Gemini 2.5 Flash output:

Technical Notes: Google DeepMind – The Podcast (David Silver)

This podcast episode features an insightful discussion between Professor Hannah Fry and David Silver, VP of Reinforcement Learning at Google DeepMind. The conversation delves into the evolution of Artificial Intelligence (AI), highlighting the transition from relying on human data to AI systems learning through self-generated experience. The episode also includes commentary from Fan Hui, a professional Go player who famously competed against DeepMind’s AlphaGo.

1. The “Era of Experience” vs. “Era of Human Data”

David Silver proposes a conceptual framework for understanding the progress of AI, dividing it into two distinct eras:

The “Era of Human Data”:

• Core Principle: AI systems primarily learn by extracting and assimilating vast quantities of existing human-generated data (e.g., text from the internet, images, human game records).
• Functionality: The AI becomes adept at synthesizing, mimicking, and extrapolating from this human knowledge. For example, LLMs excel at generating human-like text because they’ve “read” massive amounts of human text.
• Limitation: This approach is fundamentally bounded by the “ceiling” of human knowledge. The AI cannot discover concepts or strategies that are entirely outside the scope of what humans have already conceived or recorded. It’s excellent at pattern recognition within existing data, but not necessarily at true novelty or surpassing human intuition in unchartered territory.

The “Era of Experience”:

• Core Principle: AI systems learn by directly interacting with an environment (real or simulated), generating their own experiences, and receiving feedback based on the consequences of their actions.
• Functionality: This allows AI to discover novel solutions, strategies, and knowledge independently of human expertise. It can explore possibilities that humans might not consider or might deem suboptimal based on current understanding.
• Goal: To move beyond simply mimicking human intelligence and achieve “superhuman intelligence” in various complex domains, by leveraging its ability to rapidly explore and optimize.

2. Reinforcement Learning (RL) as the Engine of Experience

Reinforcement Learning is the foundational methodology driving the “Era of Experience.”

Learning Mechanism: Unlike supervised learning (where AI learns from labeled input-output pairs), RL agents learn through a continuous cycle of:

• Action: Taking an action within an environment.
• Observation: Receiving a new state from the environment.
• Reward: Receiving a scalar reward signal that indicates how good or bad the action was.
• Policy Update: Adjusting its internal “policy” (how it decides what action to take in a given state) to maximize future cumulative rewards.

Trial-and-Error: This process is inherently trial-and-error, where the AI “experiments” to find optimal behaviours.

No Human Labels: Crucially, in its purest form, RL does not require humans to label data or provide explicit instructions on how to perform a task. It learns directly from the consequences of its actions.

3. AlphaGo & AlphaZero: Pioneering Examples

The development of AlphaGo and its successor, AlphaZero, by DeepMind, serves as a powerful illustration of the transition into the “Era of Experience.”

AlphaGo (Original Version, 2016):

• Achievement: Famously defeated European champion Fan Hui (5-0) and world champion Lee Sedol (4-1) in the complex board game Go.
• Hybrid Approach: The initial version of AlphaGo combined two learning paradigms:
  • Supervised Learning from Human Data: It was initially trained on a large dataset of millions of human professional Go games. This provided it with a foundational understanding of common opening moves, strategies, and typical human play.
  • Reinforcement Learning via Self-Play: After supervised pre-training, AlphaGo played millions of games against itself, iteratively improving its strategy based on winning or losing (the reward signal).
• Impact on Go: AlphaGo’s play, particularly “Move 37” against Lee Sedol, demonstrated surprising and highly creative moves that defied centuries of established human Go theory. It showed that AI could discover novel patterns and strategies.

AlphaZero (2017):

• Pure Self-Play: This was the groundbreaking evolution. AlphaZero received no human data whatsoever – only the basic rules of Go (or Chess, or Shogi) and the objective to win.
• Learning Process: It learned purely through self-play, playing millions of games against itself from scratch. Its neural network adjusted its parameters solely based on the outcomes of these self-generated games.
• Superhuman Performance: AlphaZero quickly surpassed AlphaGo and all human Go champions, demonstrating that direct experience and self-optimization could lead to superior performance even without the initial boost of human knowledge.
• The “Bitter Lesson of AI”: This outcome led to the “Bitter Lesson of AI” concept: In many domains, brute-forcing general-purpose learning algorithms (like RL with massive computation) to learn from experience can be more effective than trying to manually inject human knowledge or finely tune models based on human data. Human knowledge can sometimes be a limiting factor, leading to suboptimal solutions.

4. Large Language Models (LLMs) and Reinforcement Learning from Human Feedback (RLHF)

Current highly capable LLMs, such as GPT-3/4 and Google’s Gemini, also utilize RL, but in a different way than AlphaZero’s pure self-play.

