A schism lies at the heart of the field of . 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.
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Today, connectionism dominates the world of . The emergence of , which is a quintessentially connectionist technique, has driven the worldwide explosion in activity and funding over the past decade. Deep learning’s recent accomplishments have been nothing short of astonishing. Yet as spreads, its limitations are becoming .
If 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 .
Two Divergent Philosophies of : A Brief History
Symbolic approaches to 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. […]
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