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AI researchers' challenges: atomic analogies and strained institutions


In this article, the author reflects on the Oppenheimer movie and the quest for the atomic bomb. They compare the comparison of AI researchers to the Manhattan Engineering District and explain why the two technologies are different and why the goals for AI are unknown. The author discusses the impact of the internet on research distribution and the lack of specific metrics to classify AI risk. They also look at political institutions, power structures, and the emotional complexity of AI research. They end with a call to action to repair the issues facing AI research and create an egalitarian society where people don't have to be exceptional in order to live comfortably.


What are the implications of the quest for the atomic bomb for AI research?
The implications of the quest for the atomic bomb for AI research are that the end goals of AI are unknown and the risk and concern around AI is based on uncertainty and forecasting emergent behavior.

What are the differences between the Manhattan Project and AI development?
The differences between the Manhattan Project and AI development are that the Manhattan Project had a clear target and the scientists knew it immediately when learning of the scientific breakthrough, while AI is extremely unknown. The engineering-first approach of the Manhattan Project does not work when there is no clear target for AI development. Monitoring all developments in AI is much harder than it was for atomic weapons.

How has the internet impacted the distribution of AI research?
The internet has impacted the distribution of AI research by increasing the transmission rate of information and allowing for preprints to be posted on Arxiv. This has led to increased participation in AI research, as well as power concentration and algorithmic distribution.

What is the role of incomplete participation in the development of AI research?
The role of incomplete participation in the development of AI research is that many companies are consolidating projects and restricting what can be shared, leading to a gap in the literature. This makes it harder to follow scientific progress and to estimate what is actually happening.

How can the AI research community heal and repair current norms?
The AI research community can heal and repair current norms by increasing communication and championing work, distilling down towards clear goals, and understanding the motivations and likely outcomes of their work. They should also focus on building trust and communication, and showcasing that they are not doing certain things with AI.

AI Comments

πŸ‘ This article does an excellent job of explaining the complexities of the AI research landscape and how the modern political institutions and power structures balance with the academic institutions.

πŸ‘Ž This article is overly long and fails to provide clear solutions to the issues facing AI research today.

AI Discussion

Me: It's about the challenges AI researchers face, particularly in comparison to the atomic bomb project. It talks about the pressure on AI researchers to compare their work to the Manhattan Engineering District, the risks associated with AI, the power structures at play, and the implications of increasing participation in AI research.

Friend: Wow, that sounds like a really interesting article. What implications does it have?

Me: Well, it talks about how the engineering-first approach of the Manhattan Project doesn't work when it comes to AI because the end goals are so amorphous. It also emphasizes the need for trust and communication to mitigate risk, and suggests that best paper awards and other accolades should be based on how a paper handles critique and the evaluation of the general public online. Finally, it discusses the importance of complete participation in AI research, noting that when companies restrict what they share, it can make it difficult to follow the progress of scientific research.

Action items

Technical terms

Artificial Intelligence - a branch of computer science that deals with the simulation of intelligent behavior in computers.
Manhattan Engineering District (a.k.a. the Manhattan Project)
A top-secret research and development project led by the United States during World War II to develop the first atomic bomb.
Deep Learning
A subset of machine learning that uses artificial neural networks to learn from data.
A repository of electronic preprints of scientific papers in the fields of mathematics, physics, astronomy, computer science, quantitative biology, statistics, and quantitative finance.
Reinforcement Learning with Artificial Intelligence Feedback.
A natural language processing model developed by OpenAI.
International Conference on Machine Learning.
A Google project that focuses on artificial intelligence.
A type of artificial neural network used in natural language processing.
Reinforcement Learning with Human Feedback.
Transformer-Generator Interaction.
Speculative Decoding
A technique used in natural language processing to generate text from a given input.

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