment. Because these values may not be the best ones to use, we should change them until getting the best ones. In some cases, these values are generated by complex functions that we cannot solve manually easily. But it is very important to do optimization because a classifier may produce a bad classification accuracy not because, for example, the data is noisy or the used learning algorithm is weak but due to the bad selection of the learning parameters initial values. As a result, there are different optimization techniques suggested by operation research (OR) researchers to do such work of optimization. According to [1], optimization techniques are categorized into four main categories:  .... " 

  (Update) A comment I got made me add this.  'Optimization' in business practice implies you can get the provably, best possible solution to a problem.   But in reality it almost always means you only can get the best solution within some specific context.     A context can include structure, constraints and goals.    It may also vary over time.    It may be wrong because its too hard to completely understand the problem.  But its still often useful to get a better solution, even if not provably optimal, if its better than todays practice.     Further if you can calculate this 'theoretical' best solution, it can give you better understanding of a problem, and what to strive for.    - FAD 

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Friday, March 16, 2018

Augmented Beauty by Modiface at L'Oreal

An area we did lots of research and development on.  Now based on this piece, it seems that the tech has finally caught up to the needs.   But will it practically work as a marketing, sales and operational tool?  Remains to be seen.   See images at the link.

L’Oreal acquires Modiface, a major AR beauty company
By Ashley Carman @ashleyrcarman  in TheVerge

L’Oreal announced today that it has acquired Modiface, a company that’s had a hand in the creation of many custom augmented reality beauty apps, including those from Sephora and Estée Lauder. L’Oreal didn’t disclose the amount spent, but it did tell Reuters that it now owns Modiface’s numerous patents that help users visualize makeup and hairstyles on themselves. The partnership makes sense in that Modiface has already worked with L’Oreal multiple times, including on the launch of its Style My Hair mobile app, which lets users try on different hairstyles. For that app, Modiface manually annotated 22,000 facial images to create the experience.  ... "

Iterative Random Forests

New Learning Method:  Sees the Forest and the Trees

".... Researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and University of California, Berkeley have created a novel machine learning method that enables scientists to derive insights from highly complex systems in record time.

In a paper published recently in the Proceedings of the National Academy of Sciences, the researchers describe a technique called "iterative Random Forests," which they say could have a transformative effect on any area of science or engineering with complex systems, such as biology. ... "

Amazon Pickup Service in Whole Foods

Witnessed the set up of this in a nearby Whole Foods today.  No additional crowding as yet.   Can see it as a specialized service offering, volume involved unclear.  Other uses when the infrastructure is operating?

Amazon/Whole Foods planning store pickup service from third-party retailers  by George Anderson in Retailwire.  with further expert comments:

Amazon.com wants to negate one advantage that rivals such as Walmart, Target, Kroger and others have — store pickup. The e-tailing giant is looking to offer a pickup service at Whole Foods’ stores that will not only include orders from the organic grocery chain, but also from a host of other retailers.

According to the reports, Amazon is seeking a finance manager that will help build a pickup business from the ground up. The job posting, which was first reported on by the Puget Sound Business Journal, said the person hired would be behind “the Whole Foods delivery and pick-up service on the ultra-fast Prime Now app and enable our Prime customers to shop from a set of marquee third-party retailers.”

What potentially makes the described service different from those offered by Walmart and others is that it would appear to offer pickup from online orders placed with Whole Foods, Amazon and perhaps others, as well.  ... " 

Ring and Amazon

I am a user of the Ring Doorbell, have been since their beginning.   So intrigued by the implications. New kinds of image data collection?  Amazon Key service has been covered here.  Privacy of behavior in the home.

What does Ring mean for Amazon?   in Retailwire  by Chris Petersen with expert comments. 

Through a special arrangement, presented here for discussion is a summary of a current article from the IMS Results Count blog.

Amazon.com in late February acquired Ring, a maker of internet-connected doorbells and cameras, for about $1.1 billion.

Ring is best known for its Wi-Fi enabled doorbells that are equipped with cameras to detect when someone is at the door. Users receive an alert and then are able to view and talk to the individual outside their door through their smartphone.

On the surface, Ring is a powerful acquisition, which launches Amazon further into the home security space. Last year it began selling Amazon Cloud Cam, an indoor security camera of its own design. In December it acquired Blink, a maker of inexpensive internet security cameras and doorbells. Amazon also moves further into the IoT space with more popular products that can connect to Alexa. Google’s Nest also offers a home security system.

