Demis Hassabis, co-founder and CEO of DeepMind

• Fresh trim Demis 😉

Intro to DeepMind

• DeepMind’s initial aim was a programme to achieve Artificial General Intelligence (AGI)
  • Step 1: solve intelligence.
  • Step 2: solve everything else.
    • “To advance science and benefit humanity”
• Two approaches to build AI
  • Expert systems
    • Limited to what they have already seen
    • Relies on hardcoded knowledge
  • Learning systems
    • Can generalise and learn new tasks
    • Inspired by neuroscience
• Merge deep learning and reinforcement learning
  • Deep reinforcement learning systems can learn new knowledge through a process of trial and error
  • Feedback loop: Observations —> Agent —> Actions —> Environment —>

AlphaGo

• The Game of Go

  • The search space is huge. Number of configurations of the board is more than the number of atoms in the universe
    • All the computers in the world working for 1 million years couldn’t calculate all the possible moves.
  • Impossible to write evaluation functions unlike in Chess
  • Go players rely on intuition rather than calculation “this felt right”

• How do we do it (AlphaGo Zero)? - V1 neural network (NN) plays against itself 100k times. It picks moves at random. This generates a dataset - V2 neural net is trained on V1 - V2 NN plays V1 NN 100 times - Best NN (55% win rate) then plays against itself another 100k times - Cycle repeats

  • Combine the neural network with Monte Carlo search for efficient move choice.

• Beat Lee Sedol (18 time world champion) 4-1.

• Changed the way human beings view the game of Go.

  • Move 37 will go down in history.

• Generalisation

  • AlphaZero generalised AlphaGo to 3 games: Chess, Go and Shogi
    • New style of chess: Mobility over Materiality
    • It will sacrifice pieces to get more mobility for it’s remaining ones; The Immortal Zugzwang Game
    • Human Chess Grandmaster searches 100 moves per decision, Stockfish: 100K, AlphaZero: 10K

Scientific Discovery

• What makes for a suitable problem?

  • Massive combinatorial search space
  • Clear objective function
  • Lots of training data or an accurate simulator to generate data.

• Protein folding problem

  • Can you predict the 3D form of a protein based only on its Amino Acid sequence?
  • Proteins are essential to life. Their 3D structure determines their function
  • It takes one PhD student (4 years) per protein structure.
  • Levinthal’s paradox: 10^300 possible confirmations for an average protein. Yet nature solves this spontaneously.
  • It was a long road to getting fully into protein folding. 90s intro at Uni full start 2016.

• AlphaFold 2

  • AlphaFold 2 achieved atomic accuracy for protein structure prediction in 2020 CASP.
  • Needed 32 component algorithms. 60 pages of supplementary information. Every part was a requirement for AlphaFold 2 based on ablation studies
  • Made the system completely end to end, rather than relying on an intermediate step.
  • Architecture: attention based neural net.
  • Included biological and physical constraints that didn’t impact the learning.
  • Start with domain knowledge. Generality is secondary.
  • 2 weeks to train. Prediction now takes minutes.
  • Predicted 20K proteins encoded by the human genome.

Roadmap

• Prioritise neglected tropical disease for proteins structure prediction.

• Plan to solve 100m proteins over the next year. All proteins currently known to humans

• New era: digital biology

  • Can you derive the laws of motion of a cell? Can you learn them?

• Demis has been shipping. Ticking off childhood dream projects.

• Many products you use will probably have interacted with code from DeepMind.

  • Data centre and grid optimisation.
  • YouTube bit rates

• Ethics:

  • Has been central to their mission from the beginning.
  • We should not move fast and break things.
  • Use the scientific method: deliberation and foresight. Control tests. Update based on empirical data. Aim to get a better understanding.