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Monday, October 28, 2019

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.

Helping Make Decisions
The base technology for this advancement is the already well developed field of recommendation engines.  Thus far these have been the bread and butter of ecommerce whether recommending books, airplane flights, or love interests.  What is coming is the expansion of this tech from predicting things you might like, to actions you might take and then helping you make that decision.

One element of this problem is that we have to add information to make these more sophisticated recommendations.  The close-in sources are our calendars, email, and texts with other sources added as the field develops.  As it happens, calendar and email-aware intelligent assistants are in early research and development, making this a lead candidate for our next break through.

But beyond adding information sources, the challenge is how to integrate this into a useful tool and that requires a deeper understanding of how people make decisions.  For example, your intelligent assistant may be able to predict what your next action could be, but how comfortable will you be if the AI simply says ‘now do this’.  .... " 

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