The apps and Ring subscriptions will create recurring revenue. All well and good in itself, but several reports on the acquisition focused on how Ring’s technology may build on Amazon Key, a service launched last October that allows Prime members to have orders delivered inside their homes to help deter theft and prevent fresh food from spoiling. .... " 

Google and Marketing Measurement

Always have been impressed by Google's aim at better measurement, it is foundational, and not  enough attention is paid to it.  Here some of their latest:

Measurement matters: Laying a foundation for better measurement, today and tomorrow  By Babak Pahlavan Mar 2018 Data & Measurement

When we talk to marketers about their challenges and needs in digital, measurement always finds its way to the center of the conversation. We've heard from advertisers large and small that measurement on digital can be difficult and often complex. But it’s also critical to address, because effective measurement is foundational to growth.

That might sound a bit lofty, but it’s true. Better measurement helps businesses uncover the best ways to invest their limited marketing resources. Which leads to better marketing, which leads to new customers and continued growth.  

But how do you define better measurement? We’ve invested a lot of time listening to our advertisers and industry partners, and we’ve consistently heard that, to be effective, measurement solutions must be:

Trustworthy: They must be transparent and easily verified by advertisers, publishers, and third parties, including technology providers and industry standards groups.

Intelligent: They must uncover the insights that really matter to a business—which often means using the latest advancements in areas like machine learning and going way beyond simple reporting.

Actionable: They must be easy to act on, so advertisers can quickly fine-tune or change their strategy, turning metrics and insights into real business impact. .... " 

Thursday, March 15, 2018

Human in the Loop Machine Learning

Attended a very good webinar today in the DSC series.  Strongly recommend joining DSC and taking advantage of their free resources.

This Webinar answers the question you will have as a data scientist.  Where will I get the data to train my models, when its mostly held by people?

Robert Munro, CTO of CrowdFlower answers in this recorded Webinar: 

"    ... Curious about what human-in-the-loop machine learning actually looks like? Join CrowdFlower and learn how to effectively incorporate Active Learning, Transfer Learning, and Annotation Quality in your ML projects to achieve better results. 

Join us in this latest Data Science Central webinar, where we will cover the following topics:

When to use the human-in-the-loop as an effective strategy for machine learning projects

How to set up an effective interface to get the most out of human intelligence

How to ensure high-quality, accurate training data sets

How to use ML models from different domains to improve your own labeling

​This webinar will include an end-to-end look at setting up and running a job that generates high-quality training data, and shows how to incorporate that training data into human-in-the-loop machine learning systems that you can run in your own environment.

Speaker: Robert Munro, Chief Technology Officer -- CrowdFlower
Hosted by: Bill Vorhies, Editorial Director -- Data Science Central .... " 

Decision Support for Health Data

Good overview talk this morning on the complexity of gathering and analyzing health data for decision support.  With some emphasis on dementia data.   'AI' is used here as a description of the analytcs used, as well as the direct decision process.   Like that.  Plans are to have a followup talk on this topic

Speaker:  Mark van Gils:  “AI for Decision Support in Health” VTT Finland

Slides are here.   Full voice /video recording will be placed here later

Augmented Reality Sensing Form and Depth

Will this drive us to better augmented reality shopping?   At IKEA a few days ago I used some of their on floor furniture placement and design Apps, nicely done, but could have used better space understanding capabilities.  What really engages for product usage and selection  in-place?

Depth-Sensing, Algorithms And Retail Shopping Allowing AiFi To Push The Boundaries Of Interactivity

AiFi is combining artificial intelligence with mixed and augmented reality.   By Nina Salomons  in VRFocus.

Founded by former Google and Apple engineers, AiFi is combining artificial intelligence (A.I.) with ARKit on Apple products such as iPhones and iPads. Speaking to VRFocus, co-founder and CEO Steve Gu explained how AiFi has enabled consumer products to understand detailed 3D shapes and activities, including individuals and their surroundings.   .... " 

Optimizing the Usefulness of Chatbots

Still awaiting reasonably adept and useful chatbots that can do real conversation.  So below starts some key thoughts.  Tracking what the base of their knowledge looks like, and how they are being effectively used.   And how they need to be maintained.  Ultimately relevant common sense and common context will also be key to understand.

Using machine learning to monitor and optimize chatbots

The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.   By Ben Lorica

In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer interaction). We are still in the early days of chatbots, but if current trends persist, we’ll see bots deployed more widely and take on more complex tasks and interactions. Gartner recently predicted that by 2021, companies will spend more on bots and chatbots than mobile app development.

