A Truth Seal via Predictive Markets

A recent inquiry had me looking at the detection and analysis of 'disinformation'.  Much in and of the news.  That led me to work by Rick Hayes-Roth, who was the CEO of an AI company called Teknowledge that I have written about here before (See tag) .  Teknowledge was one of the AI giants in the late 80s. We used their capabilities, even bought a major equity position with them.  Did some great things, but alas, they are no more.

Around 2011 Hayes-Roth and his colleagues came up with an idea called 'Truth Seals', related to the and driven by Predictive Markets, where you could deliver some measure of the validity of information.  Now needed more than ever.    The startup existed until at 2014 and then folded.  But in my research I noted that Rick, now Prof Emeritus at the Naval Postgraduate School, had written a document that did a post mortem, ala After-Action-Review (AAR) that covered what was done.  Very informative.  Also points to some of the intellectual property developed.    Useful for anyone thinking about the topic.    Reviewing.

I also notice that the idea of 'Predictive Markets' is far less talked about recently, any pointers to work still going on there?

Stanford 100 Year AI Study

Just received.  We did a great deal of work both with Stanford and with companies that came out of that program in the late 80s.  We were among the first enterprises that got lasting value from the 'intelligent' systems of the time.  Similar studies were done then to project AI's future. .   Considerable depth in this work, which I have just started to examine.   Thoughts?

Stanford : One Hundred Year Study on Artificial Intelligence (AI100)

Stanford University has invited leading thinkers from several institutions to begin a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.

This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.

Now, together with Russ Altman, a professor of bioengineering and computer science at Stanford, Horvitz has formed a committee that will select a panel to begin a series of periodic studies on how AI will affect automation, national security, psychology, ethics, law, privacy, democracy and other issues.

"Artificial intelligence is one of the most profound undertakings in science, and one that will affect every aspect of human life," said Stanford President John Hennessy, who helped initiate the project. "Given's Stanford’s pioneering role in AI and our interdisciplinary mindset, we feel obliged and qualified to host a conversation about how artificial intelligence will affect our children and our children’s children."  ..... ' 

Jerry Kaplan on the State of AI: Humans Can Still Apply

 Jerry Kaplan talk at Google.  Kaplan was one of the founders of Teknowledge, who we partnered with in the 1980s.  The partnership led , among other efforts, to a coffee blending system that saved many millions of dollars in costs, by specifically emulating human expertise, connecting it to analytical methods and extending the use of that expertise.  Now that we are addressing the challenge again, the book should be worth reading.

" ... Serial entrepreneur, and futurist Jerry Kaplan visited Google’s office in Kirkland, WA on Nov. 4, 2015 to discuss his book: “Humans Need Not Apply A Guide to Wealth and Work in the Age of Artificial Intelligence”. 
... " 

My notes on the talk:  "  ... we worked with him at Teknowledge.  I remember him being quite level headed on the idea of AI at the time and has continued that view ...  its not 'AI', its really much better automation.   Also its really about tasks, not jobs.  So we still need to think of discrete tasks.   Watson is just a very clever search and prioritization method, not intelligence.  This is all good news, makes it likely all this work will be useful for industry in many ways.   Industry people, like ourselves, have to make sure that the systems called AI or machine learning or whatever, need to make sure they deliver.  In the past management was too quickly enamored with the AI thing.  But note also that some very useful things came out of the work done .... that is still very possible.  The marketing people are very willing to take advantage of industry to sell more solutions.   ... "

AI Becomes Cognitive. Is it Now Ready for Sustained Value?

Is AI finally ready to roll?  Again? Spent years in the 90s working on it, with successes but also ultimately descent into a winter of discontent.  Back then there were a small number of well funded startups that drove the efforts. The last of these, Teknowledge, went away just a few years ago. I am currently reviewing some of the work done with them.

Now the players are the likes of IBM, Google, Apple, Facebook, Microsoft and Amazon, which already have embedded AI technologies deep in their businesses. That's a good sign, they are selling practical information technology, not the promise of disembodied intelligence.  Now can they get non technology companies, like Procter & Gamble, to pay attention?  Or do the AI, now cognitive, solutions need to remain deeply buried in the code?  Formulated in an algorithm?

And it is still hard to track how much progress is being made, and what that progress looks like.  CWorld covers some of the old efforts, and the new.  Tries to make sense of it all. I am along for the ride for now will report on it here.

Need Business Analytics Help?

Need help with business analytics?  Big Data visualization, analysis and mining?   Writing, Speaking, Managing.  Strategy and Tactics.  Over thirty years of experience.  Available.

My updated resume.

Use the contact information at link, or click below for text resume.

Feigenbaum Wins IEEE Pioneer Award

Edward Feigenbaum, known as the father of expert systems, Stanford Professor Emeritus, has been awarded the Computer Society Pioneer Award.    We consulted with him on the use of expert systems for some of our earliest knowledge systems in the 1980s, and his Teknowledge startup, resulting in millions of dollars in savings.  " ... he and Nobel laureate biologist Joshua Lederberg started the DENDRAL project, producing the world's first expert system. DENDRAL's groundbreaking accomplishments inspired an evolution of expert systems, moving artificial intelligence out of the laboratory and into countless software applications. It also changed the framework of AI science: the power of an AI program came to be seen as largely in its knowledge base, not in its inference processes. ... "  We studied the Dendral system as an example of expertise systems.    Although somewhat before their time then, they have formed the basis for systems like IBM's Watson.

Where is Artificial General Intelligence?

We expected Artificial General Intelligence (AGI) to follow quickly when we formed an artificial intelligence team and lab in the late 1980's.  Surely once we started to build incremental intelligence into the expert systems of the day they would soon be interconnected into 'general' intelligence that would guide the enterprise forward.  Yet that did not happen. Five years later our AI team was disbanded.  Despite some successes, they were all in focused, limited domains.   We invested in companies  that looked like they would become giants of AI, like Teknowledge.  But these companies also soon sank into limited domains.   Even successes like IBM's Watson are mashups of the ideas we used then.  What happened?  Why have there been no advances for AGI  in decades?

Discussed in Kurzweil AI.   Good thoughts.  One answer:  The hardware has not caught up yet.  Despite all the suggestion that the brain's neural methods would soon be replicable, it has not happened yet.