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Handol Kim of Variational AI On The Future Of Artificial Intelligence

Recognition of the strategic importance of AI. The importance of AI is being elevated to a national level where capability is seen as a nationally-strategic priority. While that means more government funding for research, it also means that some nation-states are closing themselves off to the free flow of research and circling their wagons. This doesn’t do anyone any good. With the increasing stratification of nation-states along ideological/geopolitical lines, I don’t see this getting any better in the near term and that is very concerning.

  1. Growing open-ness to AI as a potentially valid drug discovery modality within the biopharma industry. When we started Variational AI almost three years ago, we were met with way many more eyerolls than nods of agreement when we met with biotech and pharma folks. AI was widely seen as just another fad that promised to revolutionize drug discovery/development but was doomed to be crushed under the weight of its own hype, becoming just another tool in the drug hunter’s toolkit. But over the last six months or so, we’ve been seeing far more open-ness to AI, and it almost seems that if you’re launching a new biotech start-up now you need to have a pronounced AI/ML or data science angle. It reminds of me of where software/tech was a few years ago, where if you didn’t have an AI angle it was hard to get funded.
  2. Increasingly powerful compute. That last NVIDIA architecture jump from Turing to Ampere really accelerated our training times. We’re also seeing some new AI/deep learning optimized processors that hold some great promise. Von Neumann architectures might be going extinct (for AI at least) but thank God Moore’s Law still holds!
  3. Recognition of the value of good (training) data. The proven impact of AI across industries has driven the valorization of quality data. Data is increasingly seen not simply as a by-product of business processes to be locked away and held until an organization has figured out how to use it, but as a strategic asset that when unlocked could deliver multiples of value
  4. Recognition of the strategic importance of AI. There is a recognition that research and implementation of advanced AI is of strategic importance and a hyperplane that separates the winners from the losers. This recognition is almost dogma in digital industries, where we’re seeing the operationalization of AI at scale, but we’re also seeing the growing realization in traditional non-digital, industries where the product is a physical good (atoms not bits).
  1. Growing open-ness to AI as a potentially valid drug discovery modality within the biopharma industry. This is related to my first concern. There are a large number of AI for drug discovery companies all jostling for competitive advantage, but the vast majority of them don’t actually have true AI/ML researchers/scientists. Specifically, their AI efforts are led by computational chemists or bioinformaticians who use machine learning as a technique. They are domain experts who have learned AI post-hoc. There is nothing wrong with this and certainly machine learning is a valid technique to improve what a computational chemist or a biophysicist does, but it isn’t machine learning as a science.



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Authority Magazine

In-depth interviews with authorities in Business, Pop Culture, Wellness, Social Impact, and Tech