What AI Can Tell Us About Intelligence
If there is one constant in the field of artificial intelligence it is exaggeration: there is always breathless hype and scornful naysaying. It is helpful to occasionally take stock of where we stand.
The dominant technique in contemporary AI is deep learning (DL) neural networks, massive self-learning algorithms which excel at discerning and utilizing patterns in data. Since their inception, critics have prematurely argued that neural networks had run into an insurmountable wall — and every time, it proved a temporary hurdle. In the 1960s, they could not solve non-linear functions. That changed in the 1980s with backpropagation, but the new wall was how difficult it was to train the systems. The 1990s saw a rise of simplifying programs and standardized architectures which made training more reliable, but the new problem was the lack of training data and computing power.
In 2012, when contemporary graphics cards could be trained on the massive ImageNet dataset, DL went mainstream, handily besting all competitors. But then critics spied a new problem: DL required too much hand-labelled data for training. The last few years have rendered this criticism moot, as self-supervised learning has resulted in incredibly impressive systems, such as GPT-3, which do not require labeled data.
Today’s seemingly insurmountable wall is symbolic reasoning, the capacity to manipulate symbols in the ways familiar from algebra or logic. As we learned as children, solving math problems involves a step-by-step manipulation of symbols according to strict rules (e.g., multiply the furthest right column, carry the extra value to the column to the left, etc.). Gary Marcus, author of “The Algebraic Mind”and co-author (with Ernie Davis) of “Rebooting AI,”recently argued that DL is incapable of further progress because neural networks struggle with this kind of symbol manipulation. By contrast, many DL researchers are convinced that DL is already engaging in symbolic reasoning and will continue to improve at it.
At the heart of this debate are two different visions of the role of symbols in intelligence, both biological and mechanical: one holds that symbolic reasoning must be hard-coded from the outset and the other holds it can be learned through experience, by machines and humans alike. As such, the stakes are not just about the most practical way forward, but also how we should understand human intelligence — and, thus, how we should pursue human-level artificial intelligence.
Symbolic reasoning demands precision: symbols can come in a host of different orders, and the difference between (3-2)-1 and 3-(2-1) is important, so performing the right rules in the right order is essential. Marcus contends this kind of reasoning is at the heart of cognition, essential for providing the underlying grammatical logic to language and the basic operations underlying mathematics. More broadly, he holds this extends into our more basic abilities, where there is an underlying symbolic logic behind causal reasoning and reidentifying the same object over time.
The field of AI got its start by studying this kind of reasoning, typically called Symbolic AI, or “Good Old-Fashioned” AI. But distilling human expertise into a set of rules and facts turns out to be very difficult, time consuming, and expensive. This was called the “knowledge acquisition bottleneck.” While simple to program rules for math or logic, the world itself is remarkably ambiguous, and it proved impossible to write rules governing every pattern or define symbols for vague concepts.
This is precisely where neural networks excel: discovering patterns and embracing ambiguity. Neural networks are a collection of relatively simple equations that learn a function designed to provide the appropriate output for whatever is inputted to the system.
