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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: PyTorch 1.5

PyTorch has established itself as one of the top two frameworks for deep learning developments. Initially incubated by Facebook, PyTorch rapidly developed a reputation from being an incredibly flexible framework for rapid experimentation and prototyping gaining thousands of fans within the deep learning community. For instance, AI powerhouse OpenAI announced that it was standardizing on PyTorch as the default framework to power its deep learning research work. Outside Facebook, this is arguably the biggest endorsement for PyTorch within the deep learning world.

This week Facebook announced the released of PyTorch 1.5. The new version focuses on providing tools and frameworks to make PyTorch workflows production-ready. The most notable aspect of this release has been the collaboration between AWS and Facebook in two projects: TorchServe for model serving and Torch-Elastic Kubernetes for distributed training. This release contributes greatly to make PyTorch a more viable option for many enterprises starting in their machine learning journey.

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

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AI Research

DeepMind published an analysis of how reinforcement learning agents can game their target tasks producing undesired outcomes.

>Read more in this blog post from the DeepMind team

Evolutionary Meta Learning

Google AI researchers published a paper proposing a new meta-learning method based on evolutionary strategies.

>Read more in this blog post from Google AI

Quant-Noise

Facebook AI researchers published a paper introducing Quant-Noise, a new method for compressing neural networks without affecting their performance.

>Read more in this blog post from Facebook Research

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Cool AI Tech Releases

Facebook released PyTorch 1.5 which includes several projects like TorchServe and TorchElastic that are based on the collaboration between Facebook and AWS.

>Read more in this blog post from the PyTorch team

TensorFlow Profiler

Google open sourced the TensorFlow profiler, a new set of tools that you can use to measure the training performance and resource consumption of TensorFlow models.

>Read more in blog post from the TensorFlow team

Apache SINGA 3.0

Apache SINGA is one of the most popular deep learning projects incubated in Asia. The distributed deep learning library just released version 3.0.

>Read more in this announcement from the SINGA team

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AI in the Real World

Researchers from MIT unveiled a method that can reduce the carbon footprint used to train and operate AI models.

>Read more in this coverage from MIT News

CometML’s New Funding Round

CometML is one of our favorite platforms for machine learning management and they just raised a new round of funding.

>Read more in this coverage from TechCrunch

Peak AI Raises $12 Million

Enterprise AI startup Peak AI announced a new $12 million funding round to help enterprises adopt AI solutions.

>Read more in this coverage from VentureBeat

Written by

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

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