That’s specifically true in the context of unknown and unpredictable problems.

Ben Dickson, “Why AI can’t solve unknown problems,” Venture Beat, 04-02-2021, https://venturebeat.com/ai/why-ai-cant-solve-unknown-problems/

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 StarCraft but 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 asserts. 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.

Representations in symbolic AI

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.”

So, despite its remarkable reasoning capabilities, symbolic AI is strictly tied to representations provided by humans.

Representations in machine learning

Machine learning provides a different approach to AI. Instead of writing explicit rules, engineers “train” machine learning models through examples. “[Machine learning] systems could not only do what they had been specifically programmed to do but they could extend their capabilities to previously unseen events, at least those within a certain range,” Roitblat writes in Algorithms Are Not Enough.

The most popular form of machine learning is supervised learning, in which a model is trained on a set of input data (e.g., humidity and temperature) and expected outcomes (e.g., probability of rain). The machine learning model uses this information to tune a set of parameters that map the inputs to outputs. When presented with previously unseen input, a well-trained machine learning model can predict the outcome with remarkable accuracy. There’s no need for explicit if-then rules.

But supervised machine learning still builds on representations provided by human intelligence, albeit one that is more loose than symbolic AI. Here’s how Roitblat describes supervised learning: “[M]achine learning involves a representation of the problem it is set to solve as three sets of numbers. One set of numbers represents the inputs that the system receives, one set of numbers represents the outputs that the system produces, and the third set of numbers represents the machine learning model.”

Therefore, while supervised machine learning is not tightly bound to rules like symbolic AI, it still requires strict representations created by human intelligence. Human operators must define a specific problem, curate a training dataset, and label the outcomes before they can create a machine learning model. Only when the problem has been strictly represented in its own way can the model start tuning its parameters.

“The representation is chosen by the designer of the system,” Roitblat writes. “In many ways, the representation is the most crucial part of designing a machine learning system.”

One branch of machine learning that has risen in popularity in the past decade is deep learning, which is often compared to the human brain. At the heart of deep learning is the deep neural network, which stacks layers upon layers of simple computational units to create machine learning models that can perform very complicated tasks such as classifying images or transcribing audio.

[Figure Omitted]

But again, deep learning is largely dependent on architecture and representation. Most deep learning models needs labeled data, and there is no universal neural network architecture that can solve every possible problem. A machine learning engineer must first define the problem they want to solve, curate a large training dataset, and then figure out the deep learning architecture that can solve that problem. During training, the deep learning model will tune millions of parameters to map inputs to outputs. But it still needs machine learning engineers to decide the number and type of layers, learning rate, optimization function, loss function, and other unlearnable aspects of the neural network.

“Like much of machine intelligence, the real genius [of deep learning] comes from how the system is designed, not from any autonomous intelligence of its own. Clever representations, including clever architecture, make clever machine intelligence,” Roitblat writes. “Deep learning networks are often described as learning their own representations, but this is incorrect. The structure of the network determines what representations it can derive from its inputs. How it represents inputs and how it represents the problem-solving process are just as determined for a deep learning network as for any other machine learning system.”