The One Hundred Year Study on Artificial Intelligence (AI100), a longitudinal study that assesses AI’s development and societal impact every five years, released its third report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI. Led by an international Standing Committee of AI experts and administered out of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), AI100 examines AI’s evolution across technical, ethical, economic, and societal domains.
Artificial Intelligence is achieving increasingly impressive results in drug discovery. Yet as these models become more powerful, they also raise a fundamental scientific question: when do they produce reliable scientific insights, and when do they merely generate plausible predictions?
The past decade of AI was largely driven by one question: how to make large language models work at all. How to scale them, stabilize them, and push their capabilities far enough to be usable.
A major success for the b-it: The European Research Council (ERC) is providing millions of euros for projects in the fields of computer science, economics, and evolutionary biology. Prof. Dr. Lucie Flek from Data Science & Language Technologies Group has been awarded with the popular EU funding to continue her research on social parameters in AI.
The past decade of AI was largely driven by one question: how to make large language models work at all. How to scale them, stabilize them, and push their capabilities far enough to be usable.
New study conducted by Prof. Dr. Bajorath and Sanjana Srinivasan at b-it and the Lamarr-Institute at the University of Bonn show the potential of language models in finding new medications. The researchers have created a chemical language model comparable to ChatGPT to predict potential active ingredients with special properties. Following a training phase, the AI was able to exactly reproduce the chemical structures of compounds with known dual-target activity that may be particularly effective medications.
The past decade of AI was largely driven by one question: how to make large language models work at all. How to scale them, stabilize them, and push their capabilities far enough to be usable.
The past decade of AI was largely driven by one question: how to make large language models work at all. How to scale them, stabilize them, and push their capabilities far enough to be usable.
Bias in large language models is a well-known and unsolved problem. In our new paper “Do Multilingual Large Language Models Mitigate Stereotype Bias?” we address this challenge by investigating the influence of multilingual training data on model bias reduction.
A visit to Stanford University, Microsoft and Google – Professor Dr. Lucie Flek, Professor of Data Science & Language Technologies at b-it, accompanied NRW Minister President Hendrik Wüst on a delegation trip to the USA.
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