AI Scientist Created by Sakana AI Aims to Revolutionize Scientific Research and Discovery

Sakana AI has created an 'AI Scientist' that's able to automate scientific research and discovery - SiliconANGLE

Sakana AI has created an 'AI Scientist' that's able to automate scientific research and discovery Tokyo-based artificial intelligence startup Sakana AI today unveiled what it says is the first generative AI model in the world that's designed to conduct scientific research on its own. The model, called the AI Scientist, is the result of a collaboration with scientists from the University of Oxford and the University of British Columbia, and is said to have the potential to automate many of the processes involved in scientific discovery. According to its creators, the AI Scientist is based on large language models that are able to mimic the scientific discovery process, beginning with idea generation, then running experiments, and writing full scientific papers that outline their discoveries. "We propose and run a fully AI-driven system for automated scientific discovery, applied to machine learning research," Sakana AI's team wrote in an academic paper outlining the new model. The study outlines how the AI Scientist is designed to perform research in the area of machine learning specifically, and is capable of suggesting ideas such as new techniques for transformer-based language models, diffusion models and analyzing learning dynamics. Sakana AI claims that the AI Scientist can produce a complete research paper at a cost of just $15, based on the computing resources it uses. Moreover, its papers "exceed the acceptance threshold" of a well known machine learning conference, as judged by an automated review process. "From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI Scientist opens a new era of AI-driven scientific research and accelerated discovery," the researchers wrote. In a blog post, Sakana AI's team said the AI Scientist is able to utilize a broad research direction and a starting codebase, such as the open-source code base of older research on GitHub, and start its own discovery process. It also claimed that the AI model can follow the procedure of AI researchers and conduct literature searches, experiment planning, figure generation, manuscript reviewing, and more. It can also run in an open-ended loop to improve the next generation of ideas, based on the previous generation. The AI Scientist appears to be a significant breakthrough in generative AI, with its ability to conduct research processes entirely autonomously suggesting that Sakana AI's team has achieved a level of creativity and reasoning that's much closer to that of humans. The implications of such a system are significant, as it could potentially pave the way for continuous scientific research, overcoming the limitations of human researchers, who need to take coffee breaks, eat lunch and go home at the end of each day. It could lead to more rapid advances in various fields, such as materials science, drug discovery and climate change mitigation. That said, it appears that a lot of work needs to be done before the day when the AI Scientist can compete on a par with genuine human scientists. For now, the model is restricted to only one specific research area, namely AI software development, and there may also be hardware limitations. In addition, the AI Scientist still needs an existing codebase as a starting point for its research, which calls into question its ability to truly innovate and conceive new ideas. Sakana AI's team also pointed out that the AI Scientist does not have any computer vision capabilities, which limits its ability to fix any visual issues with the papers it produces. It can also be prone to AI hallucinations, which means it can sometimes incorrectly implement ideas or make unfair comparisons to baselines, contaminating its results. In addition, they admitted that the model sometimes makes "critical errors when writing and evaluating results", especially when comparing the magnitude of two numbers. While it's clear the AI Scientist demonstrates an impressive ability to innovate on existing ideas, it remains to be seen if the system will ever be able to propose and outline genuinely original and paradigm-changing ideas. AI systems excel at processing vast amounts of data and identifying patterns within it, but it's not clear if they will ever match the level of human intuition, creativity and judgment that's required to steer scientific inquiry into meaningful and beneficial outcomes.

