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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:

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From the Editor: Self-Supervised vs. Supervised vs. Reinforcement Learning

Traditional machine learning theory divides the world in two fundamental schools: supervised and unsupervised learning. However, we know that picture is incomplete. Reinforcement learning(RL) has been at the center of some of the most exciting developments in the recent years of artificial intelligence(AI). From DeepMind’s AlphaGo beating Go’s World Champion Lee Sedol to breakthroughts in games like Star Craft or Dota2, RL systems have come the closesto show sparks of intelligence. Given that RL systems learn by interacting with an environment instead of just being trained like supervised models, many experts believe that they are the key to achieve artificial general intelligence(AGI).

The current situation of machine learning systems can be summarized as this: supervised learning works but requires a lot of training data, unsupervised learning remains unpractical and RL applications applications have been mostly constrained to games. As a result, some of the top minds in the AI community have started turning their attention onto a new exciting area known as self-supervised learning. Conceptually, self-supervised learning focuses on building systems that convert an unsupervised problem into a supervised one and can learn with unlabeled data. This is analogous as babies develop a model of the world without being trained or interact physically too much with it but by simply observing. This week AI legends Yann LeCun and Yoshua Bengio described self-supervised learning as the future of AI.

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

AI Research

More Efficienty AI

OpenAI published an analysis demonstrating than algorithmic improvement has yield higher levels of efficiency compared to hardware advancements.

>Read more in this blog post from OpenAI

Understanding the Shape of Large Datasets

Researchers from Google published a fascinating paper proposing a graph-based method to understand patterns in large datasets.

>Read more in this blog post from Google Research

How Alexa Knows When You Are Talking to Her

Amazon researchers published a paper a method based on semantic and syntactic features to improve the detection of device-directed speech.

>Read more in this blog post from Amazon Research

Cool AI Tech Releases

Better TensorFlow-Spark Integration

Engineers from LinkedIn open sourced Spark-TFRecord, a new library to leverage TensorFlow’s TFRecord as native Spark datasets.

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

StellarGraph

StellarGraph, a new framework focused on state-of-the-art graph machine learning, is now open source.

>Read more in this blog post from the StellarGraph team

AI in the Real World

AI Legends Believe in Self-Supervised Learning

At the ICLR conference, Turing award winners Yann LeCun and Yoshua Bengio discussed how self-supervised learning can be the key to human-level intelligence.

>Read more in this coverage from VentureBeat

A New AI Fund

Runa Capital has closed its third fund with $157 million dedicated to deep tech investments including AI.

>Read more in this coverage from TechCrunch

An AI Model Inspired on How Kids Learn

Researchers at Carnegie Mellon University unveiled a machine learning algorithm that progressively learn more details in order to classify objects imitating how children develop knowledge.

>Read more in this coverage from MIT Technology Review

Written by

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

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