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Nando de Freitas

@NandoDF

Oct 24

Why does RL lead to causal understanding? 🧵🪡

GPT-4o: Reinforcement learning (RL) can lead to causal understanding because, by interacting with an environment, an agent learns not just correlations between actions and outcomes, but also the underlying cause-effect relationships. This is because RL involves exploration and experimentation, where an agent tests various actions and observes the consequences in the environment. Through trial and error, the agent begins to infer which actions cause which outcomes, helping it to form a model of how its actions impact the world.

Here are some key reasons RL can lead to causal understanding:

1. **Intervention-Based Learning**: In RL, the agent takes actions that serve as interventions in the environment. By observing how these actions (or interventions) change the state of the environment, the agent can identify causal relationships. This is different from supervised learning, which often relies on passive observation rather than active intervention.

2. **Temporal Structure**: RL typically operates in environments where actions are taken over time, with delayed consequences. This temporal structure allows the agent to link actions (causes) to their effects over different time scales, enabling the discovery of causal chains rather than just immediate associations.

3. **Exploration of Counterfactuals**: In RL, the agent may explore different actions in the same or similar situations, allowing it to evaluate counterfactual scenarios (e.g., "what would have happened if I had taken a different action?"). This capacity to explore alternative actions strengthens its understanding of cause-effect dynamics.

4. **Reward Signals as Feedback**: The reward signal in RL serves as feedback, guiding the agent to understand which sequences of actions lead to desirable or undesirable outcomes. Over time, this feedback loop helps the agent distinguish which actions are responsible for certain rewards or penalties.

5. **Model-Based RL and Causal Models**: In model-based RL, the agent builds an internal model of the environment that predicts future states and rewards. These models can often incorporate causal reasoning, where the agent explicitly learns causal dynamics rather than just action-state associations.

Thus, RL agents can develop a deeper understanding of their environment by not only learning what works but also why it works, which reflects a form of causal reasoning.

Do agents need to have a notion of self in order to understand that it’s them taking the actions, as opposed to observing?

GPT-4o: Agents do not necessarily need a fully developed notion of "self" in the human sense to understand that they are the ones taking actions, but they do need a minimal form of self-awareness or agency recognition. This basic self-awareness allows the agent to distinguish between actions it initiates and external events it merely observes. In reinforcement learning, this distinction is implicit in how the agent interacts with its environment, particularly through the following mechanisms:

1. **Action-Effect Feedback Loop**: In RL, an agent takes an action and then observes the resulting change in the environment. The agent recognizes that the action it took is directly responsible for the observed state transition or reward. This feedback loop builds the understanding that the agent’s actions have consequences, which inherently differentiates its own actions from passive observations.

2. **Explicit Reward Signals**: The reward or feedback provided to the agent is tied to the agent’s actions. Even without a sophisticated "self" model, the agent learns through trial and error which actions lead to positive or negative outcomes. The mere fact that the agent’s actions are the ones being rewarded or punished helps the agent distinguish between its active role and passive observation.

3. **Learning from Interventions**: As the agent interacts with the environment, it essentially performs interventions—actions that cause changes in the environment. Through these interventions, the agent implicitly understands that it is the one causing the observed effects. This causal link between action and consequence reinforces the notion that it is the agent driving the changes, not just a passive observer.

4. **Exploration vs. Observation**: Agents that explore and try different strategies recognize that different actions lead to different outcomes. This recognition doesn't require a rich sense of "self" but rather an understanding that certain actions under the agent’s control are responsible for the effects seen in the environment. It forms a rudimentary self-awareness, primarily in the sense of understanding one's role as the source of action.

5. **Embodied Learning (in some cases)**: In more complex or embodied agents (like robots or advanced AI), having a model of one's own body or system within the environment might be necessary for more advanced tasks, such as physical interaction. This could lead to a more explicit sense of self, but this level of sophistication isn't required for basic RL tasks.

