November AI — Breakthrough in Biology after 50 years
By: Harshit Tyagi
Tags: Biology, Apple, Machine Learning, Artificial Intelligence, Programming

So, November is over and I’ve come across a good list of blogs, research papers, books, and datasets that are worth deep-diving into.

This is the second part of the AI Monthly webcast, you can find the first one here.

Here’s what we are going to cover in the November AI updates:

Firstly, we’ll look at 2 interesting pieces of news that you’d have heard:

The first one comes from DeepMind. The latest version of their AI system AlphaFold has been recognized as a solution to figuring out the shape of protein structure in 3D. The second is for TensorFlow users of Intel Macs or Macs powered by Apple’s new M1 chip can now take advantage of accelerated training using Apple’s Mac-optimized version of TensorFlow 2.4 and the new ML Compute framework.

Next, we are going to go over two interesting datasets :

The first one is the Objectron Dataset by Google Research — It is a collection of short, object-centric video clips capturing a larger set of common objects from different angles. MedMNIST dataset which is a collection of 10 pre-processed medical image datasets with Creative Commons (CC) Licenses.

The second half of the blog will focus on learning and improving scientific/analytic practices. I’ve shared what books, courses, and research papers I am following these days to be less wrong about data science and AI with each day passing.

AlphaFold 2 — Solution to the protein folding problem

On November 30, DeepMind published a blog titled AlphaFold: a solution to a 50-year-old grand challenge in biology. They have solved the long-standing problem of protein folding with their latest version of the AI system called AlphaFold 2.

Proteins are indispensable to life, supporting everything we do. They are large complex chains of amino acids that help us carry molecules and decode the DNA.

Protein folding problem

This functionality of a protein largely depends on its unique 3D structure. A part of biological research has been centered around predicting the shape of the protein over the past 50 years.

Now, figuring out what shapes proteins fold into is known as the “protein folding problem”.

The solution requires accuracy of above 87 GDT on CASP competition.

It has been a well-defined grand challenge and the Protein Structure prediction center has been organizing a biennial competition called CASP(Critical Assessment of protein Structure Prediction) where they invite teams to submit their solutions.

A bit of History

The Alphafold team first entered CASP13 in 2018 with an initial version of AlphaFold, which achieved the highest accuracy among participants. They wrote a paper explaining their work and inspired the community to further make open-source contributions.

And this year they went with new deep learning architectures that brought about changes in their methods for CASP14, enabling them to achieve incredible levels of accuracy.

The AlphaFold system achieved a median score of 92.4 GDT overall across all targets. GDT stands for Global Distance Test which ranges from 0–100. DeepMind tried to explain it as follows:

GDT can be approximately thought of as the percentage of amino acid residues (beads in the protein chain) within a threshold distance from the correct position.

The team has researchers, scientists, engineers from fields of biology, physics, and machine learning, as well as of course the work of many scientists in the protein folding field over the past half-century.

Speculating the improved architecture of AlphaFold 2

What changed in AlphaFold that led to such high scores will only be clear after the research paper is published but we can speculate.

There are speculations that AlphaFold 2 uses transformers instead of CNNs and they are now using evolutionary sequences + multiple sequence alignment as part of the iterative learning process.

How big is this achievement?

While testing the AlphaFold 2 during experiments, the team has observed this structure prediction of a protein could be useful in future pandemic response efforts.

Earlier this year, they predicted several protein structures of the SARS-CoV-2 virus, including ORF3a, whose structures were previously unknown.

At CASP14, they predicted the structure of another coronavirus protein, ORF8. Experimentalists have now confirmed the structures of both ORF3a and ORF8. And they have achieved a high degree of accuracy on both of the predictions when compared to their experimentally determined structures.

I’d be publishing a more detailed blog on this breakthrough soon.

Apple’s Accelerated tensorflow 2.4

For all the model trainers using Macs, we now have Apple’s Mac-optimized version of TensorFlow 2.4 and the new ML Compute framework.

We all know that Macbook is the most preferred platform for developers, engineers, and researchers. Apple announced an updated lineup of Macs that contain the new M1 chip in November. And now Apple claims that the Mac-optimized version of TensorFlow 2.4 leverages the full power of the Mac with a huge jump in performance.

Here are a few charts that will help you get a better understanding of the performance enhancement across different Macs(Intel-based vs M1)

Now, to get started with the new accelerated tensorflow, you don’t need to make any changes to your existing projects and scripts to use the ML compute framework, all you need to do is head over to apple’s GitHub repo for the installation instructions and prerequisites.

From there, you just simply need to install the mac-optimized tensorflow 2.4 and you are done. They are saying that they’ll be making these updates easier by integrating it in the tensorflow master branch but let’s see how long it takes!