Like any other software application, as bots get deployed in real-world applications, companies will need tools to monitor their performance. For a single, simple chatbot, one can imagine developers manually monitoring log files for errors and problems. Things get harder as you scale to more bots and as the bots get increasingly more complex. As in the case of other machine learning applications, when companies start deploying many more chatbots, automated tools for monitoring and diagnostics become essential.  .... " 

Wednesday, March 14, 2018

Talk: AI for Decision Support in Health - how to make it work

Upcoming CSIG Meeting:

Date and Time :  Mar 15, 2018 - 10:30am US Eastern
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
Website: http://cognitive-science.info/community/weekly-update/  (Slides, Recording)

{ Also presented at #OpenTechAI workshop in Helsinki https://developer.ibm.com/opentech/2018/01/29/helsinki-march-2018-opentech-ai-workshop/ }

Talk Title: AI for Decision Support in Health - how to make it work
Presenters: Mark van Gils (VTT)

Mark van Gils is an experienced research & development professional, specializing in data-analysis solutions for health and wellbeing applications. A successful track-record in setting-up, carrying out and managing data-analysis projects with healthcare professionals and SMEs and global companies operating in the health and wellness area.

• Impact through development of data analytics solutions that are meaningful and used in practice, impact through scientific co-operations and publications; (co-)author of over 120 articles in the field.
• Over 20 years experience in machine learning, statistics, signal processing, artificial intelligence methods.
• Leadership and management of multi-location team (>15 R&D professionals), co-ordination of large international multi-disciplinary R&D projects
• Communication of data analytics results and providing insights for different stakeholders
• Taking care of customer relationships
• Ph.D. in artificial intelligence/biomedical engineering, M.Sc. in applied physics
• Lecturing courses and guiding students and researchers


Healthcare is one of the most conservative fields in the uptake of new technologies. Reasons for this range from regulatory considerations to (informal and formal) processes that are difficult to change, but also technical issues, such as problems with the data and the difficulty of proving performance play a strong role. In this tutorial we will discuss issues we may run into when considering AI approaches for health applications. Subjects include (but are not limited to): how to get the input data right (poor quality data, missing data, harmonization), (lack of) Gold Standards and objective measures, black-box approaches vs. explainable models, data visualization, usability, classification performance vs cost-effectiveness vs practical meaningfulness. Examples of the issues and practical hints will be given based on real-life example cases of implemented systems. .... " 

Optimization vs AI to make things Better

I was reminded that my early experiences with government and enterprise systems dealt with the optimization of systems.  That is, the mathematical means of linking a specific mathematical statement of a problem, with value goals and constraints, to a specific best possible solution.   We used the predecessors of ILOG, and CPlex directly for these problems.    We saved millions using these methods.  Of course optimization does not have the current hype.

Now how is AI, as it currently defined,  dissimilar from Optimization?    Usually because the Optimization approach is more specifically and numerically defined.     If AI uses human-like intelligence, it is usually not precisely mathematical.   And unfortunately not as closely tied to specific business process.   Not saying that AI cannot use optimization methods, it just usually does not.   So there should be a strong consideration towards using more precise and direct and process oriented methods.

Was pointed to this company that works the space, have never worked with them:

Optimization Direct Inc., co-founded by Dr. Robert Ashford, a pioneer in the field of optimization, and Dr. Alkis Vazacopoulos, a leader in the industry, markets IBM® ILOG® CPLEX Optimization Studio®, the world's leading software product for modeling and optimization.

CPLEX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment.

CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to:

Optimize business decisions with high-performance optimization engines.

Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios.

Create real-world applications that can significantly improve business outcomes. ...... "

The Semantics of Image Deep Learning

Google once again shows its impressive advanced AI/Deep Learning capabilities.     Which made me recall that it is often the 'semantic', or meaning in context aspects that are most important for an AI or analytic method to be useful.   And that assigning tags also implies we will need to maintain the tags as context changes.  Below is technical, look at the link for some image examples that make this clearer.

Semantic Image Segmentation with DeepLab in Tensorflow

Posted by Liang-Chieh Chen and Yukun Zhu, Software Engineers, Google Research

Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection. ... "

Assistants and Common Sense

Its actually not too often that assistants speak gibberish, they more often just admit to not knowing what was asked, when a human would understand readily.  That's usually better to diminish risk.  See my previous posts on Common Sense reasoning, which we worked on in the enterprise.  The idea of a challenge is good, it will at least scope the problem in contextual and current terms.   See the examples at the link below.  Also the notes on what 'fundamental limitations' are. 

