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The Underlying Model for Models

September 16, 2019
Imagine a machine with a hundred dials that you can turn, each of which controls a single parameter. The device is performing "okay," but we know it could perform better if we could find the right ways to turn the dials. Optimization techniques will help to traverse each dial and smartly explore our ample dimensional space to find a more optimal solution. We know the answer is likely not "the perfect solution," but we know it was better than the original initial conditions.

Let's pose a slightly different situation. The machine is performing fine, but the system has changed in some way, and you need to adjust these dials to fit the new task better. We can start the optimization process again, but it's laborious to traverse all those dials. Even more problematic, the system changes continuously, and by the time we learn the new model, we need to start again.

Meta-learning recognizes that there is a relation between all the systems influencing the machine, and therefore the dials are also connected. As we learn each new system, we start to see the connection between dials, and there is a meta-dial that controls and restricts the dials' parameter spaces. This meta-dial finds the right model more efficiently and speeds up learning.

Ideally, faster optimization would not be the only advantage. Our machine learning models typically move through a changing world. If we could find that meta-dial, we could better understand the rules that rule our models.

AI has ventured into the science world:

CASP13 is an initiative to provide an independent evaluation of the current algorithm design in protein structure modeling. Protein structure modeling is a long-studied problem in computational biology. Scalable solutions would improve drug discovery and help us better understand the disease. The teams that enter CASP work in a lab typically that has been studying this area for decades.

Last year, DeepMind participated in CASP for the first time and sort of rocked the community by outperforming. In the case of the protein structures where homologous structures were known, DeepMind's AlphaFold exceeded in the 25 of 43 cases. The second-place finisher won three of the same 43 cases. Several scientists pointed out the AlphaFold algorithm relied on some of the best performing existing methods.

DeepMind team will probably move on to the next problem, but I hope not. I spent several years studying protein sequence-structure-function back in my academic days, and this is only scratching the surface. We often forget these structures are not rocks that never change shapes. Proteins move about, unfold and fold in new ways (usually to bind DNA, RNA, and other proteins). If we had a model that could represent many to most proteins, we could dive in and ask new questions. Possibly, learn that meta-dial that makes everything possible (literally). After spending 20 years in physics and then biology, I can not imagine making the steps in scientific discoveries without AI.

The above was inspired by reading this some time ago.

Written by

Angela Wilkins

I like science, machine learning, start-ups, venture capital, and technology.
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