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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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Sentier was founded in 2009 and is headquartered in Austin, Texas USA. We are a woman-owned small business. We are HIPAA compliant and SAM registered.   ...

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. .... "