Every week, my team at Invector Labs publishes a newsletter to track 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: Data Privacy and AI

Data privacy is one of the biggest challenges of modern machine learning applications. In order to build machine learning models, researchers need to have complete access to datasets that often contain sensitive data. The idea of models that work effectively with encrypted datasets has long been an elusive goal of the machine learning space. While research in areas such as homomorphic encryption or secure, multi-party computation has been rapidly advancing, its adoption in machine learning stacks remains limited at best.

Incorporating data privacy techniques in mainstream deep learning frameworks is essential to unlock many scenarios in regulated industries or even mobile applications. In the past, we have seen companies such as DeepMind pioneer some efforts to build data privacy frameworks for deep learning solutions. This week, Facebook took an important step open sourcing a new project that brings data privacy capabilities to PyTorch. Certainly, in the next few years data privacy frameworks should become a common component of deep learning stacks.

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

AI Research

Massively Multi-Lingual Machine Translation

Google Research published a paper proposing a new neural machine translation method that can scale massively across hundreds of languages.

>Read more in this blog post from Google Research

An AI that can Answer Questions About Images

Microsoft Research published a paper about a new technique that can answer textual questions about large scenic images.

>Read more in this blog post from Microsoft Research

Inside LinkedIn Caption Generation Techniques

LinkedIn published a blog post detailing how they automatically generate captions for uploaded images.

>Read more in this post from LinkedIn engineering

Cool AI Tech Releases

PyTorch 1.3

Facebook open sourced the new version of PyTorch.

>Read more in this blog post from the PyTorch team

Data Privacy for Deep Learning

As part of the PyTorch release Facebook also open sourced CrypTen, a framework for native data privacy in deep learning solutions.

>Read more in blog post from the Facebook Engineering team

An Interpretability Framework for PyTorch

This week Facebook also open sourced Captum, an interpretability library for PyTorch.

>Read more in this blog post from the Faceook Engineering team

AI in the Real World

Chinese AI Companies Blacklisted

The US-China trade war affected some to China’s top AI companies.

>Read more in this coverage from VentureBeat

Fighting Deepfakes with Deepfakes

Tech giants are creating AIs that generate deepfakes in order to train detection models.

>Read more in this coverage from MIT Technology Review

A National Facial Recognition System

India is creating a national facial recognition system to modernize the police force and criminal identification but there are some concerns about the increasing levels of surveillance.

>Read more in this coverage from BuzzFeed

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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