How can you help neural networks learn from human feedback?

1 Reinforcement learning

One way to help neural networks learn from human feedback is to use reinforcement learning (RL), a type of AI that learns from rewards and penalties. You can design a reward function that specifies the desired outcomes and behaviors of the network, and provide feedback signals that indicate how well the network is doing. For example, you can train a network to play a game by rewarding it for winning and penalizing it for losing. RL can help neural networks learn complex and dynamic tasks, but it can also be tricky to define the right reward function and avoid unintended consequences.

2 Interactive learning

Another way to help neural networks learn from human feedback is to use interactive learning, a type of AI that learns from direct human input. You can interact with the network through natural language, gestures, or other modalities, and provide feedback that helps the network understand your intentions, preferences, or corrections. For example, you can teach a network to recognize objects by pointing at them and naming them, or correct a network's mistakes by providing alternative answers. Interactive learning can help neural networks learn from natural and diverse feedback, but it can also be costly and time-consuming to collect and process human input.

3 Inverse reinforcement learning

A third way to help neural networks learn from human feedback is to use inverse reinforcement learning (IRL), a type of AI that learns from observing human behavior. You can infer the reward function that motivates human actions, and use it to train the network to imitate or optimize human behavior. For example, you can train a network to drive a car by observing how humans drive, or train a network to make ethical decisions by observing how humans judge moral dilemmas. IRL can help neural networks learn from implicit and rich feedback, but it can also be challenging to model human rationality and variability.

4 Active learning

A fourth way to help neural networks learn from human feedback is to use active learning, a type of AI that learns from selective human input. You can let the network decide when and what to ask for human feedback, based on its uncertainty, curiosity, or confidence. For example, you can train a network to classify images by asking for human labels only for the most ambiguous or novel images, or train a network to generate text by asking for human ratings only for the most creative or relevant texts. Active learning can help neural networks learn from efficient and informative feedback, but it can also be difficult to design the right criteria and strategies for requesting feedback.

5 Cooperative learning

A fifth way to help neural networks learn from human feedback is to use cooperative learning, a type of AI that learns from collaborative human input. You can combine the network's abilities and limitations with the human's expertise and intuition, and create a feedback loop that enhances both. For example, you can train a network to diagnose diseases by working with a human doctor, or train a network to create art by working with a human artist. Cooperative learning can help neural networks learn from synergistic and adaptive feedback, but it can also be complex and sensitive to coordinate and communicate with humans.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?