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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: Uber’s Open Source Machine Learning Stack

Welcome to our first newsletter of 2020! This time we start where we left off in 2019: talking about machine learning technologies in the real world.

Artificial intelligence(AI) is a trend that differs from many of its predecessor technology movements in the sense that a substantial percentage of the innovation is not coming from startups but from the big corporate labs. Companies like Microsoft, Google, Amazon, Facebook, Uber are actively contributing to the rapid evolution of AI research and technology. Among those technology giants, Uber has quietly become one of the most active contributors to open source machine learning technologies of the last few years.

A few days ago, Uber announced the latest addition to its open source machine learning stack. Manifold is a toolset for debugging and interpreting machine learning models at scale. The release of Manifold joins a deep stack of open source machine learning technologies that include the following:

· Ludwig :A TensorFlow based toolbox that allows to train and test deep learning models without the need to write code.

· Pyro : A deep probabilistic programming language

· Plato: A Framework for Building Conversational Agents at Scale

· Horovod: A framework for parallelizing the training of deep learning models

Uber’s contributions to the open source machine learning community are helping to advance the space and provide data scientists with tools and frameworks that have been tested at Uber’s scale.

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

AI Research

Dopamine as an Inspiration for Reinforcement Learning

DeepMind started the year publishing a groundbreaking paper that proposes a new reinforcement learning algorithm inspired by the role of dopamine in the human brain.

>Read more in this blog post from DeepMind

Solving Advanced Mathematical Equations using Deep Neural Networks

Facebook AI Research(FAIR) published a paper describing a model that can solve complex math equations using symbolic reasoning.

>Read more in this blog post from the FAIR team

Quantifying Model Uncertainty

Google AI researchers published a paper that benchmark the uncertainty of state-of-the-art deep learning models as they are exposed to new data distributions.

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

Cool AI Tech Releases

PyTorch 1.4

Facebook released a new version of the PyTorch deep learning framework that includes improvements in mobile, an excitement new mode for parallel training, Java bindings and several other cool features.

>Read more in this blog post from the PyTorch team

AWS Gets Into AutoML with AuGluon

AutoGluon is a new framework that allows developers to author deep learning models with a few lines of code.

>Read more in this blog post from the Amazon research team

Manifold Allows You to Debug Models Visually

Uber open sourced Manifold, a tool for visually testing and debugging machine learning models.

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

AI in the Real World

AI Startups Raised $18.5 billion in 2019

A new report by the National Venture Capital Association claims that 1,356 AI-related companies in the U.S. raised $18.457 billion in venture capital last year.

>Read more in this coverage from VentureBeat

Apple Buys AI Startup for $200M

Apple has acquired, a startup that has powering machine learning models in edge devices.

>Read more in this coverage from TechCrunch

AI in Hollywood

Hollywood studios are relying more and more on AI models to optimize the chances of success of new films.

>Read more in this article from the Guardian

Written by

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

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