An AI Marie Curie or Robo-Galileo might make the next big science discovery

AI research is leading to AI that can do research all on its own. That's the dream of Japanese firm Sakana AI, which has shared what they claim is the first "AI Scientist." The embedded generative AI model is designed to conduct scientific research autonomously and could lead to discoveries and innovations faster than would be possible with human researchers alone. Sakana worked with the University of Oxford and the University of British Columbia to design the AI Scientist, publishing a research paper on what they've done so far. The AI Scientist is supposedly capable of emulating the entire scientific discovery process, including everything from coming up with ideas to designing and running experiments and even writing full scientific papers detailing its findings for publishing. Sakana said it would be a bargain, too, as the model can produce a complete research paper for as little as $15. All it takes is a broad subject and access to data to start. Then, the AI can review previous research and look for unanswered questions and ways of producing experiments based on those ideas. Then, it can run in a loop and refine its ideas based on the outcomes of its previous research. For now, the AI Scientist is focused on machine learning, an apropos choice as that's the seed of its own creation. So far, the AI Scientist has come up with previously unexplored techniques for different language models and ideas for analyzing how a model evolves from training. That might lead to better machine learning algorithms and, in a way, the AI Scientist's successors. "The AI Scientist automates the entire research lifecycle, from generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript. Sakana explained in a blog post. "We believe this work signifies the beginning of a new era in scientific discovery: bringing the transformative benefits of AI agents to the entire research process, including that of AI itself. The AI Scientist takes us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems." The AI Scientist could theoretically expand into almost any field of research. Environmental protection, medical research, material science, and more might benefit from the model's efforts. Any such revolution in science is a long way off, though. AI software is about the limit of the AI's expertise, and expanding its capabilities to other scientific disciplines would require a lot more work. Like any other AI model, hallucinations could arise, meaning the research would be incorrect or outright nonsensical and worthless to scientific research. Sakana isn't unique in seeing how AI could someday do its own research. In fact, there may be quite a lot of talk around it soon. The frantic rumors around the pending OpenAI project "Strawberry" suggest the ChatGPT-maker will have its own AI models capable of performing research on its own, too. Admittedly, the idea for Strawberry seems more about the AI doing follow-up research online and thinking ahead to anticipate human questions, but it's easy to see how that could be adapted to scientific research.

Sakana AI creates an 'AI Scientist' to automate scientific research and discovery - SiliconANGLE

Sakana AI creates an 'AI Scientist' to automate scientific research and discovery Tokyo-based artificial intelligence startup Sakana AI today unveiled what it says is the first generative AI model in the world that's designed to conduct scientific research on its own. The model, called the AI Scientist, is the result of a collaboration with scientists from the University of Oxford and the University of British Columbia, and is said to have the potential to automate many of the processes involved in scientific discovery. According to its creators, the AI Scientist is based on large language models that are able to mimic the scientific discovery process, beginning with idea generation, then running experiments, and writing full scientific papers that outline their discoveries. "We propose and run a fully AI-driven system for automated scientific discovery, applied to machine learning research," Sakana AI's team wrote in an academic paper outlining the new model. The study outlines how the AI Scientist is designed to perform research in the area of machine learning specifically, and is capable of suggesting ideas such as new techniques for transformer-based language models, diffusion models and analyzing learning dynamics. Sakana AI claims that the AI Scientist can produce a complete research paper at a cost of just $15, based on the computing resources it uses. Moreover, its papers "exceed the acceptance threshold" of a well known machine learning conference, as judged by an automated review process. "From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI Scientist opens a new era of AI-driven scientific research and accelerated discovery," the researchers wrote. In a blog post, Sakana AI's team said the AI Scientist is able to utilize a broad research direction and a starting codebase, such as the open-source code base of older research on GitHub, and start its own discovery process. It also claimed that the AI model can follow the procedure of AI researchers and conduct literature searches, experiment planning, figure generation, manuscript reviewing, and more. It can also run in an open-ended loop to improve the next generation of ideas, based on the previous generation. The AI Scientist appears to be a significant breakthrough in generative AI, with its ability to conduct research processes entirely autonomously suggesting that Sakana AI's team has achieved a level of creativity and reasoning that's much closer to that of humans. The implications of such a system are significant, as it could potentially pave the way for continuous scientific research, overcoming the limitations of human researchers, who need to take coffee breaks, eat lunch and go home at the end of each day. It could lead to more rapid advances in various fields, such as materials science, drug discovery and climate change mitigation. That said, it appears that a lot of work needs to be done before the day when the AI Scientist can compete on a par with genuine human scientists. For now, the model is restricted to only one specific research area, namely AI software development, and there may also be hardware limitations. In addition, the AI Scientist still needs an existing codebase as a starting point for its research, which calls into question its ability to truly innovate and conceive new ideas. Sakana AI's team also pointed out that the AI Scientist does not have any computer vision capabilities, which limits its ability to fix any visual issues with the papers it produces. It can also be prone to AI hallucinations, which means it can sometimes incorrectly implement ideas or make unfair comparisons to baselines, contaminating its results. In addition, they admitted that the model sometimes makes "critical errors when writing and evaluating results", especially when comparing the magnitude of two numbers. While it's clear the AI Scientist demonstrates an impressive ability to innovate on existing ideas, it remains to be seen if the system will ever be able to propose and outline genuinely original and paradigm-changing ideas. AI systems excel at processing vast amounts of data and identifying patterns within it, but it's not clear if they will ever match the level of human intuition, creativity and judgment that's required to steer scientific inquiry into meaningful and beneficial outcomes.