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: 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:
More Efficienty AI
OpenAI published an analysis demonstrating than algorithmic improvement has yield higher levels of efficiency compared to hardware advancements.
Understanding the Shape of Large Datasets
Researchers from Google published a fascinating paper proposing a graph-based method to understand patterns in large datasets.
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.
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.
StellarGraph, a new framework focused on state-of-the-art graph machine learning, is now open source.
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.
A New AI Fund
Runa Capital has closed its third fund with $157 million dedicated to deep tech investments including AI.
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.