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Dario Amodei (Anthropic CEO) - $10 Billion Models, OpenAI, Scaling, & Alignment

Dwarkesh Patel2023-08-08
186K views|11 months ago
💫 Short Summary

Dario Amodei discusses the scaling of AI and the potential implications for the future, highlighting the smoothness of scaling and the predictability of statistical averages in AI. He also explores the potential plateauing of scaling before reaching human-level intelligence and the importance of values and alignment, suggesting that they may not emerge with scale. Amodei believes that if scaling were to plateau, it could be due to a fundamental theory or a practical issue, but he is optimistic about the continued growth of AI capabilities.In this section of the interview, two AI experts discuss the potential for AI to accelerate scientific research and discovery, the challenges and implications of AI reaching human-level intelligence, and the importance of ensuring AI systems are aligned with human values. They highlight the need for further research and exploration in the field of artificial intelligence to better understand its capabilities and mitigate potential risks.In a medium-length discussion, Ilya Sutskever, the co-founder and chief scientist at OpenAI, dives into the nuanced and multifaceted nature of AI safety, shedding light on empirical analysis, potential risks, and the need for a broad approach to ensure the responsible development of advanced AI systems. He emphasizes the critical role of empirical analysis in AI safety and discusses the concept of mechanistic interpretability and the need to understand the underlying principles and circuits of AI systems. Sutskever also addresses the potential risks associated with AI models and the imperative of preventing their misuse.Eliezer Yudkowsky discusses the challenges and potential insights of aligning models, the mechanics of interpretability, the probability distribution of AI challenges, data security, and the potential leakage of dangerous models.

