To Understand The Future of AI, Study Its Past
A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to “solve” intelligence, with differing research agendas and sometimes bitter relations.
Today, connectionism dominates the world of AI. The emergence of deep learning, which is a quintessentially connectionist technique, has driven the worldwide explosion in AI activity and funding over the past decade. Deep learning’s recent accomplishments have been nothing short of astonishing. Yet as deep learning spreads, its limitations are becoming increasingly evident.
If AI is to reach its full potential going forward, a reconciliation between connectionism and symbolism is essential. Thankfully, in both academic and commercial settings, research efforts that fuse these two traditionally opposed approaches are beginning to emerge. Such synthesis may well represent the future of artificial intelligence.
Symbolic approaches to AI seek to build systems that behave intelligently through the manipulation of symbols that map directly to concepts—for instance, words and numbers. Connectionist approaches, meanwhile, represent information and simulate intelligence via massive networks of interconnected processing units (commonly referred to as neural networks), rather than explicitly with symbols.
In many respects, connectionism and symbolism represent each other’s yin and yang: each approach has core strengths which for the other are important weaknesses. Neural networks develop flexible, bottoms-up intuition based on the data they are fed. Their millions of interconnected “neurons” allow them to be highly sensitive to gradations and ambiguities in input; their plasticity allows them to learn in response to new information.
But because they are not explicitly programmed by humans, neural networks are “black boxes”: it is generally not possible to pinpoint, in terms that are meaningful to humans, why they make the decisions that they do. This lack of explainability is a fundamental impediment to the widespread use of connectionist methods in high-stakes real-world environments.
Symbolic systems do not have this problem. Because these systems operate with high-level symbols to which discrete meanings are attached, their logic and inner workings are human-readable. The tradeoff is that symbolic systems are more static and brittle. Their performance tends to break down when confronted with situations that they have not been explicitly programmed to handle. The real world is complex and heterogeneous, full of fuzzily defined concepts and novel situations. Symbolic AI is ill-suited to grapple with this complexity.
At its inception, the field of artificial intelligence was dominated by symbolism. As a serious academic discipline, artificial intelligence traces its roots to the summer of 1956, when a small group of academics (including future AI icons like Claude Shannon, Marvin Minsky and John McCarthy) organized a two-month research workshop on the topic at Dartmouth College. As is evident in the group’s original research proposal from that summer, these AI pioneers’ conception of intelligence was oriented around symbolic theories and methods.
Throughout the 1960s and into the 1970s, symbolic approaches to AI predominated. Famous early AI projects like Eliza and SHRDLU are illustrative examples. These programs were designed to interact with humans using natural language (within carefully prescribed parameters). For instance, SHRDLU could successfully respond to human queries like: “Is there a large block behind a pyramid?” or “What does the box contain?”
At the same time that symbolic AI research was showing early signs of promise, nascent efforts to explore connectionist paths to AI were shut down in dramatic fashion.