AI assistants don’t have the common sense to avoid talking gibberish
A new test could prove that when it comes to language, today’s best AI systems are fundamentally limited.   by Will Knight in Technology Review

Siri and Alexa are clearly far from perfect, but there is hope that steady progress in machine learning will turn them into articulate helpers before long. A new test, however, may help show that a fundamentally different approach is required for AI systems to actually master language.

Developed by researchers at the Allen Institute for AI (Ai2), a nonprofit based in Seattle, the Arc Reasoning Challenge (ARC) will pose elementary-school-level multiple-choice science questions. Each question will require some understanding of how the world works.  .... " 

Wal-Mart Brings You Fresher Groceries through Eden

In Wal-Mart's blog:

Eden: The Tech That’s Bringing Fresher Groceries to You   By Parvez Musani   Vice President – Supply Chain Technology, Walmart

What’s for dinner tonight?

No matter the answer, there are some givens: It has to taste good, be good for you, and be affordable. But when you’re shopping with limited time, how can you be sure you’re buying the freshest apples, milk that will last, or perfectly ripe bananas?

We think our new intelligent food system called Eden can help. Developed in just six months by our own associates, it is improving the quality and flow of fresh groceries from farm to shelf.

Eden is the result of a friendly competition, or hackathon, among the engineers on our fresh merchandising teams. Our goal was to figure out the best way to keep track of food freshness all the way from the farms to our stores. The winning team determined that building a digital library of food standards was the answer. So they gathered the many chapters of food product specifications set by the USDA, layered on Walmart’s own rigorous product standards, and combined all of this information with more than a million photos to create a freshness algorithm that prioritizes the flow of perishable goods worldwide.  ... " 

Robert Hetu of Gartner also discusses this here:
Walmart’s Freshness Algorithm; A Great Example of Algorithmic Retailing   by Robert Hetu   

Neural Nets Remembering

Well yes, all neural nets 'remember'.  We examined that feature in their very early days.    But they don't often remember in the way we would like memory to work.    Very quickly we needed to rework their memory with new infrastructure, based on new context.  Often with new metadata.  So they remember in strict context.   Useful, but you have to be careful about the term.

Following piece is on the topic and interesting

The Neural Network That Remembers
With short-term memory, recurrent neural networks gain some amazing abilities  By Zachary C. Lipton and Charles Elkan .... 

Future of AI Assistants

Useful perspective from a number of players.

SXSW 2018: The Future of AI Assistants
Alexa, Google Home, Siri, and Cortana will learn to adjust to your changing life   By Stephen Cass

In the years to come, what will be the biggest improvement in AI-powered digital assistants? It’s likely to be the ability to accommodate a fundamental aspect of being human: The fact that we all have different personas, we show different facets of ourselves depending on where we are and who we are with, and our personas change over time. And different personas want different things from their AI assistants. Assistants that can understand your personal circumstances are less likely to remind you to pick up your rash prescription as you drive by the pharmacy if there are other people in the car, bug you about work email at home, or keep suggesting fun nightclubs if you’ve just had a baby.

That was the message from Sunday’s panel on “Designing the Next Wave of Natural Language and AI” at the SXSW festival in Austin, Texas. The panel included Ben Brown from Google; Ed Doran from Microsoft; Karen Giefer from Frog; and Andrew Hill from Mercedes-Benz. .... " 

Tuesday, March 13, 2018

Google Assistant on iPad

Interesting development.  As tablets can be seen as more readily mobile devices that we carry along for convenience, why not  integrate an assistant.   And the screen broadens visual communications.  So is this still primarily voice oriented?  Like Cortana.

Have loaded Google Assistant onto a minimal iPad, and works very well.  Have had it on an iPhone, but did not use it much there.

Introducing the Google Assistant on iPad
Product Manager, Google Assistant

Last year we brought the Google Assistant to iPhones and today, iPads are joining the party. The Assistant on iPad can do everything the Assistant on your iPhone can do, with the added benefit of a bigger screen that supports both portrait and landscape mode. ... " 

Will AI Address Engineering Grand Challenges?

The statement of the challenges alone is interesting.

Jeff Dean Thinks AI Can Solve Grand Challenges–Here’s How
Alex Woodie in Datanami

In 2008, the National Academy of Engineering presented 14 Grand Challenges that, if solved, had the potential to radically improve the world. Thanks to recent breakthroughs in artificial intelligence – specifically, the advent of deep neural networks — we’re on pace to solve some of them, Google Senior Fellow Jeff Dean said last week at the Strata Data Conference.