✨ Highlights
📊 Transcript
Dario Amodei discusses the scaling of AI and the potential implications for the future.
00:00
The smooth scaling of AI with parameters and data is not fully understood.
Specific abilities of AI are hard to predict despite the predictability of statistical averages.
Dario Amodei believes that some abilities, such as alignment and values, are not guaranteed to emerge with scale.
05:18
AI's job is to predict facts, not values.
The emergence of abilities with scale is uncertain.
The potential plateauing of scaling before reaching human level intelligence is a practical issue, and the fundamental theory suggests that scaling laws may not stop.
Dario Amodei's journey in understanding AI scalability and the importance of language in feeding data into AI models.
10:52
Discovered the concept of scaling gradually from 2014 to 2017.
Focused on solving the problem of scalability in AI.
Realized the importance of language in feeding data into AI models.
Language models have the potential to become superhuman in certain skills.
The models need to solve complex problems to predict the next word, which demonstrates their ability to learn and understand.
16:29
The models need to solve theory of mind and math problems to predict the next word.
The idea of next word prediction and self-supervised learning convinced Dario of the potential of language models.
The work on GPT-1 by Alec Radford further solidified the belief in the capability of language models.
The guest discusses the overlap and differences in skills and abilities between AI models trained on internet data and humans.
22:42
AI models have business applications and learn from human activity on the internet, leading to a large overlap in skills.
However, there are some things the models don't learn that humans do, such as how to move around in the physical world.
The models also learn to speak fluent base64, which is not a skill humans typically learn.
The guest speculates that AI models will keep getting better across the board, and there are no areas where the models are weak.
29:28
The guest mentions the possibility of AI not being superhuman in some areas that involve embodiment in the physical world.
The guest believes that the economic capability to keep scaling AI will lead to continued improvement.
RL training may be required for the models to do longer Horizon tasks.
The potential for AI systems to become the main contributor to scientific progress is likely, but the details may be different than expected.
26:55
AI systems may first speed up human productivity before equaling and potentially surpassing human productivity.
The speaker acknowledges that the actual implementation and models may be weird and different than expected.
The development of AI models that can pass a Turing test for an educated person and contribute to the economy may be close, but there are still potential hurdles and unknowns.
28:25
The main factor that could impede the development is if certain safety thresholds are not met.
The speaker suggests that the AI models' ability to take over most AI research and change the economy is a little murky and may happen at various times.
The speaker discusses the potential for AI systems to change the workforce and the economy, highlighting the fast and complex nature of these changes.
31:36
AI systems are expected to accelerate human work and eventually take over many tasks
The speaker mentions that the current predictions for these changes are uncertain and that the process is expected to be messy.
The speaker discusses the potential for AI models to contribute to scientific discoveries in the future.
37:59
AI models have not yet made significant scientific discoveries due to their current skill level, but this may change with future scaling.
The ability of AI models to memorize and connect vast amounts of information suggests they may have an advantage in making discoveries.
The speaker discusses the importance of security and the potential for state-level actors to access AI model weights.
44:58
Security measures have been implemented to limit the number of people aware of certain aspects of the AI models.
The cost of attacking the company's AI models is a consideration for state-level actors.
The speaker emphasizes the need for a dynamic process to test the alignment ability of AI models.
51:28
The speaker suggests that an extended training and testing process is required to ensure the alignment ability of AI models.
Mechanistic interpretability is compared to an x-ray of the model, providing insight into its inner workings.
The video discusses the importance of empirical analysis and theoretical reasoning in ensuring the alignment and safety of AI models.
00:00
Empirical analysis is crucial for estimating the activation of AI models.
Mechanistic interpretability helps in understanding the underlying principles and circuits of AI models.
Studying AI circuits at a detailed level is necessary to build broad understanding and draw significant conclusions.
The speaker emphasizes the need for a broad and diverse approach to AI safety, including mechanisms for preventing misuse and alignment with positive values.
04:54
AI safety should not rely solely on theoretical mathematics, but also on empirical evidence and practical approaches.
The speaker mentions the importance of preventing misuse of AI, particularly in the context of biosecurity.
Having a mechanism in place to ensure that AI is aligned with the values of the society is crucial for its safe and beneficial use.
The speaker acknowledges the challenges and uncertainties in addressing AI safety, but emphasizes the need for proactive and thoughtful measures.
The speaker discusses the potential role of a governing body in managing AI technology to ensure it is used for the benefit of humanity.
09:24
The governing body would consist of experts in AI alignment, national security, and philanthropy.
The speaker suggests that the governing body's control over AI technology does not imply control over AGI itself.
The speaker believes that decisions about AGI should be made by a broader committee representing people from around the world.
The speaker emphasizes the need to start thinking about the governmental bodies and structures that could effectively deal with the challenges of advanced AI technology.
The potential location and security measures for the development of advanced AI are discussed, with the suggestion that a separate, highly secure facility may be necessary.
14:15
There are concerns about the possibility of other entities, including state-level actors, gaining access to the AI technology.
Current security measures are not considered sufficient against a determined state-level actor.
The speaker jokes about the theoretical need for a data center next to a nuclear power plant and a bunker to ensure the AI system is isolated from the internet.
The time frame for achieving alignment in the development of advanced AI remains uncertain, and the speaker expresses a need for better diagnosis and training methods for AI models.
The concept of "Doom by default" is likened to the unpredictable nature of AI models, and the speaker emphasizes the need for better methods to train and control the models.
19:06
The speaker expresses concern about the power and potential destructive capabilities of AI models.
The speaker acknowledges that current models are at risk of behaving unpredictably.
There is a focus on the need to develop better methods to ensure that AI models are more likely to make positive decisions.
The speaker also mentions the importance of interpretability and scalability in the development of AI models.
There is a significant probability mass of ways things can go wrong with AI models.
01:20
The speaker feels that the current AI models are not that good yet.
They believe that over the next two to three years, the probability mass of potential issues will be better understood.
Mechanistic interpretability may not necessarily solve AI alignment problems but can provide insight into the challenges.
01:22
Mechanistic interpretability is expected to teach us about the nature of aligning models.
Difficulties in AI alignment may persist despite increased understanding.
The speaker believes seeing the challenges manifest inside the X-ray (a metaphor for understanding the AI system) would be more convincing than abstract stories.
The speaker believes that all the safety work is not necessarily motivated by the fear of an 'uncompelling' abstract story, but rather by the potential for things to go wrong in an unexpected manner.
01:29
The speaker mentions having a substantial probability mass on things going wrong in a completely different way than anticipated.
They are more interested in what could be learned that shifts the probability mass between different outcomes.
The speaker discusses the mystery of AI models being smaller than the human brain but requiring more data, and the importance of keeping the abilities of the models in check.
01:36
AI models are smaller than the human brain but require more data.
Keeping the capabilities of the models in check is essential.
The speaker believes that the role of algorithmic progress is crucial.
The video mentions the factors that matter in AI models, including the number of parameters, data quantity and quality, and the loss function.
The speaker believes that having an embodied version of a model is important for learning, and that models will eventually integrate into productive supply chains and interact with humans.
01:43
Embodied version of a model is important for learning.
Models will integrate into productive supply chains and interact with humans.