Michael Bronstein
Michael Bronstein (Hebrew: מיכאל ברונשטיין; born 1980) is a British-Israeli computer scientist and entrepreneur. He is a computer science professor at the University of Oxford and scientific director of Aithyra Institute at the Vienna Biocenter in Austria.
Quotes
[edit]- I think there is some broadness to the definition of what counts as a foundation model, and more generally, artificial intelligence.
- François Chollet posits that intelligence is not about how well a specialized model performs, but how good it is in learning new things, or in other words,
- How well it can generalize across tasks.
- In this sense we are probably still far away from general intelligence and this is one of the reasons why I personally don’t like the term artificial intelligence.
- We don’t really understand and agree on what intelligence is.
- In large language models such as ChatGPT, the key to their success was the scale, being able to create very large models that can be trained on huge amounts of data.
- We don’t yet have anything of comparable scale in biology, and probably the main limitation is the amount of data we have.
- It is very expensive to obtain experimental data.
- It will be possible to overcome the lack of experimental data with simulation and it’s an interesting question how to combine simulated data.
- The range and diversity of problems in biology is significantly bigger than in language.
- It could be that in some applications we don’t necessarily need the kind of scale we find in ChatGPT.
- Biotech and pharma companies try to do it and successfully in many cases.
- One example is Recursion that scaled-up cell-painting technologies, allowing to image hundreds of millions of cells and see what happens to the cells. *When you perturb them either chemically or genetically.
- What I would like to do is to take a step back and look at the next generation of data sources where the consumer of the data will not be a human but a machine.
- If we say that the data does not necessarily need to be viewed by human scientists, we can come up with completely new experimental data sources.
- I am honoured and delighted to join Oxford, which has unparalleled expertise in AI and related fields and amazing students.
- I am looking forward to forging new collaborations and synergies within the Department and beyond that would allow us to develop the next generation.
- Learning methods that solve real-world problems and at the same time have the trust of domain experts and the broader public.
External links
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