The Academy certainly didn’t lack for ambition 10 years ago when it drew up the 14 Grand Challenges. Delivering a solution for any one of them – such as providing energy from nuclear fusion or finding out how to sequester carbon – could have a dramatic impact billions of people’s lives.

As a result of advances in deep learning techniques, the presence of enormous data collections, and the availability of massive server clusters, we will be able to compute our way toward solving them, Dean told a packed room of attendees during his presentation Thursday afternoon at the San Jose McEnery Convention Center. ... " 

Wal-Mart Ups Robot Mileage

Technology Review writes that Robot mileage has been considerable for the Wal-Mart large scale robot test. Reported consumer interaction is interesting, similar what we say in laboratory tests.   Short piece.    " ... Walmart’s shelf-scanning robots have been on the move. In the four months since the company announced it was deploying them in 50 of its stores, the automated (and vaguely llama-looking) machines have traveled nearly 2,000 miles through the aisles. ... " 

Build a Retail Chatbot

Brought to my attention ...

Technical, for the development savvy. But perusing this could give you an idea of how to design and understand the possibilities.  Then you will likely need a coder to get something started.   Like the use of Watson Discovery.

Create a “cognitive” retail chatbot  

Build a configurable, retail-ready chatbot ... 


As a Python developer, you can use this pattern to learn how to add features such as a shopping cart, context store, and custom inventory search into your chatbot. When you’ve completed the pattern, you will understand how to create a chatbot dialog using Watson Conversation, a Cloudant NoSQL database, Watson Discovery, and a Slack group.  ... 

Chatbots are a hot topic in the retail industry, but so far the execution has mostly amounted to little more than a novelty experience for customers. Interested in adding a chatbot? In this developer code pattern, learn how you can create an easily configurable, retail-ready Watson Conversation-based chatbot that lets a user find items to purchase and then add and remove items from their cart. .... " 

Algorithm Reads Brain Reaction to Music

We experimented with fMRI to attempt to discern consumer reactions to product.    So assume this is a measure of the reaction of people to different kinds of music.  How would that end in their behavior to listening or buying or engaging with background music?

Algorithm Allows for Potential 'Brain-Reading' 
Digital Journal via the ACM
By Tim Sandle

Researchers at the D'Or Institute for Research and Education in Brazil have developed a machine learning algorithm capable of identifying pieces of music from functional magnetic resonance imaging (fMRI) scans of the listener. The team first mapped brain responses triggered by listening to the music, and then used the collected information to identify novel musical pieces based on fMRI imaging data alone. FMRI visualizes cerebral blood flow and neuronal activation, because when an area of the brain is in use, blood flow to that region increases. The implication of the research is that by interpreting the right mapping of musical features to the brain, scientists can predict and decode any unique musical piece. The researchers say the model was based on analyzing six participants who listened to 40 distinct pieces of music. Through this method, the algorithm encoded the listeners' fMRI responses for individual pieces of music, evaluating specific features such as tonality, dynamics, rhythm, and timbre.  ... " 

Monday, March 12, 2018

Alexa Stain Detective

Have been exploring alternative skills and their distribution by type on various voice driven assistants.   Found  the 'Tide Stain Remover',  as an Alexa Skill.   Based originally in part on some content in the book:   'Clean it fast, Clean it Right',  by Jeff Bredenberg.   Written by our AI team, long ago.

On a historical note this was originally written in a form that was delivered via a CD Rom in pre common Internet days, then converted to an App that ran under popular smartphone OS's.   It now runs on the Amazon Alexa system via voice interaction.   I also found that the IOS app version has stopped working after about IOS version 9.0, says it needs to be upgraded, which is not good  publicity.    Either fix it or remove it.

The skill idea was used as an early model for Constructing query -> Subtasks-> Cautions->Tasks  -> Solution Models.    By our AI team and later  P&G Productions.

If anyone has more knowledge of its more recent history, contact me and I will be glad to update its history in the Service Skills domain.

Leveraging AI for Max ROI

For AI job we did with signiicant cost and potential results, we generated an ROI estimate, and also a risk model.  Makes sense if you want to continue funding.

Leveraging AI for Maximum ROI  in O'Reilly

In this O'Reilly Radar Podcast, Rachel Roumeliotis of O'Reilly Media and Atif Kureishy, global VP of emerging practices and artificial intelligence and deep learning at Teradata, discuss how enterprises are currently investing in AI, which industries are seeing the most impact, barriers to AI implementation, and future trends. 

