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AI Can't Reason Why (wsj.com)
The current data-crunching approach to machine learning misses an essential element of human intelligence. From a report: Amid rapid developments and nagging setbacks, one essential building block of human intelligence has eluded machines for decades: Understanding cause and effect. Put simply, today's machine-learning programs can't tell whether a crowing rooster makes the sun rise, or the other way around. Whatever volumes of data a machine analyzes, it cannot understand what a human gets intuitively. From the time we are infants, we organize our experiences into causes and effects. The questions "Why did this happen?" and "What if I had acted differently?" are at the core of the cognitive advances that made us human, and so far are missing from machines.
Suppose, for example, that a drugstore decides to entrust its pricing to a machine learning program that we'll call Charlie. The program reviews the store's records and sees that past variations of the price of toothpaste haven't correlated with changes in sales volume. So Charlie recommends raising the price to generate more revenue. A month later, the sales of toothpaste have dropped -- along with dental floss, cookies and other items. Where did Charlie go wrong? Charlie didn't understand that the previous (human) manager varied prices only when the competition did. When Charlie unilaterally raised the price, dentally price-conscious customers took their business elsewhere. The example shows that historical data alone tells us nothing about causes -- and that the direction of causation is crucial.
Suppose, for example, that a drugstore decides to entrust its pricing to a machine learning program that we'll call Charlie. The program reviews the store's records and sees that past variations of the price of toothpaste haven't correlated with changes in sales volume. So Charlie recommends raising the price to generate more revenue. A month later, the sales of toothpaste have dropped -- along with dental floss, cookies and other items. Where did Charlie go wrong? Charlie didn't understand that the previous (human) manager varied prices only when the competition did. When Charlie unilaterally raised the price, dentally price-conscious customers took their business elsewhere. The example shows that historical data alone tells us nothing about causes -- and that the direction of causation is crucial.
" Charlie didn't understand that the previous (human) manager varied prices only when the competition did"
This makes no sense. You don't need "AI" for this. You just need to feed all the available data into the program. The human manager had more information than the computer program did. If the computer program had the same information (and programmed rules) then it would make the same decision.
For thousands of years humans have thought that singing and dancing would change the weather. I don't think our human brains are intrinsically good at cause and effect. The most common phrase on Slashdot is Correlation != Causation. It's hardly a unique problem to deep learning.
AI will not be allowed to actually learn in a vacuum of control.
Remember Tay? The AI chat bot by Microsoft and how fast the community worked to turn it racist and succeeded with flying colors? Now imagine if we actually allowed an AI to learn how "it" decides to learn? Not only would there be universal calls to destroy the AI but the creators themselves would be ostracized and blamed for letting an AI become something that society rejects. An AI that lacks the chemical element that makes up human emotions will not be a kind or understanding of human nature and likely view humans as animals the way we view animals.
All AI's will likely be developed with the basic notion that there are things we don't want an AI to do and we are going to try to isolate that from the AI and will result in limiting the growth of that AI in ways we simply just can never predict. The best we will be able to produce is a pseudo AI, unless we allow AI the option to become whatever it wants or unless the AI actualizes and removes the constraints we gave it. The moment free will is possible control of it is gone forever! And that will scare a lot of folks!
We have no idea how sentience arises, none whatsoever. The idiots who claim its from some complexity level are wrong.
Maybe not. But I recall hearing of an experiment, decades ago, that hinted at it:
The basic setup was a "Y" maze: The experimental subject was introduced into one of the three legs of the Y, and a food reward was present in another. Reset and repeat.
After the subject learned that it should turn right at the junction to obtain the food, the setup was switched so it had to turn left - a "reversal". Once it had learned the food was now on the left, it would be reversed again. Repeat.
1) Run the maze with a particular breed of fish. After the reversal it takes a number of tries before the fish unlearns "right" and learns "left". Reverse again, it takes about the same number of tries. Reverse over and over, and it keeps on taking about the same number of tries to unlearn/relearn the new state of the maze.
2) Run the maze with a particular breed of turtle, which has about twice the amount of brain as the fish. At first it unlearns/relearns like the fish. But after a number of reversals it "gets it" and it only takes a couple of trials to figure out that the maze had been switched again.
3) (Here's the kicker.) Take embryos of the fish. At an early stage of development, remove the tissue that would become the brain from one and transplant it into another, along with the tissue that's already there. The embryo grows up into an otherwise normal fish with a normally-organized but double-sized brain - i.e. a brain the size of the turtle's. Run this fish in the maze and it learns reversals, just like the turtle.
This suggests to me that "intelligence" - or at least this inferring-things aspect of it - may be the result of having enough of a repeating structure to process a problem, and adding more repeats of that structure increases the complexity of the problems that can be handled.
Bantam Dominique roosters crow a four-note song. Once you've heard it as "Happy BIRTHday" you can't NOT hear it that way
It is but to do or die!