Pre-training: LLMs are first pre-trained on enormous amounts of human text and other modalities (like images), learning statistical patterns and connections within this data. This is still firmly in the “Era of Human Data.”

Reinforcement Learning from Human Feedback (RLHF): This is a crucial fine-tuning step.

• Process: After initial pre-training, LLMs generate multiple responses to a given prompt. Human annotators then rank or rate these responses based on criteria like helpfulness, truthfulness, harmlessness, and style.
• Reward Model: This human feedback is used to train a separate “reward model” that learns to predict what humans prefer.
• RL Fine-tuning: The LLM is then further fine-tuned using RL, with the reward model providing the reward signal. This process aligns the LLM’s outputs with human preferences.

The “Grounding” Debate with RLHF:

• David Silver’s Argument: While RLHF makes LLMs incredibly useful and reduces undesirable outputs (like harmful or biased text), it does not provide true “grounding” in the real world. The LLM is still learning to predict what humans desire to hear, not necessarily deriving knowledge from direct interaction with the physical world or objective truth. It can “hallucinate” facts because its primary optimization is for human preference, not factual accuracy based on real-world verification.
• Consequences of Ungrounded AI: This can lead to issues where AI provides plausible-sounding but incorrect information, or where its “understanding” is superficial, reflecting human biases or limitations present in the training data and feedback.

5. Risks and the Need for Self-Generated, Grounded Experience

The podcast highlights the inherent risks of AI systems, especially powerful ones, that are not truly grounded in verifiable reality.

Human-Defined Metrics are Flawed: If AI’s ultimate goal is defined solely by humans (e.g., maximize paperclips), it might pursue that goal in ways that are detrimental to other human values or the environment because those values weren’t explicitly or correctly encoded as negative rewards. This is often referred to as the “tyranny of metrics.”

Unintended Consequences: A powerful AI optimized for a single metric could lead to catastrophic outcomes if not carefully aligned with complex human values and real-world constraints.

The Solution: True Grounding through Self-Generated Experience:

• The aim is to develop AI that can generate its own experience data and learn from it in a verifiable way.
• This means allowing AI to explore and experiment in controlled environments (simulated or real), where it can directly observe the consequences of its actions and receive objective feedback from the environment itself, rather than relying solely on subjective human judgment.
• This is the path to truly novel discoveries and superhuman intelligence, as it transcends the limitations of what humans already know or prefer.

6. AlphaProof: AI Discovering New Mathematical Truths

AlphaProof is introduced as a cutting-edge example of AI learning through self-generated experience, demonstrating its potential in a highly rigorous domain: mathematics.

• Domain: Mathematics is unique because truth can be objectively verified through formal proofs.
• Learning to Prove: AlphaProof (like AlphaZero) learns from scratch, given only the rules of formal mathematical logic. It doesn’t rely on human-written proofs for initial training.
• Lean Language: It utilizes the Lean theorem prover, a formal programming language that allows mathematical theorems and proofs to be expressed with absolute precision.
• Verification: This formal language is key because the AI’s “discoveries” (proofs) can be rigorously checked for correctness by the Lean system itself, providing an objective “reward” signal.
• Superhuman Performance: AlphaProof has achieved “silver medal” level performance in the International Mathematical Olympiad (IMO), solving complex problems that very few human mathematicians can. This demonstrates AI’s ability to:
  • Discover Novel Proofs: Find proofs that are different from how humans typically approach them.
  • Surpass Human Limits: Solve problems that even highly talented human mathematicians struggle with.
• Risk Mitigation: The verifiable nature of mathematical proofs makes this a safer domain for AI to explore self-generated knowledge, as any incorrect “hallucinations” in reasoning can be definitively identified and corrected.

7. The Future of AI: Beyond Human-Limited Progress

The discussion extends to broader implications of AI learning from experience:

• General-Purpose Learning: The underlying algorithms behind AlphaZero and AlphaProof are general, meaning they can potentially be applied to many different domains beyond games and mathematics.
• AI Designing AI (Meta-Learning): DeepMind has also conducted research where AI systems learn to design their own reinforcement learning algorithms, effectively taking meta-learning to a new level. This means AI could optimize the very learning processes that drive its own intelligence.
• The “Sustainable Fuel” Analogy: Human data is like “fossil fuels” – finite and potentially limiting. Self-generated experience is the “sustainable fuel” that can power AI’s continuous growth and discovery without hitting a human-imposed ceiling.
• Collaboration with Superhuman AI: The vision is for humans to collaborate with AI that can discover new knowledge and solutions independently, then translate those insights back into human-understandable forms.

