Contextual Intelligence: A Next Big Thing

Good overview piece in DSC.   I add some of our own experience using AI techniques.

We struggled with this a number of times.   All assistance exists in some context.  The context itself can be complex or not.   So for example we built a pump replacement system for choosing industrial pumps based on a dozen criteria.   Though this was technical, even including some predictive analytics,  it was straightforward.    Also easy to explain its details to the decision makers involved.   It also produced some additional meta information:  " how much did we, and would we spend on XYZ pumps next year, and what should their maintenance cost be?

But once we looked at complex systems.  Ones that depended on human behavior, required common sense decisions,  competitive reaction, this needed many sensory inputs, depended on the decisions of many other people and systems, the whole thing became difficult.  All this was asked in questions like "What profit is the company likely to make on new product X in the next year?"   At one point we estimated that such a question required several hundred external inputs.  Assistance in complex context is hard.  And you always end up with some risk/uncertainty attached to the assistance.     Even how the decision is implemented can add considerable risk and uncertainty.   Much work to be done.

Contextually Intelligent NLP Assistants – AI’s Next Big Technical Challenge   Posted by William Vorhies 

Summary:  Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML.  The tech is already here from recommendation engines.  The need to be more efficient and to become AI-augmented in our decision making is now.  Getting the contextual awareness is the hard part.

Last week we took the position that from a technical standpoint, ‘deeply inclusive and contextually sensitive’ AI is one of the two ‘next big things’ in AI.

In retrospect I wish there were a more concise agreed naming convention for this bit of technical legerdemain.  “Inclusive” and “contextually sensitive” are in the category of those ‘suitcase words’ Marvin Minsky called out as being so dependent on the user’s experience that agreement on meaning is difficult.

What we’re not talking about is the ability of NLP to hold a contextually appropriate conversation, such as making a reasonable response or request for clarification based on the topic at hand.  For the most part, short of performing psychoanalysis, chatbots can do pretty well with human ad hoc conversation.

Also, we’re not talking about being culturally inclusive as in detecting and eliminating bias.  Important, but not what we’re getting at.

What we’re describing is the next big step in NLP utility in which the NLP puts together facts it knows about us and proactively takes action or makes suggestions that make our life easier.

The example we gave in our previous article is about having the NLP assistant remind me of my mother’s upcoming birthday in a week or so without my having explicitly created a reminder.  More importantly my NLP assistant could make a recommendation for a present.  Presumably my past communications with her both in fact and tone contain some strong signals about my mom’s demographics and perhaps even her interests so why not predict a short list of appropriate gifts.  Now that would be valuable.

So perhaps a better description of this behavior then would be ‘contextually intelligent’.  We’ll stick with that.

Considering the Data Science Behind AI

Nicely done piece from DSC by William Vorhies,  an overview.  Good exec and introductory piece.  Like in general the way this is presented.  Would add that I have started to call it Augmented Intelligence (AI),  which tempers the expectations involved.

The Data Science Behind AI    Posted by William Vorhies  

Summary:  For those of you traditional data scientist who are interested in AI but still haven’t given it a deep dive, here’s a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI).

We and others have written quite a bit about the various types of data science that make up AI.  Still I hear many folks asking about AI as if it were a single entity.  It is not.  AI is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use.  In each of these areas however, we’ve made a lot of progress and that’s caught the attention of the popular press.

This article is not intended to be a deep dive but rather the proverbial 50,000 foot view of what’s going on.  If you’re a traditional data scientist who’s read some articles but still hasn’t put the big picture together you might find this a way of integrating your current knowledge and even discovering where you’d be interested in focusing.  .... " 

Using IBM Watson in Business

This oversimplifies the task of using a capability like IBM/Watson, but is still a good introduction. Non technical, suggestive as to the possibilities.   Also an excellent list of companies using Watson.  Pointers to more detail.  By William Vorhies in DSC:

" ... In our recent reviews of historical Watson and the modern Watson of today we concluded that IBM’s Watson Group may have the first or at least the current strongest comprehensive AI platform. 

 This is the first time that we know of that all three elements of AI have been brought together in a single user friendly platform: image processing, text and speech processing, and knowledge retrieval. 

This is not so much a platform for data scientist to use to expand the capabilities of AI as it is a platform for business users (with the aid of data scientists) to exploit the capabilities of modern AI by building new products and services.

To wrap up this review of Watson, we wanted to provide some thought-starters on what new services or even new businesses you might build on Watson.

Oh, and regarding new businesses, did we mention that developers who join the Watson Ecosystem are eligible to become a Watson “partner” with a shot at the $100 Million funding IBM is making available to startups plus support and access from IBM business and technology advisors. ... " 

From Shallow Nets to Beyond Deep Learning

We called this  'artificial neural nets' (ANN) and we used them actively, but shallowly.  These have been deepened to be called 'Deep Learning'.   And very successful at categorizing things like images.   Our nets were much shallower,  a handful of layers at most.  These worked for things we had in the the past used pretty standard and long known statistical methods.

So now William Vorhies  writes in DSC about Beyond Deep Learning – 3rd Generation Neural Nets  ... Summary:  If Deep Learning is powered by 2nd generation neural nets.  What will the 3rd generation look like?  What new capabilities does that imply and when will it get here?  ... ' 

Nicely done, he mentions some of the limitations of neural nets and their use, which were the same things that we experienced with our 'nets'.   These are very data intense methods, required tagged data for supervised learning and mostly did classification.  Though the last could easily be stretched. They were trained with much data, then used to classify and test new examples.   It was easy to determine their accuracy and try again after engineering some parameters.  Art and Science.

And then he takes it to what might be next.  Good read without too much technology depth.  And insightful for those with a taste of the idea already.   I am about to take a deeper dive.

