There’s been a lot of talk on the extendability of current AI this past week and it appears that while we may have a good deal of runway left on the machine learning/deep learning/pattern recognition, there’s something ahead that we don’t understand.
Let’s start with MIT IQ (Intelligence Quest), which is essentially a moon shot project to understand and replicate human intelligence. The Quest is attempting to answer “How does human intelligence work, in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines, to the benefit of society?“.
Where’s HAL?
The problem with AI’s deep learning today is that it’s fine for pattern recognition, but it doesn’t appear to develop any basic understanding of the world beyond recognition.
Some AI scientists concede that there’s more to human/mamalian intelligence than just pattern recognition expertise, while others’ disagree. MIT IQ is trying to determine, what’s beyond pattern recognition.
There’s a great article in Wired about the limits of deep learning, Greedy, Brittle, Opaque and Shallow: the Downsides to Deep Learning. The article says deep learning is greedy because it needs lots of data (training sets) to work, it’s brittle because step one inch beyond what’s it’s been trained to do and it falls down, and it’s opaque because there’s no way to understand how it came to label something the way it did. Deep learning is great for pattern recognition of known patterns but outside of that, there must be more to intelligence.
The limited steps using unsupervised learning don’t show a lot of hope, yet
“Pattern recognition” all the way down…
There’s a case to be made that all mammalian intelligence is based on hierarchies of pattern recognition capabilities.
That is, at a bottom level human intelligence consists of pattern recognition, such as vision, hearing, touch, balance, taste, etc. systems which are just sophisticated pattern recognition algorithms that label what we are hearing as Bethovan’s Ninth Symphony, tasting as grandma’s pasta sauce, and seeing as the Grand Canyon.
Then, at the next level there’s another pattern recognition(-like) system that takes all these labels and somehow recognizes this scene as danger, romance, school, etc.
Then, at the next level, human intelligence just looks up what to do in this scene. Almost as if we have a defined list of action templates that are what we do when we are in danger (fight or flight), in romance (kiss, cuddle or ?), in school (answer, study, view, hide, …), etc. Almost like a simple lookup table with procedural logic behind each entry
One question for this view is how are these action templates defined and how many are there. If, as it seems, there’s almost an infinite number of them, how are they selected (some finer level of granularity in scene labeling – romance but only flirting …).
No, it’s not …
But to other scientists, there appears to be more than just pattern recognition(-like) algorithms and lookup and act algorithms, going on inside our brains.
For example, once I interpret a scene surrounding me as in danger, romance, school, etc., I believe I start to generate possible action lists which I could take in this domain, and then somehow I select the one to do which makes the most sense in this situation or rather gets me closer to my current goal (whatever that is) in this situation.
This is beyond just procedural logic and involves some sort of memory system, action generative system, goal generative/recollection system, weighing of possible action scripts, etc.
And what to make of the brain’s seemingly infinite capability to explain itself…
Baby intelligence
Most babies understand their parents language(s) and learn to crawl within months after birth. But they haven’t listened to thousands of hours of people talking or crawled thousands of miles. And yet, deep learning requires even more learning sets in order to label language properly or learning how to crawl on four appendages. And of course, understanding language and speaking it are two different capabilities. Ditto for crawling and walking.
How does a baby learn to recognize these patterns without TB of data and millions of reinforcements (“Smile for Mommy”, say “Daddy”). And what to make of the, seemingly impossible to contain wanderlust, of any baby given free reign of an area.
These questions are just scratching the surface in what it really means to engineer human intelligence.
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MIT IQ is one attempt to try to answer the question that: assuming we understand how to pattern recognition can be made to work well on today’s computers what else do we need to do to build a more general purpose intelligence.
There are obvious ethical questions on whether we want to engineer a human level of intelligence (see my Existential risks… post). Our main concern is what it does (to humanity) once we achieve it.
But assuming we can somehow contain it for the benefit of humanity, we ought to take another look at just what it entails.
Photo Credits: Tech trends for 2017: more AI …., the Next Silicon Valley website.
HAL from 2001 a Space Odyssey
Design software test labeling…
Exploration in toddlers…, Science Daily website
Read an article in The Atlantic this week (
In any case, better men than I have taken up the checkers challenge over time. And Schaeffer had made it his life’s work to conquer checkers and did it with his program, Chinook.
But even after Tinsley’s death, Schaeffer kept on improving Chinook.
As computing power increased, Chinook’s end game database started earlier in the game with more pieces on the board and his opening database started working towards later into the game, following opening moves farther into the mid game.
