Systems Neuroscience and AGI

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Created: March 18, 2016 / Updated: November 2, 2024 / Status: finished / 5 min read (~816 words)
Artificial General Intelligence

This article contains most of the content of the slides of the presentation by Demis Hassabis available at https://www.youtube.com/watch?v=IjG_Fx3D0o0.

  • How can we know/measure we're making progress toward AGI
  • Non-biological approach vs biological approach
  • Issues with the non-biological approach
    • Brittle
    • Time-consuming to train
    • Poor at general learning
    • Difficult to acquire/generate new symbols
    • How do you refer to things outside of the agent? (symbol grounding problem)
  • Biological approach
    • The brain as a blueprint
    • Covers a large class of approaches
  • Different search spaces of possible AGI solutions
    • Regime 1: Small and dense search space
      • Not worth too much relying on the human brain design
    • Regime 2: Large and sparse search space
      • Worth a lot to rely on the human brain design
  • Evidence points to regime 2:
    • Evolution has only produced human level intelligence once
    • Large non-biological projects failed to make progress

  • Cognitive science architectures: SOAR (Laird/Newell), ACT-R (Andersen), OpenCog (Goertzel)
    • Unsatisfactory because they're besed on introspection and when changes in knowledge occurs, they have to modify their model to fit in this new understanding
  • System neuroscience: the brain algorithms
  • Brain emulation: Blue Brain (Markram), SyNAPSE (Modha)
    • Not telling us about the internal processes/functions going inside the brain
    • Relying on very intricate imaging techniques (at what level do we need to stop? Calcium ion channels? Atoms?)

  • Computational: What - the goals of the system
  • Algorithmic: How - the representations and algorithms
  • Implementation: Medium - the physical realisation of the system

  • Revolution in cognitive neuroscience
  • New experimental techniques
  • Sophisticated analysis tools
  • Exponential growth in understanding
  • Actively conduct neuroscience research useful for building AGI

  • Likely that neuroscience will have a big role in building AGI
  • As an orthogonal source of information to Machine Learning
  • Provides direction: inspiration for new algorithms/architectures
  • Validation testing: does an algorithm consistute a viable component of an AGI system?
  • How can it not be a net benefit in the quest for AGI systems to add neuroscience knowledge into the mix?

  • Combine the best of machine learning and neuroscience
  • Where we know how to build a component
    • Use the latest state-of-the-art algorithms
  • Where we don't know how to build a component
    • Continue to push pure machine learning approaches hard
    • In parallel, also look to systems neuroscience for solutions

  • Extract the principles behind an algorithm the brain uses
  • Creatively re-implment that in a computational model
  • Result: a state-of-the-art technique and AGI component

  • Full embodied physical robots: throws up complex engineering problems whilst distracting from the main problem of intelligence
  • Toddler AGI: AI-controlled robot that display qualitatively similar cognitive behaviours to a young human child (~3yo)
  • Massive breadth of capabilities required = extremely hard

  • Core capabilities:
    • Conceptual knowledge acquisition/representation
    • Planning and prediction abilities

  • Knowledge in the brain
    • Symbols
    • Conceptual
    • Perceptual
  • Equivalent machine learning algorithms
    • Logic networks
    • ???
    • DBN, HMAX, HTM
  • So how does the brain acquire conceptual knowledge?

  • Hippocampus sits at the apex of the sensory cortex
  • High-level neocortex: association and prefrontal cortex
  • Stores the memories of recent experiences or episodes
  • Replays those memories during sleep at speeded rates
  • Gives high-level neocortex samples to learn from
  • Memories selected stochastically for replay
  • Rewarded: emotional or salient memories replayed more
  • Circumvents the statistics of the external environment
  • (Hypothesis) Leads to abstraction and semantic knowledge

  • Build knowledge on top of existing knowledge
  • Abstract classification: classification of empty/full containers
  • Discovery of higher-order structures (eg. 123456789101112131...) What is the next number? Statistics is not enough
  • Algorithms that can build sophisticated models of the environment (eg. play any card game just by observing a raw perceptual stream)
  • Transfer learning: learning a response in one perceptual context, abstracting a rule, and applying it correctly in a new context
  • Some impressive things have already happened:
    • MoGo - first program to beat a professional human go player
    • IBM's Watson - taking on human champions at Jeopardy quiz show

  • One approach: measure success across a suite of tasks
  • Ideally we'd like a more integrated measure of progress
  • Algorithmic Intelligence Quotient (AIQ)

  • Systems neuroscience understanding will help inspire to several key components of the overall AGI puzzle
  • System with transfer learning and conceptual knowledge acquisition capabilities will appear in the next 5 years
  • Measurement tools charting progress are improving all the time
  • Once interim milestones have been achieved, we will have a better understanding of of intelligence and the safety issues involved
  • Probably ~20+ years for full human-level AGI but lots of interesting technologies will be built on the way