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Next chess move suggester how to#

They can estimate the age of the layers by looking at the iron-bearing minerals they contain which reflect the state of the earth’s magnetic polarity at the time they were preserved.Īt Ought, we’ve been thinking about scientific literature review as a task where we expect to arrive at correct answers only when it’s based on a good process. Archeologists expect their conclusions about the age of the first stone tools to be more or less correct because they can reason about the age of the sediment layer the tools are in.Programmers expect their algorithms to implement the intended behavior because they reason about what each function and line does and how they go together to bring about the behavior they want.Engineers and astronomers expect the James Webb Space Telescope to work because its deployment follows a well-understood plan, and it is built out of well-understood modules.We trust the result because of this reasoning, not because we’ve observed final results for very similar tasks:

Next chess move suggester how to#

If you’re not optimizing based on how well something works empirically (outcomes), then the main way you can judge it is by looking at whether it’s structurally the right thing to do (process).įor many tasks, we understand what pieces of work we need to do and how to combine them. It’s just a few functions that, if chained together, are useful for predicting reward-maximizing actions. However, because the networks are optimized end-to-end to jointly maximize expected rewards and to be internally consistent, they need not capture interpretable dynamics or state. Superficially, this looks like an architecture with independently meaningful components, including a “world model” (dynamics network).

A prediction network, mapping state to value and distribution over next actions.

A dynamics network, mapping state and action to future state, and.

A representation network, mapping observations to states.

MuZero is a reinforcement learning algorithm that reaches expert-level performance at Go, Chess, and Shogi without human data, domain knowledge, or hard-coded rules. MuZero is an example of a non-trivial outcome-based architecture. In each case, the system is optimized based on how well it’s doing empirically.

Policy gradient optimizes policy neural nets to choose actions that lead to high expected rewards.

Neural architecture search optimizes architectures and hyperparameters to have low validation loss.

SGD optimizes weights in a neural net to reduce its training loss.

Local components are optimized based on an overall feedback signal: Supervision of outcomes is what most people think about when they think about machine learning. In a follow-up post, we’ll explain why we’re building Elicit, the AI research assistant. We only describe our background worldview here. To the extent that there are new ideas, credit primarily goes to Paul Christiano and Jon Uesato. We’re reframing the well-known outer alignment difficulties for traditional deep learning architectures and contrasting them with compositional approaches.

So it’s crucial to push toward process-based training now.

Whether the most powerful ML systems will primarily be process-based or outcome-based is up in the air.

This lock-in applies much more to outcome-based systems.

Both process- and outcome-based evaluation are attractors to varying degrees: Once an architecture is entrenched, it’s hard to move away from it.

In the long term, process-based ML systems help avoid catastrophic outcomes from systems gaming outcome measures and are thus more aligned.

These tasks include long-range forecasting, policy decisions, and theoretical research.

In the short term, process-based ML systems have better differential capabilities: They help us apply ML to tasks where we don’t have access to outcomes.

This post explains why Ought is devoted to process-based systems.

Outcome-based systems are built on end-to-end optimization, with supervision of final results.

Process-based systems are built on human-understandable task decompositions, with direct supervision of reasoning steps.

We can think about machine learning systems on a spectrum from process-based to outcome-based: