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: Visualizing Neural Networks

Interpretability remains one of the biggest challenges in modern machine learning. Disciplines such as deep learning have increase the sophistication of neural networks but that sophistication has introduced challenges in terms of understanding how these systems make decisions. The accuracy-interpretability dilemma is at the center of the evolution deep learning. That dilemma describes the friction between being able to accomplish complex knowledge tasks and understanding how those tasks were accomplished. In essence, interpretable models are not very accurate and accurate models tend to be hard to understand.

In addition to the interpretability-accuracy friction, understanding deep learning models requires a new generation of debugging tools. As a result of these challenges, data scientists typically rely on visualization tools to understand the decision making process of deep learning models. This week we saw a major release in this area when OpenAI open sourced Microscope and the Lucid library, two efforts focused on creating visual representations of deep neural networks. Visualization techniques and debugging tools are certainly a key area of improvement in order to accelerate the mainstream adoption of deep learning technologies.

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

AI Research

Offline Reinforcement Learning

Google Research published a paper proposing a method to train reinforcement learning methods from logged experiences.

>Read more in this blog post from Google Research

Learning Related Tasks with Minimum Data

Researchers from Amazon published a paper outlining a meta-learning method that allow models to learn new tasks using unlabeled datasets

>Read more in this blog post from Amazon Research

Scalable Object Detection

Google Research published a new paper detailing EfficientDet, a method for building scalable and efficient object detectors.

>Read more in this blog post from Google Research

Cool AI Tech Releases

Visualizing Neural Networks

OpenAI unveiled Microscope, a new series of visualizations to interpret well-know neural network models.

>Read more in this blog post from OpenAI

TensorFlow Lite Model Maker

Google open sourced Model Maker, a tool that uses transfer learning to adapt dapts state-of-the-art machine learning models to custom data sets.

>Read more in this blog post from the TensorFlow team


Facebook added new features to Nevergrad, an open source framework for model optimization.

>Read more in this blog post from Facebook Research

AI in the Real World


AI startup MinsDB raised $3 million to accelerate its platform that allow data scientists to rapidly train and deploy machine learning models.

>Read more in this coverage from VentureBeat

Microsoft CTO Book

Kevin Scott, CTO of Microsoft, has published a new book about how AI can reprogram the American Dream.

>Read more in this coverage from the Wall Street Journal

AI for Measuring Social Distancing

Andrew Ng’s AI startup Landing AI created a tool that uses image analysis to measure social distancing in the workplace.

>Read more in this coverage from MIT Technology Review

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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