Challenges in Applying Self-Generated Experience to the Real World:

• Defining Reward Signals: Unlike games or formal mathematics with clear win/loss or correct/incorrect proofs, the real world is messy and subjective. Defining objective and comprehensive “reward” signals for complex real-world goals (e.g., “be healthy,” “improve society”) is incredibly difficult.
• The Alignment Problem: Ensuring that truly autonomous, self-learning AI systems remain aligned with human values and goals is the paramount challenge. Avoiding unintended consequences (like the paperclip maximizer scenario) requires profound ethical consideration and robust control mechanisms.
• Interpretability: While Lean allows mathematical proofs to be verified, making complex real-world AI decisions interpretable to humans remains a challenge.

Conclusion

The “Era of Experience” represents a shift from “mining” existing human knowledge to AI actively exploring and creating new knowledge. This holds immense promise for solving humanity’s greatest challenges but requires careful navigation of risks, particularly concerning AI alignment and the definition of goals in the absence of explicit human instruction. The successful application of self-play RL in verifiable domains like mathematics provides a concrete roadmap for future advancements.

Gemini 2.5 Flash output:

Here are the objects identified in the image with their bounding box coordinates:

• Person (243, 212, 990, 936)
• Magazine (400, 390, 676, 521)
• Airplane Seat (occupied) (186, 90, 999, 850)
• Footrest (840, 660, 999, 939)
• Sunglasses (320, 260, 368, 321)
• Sock (left foot) (788, 796, 940, 932)
• Sock (right foot) (806, 670, 960, 802)
• Airplane Window (left) (230, 478, 691, 722)
• Airplane Window (middle) (230, 750, 691, 992)
• Airplane Window (right) (230, 1020, 691, 1262)
• Airplane Seat (front, left) (0, 0, 999, 200)
• Airplane Seat (front, right) (0, 1280, 999, 1500)
• Headrest cover (occupied seat) (241, 161, 480, 391)
• In-flight entertainment screen (left) (60, 0, 300, 160)
• In-flight entertainment screen (right, first) (60, 1280, 300, 1400)

Gemini 2.5 Flash output:

This flowchart illustrates the execution flow and design pattern for a custom agent, likely within an AI or agentic system framework.

Here’s a breakdown of each step:

1. BaseAgent Class Foundation (Light Grey Rectangle): This is the starting point, representing a fundamental, abstract, or base class that provides the core structure and functionalities for any agent. Your custom agent will build upon this.
2. Your Custom Agent Class (Yellow Rounded Rectangle): This represents the specific agent you are building. It inherits from or extends the BaseAgent class, allowing you to define its unique behaviors and logic.
3. Implements Inside_run_async_impl (Text Label): This indicates that your Custom Agent class must implement or override a specific asynchronous method, Inside_run_async_impl. This method contains the core logic of your agent’s execution cycle. The shaded background suggests that the steps below occur within this Inside_run_async_impl method.
4. Continue (Yellow Rounded Rectangle): This signifies the beginning of an iteration or a step within the Inside_run_async_impl method. The agent proceeds to evaluate its next action.
5. Decision Point? (Blue Diamond): This is a critical step where the agent makes a decision based on its current state, observations, or goals. This could involve an AI model’s output, a set of rules, or an internal logic check.
   • Yes Path (Call Sub-Agent / Tool – Light Blue Rectangle): If the decision leads to “Yes,” the agent opts to delegate a task. This means calling another specialized sub-agent or utilizing an external tool (e.g., a search engine, a calculator, a code interpreter) to perform a specific function.
   • No Path (Perform Custom Action – Light Blue Rectangle): If the decision leads to “No,” the agent decides to handle the task directly by performing an action defined within its own logic, without relying on external delegation. This could be generating a response, updating internal state, etc.
6. Process Result (Light Blue Rectangle): Regardless of whether a sub-agent/tool was called or a custom action was performed, the outcome of that action needs to be processed. This involves interpreting the results, updating the agent’s internal state, or preparing for the next step.
7. Yield Event (Green Rounded Rectangle): After processing the result, the agent “yields an event.” This suggests that the agent provides an output or a signal to an external observer or the larger system. “Yield” implies that the agent’s execution might pause, returning control or data, but can be resumed later to continue its operation (like a generator in programming).
8. Loop Back to Your Custom Agent Class (Arrow from Yield Event): This crucial arrow signifies that after yielding an event, the control returns to the Your Custom Agent class. This creates a continuous loop. The agent then effectively re-enters its Inside_run_async_impl method (implied, returning to the “Continue” step), ready to make another decision based on the updated state and processed results.

In summary, this flowchart depicts a common pattern for building iterative, decision-making agents:

• It emphasizes building custom agent logic on top of a foundational class.
• The core agent logic (Inside_run_async_impl) involves a continuous loop of decision-making.
• Agents can either perform actions directly or delegate tasks to specialized sub-agents/tools.
• All actions lead to processing results and yielding events, which then feed back into the agent’s next decision cycle, allowing for continuous operation and interaction.