From Narrow to General AI

and from External to Internal Intelligence

Peter Voss
Oct 3, 2017 · 8 min read
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A Brief History

The original vision of AI some 60 years ago was to build machines that can think, learn, and reason like humans. Initial optimism of achieving this in just a few years was grossly misplaced, and indeed continued to haunt AI for decades. As researchers failed to get anywhere near the flexibility and general cognitive ability of humans they turned their focus to solving very specific, narrow problems of ‘intelligence’ — And to this day ‘AI’ is practiced almost entirely this way. In fact, a good number of current AI researchers and developers typically aren’t even aware of the original meaning of AI!

Limitations of Narrow AI

The core problem of current AIs is not so much that they are narrow — specialization can be very helpful — but that they are inherently narrow (narrow by design), and that they are rigid, fixed. Both traditionally programmed as well as the newer, ‘trained’ AIs suffer the same basic limitation: whatever capabilities they have, are pretty much frozen in time.

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So, what is Intelligence?

Intelligence, in general, is the cognitive ability to understand the world; to help achieve a wide variety of goals; and to integrate new knowledge and skills in ongoing learning. It must operate in real time, in the real world, and with limited knowledge and time.

  1. To truly understand language, have meaningful conversation, and be able to reason contextually, logically and abstractly. Moreover, it must be able to explain its conclusions!
  2. To remember recent events and interactions (short-term memory), and to understand the context and purpose of actions, including those of other actors (theory of mind).
  3. To proactively use existing knowledge and skills to accelerate learning (transfer learning).
  4. To generalize existing knowledge by forming abstractions and ontologies (knowledge hierarchies).
  5. To dynamically manage multiple, potentially conflicting goals and priorities, and to select the appropriate input stimuli and to focus on relevant tasks (focus and selection).
  6. To recognize and appropriately respond to human emotions (have EQ, emotional intelligence), as well as to take its own cognitive states — such as surprise, uncertainty or confusion — into account (introspection).
  7. Crucially, to be able to do all of the above with limited knowledge, computational power, and time. For example, when confronted with a new situation in the real world, one cannot afford to wait to re-train a massive neural network over several days on a specialized supercomputer.

How do we get to Real AI?

Achieving truly intelligent, general AI will require both engineering solutions as well as the right commercial dynamic.

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  • Having a large baseline of common knowledge and skills that can be applied to multiple domains
  • The ability to learn and integrate new knowledge and skills instantaneously
  • Easily, and almost instantaneously share knowledge and skills with other AGIs (as appropriate)
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Intuition Machine

Deep Learning Patterns, Methodology and Strategy

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