On Hallucinatory Intelligence

What do you see?

An Architecture

Figure 1: Basic, feed-forward neural architecture
Figure 2: Neural net with information flowing forwards and backwards, with “combiners” (represented as circles) to merge the data coming from the backward and forward layers.

The Future

Figure 3: The six levels of mental processes, as described in Part V of The Emotion Machine by Marvin Minsky.

Contributions

  1. Argued that a form of mild hallucination is at the heart of how human minds work, and that information flows in all directions at once.
  2. Proposed a design pattern for creating neural network architectures that allow information to flow both forward and backward, in which the conclusions of later layers can back-influence what the earlier layers see as their input.
  3. Argued that deep learning is poised for a strong partnership with classical AI if the two are connected with such two-way data-flow. In particular, proposed that deep learning handles low-level perception and learned reactions; classical AI techniques provide deliberation, conceptual reasoning and reflection; and the interface between them allows raw perception to influence high-level reasoning, while also allowing the reverse: high-level reasoning back-influences lower-level reasoning and even raw perception.

Acknowledgments

  • Professor Gerry Sussman, who got me thinking about these problems, and planted many of the core ideas in my head that enabled me to write this essay.
  • Danny Hillis, for reminding me and my peers that in the early days of AI, there was a 6-step plan for building full AI, in which perception and pattern recognition would sit at the first layer — and for pushing us to think about the other 5 steps, now that we finally have the first one more-or-less solved!

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Blake Elias

Blake Elias

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