How quickly is AI advancing? And should you be working in the field? (with Danny Hernandez)
Aug 23, 2023
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Danny Hernandez, an early AI researcher at OpenAI and Anthropic, dives into the rapid advancements in AI technology. He discusses the remarkable growth rates in computing power and data, emphasizing their implications for the future of work. The conversation also touches on why some perceive AI as limited while exploring its automation potential, particularly in fields like radiology. Hernandez encourages more people to get involved in AI, highlighting the importance of aligning personal values with career paths and the decision-making strategies inspired by AI thinking patterns.
AI progress is driven by three exponential trends: Moore's Law, increased spending on AI systems, and algorithmic progress.
As AI models become more capable due to exponential growth and improved algorithms, they also demonstrate better generalization and few-shot learning abilities.
Working on AI is not only about the potential societal and technological impact, but also about finding deep personal meaning in the work.
Deep dives
AI Progress: Exponential Trends and their Implications
AI progress is driven by three exponential trends: Moore's Law, increased spending on AI systems, and algorithmic progress. These trends have led to significant improvements in AI capabilities over the years. The exponential growth in computation power, along with advancements in algorithms, has made AI systems more efficient and capable. The progress in AI is expected to continue for the next decade or more, leading to major changes in various fields. The implications of these trends suggest that AI could be the biggest technological advancement since the internet, with the potential to reshape the world in ways that are unrecognizably different.
Generalization and Few-shot Learning in AI Systems
As AI models become more capable due to exponential growth and improved algorithms, they also demonstrate better generalization and few-shot learning abilities. This means that the models can perform tasks with a smaller number of examples and generalize their understanding to unseen scenarios. This shift from training models on specific tasks to models that can learn with minimal supervision has significant implications for various industries. Few-shot learning capabilities have the potential to revolutionize fields like natural language processing and image recognition, making AI systems more versatile and adaptable.
Meaning and Motivation in Working on AI
Working on AI is not only about the potential societal and technological impact, but also about finding deep personal meaning in the work. The exponential growth and transformative nature of AI present unique opportunities to make a significant difference. Individuals who choose to focus on AI can align their values with the pursuit of building safer and more advanced AI systems. By actively working in this field, people can find fulfillment in contributing to the progress of AI and shaping its future for the better.
Implications of AI advancements in job disruption and power dynamics
The podcast discusses the potential implications of continued AI advancements, suggesting that these exponential trends may lead to significant disruptions in the job market and a shift in power dynamics. It is predicted that certain high-skilled jobs, such as doctors and lawyers, which involve well-defined tasks and abundant training data, could be more susceptible to automation. This could result in job displacement and the need for individuals to reskill. Moreover, the increased ability of AI systems to turn capital into labor may further concentrate power among companies, potentially leading to unprecedented levels of inequality. The podcast also mentions the emergence of a new class of managers who excel at managing AI systems, which may further amplify the divide between those who benefit from AI advancements and those who face replacement.
AI policy, security, and non-technical roles to contribute towards AI developments
The podcast suggests various roles and areas where individuals interested in shaping the future of AI can contribute. These include AI policy, security, and non-technical roles such as operations or management. In terms of AI policy, the focus is on addressing issues related to regulation, liability, and ethics. Security researchers can play a crucial role in identifying vulnerabilities and ensuring the robustness of AI systems. Additionally, individuals without extensive AI expertise, such as those with a background in policy or management, can still contribute effectively to AI development by applying their skills and knowledge in relevant areas. The podcast emphasizes the need for capable individuals to explore opportunities in these fields, highlighting their potential for high impact.
Along what axes and at what rates is the AI industry growing? What algorithmic developments have yielded the greatest efficiency boosts? When, if ever, will we hit the upper limits of the amount of computing power, data, money, etc., we can throw at AI development? Why do some people seemingly become fixated on particular tasks that particular AI models can't perform and draw the conclusion that AIs are still pretty dumb and won't be taking our jobs any time soon? What kinds of tasks are more or less easily automatable? Should more people work on AI? What does it mean to "take ownership" of our friendships? What sorts of thinking patterns employed by AI engineers can be beneficial in other areas of life? How can we make better decisions, especially about large things like careers and relationships?
Danny Hernandez was an early AI researcher at OpenAI and Anthropic. He's best known for measuring macro progress in AI. For example, he helped show that the compute of the largest training runs was growing at 10x per year between 2012 and 2017. He also helped show an algorithmic equivalent of Moore's Law that was faster, and he's done work on scaling laws and mechanistic interpretability of learning from repeated data. He is currently focused on alignment research.
