Hub AI research

Artificial Intelligence is already part of everyday life. Since the ImageNet moment in 2012, when Deep Learning proved its great utility in combination with a lot of computing power and gigantic amounts of data, only about ten years have passed – yet there has been more progress than in half a century before.

Still, many questions remain: Will Deep Learning remain the gold standard of AI development? What are the performance limits of large AI systems? Do we need symbolic systems? What technology will lead to artificial general intelligence? Finally, what actually is intelligence, anyway?

We explore these topics and more in our Artificial Intelligence Research Content Hub.

AI research
Update

AI coding can make developers slower even if they feel faster

AI research

AI agents outperform human teams in hacking competitions

AI research

Wait a minute! Researchers say AI's "chains of thought" are not signs of human-like reasoning

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AI research
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Researchers at UC Berkeley and Microsoft Research have developed Gorilla, a large language model that excels at generating accurate API calls. This LLaMA-based model outperforms state-of-the-art LLMs such as GPT-4 by mitigating the problem of hallucination and adapting to document changes at test time. Gorilla is trained on massive datasets from Torch Hub, TensorFlow Hub, and Hugging Face.

Gorilla's code, model, data, and demo are now available on GitHub, with plans to add more domains such as Kubernetes, GCP, AWS, and OpenAPI.

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A study comparing the performance of OpenAI's ChatGPT (GPT-3.5), Google Bard, and Microsoft Bing (Precision mode) in answering 77 physiology case vignettes showed that ChatGPT significantly outperformed the others (ChatGPT 3.19±0.3, Bard 2.91±0.5, Bing Chat 2.15±0.6, on a scale of 0 to 4). Two physiologists independently scored the responses of the LLMs for accuracy.

While the results highlight the potential for incorporating AI systems into medical education, the study acknowledges the need for further research to determine the effectiveness of these models in different medical fields. It's also possible that specific AI models fine-tuned for medical tasks will win the race, such as Google's recently unveiled Med-PaLM M, which also incorporates vision.

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