Patrick Schwab (@schwabpa) 's Twitter Profile
Patrick Schwab

@schwabpa

Senior Director Machine Learning & AI @GSK. Prev: ML @Roche, PhD @ETH. ML for Drug Discovery and Health.

ID: 1610691422

linkhttp://schwabpatrick.com/ calendar_today21-07-2013 14:49:39

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Yarin (@yaringal) 's Twitter Profile Photo

We have a senior postdoc position available with OATML_Oxford (closing 19/05) to lead work on LLM based causal reasoning with GSK. Please share with anyone you think this might be relevant to! my.corehr.com/pls/uoxrecruit…

We have a senior postdoc position available with <a href="/OATML_Oxford/">OATML_Oxford</a> (closing 19/05) to lead work on LLM based causal reasoning with GSK. Please share with anyone you think this might be relevant to!
my.corehr.com/pls/uoxrecruit…
Patrick Schwab (@schwabpa) 's Twitter Profile Photo

whenever you think you are seeing an exponential that, in theory, leads to some unbelievable singularity state, you are most likely at best looking at a sigmoid that does not lead to a singularity. Why? everything rooted in the physical world has physical constraints that make

Patrick Schwab (@schwabpa) 's Twitter Profile Photo

Cautious optimism is the optimum on the optimism-pessimism spectrum - sufficiently optimistic to believe things can change, but cautious enough to not think that just doing anything will be enough to get there.

ICLR Nucleic Acids Workshop (@ai4na_workshop) 's Twitter Profile Photo

🔥 Our workshop will take place in less than a week! In the meantime, check out the full schedule on our website and the strong lineup of invited speakers and panelists 💪 Looking forward to seeing you there!

🔥 Our workshop will take place in less than a week! In the meantime, check out the full schedule on our website and the strong lineup of invited speakers and panelists 💪 Looking forward to seeing you there!
Patrick Schwab (@schwabpa) 's Twitter Profile Photo

It's not wrong that most Bio AI research today misses the mark in improving drug discovery (i.e., it most likely does not lead to a future where we are able to develop substantially more medicines). Mostly, because most of it already starts with the wrong premise of what the

It's not wrong that most Bio AI research today misses the mark in improving drug discovery (i.e., it most likely does not lead to a future where we are able to develop substantially more medicines). Mostly, because most of it already starts with the wrong premise of what the
Patrick Schwab (@schwabpa) 's Twitter Profile Photo

Why is this notion so wide-spread in the community despite being obviously flawed? If I were to guess there are probably two main reasons: 1- false equivalence to other domains: people tend to do what they are familiar with/see done elsewhere without questioning it. Supervised

Patrick Schwab (@schwabpa) 's Twitter Profile Photo

For AI in drug discovery to succeed, it also needs to emancipate itself. Today, the field often recycles ideas and methods from computer vision, natural language and web applications - directly and frequently without further thought as to whether those ideas transfer to the

Patrick Schwab (@schwabpa) 's Twitter Profile Photo

Much of the focus in medical innovation today is on the invention step and the inventors - i.e., on who was first to state the core idea in a permanent medium. In a world where approximately every possible permutation of disease, gene, biomarker, pathways, etc. has already been

Patrick Schwab (@schwabpa) 's Twitter Profile Photo

A function of increased competition for attention - the volume of publications is growing exponentially, but scholars' bandwidth to read remains constant. Researchers may have been able to read the top 1% of recent papers in their field in the 1980s - now its only the top 0.1%.