Microsoft Research has just posted a talk by Kevin Buzzard of the Imperial College of London. Don’t worry, no advanced mathematical knowledge is assumed in the talk.

Slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/The-Future-of-Mathematics-SLIDES.pdf 

From the video description:

As a professor of pure mathematics, my job involves teaching, research, and outreach.

Two years ago I got interested in formal methods, and I learned how to use the Lean theorem prover developed at MSR. Since then I have become absolutely convinced that tools like Lean will play a role in the future of mathematics.

Machine Learning can be confusing sometimes.

From the esoteric terms to elevated expositions it seems like a terribly difficult area to get into.

Seth Juarez, like me, started off as a developer, and he tackles the one term that is used all of the time in Machine Learning: the elusive “model.

From the description:

First we set up how machine learning is different, how to think about it, and finally what a model actually is (spoiler alert – think “a function written a different way”). Would love your feedback

https://aka.ms/MachineLearningModels

Leila Etaati, a Data Soup Summit speaker,  will be in the USA for a month and presenting a one day workshop at the following locations

 

MLOps (also known as DevOps for machine learning) is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production machine learning (ML) lifecycle.

Azure Machine Learning service’s MLOps capabilities provide customers with asset management and orchestration services which enable effective ML lifecycle management.

Learn more about MLOps:
https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment

Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:
https://colab.research.google.com/drive/19N9VWTukXTPUj9eukeie55XIu3HKR5TT

Demo project:
https://github.com/jaxball/advis.js

 

Microsoft Research explores how the brains beget the mind.

How do molecules, cells, and synapses effect reasoning, intelligence, language, science? Despite dazzling progress in experimental neuroscience we do not seem to be making progress in the overarching question — the gap is huge and a completely new approach seems to be required.

As Richard Axel recently put it: “We don’t have a logic for the transformation of neural activity into thought.” What kind of formal system would qualify as this “logic”? I will sketch a possible answer.

(Joint work with Santosh Vempala, Dan Mitropolsky, Mike Collins, Wolfgang Maass, and Larry Abbott.)

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/A-Calculus-for-Brain-Computation-SLIDES.pdf