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:

From the Editor: An Interesting Message at AI’s Biggest Party

The NeurIPS conference is considered by many the top artificial intelligence(AI) academic event of the year. Last week, over 13000 AI researchers descended over the city of Vancouver to participate in the latest edition of NeurIPS. While previous editions of NeurIPS were filled with overly optimistic pictures about the future, this year saw a shift of focus towards addressing some of the most important limitations of the latest generation of AI systems.

To use some examples, deep learning legend Yoshua Bengio used his keynote to highlight the very narrow learning mechanisms of most AI models and the difficulty of reusing knowledge across AI systems. Similarly, Google’s Blaise Aguera y Arcas called AI researchers to pay attention to the “biological nature” of intelligence and to incorporate those mechanisms into deep learning systems. Overall, NeurIPS celebrated the recent advancements in AI research but emphasized a very sober and pragmatic view of the near term future of AI.

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

AI Research

Neurosymbolic AI

Microsoft researchers published a paper proposing a method that uses symbolic representations to understand the knowledge of neural networks.

>Read more in this blog post from Microsoft Research

NeurIPS Keynotes

Some amazing keynotes from the NeurIPS conference are now online including Turing Award winner’s Yoshua Bengio.

>Watch Dr. Bengio’s keynote about how to move from mimicking system 1 to system 2 intelligence here.

Deep Learning Training at Scale

Uber published a blog post describing a very innovative architecture for productionizing the XGBoost method used to train deep learning models at scale.

>Read more in this blog post from the Uber engineering team

Cool AI Tech Releases

Fairness Indicators

Google open sourced Fairness Indicators, a suite of tools that enable regular computation and visualization of fairness metrics.

>Read more in this blog post from the Google AI team

Deep Fake Detection Challenge

Facebook announced a new Kaggle challenge and a new dataset to advance the research in deep fake detections.

>Read more in this blog post from the Facebook AI Research team

A Better Dataset for Object Detection

Researchers from the Massachusetts Institute of Technology(MIT) released ObjectNet, a new dataset that attempts to bridge the gap between AI systems and humans when comes to object recognition.

>Read more in this article from MIT News

AI in the Real World

Google Discusses AI Trends for 2020

Google AI chief Jeff Dean gave a vert insightful interview about some of the interesting AI trends that are likely to see traction next year.

>Read the entire interview in VentureBeat

AI R&D in 2019

The AI Index Report its one of the most anticipated documents in the field of AI. This year’s edition covered a lot of ground including AI research and adoption.

>Read more in this coverage from The Verge

AI and the Search for Alien Life

NASA is using deep learning to interpret data that will be collected by future telescopes like the James Webb Space Telescope or the Transiting Exoplanet Survey Satellite mission.

>Read more in this coverage from Space.com

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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