The AI Scientist: Towards Fully Automated Open-Ended Scientific
  Discovery
One of the grand challenges of artificial general intelligence is developing
agents capable of conducting scientific research and discovering new knowledge.
While frontier models have already been used as aids to human scientists, e.g.
for brainstorming ideas, writing code, or prediction tasks, they still conduct
only a small part of the scientific process. This paper presents the first
comprehensive framework for fully automatic scientific discovery, enabling
frontier large language models to perform research independently and
communicate their findings. We introduce The AI Scientist, which generates
novel research ideas, writes code, executes experiments, visualizes results,
describes its findings by writing a full scientific paper, and then runs a
simulated review process for evaluation. In principle, this process can be
repeated to iteratively develop ideas in an open-ended fashion, acting like the
human scientific community. We demonstrate its versatility by applying it to
three distinct subfields of machine learning: diffusion modeling,
transformer-based language modeling, and learning dynamics. Each idea is
implemented and developed into a full paper at a cost of less than $15 per
paper. To evaluate the generated papers, we design and validate an automated
reviewer, which we show achieves near-human performance in evaluating paper
scores. The AI Scientist can produce papers that exceed the acceptance
threshold at a top machine learning conference as judged by our automated
reviewer. This approach signifies the beginning of a new era in scientific
discovery in machine learning: bringing the transformative benefits of AI
agents to the entire research process of AI itself, and taking us closer to a
world where endless affordable creativity and innovation can be unleashed on
the world's most challenging problems. Our code is open-sourced at
https://github.com/SakanaAI/AI-Scientist
66bac463046cedd837a748c7
Chris Lu
claimed_verified
2024-08-13T07:38:21.875Z
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Chris Lu
user
chrlu
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Cong Lu
admin_assigned
2024-08-13T07:42:27.651Z
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Cong Lu
user
conglu
66bac463046cedd837a748c9
Robert Tjarko Lange
claimed_verified
2024-10-01T13:10:47.063Z
65e0355ee6e8f7711369ed08
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Robert Lange
user
RobertTLange
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Jakob Foerster
claimed_verified
2024-08-15T10:11:55.520Z
60b4acc69c978cce68723b34
https://cdn-avatars.huggingface.co/v1/production/uploads/60b4acc69c978cce68723b34/eEnAT3CgDcnYKa7PIj5FB.jpeg
Jannes Stubbemann
user
stubbi
66bac463046cedd837a748cb
Jeff Clune
66bac463046cedd837a748cc
David Ha
admin_assigned
2024-08-13T08:29:11.154Z
6335ea47b5a0e02f80538bf3
/avatars/fce4e08ae86339952e35ff73680da7e9.svg
David
user
davidha
The AI Scientist: Towards Fully Automated Open-Ended Scientific
  Discovery
One of the grand challenges of artificial general intelligence is developing
agents capable of conducting scientific research and discovering new knowledge.
While frontier models have already been used as aids to human scientists, e.g.
for brainstorming ideas, writing code, or prediction tasks, they still conduct
only a small part of the scientific process. This paper presents the first
comprehensive framework for fully automatic scientific discovery, enabling
frontier large language models to perform research independently and
communicate their findings. We introduce The AI Scientist, which generates
novel research ideas, writes code, executes experiments, visualizes results,
describes its findings by writing a full scientific paper, and then runs a
simulated review process for evaluation. In principle, this process can be
repeated to iteratively develop ideas in an open-ended fashion, acting like the
human scientific community. We demonstrate its versatility by applying it to
three distinct subfields of machine learning: diffusion modeling,
transformer-based language modeling, and learning dynamics. Each idea is
implemented and developed into a full paper at a cost of less than $15 per
paper. To evaluate the generated papers, we design and validate an automated
reviewer, which we show achieves near-human performance in evaluating paper
scores. The AI Scientist can produce papers that exceed the acceptance
threshold at a top machine learning conference as judged by our automated
reviewer. This approach signifies the beginning of a new era in scientific
discovery in machine learning: bringing the transformative benefits of AI
agents to the entire research process of AI itself, and taking us closer to a
world where endless affordable creativity and innovation can be unleashed on
the world's most challenging problems. Our code is open-sourced at
https://github.com/SakanaAI/AI-Scientist
The AI Scientist is a framework that enables independent scientific research through automatic idea generation, experimentation, and paper writing using large language models.
diffusion modeling transformer-based language modeling learning dynamics automated reviewer