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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:

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From the Editor: Building Machine Learning Systems in the Real World

We are in the golden age or artificial intelligence(AI) research and development. Every week, there are literally hundreds of new research papers published proposing shiny new AI techniques as well as new open source frameworks and libraries. However, building machine learning systems at scale remains an incredibly difficult and expensive task. From the talent cost to the lack of tooling and processes, the implementation of machine learning applications remains a challenge for most organizations. While machine learning libraries and frameworks are evolving at a very fast pace, the guidelines, best practices and processes for building real-world, machine learning applications remain incredibly nascent.

Every aspect of the lifecycle of machine learning applications brings a unique set of challenges that we haven’t seen before. From model training to serving and optimization, machine learning teams are regularly confronted with situations from which there are not well established solutions. At Invector Labs, we’ve seen enough of these challenges and have developed prescriptive solutions that help us streamline the implementation of machine learning systems for our clients. This week, we presented some of these best practices at a machine learning symposium in Munich, Germany. The slides are now publicly available and we hope you find them useful.

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

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AI Research

Microsoft AI researchers published a paper proposing a technique to generate images based on natural language descriptions.

>Read more in this blog post from Microsoft Research

The Facebook AI Research(FAIR) team, published a paper proposing a method to build more efficient convolutional neural networks(CNNs).

>Read more in this blog post from the FAIR team

Google AI researchers published a paper outlining a method to train reinforcement learning agents to learn from older data.

>Read more in this blog post from Google AI Research

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Cool Tech Releases

Facebook open sourced PyRobot, a PyTorch-based framework for robotics research.

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

Salesforce expands its product offering with three new AI tools

>Read more in this article from Forbes

IntersectLabs launches a new self-service platform for machine learning workflows.

>See more in this coverage from Techcrunch

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AI in the Real World

The White House updated the National Artificial Intelligence Strategy by adding focus on public-private partnerships.

>Read more in this article from Nextgov

TextIQ, a platform for analyzing sensitive corporate data raised $12.6 million led by FirstMark Capital.

>Read more in this article from VentureBeat

Researchers from the Massachusetts Institute of Technology(MIT) have developed a novel technique for machine learning models to diagnose brain conditions.

>Read more in this article from MIT News

Written by

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

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