Thus, agents in reinforcement learning typically operate with a minimal notion of "self"—enough to distinguish between actions they initiate and the environment’s responses—but they don’t need the rich, introspective self-awareness that humans have. This rudimentary agency recognition is sufficient for the agent to understand the causal link between its actions and environmental changes.

Do chatbots trained via supervised learning have this minimal notion of “self”?

GPT-4o: Chatbots trained via supervised learning typically do not have even a minimal notion of "self" in the way reinforcement learning agents might. Supervised learning, as applied to chatbots, involves training a model to predict the next word, phrase, or sentence based on input-output pairs from large datasets of conversations. This process is fundamentally different from the interactive, trial-and-error-based learning that characterizes reinforcement learning.

Here’s why supervised learning chatbots lack a minimal notion of "self":

1. **No Active Role in Decision-Making**: In supervised learning, the chatbot is trained passively on pre-existing data. It learns patterns and correlations between inputs and responses, but it does not engage in any form of active decision-making or interaction with its environment. There is no distinction between actions it initiates and external events—it simply replicates patterns without agency.

2. **No Action-Effect Feedback Loop**: Supervised learning doesn’t provide the chatbot with feedback based on its actions in real time. Once it is trained, it generates responses based on learned patterns, but it doesn’t receive any signal that informs it about the consequences of those responses in terms of causing changes in the environment or conversation.

3. **Lack of Interactivity or Exploration**: Unlike reinforcement learning agents, which explore different actions to learn from their effects, chatbots trained via supervised learning only replicate learned behaviors. They don’t "explore" different responses and evaluate how these responses affect the conversation in real time. Their training doesn’t involve intervention in the sense of causing effects through deliberate actions.

4. **No Agency Recognition**: Since supervised learning models are trained on static data, they lack the dynamic, interactive component that would foster a sense of agency. They generate responses based on correlations, not because they understand that they are taking actions that cause changes in the conversation.

5. **Prediction, Not Causation**: Supervised learning teaches a chatbot to predict the most likely response given an input, but it doesn’t form an understanding of cause and effect. This is because supervised learning models operate purely on statistical correlations, without any feedback that ties the model's actions (responses) to specific outcomes or consequences.

In summary, supervised learning chatbots do not have a minimal notion of "self" because they operate without a sense of agency or action-driven feedback. They predict responses based on learned data patterns but do not interact with the environment in a way that would develop an understanding of their role in influencing the conversation. They lack the core components of action, feedback, and exploration that lead to even basic self-awareness in other types of learning models like reinforcement learning agents.

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Nando de Freitas

@NandoDF

May 14, 2022

Someone’s opinion article. My opinion: It’s all about scale now! The Game is Over! It’s about making these models bigger, safer, compute efficient, faster at sampling, smarter memory, more modalities, INNOVATIVE DATA, on/offline, … 1/N thenextweb.com/news/deepminds…

Solving these scaling challenges is what will deliver AGI. Research focused on these problems, eg S4 for greater memory, is needed. Philosophy about symbols isn’t. Symbols are tools in the world and big nets have no issue creating them and manipulating them 2/n

@ilyasut

Finally and importantly, @ilyasut is right 🐈

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Nando de Freitas

@NandoDF

Dec 11, 2020

@databoydg

Another long thread. Bear with me till the end please. These are my views, and not necessarily of those associated with me. Though those associated with me have been incredibly supportive. I thank everyone who messaged me. I thank @databoydg for inspiring me to say this next. 1/n

@databoydg

First, @databoydg: I hope this makes justice to what you've tried to teach me. I'm sorry if I'm a slow student. In this second part of the story, I'm a privileged academic having a drink in Montreal after a #neurips conference with @sindero 2/n

Simon says to me: I feel we need to do something about this (stark lack of minority representation in ML). We agree we'll do something about it, but it feels like we're at the bottom of Everest and have to climb it without any gear 3/n

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