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To Understand The Future of AI, Study Its Past

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.

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.

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.

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.

The book set forth mathematical proofs that seemed to establish that neural networks were not capable of executing certain basic mathematical functions.

Symbolic AI reached its mainstream zenith in the early 1980s with the proliferation of what were called “expert systems”: computer programs that, using extensive “if-then” logic, sought to codify the knowledge and decision-making of human experts in particular domains.

These systems generated tremendous expectations and hype: startups like Teknowledge and Intellicorp raised millions and Fortune 500 companies invested billions in attempts to commercialize the technology.

Amid the ashes of the discredited symbolic AI paradigm, a revival of connectionist methods began to take shape in the late 1980s—a revival that has reached full bloom in the present day.

In 1986 Geoffrey Hinton published a landmark paper introducing backpropagation, a new method for training neural networks that has become the foundation for modern deep learning.

In just the past decade, a confluence of technology developments—exponentially increased computing capabilities, larger data sets, and new types of microprocessors—have supercharged these connectionist methods first devised in the 1980s.

Recognizing the promise of a hybrid approach, AI researchers around the world have begun to pursue research efforts that represent a reconciliation of connectionist and symbolic methods.

XAI is providing funding to 13 research teams across the country to develop new AI methods that are more interpretable than traditional neural networks.

few years ago, it was not uncommon for AV researchers to speak of pursuing a purely connectionist approach to vehicle autonomy: developing an 'end-to-end' neural network that would take raw sensor data as input and generate vehicle controls as output, with everything in between left to the opaque workings of the model.

Together, the approach goes beyond what current deep learning systems can do.” Taking a step back, we would do well to remember that the human mind, that original source of intelligence that has inspired the entire AI enterprise, is at once deeply connectionist and deeply symbolic.

As philosopher Charles Sanders Peirce put it, “We think only in signs.” Any conception of human intelligence that lacked either a robust connectionist or a robust symbolic dimension would be woefully incomplete.

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