Why AI can’t solve unknown problems

When will we have artificial general intelligence, the kind of AI that can mimic the human mind in all aspect? Experts are divided on the topic, and answers range anywhere between a few decades and never.

But what everyone agrees on is that current AI systems are a far shot from human intelligence. Humans can explore the world, discover unsolved problems, and think about their solutions. Meanwhile, the AI toolbox continues to grow with algorithms that can perform specific tasks but can’t generalize their capabilities beyond their narrow domains. We have programs that can beat world champions at StarCraftbut can’t play a slightly different game at amateur level. We have artificial neural networks that can find signs of breast cancer in mammograms but can’t tell the difference between a cat and a dog. And we have complex language models that can spin thousands of seemingly coherent articles per hour but start to break when you ask them simple logical questions about the world.

In short, each of our AI techniques manages to replicate some aspects of what we know about human intelligence. But putting it all together and filling the gaps remains a major challenge. In his book Algorithms Are Not Enough, data scientist Herbert Roitblat provides an in-depth review of different branches of AI and describes why each of them falls short of the dream of creating general intelligence.

The common shortcoming across all AI algorithms is the need for predefined representations, Roitblat discusses. Once we discover a problem and can represent it in a computable way, we can create AI algorithms that can solve it, often more efficiently than ourselves. It is, however, the undiscovered and unrepresentable problems that continue to elude us.

Throughout the history of artificial intelligence, scientists have regularly invented new ways to leverage advances in computers to solve problems in ingenious ways. The earlier decades of AI focused on symbolic systems.

This branch of AI assumes human thinking is based on the manipulation of symbols, and any system that can compute symbols is intelligent. Symbolic AI requires human developers to meticulously specify the rules, facts, and structures that define the behavior of a computer program. Symbolic systems can perform remarkable feats, such as memorizing information, computing complex mathematical formulas at ultra-fast speeds, and emulating expert decision-making. Popular programming languages and most applications we use every day have their roots in the work that has been done on symbolic AI.

But symbolic AI can only solve problems for which we can provide well-formed, step-by-step solutions. The problem is that most tasks humans and animals perform can’t be represented in clear-cut rules.

“The intellectual tasks, such as chess playing, chemical structure analysis, and calculus are relatively easy to perform with a computer. Much harder are the kinds of activities that even a one-year-old human or a rat could do,” Roitblat writes in Algorithms Are Not Enough.

This is called “Moravec’s paradox,” named after the scientist Hans Moravec, who stated that, in contrast to humans, computers can perform high-level reasoning tasks with very little effort but struggle at simple skills that humans and animals acquire naturally.

“Human brains have evolved mechanisms over millions of years that let us perform basic sensorimotor functions. We catch balls, we recognize faces, we judge distance, all seemingly without effort,” Roitblat writes. “On the other hand, intellectual activities are a very recent development. We can perform these tasks with much effort and often a lot of training, but we should be suspicious if we think that these capacities are what makes intelligence, rather than that intelligence makes those capacities possible.