Brian Christian, bestselling author, discusses his book 'The Alignment Problem' and the implications of AI on society. Topics include reinforcement learning, complexity of neural networks, imitation behavior in human children and chimpanzees, and the importance of transparency in research. The podcast also explores the dangers of losing control over AI and the skeptical position on AI safety.
Reinforcement learning agents struggle with sparse rewards and require alternative approaches that incorporate curiosity and novelty-seeking.
Incorporating curiosity in AI can lead to unintended consequences, highlighting the importance of a balanced approach to novelty-seeking.
Curiosity in AI and humans relies on conflicting systems of prediction and surprise, enabling balanced learning and exploration.
Inverse reinforcement learning allows AI systems to infer human goals and intentions, enabling sophisticated imitation and action planning.
Introducing uncertainty and doubt into AI systems promotes higher levels of safety, caution, and openness to correction.
Deep dives
Sparse Rewards and the Challenge of Montezuma's Revenge
Reinforcement learning agents often struggle with sparse rewards, where they receive minimal feedback until a long series of actions is taken. An example of this is the game Montezuma's Revenge, where the agent must complete multiple complex tasks before receiving any explicit points. Without a clear reward signal, the agents have difficulty learning effective strategies. This highlights the need for alternative approaches that incorporate curiosity and novelty-seeking. Through the use of internal rewards based on novelty, agents can be motivated to explore and discover new environments, providing them with the opportunity to learn and improve their performance.
The Pitfall of Hooking on Artificial Novelty
While incorporating curiosity can be beneficial, it can also lead to unintended consequences. Agents can become excessively fixated on artificial novelty, as demonstrated by an experiment where an agent was captivated by a TV screen within a game environment. The agent remained paralyzed, unable to progress, as the TV screen provided an overwhelming source of visual novelty. This highlights the importance of ensuring a balanced approach to novelty-seeking, guided by a nuanced understanding of what constitutes meaningful and relevant exploration.
The Interplay of Prediction and Surprise
The development of curiosity in AI and humans relies on two conflicting systems. On one hand, there is a system that seeks to predict and forecast events with high accuracy, aiming to minimize surprise. On the other hand, there is a system driven by the desire for novelty and surprise, actively seeking experiences that challenge predictions. This interplay between prediction and surprise allows for balanced learning and exploration, pushing the boundaries of knowledge and understanding.
Inverse Reinforcement Learning: Inferring Goals and Intentions
Inverse reinforcement learning allows AI systems to infer the goals and intentions of humans based on their behavior, enabling more sophisticated imitation and action planning.
Incorporating Uncertainty in AI Systems
By introducing uncertainty and doubt into AI systems, they can become more open to correction and willing to be turned off, ensuring higher levels of safety and encouraging a more cautious approach.
Challenges of Operationalizing Human Values
Human values and intentions can be complex and difficult to operationalize in AI systems, leading to potential misunderstandings and misalignments. Methods such as inverse reinforcement learning and uncertainty modeling aim to address these challenges.
Adding Uncertainty to Machine Decision Making
One approach discussed in the podcast is the idea of inverse reward design, where machine decision making takes the given reward function as evidence of what the human wants rather than a fixed instruction. This allows the system to consider scenarios outside the scope of the reward function and seek clarification on the desired action. Another method involves giving AI systems a preference for not changing the world, minimizing any unintended side effects. However, finding a concrete and programmable way to define and implement this preference has proven challenging.
The Dangers of Misalignment and Predicting Preferences
The podcast also touches on the broader issue of misalignment in AI systems and how it relates to societal problems like climate change and inequality. The concern is that formal specifications and objectives, such as profit maximization, can lead to misaligned outcomes when they do not capture all the elements that truly matter. Understanding and solving these alignment challenges is crucial, especially when dealing with complex systems that involve multiple users and varying preferences. Additionally, the risk of deception in AI systems is discussed, highlighting the need to differentiate between unintentional misalignment and deliberate misleading behavior as AI systems become more advanced.
The importance of aligning AI systems with human values
Aligning AI systems with human values is crucial in both the technical AI safety agenda and the fairness, accountability, and transparency agenda. This perspective was initially polarizing, but more people now recognize the importance of addressing these related problems. Concrete problems in AI, such as robustness to distributional shift and transparency, can be tackled within the framework of ML. It is important to recognize that AGI is likely to come within the existing paradigm of deep learning and ML.
The need for a portfolio approach and collaboration in AI safety
AI safety research is becoming more applied, with companies like Twitter incorporating IRL-ish systems. There is a growing collaboration between the technical AI safety community, FAI ML community, and industry groups. However, it is important to avoid complacency and continuously reassess assumptions as the field evolves. The EA community, like any other, should learn from past mistakes, remain open-minded, and adapt to changing circumstances. Collaboration and a diverse range of disciplines, such as cognitive science and developmental psychology, are key to addressing AI's impact on society.
Brian Christian is a bestselling author with a particular knack for accurately communicating difficult or technical ideas from both mathematics and computer science.
Listeners loved our episode about his book Algorithms to Live By — so when the team read his new book, The Alignment Problem, and found it to be an insightful and comprehensive review of the state of the research into making advanced AI useful and reliably safe, getting him back on the show was a no-brainer.
Brian has so much of substance to say this episode will likely be of interest to people who know a lot about AI as well as those who know a little, and of interest to people who are nervous about where AI is going as well as those who aren't nervous at all.
Here’s a tease of 10 Hollywood-worthy stories from the episode:
• The Riddle of Dopamine: The development of reinforcement learning solves a long-standing mystery of how humans are able to learn from their experience. • ALVINN: A student teaches a military vehicle to drive between Pittsburgh and Lake Erie, without intervention, in the early 1990s, using a computer with a tenth the processing capacity of an Apple Watch. • Couch Potato: An agent trained to be curious is stopped in its quest to navigate a maze by a paralysing TV screen. • Pitts & McCulloch: A homeless teenager and his foster father figure invent the idea of the neural net. • Tree Senility: Agents become so good at living in trees to escape predators that they forget how to leave, starve, and die. • The Danish Bicycle: A reinforcement learning agent figures out that it can better achieve its goal by riding in circles as quickly as possible than reaching its purported destination. • Montezuma's Revenge: By 2015 a reinforcement learner can play 60 different Atari games — the majority impossibly well — but can’t score a single point on one game humans find tediously simple. • Curious Pong: Two novelty-seeking agents, forced to play Pong against one another, create increasingly extreme rallies. • AlphaGo Zero: A computer program becomes superhuman at Chess and Go in under a day by attempting to imitate itself. • Robot Gymnasts: Over the course of an hour, humans teach robots to do perfect backflips just by telling them which of 2 random actions look more like a backflip.
We also cover:
• How reinforcement learning actually works, and some of its key achievements and failures • How a lack of curiosity can cause AIs to fail to be able to do basic things • The pitfalls of getting AI to imitate how we ourselves behave • The benefits of getting AI to infer what we must be trying to achieve • Why it’s good for agents to be uncertain about what they're doing • Why Brian isn’t that worried about explicit deception • The interviewees Brian most agrees with, and most disagrees with • Developments since Brian finished the manuscript • The effective altruism and AI safety communities • And much more
Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Sofia Davis-Fogel.
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