Reinforcement Learning and AI

This bears some relationship to looking directly at business process and attempting to control it directly.  A kind of context simulation with rewards.   Thus similar to game play and process control.   I will follow in a post with a real world example.

Reinforcement Learning and AI   Good piece with many related examples.
Posted by William Vorhies

Summary:  At the core of modern AI, particularly robotics, and sequential tasks is Reinforcement Learning.  Although RL has been around for many years it has become the third leg of the Machine Learning stool and increasingly important for Data Scientist to know when and how to implement. ... " 
" ... The key to understanding when to use Reinforcement Learning is this:

Data for learning currently does not exist
Or you don’t want to wait to accumulate it (because delay might be costly)
Or the data may change rapidly causing the outcome to change more rapidly than a typical model refresh cycle can accommodate.

What problems fit this description?  Well robotic control for one and game play for another, both a central focus of AI over the last few years.   ... " 

(Read the full article)   See also in the WP, which does a good broad overview.

Mood Science and the Internet of Things

Anyone who works with advertising and marketing ideas wants to measure and influence aspects of mood in  a number of context.   Much has been made of neuromarketing ideas in recent years.   'Things'  that can  influence mood could be packaging, product, context, promotion, shelf and fixture.

And things that influence, can also measure and interconnect.   Measuring means data you can leverage it.  We mocked up a number of related ideas, starting with the simplest RFID tags.   Consider links with Beacons too.    Lots of interesting thoughts in the article linked to below.

These IoT Sensors Want to Know How You Feel – And Maybe Even Change Your Mood  Posted by William Vorhies  

Summary:  Sensors that know how you feel?  Sensors that want to change the way you feel?  When did that happen and better yet how?  ... 

Mood Science
" ... I’ve been tracking the uses of IoT sensors particularly those with human interaction (think Fitbit) but I didn’t see the big picture until I came across this article “Design for Mood: Twenty Activity-Based Opportunities to Design for Mood Regulation” by Pieter M. A. Desmet, a member of the Faculty of Industrial Design Engineering, Delft University of Technology.  This is one of those articles you know you should trust because it contains a reference bibliography of 169 learned articles.

For the most part it seems that in academic circles the desire to determine how to ‘regulate mood’ is pretty benign and generally couched in terms like improving subjective well-being.  After all who doesn’t want an extra helping of well-being? .... " 

Comparing Three Data Science Concepts

Nice quiz and a demonstration of the imprecision of the terms in use
AI vs Deep Learning vs Machine Learning   Posted by William Vorhies  
Summary:  Which of these terms means the same thing:  AI, Deep Learning, Machine Learning?  Are you sure?  While there’s overlap none of these is a complete subset of the others and none completely explains the others. ... " 

Starting an IOT Project

Nicely done piece by William Vorhies, which points to previous lessons as well.

IoT 101 – Lesson 3: Everything You Need to Know to Start Your IoT Project

Posted by William Vorhies on July 5, 2016  
Summary:  In this Lesson 3 we continue to provide a complete foundation and broad understanding of the technical issues surrounding an IoT or streaming system so that the reader can make intelligent decisions and ask informed questions when planning their IoT system.     ..... " 

Automating Data Science

In the DSC:

Data Scientists Automated and Unemployed by 2025!
Posted by William Vorhies

Summary:  The shortage of data scientists is driving a growing number of developers to fully Automated Predictive Analytic platforms.  Some of these offer true One-Click Data-In-Model-Out capability, playing to Citizen Data Scientists with limited or no data science expertise.  Who are these players and what does it mean for the profession of data science? ... " 

Predictive Analytics in the Supply Chain

Nicely done overview of the topic.  Sounds like an outline of areas I worked in for a long time.   I do have to ask: What has changed since we started work in this area in the 70s?   I saw evidence of similar work being done in the 50s.   So?   The problems areas are the same.  More data, especially in real time, is available now.  Statistical and fundamental math are largely the same.  Computers are now faster and processors can now be used in parallel.  Some new methods exist that allow us to model some problems closer to the way decisions are really made.   Decision makers expect better, real time results.  The last point, increased expectations, always at hand answers, may be the biggest driver of all.   It gets your decision makers tightly involved.

From the article:  Summary:  Predictive analytics are increasingly important to Supply Chain Management making the   process more accurate, reliable, and at reduced cost.  To be at the top of your game as a supply chain   manager you need to understand and utilize advanced predictive analytics. .. "    by William Vorhies   Original post in Data Magnum blog

Decision Scientists Need Game Theory

Good short piece in DSC by  William Vorhies, on the interaction of analytics and game theory.  And more generally how decisions and risk can be linked to analytical methods.   Is this important for data scientists?   If you believe that analytics  will be implemented somehow, yes, absolutely.   Ultimately you are playing an iterated game with your competitors,  the economy,  your environment and even the flow of time.   Often your process game model can be very simple, but it should always be considered.

Google Funding the Automated Statistician

Fundamentally a very old idea.  But much maligned by statisticians.   Our own experiments in this area might be called semi-automated.  Were step by step, with close connections to key needs from business process definitions, and sometimes required check-offs by formally stat trained people.   Some similarities to efforts like Watson Analytics, which focuses to specific business contexts.   Cambridge-Google announcement from last year.  And a related site.   Specific project description. See also:  Automating the Data Scientist by William Vorhies in DSC.

Risk in Modeling Check List

Good short piece on bias and Simpson's Paradox in DSC, by William Vorhies  In particular dealing with real time data.  Relatively non technical, beginners should be able to check off each of these risks before proceeding to process conclusions.   This post describes Simpson's Paradox well, I found it to be rare, but did once discover it in a particularly harmful case.   Again, I repeat, visualize your data first.  Make sure you consider the biases mentioned,  which can often be hidden.   That is a bias in itself.