+ For more ways to maximize the ROI on your AI implementations, check out the AI Business Summit in NY (April 30–May 2)

Atif Kureishy on how enterprises are investing in AI
Leveraging the potential of AI to gain maximum ROI.

By Jeff Bleiel 

Amazon Shifts to Subscription for Prime Pantry

Seems more practically useful,  to larger and more systematic users.

Amazon shifts to a subscription model for Prime Pantry
by Tom Ryan in Retailwire with further comments.

Amazon’s Prime Pantry service is shifting to a $5 monthly subscription model from the $6 it currently charges per box.

Prime Pantry, launched in 2014, offers “low-priced, every-day essentials in everyday sizes.” Items include non-perishable goods such as detergent, paper towels, canned foods, breakfast foods, beverages and beauty and personal care items.

The programs selling points include offering savings without the need to buy in bulk and eliminating the need to visit stores for regularly-purchased items. Products arrive in one to four days. The Prime Pantry’s micro-website states, “Skip the trip to the grocery store and let us do the heavy lifting.” ... ' 

Automata: States, Transitions and Context for Process

I have yet to see this directly useful, but it does teach the broad notion of automata, which is useful. Any time you are programming you are dealing with states of a device, and even states of a context being explored.   Sometimes a good way to think about a process.

Automata Based Programming

A Visual automata language called Rosmaro

To do automata-based programming is to program with states and transitions. States correspond to different behaviors. Transitions are named after events and describe how those behaviors change. ... " 

Walmart Smarter Shopping App

More on new Wal-Mart Shopping App.   We looked at similar approaches which prioritized 'smart' lists and navigation .....   Tailored notification was also a means to remind people of periodic replenishment requirements.   And linking to key expertise categories ... like cleaning, cooking. party needs  ....  Which could be expertise based models, or even linked to human provided expertise as needed.    Or  integrate consumer needs and processes with specific products.

Walmart reimagines in-store shopping for mobile     by Tom Ryan in RetailWire.

Many shoppers only seem to use their smartphones in-store to check prices on Amazon.com, but Walmart hopes to change that habit with a newly added store assistant feature to deliver a “totally re-imagined experience for in-store shopping.”

Walmart’s mobile app now features a product search bar, barcode scanner, customer reviews and Walmart Pay. Among the new and expanded features are .... "

Synthetic Biology

When we were more Pharma involved we took a look at what is called 'synthetic biology'.   and connected to neural models that could predict human behavior.  As data access brows there is more opportunity to suggest simulation and statistical models.  Reviewing the field.

From McKinsey: What do you get when you combine genetic engineering with big data analytics? A new technology for disruptive innovation.... 

Fairly broad definition in the WP:

Synthetic biology is an interdisciplinary branch of biology and engineering.
The subject combines disciplines from within these domains, such as biotechnology, genetic engineering, molecular biology, molecular engineering, systems biology, biophysics, electrical engineering, computer engineering, control engineering and evolutionary biology. Synthetic biology applies these disciplines to build artificial biological systems for research, engineering and medical applications. .... " 

Sunday, March 11, 2018

Drug Robot Finds Toothpaste Ingredient

First thought this was describing a 'robot' in a general sense,  and the physical implementation was also required. But consider that the term robotic can be full or partial automation.

AI 'scientist' finds that toothpaste ingredient may help fight drug-resistant malaria

An ingredient commonly found in toothpaste could be employed as an anti-malarial drug against strains of malaria parasite that have grown resistant to one of the currently-used drugs. This discovery, led by researchers at the University of Cambridge, was aided by Eve, an artificially-intelligent ‘robot scientist’.  .... " 

Walmart asks CPGs for higher priced products

No joke – Walmart asks CPGs for higher priced products   by Matthew Stern in Retailwire with discussion. 

Walmart is known for its commitment to low prices, but the company is discovering that shipping the lowest-priced products is making it tough to turn a profit with e-commerce. So, the chain has begun encouraging vendors to provide higher-priced items to sell on Walmart.com.

Last week, Walmart CEO Marc Lore informed big-name CPG companies like Procter & Gamble, Unilever and others that Walmart.com wants to focus on selling items that cost at least $5 and preferably more than $10, according to Reuters. .... "

Saturday, March 10, 2018

Blood Pressure Sensing from a Smart Phone

Was involved in defense department look at how to do inobtrusive, non mechanical blood pressure sensng.   Was considered impossible at one time.   The method described does not require complex calibration. Availability not mentioned. Includes video.

