Slashdot Mirror
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.
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!