In his landmark book Thinking, Fast And Slow, Nobel-winning psychologist Daniel Kahneman popularized the concepts of “System 1” thinking and “System 2” thinking.
System 1 thinking is intuitive, fast, effortless and automatic. Examples of System 1 activities include recognizing a friend’s face, reading the words on a passing billboard, or completing the phrase “War And _______”. System 1 requires little conscious processing.
System 2 thinking is slower, more analytical and more deliberative. Humans use System 2 thinking when effortful reasoning is required to solve abstract problems or handle novel situations. Examples of System 2 activities include solving a complex brain teaser or determining the appropriateness of a particular behavior in a social setting.
Though the System 1/System 2 framework was developed to analyze human cognition, it maps remarkably well to the world of artificial intelligence today. In short, today’s cutting-edge AI systems excel at System 1 tasks but struggle mightily with System 2 tasks.
AI leader Andrew Ng summarized this well: “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”
Yoshua Bengio’s 2019 keynote address at NeurIPS explored this exact theme. In his talk, Bengio called on the AI community to pursue new methods to enable AI systems to go beyond System 1 tasks to System 2 capabilities like planning, abstract reasoning, causal understanding, and open-ended generalization.
“We want to have machines that understand the world, that build good world models, that understand cause and effect, and can act in the world to acquire knowledge,” Bengio said.
There are many different ways to frame the AI discipline’s agenda, trajectory and aspirations. But perhaps the most powerful and compact way is this: in order to progress, AI needs to get better at System 2 thinking.
No one yet knows with certainty the best way to move toward System 2 AI. The debate over how to do so has coursed through the field in recent years, often contentiously. It is a debate that evokes basic philosophical questions about the concept of intelligence.
Bengio is convinced that System 2 reasoning can be achieved within the current deep learning paradigm, albeit with further innovations to today’s neural networks.
“Some people think we need to invent something completely new to face these challenges, and maybe go back to classical AI to deal with things like high-level cognition,” Bengio said in his NeurIPS keynote. “[But] there is a path from where we are now, extending the abilities of deep learning, to approach these kinds of high-level questions of cognitive system 2.”
Bengio pointed to attention mechanisms, continuous learning and meta-learning as existing techniques within deep learning that hold particular promise for the pursuit of System 2 AI.
Others, though, believe that the field of AI needs a more fundamental reset.
Professor and entrepreneur Gary Marcus has been a particularly vocal advocate of non-deep-learning approaches to System 2 intelligence. Marcus has called for a hybrid solution that combines neural networks with symbolic methods, which were popular in the earliest years of AI research but have fallen out of favor more recently.
“Deep learning is only part of the larger challenge of building intelligent machines,” Marcus wrote in the New Yorker in 2012, at the dawn of the modern deep learning era. “Such techniques lack ways of representing causal relationships and are likely to face challenges in acquiring abstract ideas….They have no obvious ways of performing logical inferences, and they are also still a long way from integrating abstract knowledge, such as information about what objects are, what they are for, and how they are typically used.”
Marcus co-founded robotics startup Robust.AI to pursue this alternative path toward AI that can reason. Just yesterday, Robust announced its $15 million Series A fundraise.
Computer scientist Judea Pearl is another leading thinker who believes the road to System 2 reasoning lies beyond deep learning. Pearl has for years championed causal inference—the ability to understand cause and effect, not just statistical association—as the key to building truly intelligent machines. As Pearl put it recently: “All the impressive achievements of deep learning amount to just curve fitting.”
Of the six AI areas explored in this article series, this final one is, purposely, the most open-ended and abstract. There are many potential paths to System 2 AI; the road ahead remains shrouded. It is likely to be a circuitous and perplexing journey. But within our lifetimes, it will transform the economy and the world.