New Smartphone Sensor Checks Your Blood Pressure
It’s more convenient than a cuff   and could help patients monitor hypertension at home
By Emily Waltz in IEEE Spectrum

For years, scores of engineers have been trying to develop a more unobtrusive, convenient device for blood pressure monitoring. Now, researchers at Michigan State University and University of Maryland appear to have succeeded.

In a paper published today in Science Translational Medicine, the researchers described a prototype blood pressure sensor that can be incorporated into a smartphone, and requires only the press of a fingertip.  

The convenient device could encourage people to check their blood pressure more often, allowing them to catch hypertension—persistently high blood pressure—sooner, says Ramakrishna Mukkamala, a biomedical engineer at Michigan State, in East Lansing, who led the study.   ... " 

AI Will Still Need Remote Human Help. Or Concierges?

Truly autonomous cars still some time away?   Will they still need rooms full of operators somewhere to take over car operations remotely in some situations?   That's what dozens of companies are preparing for.   Humans stay in the loop for now.  For legal reasons, and sometimes to protect the autonomous vehicles from other humans.  ' ... The truth is that artificial intelligence is neither artificial nor intelligent.   A.I. is made out of people .... ' 

In ComputerWorld:  by Mike Elgan

" ... California just approved licenses for self-driving cars to in fact have no human driver behind the wheel, or no human in the vehicle at all (after dropping off a passenger, or for deliveries) with one caveat: The self-driving car companies must monitor and be able to take over driving remotely.

This rule matches other parts of the country where no-driver autonomous vehicles have been allowed. There’s no human in the car, or at least the driver’s seat, but remote monitoring and remote control make that possible, safe and legal. ... "

The article further examines how humans may continue to stay in the loop for some time.   Also mentions the Facebook M example, where humans were initially designed into the system, perhaps with the goal to remove humans later?  Our own approach used a 'Concierge' model, where the AI only had to be good enough to detect and solve the easiest problems OR pass the problem off to the right expert human.  While having also gathered enough data to make the human's effort easier. 

Humans will be in many loops, somewhere, for some time.

Google Pushes Forward on Quantum Computing

Been following this concept since close to the beginning.  Most interesting, the kinds of problems that may be best solved more quickly.    Good thoughts on the testing aspects:

Google thinks it’s close to “quantum supremacy.” Here’s what that really means.

It’s not the number of qubits; it’s what you do with them that counts.
by Martin Giles and Will Knight  in MIT Tech Review

Google on Future Value of Machine Learning

Google in Think With Google writes an interesting piece regarding CES conference and Machine learning:

(Below, are summaries, links to detail from the link below)

The promise and potential of machine learning
Recently at CES, marketers gathered to check out all the latest tech and see for themselves the promise and potential of machine learning. One thing that became clear: consumer experiences are being redefined and that’s reshaping what’s required of marketers. Machine learning is enabling a world where assistance is all around us, helping brands save time, uncover new insights, and deliver stronger results. It’s already changing the game for app marketers and the travel industry, for example. Read on to see how machine learning will affect your business. ....

Machine learning for non-engineers
First things first. If all these buzzwords—artificial intelligence, machine learning, deep learning—are making your head spin, check out this primer. Google’s Adam Green outlines the areas where marketers can make the most of machine learning and truly add value.  ...

Get one step closer to relevance at scale
“Technological advances have always created new opportunities for storytelling and marketing,” explained Marvin Chow, VP of marketing at Google. Soon, he adds, marketers will be able to tailor campaigns to consumer intent in the moment. “It will be like having a million planners in your pocket.” .....

Data + machine learning = personalized experiences
Today, people expect personalized experiences when they interact with brands. And that means marketers aren’t just closing transactions, they’re building relationships. By coupling their data with machine learning, marketers can understand new audiences that are similar to their best customers. That’s a win-win. ....   "

Friday, March 09, 2018

How P&G and American Express Are Approaching AI

I am quoted in the Harvard Business Review about how P&G successfully used AI in the past to improve systems, including estimates of actual value.     This HBR article has just been reposted, and the complete article is for sale if you don't have a subscription .... Ask me for more about these efforts.  Much supporting information has also been posted here.   More details were also published in the Cognitive Systems Institute archives.

How P&G and American Express Are Approaching AI
By Thomas H. Davenport, Randy Bean

Published March 31, 2017

There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies — which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of “chief cognitive officer” roles, and certainly many firms are rushing to hire experts with deep learning expertise. “New and different” is the ethos of the day. .... 

