Last Week in AI

The newest developments this week in AI research and development.

Image for post
Image for post

Every week, Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it below. Please do so, our guys worked really hard on this:

Image for post
Image for post

From the Editor: The Challenge of Training at Scale

Training is one of the frequently overlooked elements of building machine learning solutions at scale. While training machine learning models seems relatively simple conceptually, it gets really complicated when applied to large models or to a large number of models. As a result, most organizations struggle getting past certain level of scale in their machine learning solutions given the infrastructure costs.

Despite the proliferation of a new generation of machine learning frameworks, the techniques for scaling the training of models have remained mostly constrained to large technology companies such as Google, Microsoft, Amazon, Uber or Facebook. The great thing is that those companies have started open sourcing some of their methods in the form of libraries and frameworks. Just this week, Uber and OpenAI open sourced Fiber, a new framework for parallelizing computations and training in machine learning models. Contributions like this are going to be key to streamline the implementation of large-scale machine learning solutions.

Now let’s take a look at the core developments in AI research and technology this week:

Image for post
Image for post

AI Research

Massively Scalable Reinforcement Learning

Google Research published a paper proposing SEED RL, a reinforcement learning architecture that can scale to thousands of machines.

>Read more in this blog post from Google Research

Training Self-Driving Cars in a Simulation

MIT AI researchers published a paper proposing a simulated environment with virtually infinite steering possibilities in order to train different policies in self-driving vehicles.

>Read more in this article from MIT News

Coordination and Collaboration in Multi-Agent Environments

Researchers from MIT and Harvard collaborated in a paper proposing a coordination method that facilitate collaboration multi-agent AI environments.

>Read the research paper here

Image for post
Image for post

Cool AI Tech Releases

Uber-OpenAI Fiber

Uber and OpenAI have partnered to open source Fiber, a new platform for distributed and scalable training.

>Read more in this introductory blog post

A-Z of AI

Google and Oxford University launched a new portal explaining the fundamental concepts of AI in a very intuitive way.

>Check out the A-Z of AI portal here

Cortex

Cortex is a new open source framework for rapid machine learning deployment.

>Read more in their GitHub repository

Image for post
Image for post

AI in the Real World

COVID-NET

AI Researchers from University of Waterloo developed a deep neural network that can help spot signs of Covid-19 in chest x-rays.

>Read more in this coverage from MIT Technology Review

Supercomputers to Fight Coronavirus

IBM is cooperating with the White House and the Department of Energy to offer coronavirus researchers access to supercomputers.

>Read more in this coverage from VentureBeat

Human Compatible AI

TheNextWeb published a comprehensive analysis AI-legend Stuart Russell’s new book.

>Read more in the TheNextWeb’s analysis

Written by

CEO of IntoTheBlock, Chief Scientist at Invector Labs, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store