AI, Yai AI: Memory Prediction Framework
September 25, 2014
Believe me, I am not looking for artificial intelligence information. For some reason the information finds me. I suppose smart software with a dunce cap is perpetrating this flood of content marketing objects.
I did read “The Gigaom Interview: Jeff Hawkins on Why His Approach to AI Will Become the Approach to AI.” The headline, I finally figured out, means that Jeff Hawkins has the answer to smart software problems.
In the interview, Mr. Hawkins, who “invented the Palm Pilot,” according to Wikipedia, is running Numenta. He’s a Cornell University graduate. After getting out of the handheld business, he founded a research institute and is now working to commercialize his algorithmic frameworks.
The interview is fascinating because it encapsulates the conceptual disputes within smart software and makes evident how expectations can be pumped up for the benefits of smart software. The challenge, in my view, is to move from figuring out how to make smart software to delivering applications that are smarter than those used to deliver smart bombs and Google ads. There may be some other noble applications of smart software, and that possibility is making artificial intelligence almost as trendy as Big Data.
In the interview, Mr. Hawkins states:
We’re very confident that by the end of the 2020s, we’re going to be settled on a dominant paradigm. It’s going to be quite different than the one we’re currently in today, where specific algorithms that excel at one task dominate. We believe it’s going to be based instead on the universal algorithms that work on many problems. They’re going to be memory-based, not mathematically based. They’re going to be based primarily on time-based patterns, and they’re going to be online learning paradigms.
For me this means that Google’s mathy approach is off track and Mr. Hawkins’ Hierarchical Temporal Memory, Fixed-sparsity Distributed Representations and Cortical Learning Algorithm are the solutions. Like Johnny Cash’s sideman said when asked why he kept using the same chords over and over: “Other are huntin’. I done found it.”
Mr. Hawkins revealed one component of his approach:
Now, to do vision correctly, you need to do sensory motor inference. We understand that and we’re in the process of building it out now. That’s been a major research effort for us, starting in January. We think we can get to a vision system that is cortical like. It will work the same way the brain does. I have faith that it will be better than other approaches, but I can’t prove that yet. I do have a path to get there. We’re currently working on that, but I can’t sit here today and say how our vision system performs compared to Google’s system or something like that.
Along with former Xoogler Ng (who now works at Baidu), Mr. Hawkins suggests that the Googlers may not be on the same information highway as Numenta.
Mr. Hawkins is firm in his conclusion that his approach is correct. He does a bit of Socratic analysis:
A year from now would we change something? I don’t know. Possibly. But I haven’t felt this good about Numenta for a long time. One, from the science point of view, the technology point of view, we’re making great progress. We’ve made big progress just the beginning of this year on the sensory motor stuff. We’ve proven the other technology works well. We’ve also had independent validation that the technology is valuable. We have people who have said, “Yup, this is really cool. We want to buy this. It’s worth a lot to us.”
I did not one factoid in the write up. Here it is:
Our approach is to stay small as long as possible because that gives us flexibility. That gives us the ability to change this. As soon as you start becoming big, then you’ve chosen a path. Then you become obsolete in a few years.
Quite true. As Johnny Cash sang:
That old highway’s calling me and free I gotta be
But I don’t know where I’m bound
Mr. Cash seems to have anticipated the direction of artificial intelligence.
Stephen E Arnold, September 25, 2014
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