Two good examples of combining well-established practices with cognitive technology to achieve business success are American Express and Procter & Gamble. Both firms are actively undertaking cognitive technology initiatives.  Both are well into their second centuries; they wouldn’t still be here if they weren’t able to accommodate change well and introduce new technology effectively. We spoke with top executives at each of these firms about the rise of cognitive in their organizations. Ash Gupta is President of Global Credit Risk and Information Management at American Express, and Guy Peri is Chief Data Officer and Vice President of Information Technology at P&G. Both executives have longstanding track records of success at their respective organizations, having seen business and technology change come and go for 20 years or more.

How it will impact business, industry, and society.

Both organizations have a considerable history with artificial intelligence. Gupta at American Express reminded us of the Authorizer’s Assistant, which was one of the more successful rule-based expert systems of the late 1980s. As described in a popular Harvard Business Review article on that generation of technology, the system made recommendations to human authorizers whether to approve large purchase transactions by cardholders.

P&G also built and employed a number of rule-based expert systems. In addition to Peri, the current CDO, we also spoke with Franz Dill, a retired P&G IT manager who focused on AI during the 80s and 90s. He said that the most well-known expert system they developed was one that blended Folgers coffee (no longer a P&G brand). This system, Dill noted, saved P&G in excess of $20 million dollars a year in green coffee costs. The company also built an expert system that helped advertisers at P&G to use, modify, and reuse the company’s advertising assets.

Both American Express and P&G are companies that have explored artificial intelligence over the years, and while the technology may have changed, the established yet innovative approaches that these firms take to incorporating new technologies and capabilities continues to evolve. Their fundamentally sound innovation practices provide a foundation for evolution. The attributes of their respective approaches to cognitive technology include .... " 

Bluefabric Defines the Smart Contract

As we examine the idea of a Smart Contract, we are coming to realize that it's construction will be the ultimate challenge.    Never before have we had to consider all the implications, current and future of a contract.  The risks involved.    Can it forecast and simulate its operation and goals under future conditions?  More opportunity for analytics

What is a smart contract and why it means the Future  by Bluefabric: 

"Smart” is probably one of the most used words nowadays: smart phones, smart watches, smart TVs, and even smart homes. But what is a smart contract and how are they set to shape our future?

To start with a formal definition, smart contracts are self-executing agreements between two parties (usually a buyer and a seller), which are directly written into lines of code and exist across a decentralized blockchain network. Blockchain is the technology that also underpins the much-discussed cryptocurrencies. Smart contracts allow for trusted transactions among different anonymous parties without involving a central authority or a middleman. Therefore, their primary aim is to provide better security than traditional contracts and to reduce transaction costs associated with contracting. .... " 

Voice Assistance Resonates

 In Think with Google (And surprisingly mentions Amazon):

Three ways voice assistance is resonating with baby boomers

When new technology takes off, we sometimes assume the only early adopters are the young. While millennials are no doubt talking to the new wave of voice-activated speakers, it turns out that Google Home and Amazon Echo are really resonating with baby boomers. In fact, our research has found that boomers see their voice-activated speakers as more than a simple device. They see them as empowering tools, as an opportunity to interact with brands, and even as companions. .... ".   ( And more statistics ... ) 

Windows to Integrate AI Platform

Will follow this closely, will it get capabilities closer to the Edge.  Running directly from Win 10 devices.  Or is it more important to be closer to the developer.

Windows 10’s next major update will include Windows ML, a new AI platform   By Tom Warren  @tomwarren  in TheVerge

Microsoft is planning to include more artificial intelligence capabilities inside Windows 10 soon. The software giant is unveiling a new AI platform, Windows ML, for developers today, that will be available in the next major Windows 10 update available this spring. Microsoft’s new platform will enable all developers that create apps on Windows 10 to leverage existing pre-trained machine learning models in apps.

Windows ML will enable developers to create more powerful apps for consumers running Windows 10. Developers will be able to import existing learning models from different AI platforms and run them locally on PCs and devices running Windows 10, speeding up real-time analysis of local data like images or video, or even improving background tasks like indexing files for quick search inside apps. Microsoft has already been using AI throughout Office 365, inside the Windows 10 Photos app, and even with its Windows Hello facial recognition to allow Windows 10 users to sign into PCs and laptops with their faces. ... " 

Operationalizing Machine Learning

Utimately one of the most important issues  of using AI.  Making it work in operational context.  And note too the discovery of wrong assumptions, a kind of confirmation bias, that is highlighted.   All models have assumptions, even the most sophisticated.  Rooting them out early in machine learning is too often not emphasized.

Keynote from O'Reilly Strata
Operationalizing machine learning  A short video. 

Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes that cause expensive misunderstandings. ... "