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: The Anti-Innovator’s Dilemma

Are innovations in machine and deep learning applications coming from startups or big corporate labs?

In his famous book, The Innovator’s Dilemma, Clayton Christensen outlined a series of principles that describe how startups can disrupt incumbents by driving innovation in new technologies. The Innovator’s Dilemma has been an omnipresent dynamic in technology markets with startups driving the innovation while large corporations tend to get late to the game. That dynamic seems to have completely shifted in the case artificial intelligence(AI) market with large technology incumbents consistently out innovating startups in the space.

If we look at the spectrum of innovations in AI technologies in the last few years, we can clearly determine that companies Google, Facebook, Microsoft, Amazon, Uber and many other internet giants have been responsible for advancing both the research and technological agenda in the market. Differently from other technology trends, AI seems to be a “big boys problem”. AI applications require large datasets, human and compute resources which seem to be a privilege of large corporations. This dynamic posses a traditional “rich get richer” challenge that needs to be addressed in order to foment a more balanced and fair AI.

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


Researchers from the Facebook AI Research(FAIR) lab published a paper detailing a new technique for certifying the safety of a neural network.

>Read more in this blog post from the FAIR team

Google AI researchers proposed a method for reducing the amount of labeled data used to train adversarial models.

>Read more in this blog post from Google Research

Microsoft Researchers proposed a technique for new embedding models that can be applied to many natural language processing tasks.

>Read more in this blog post from IBM Research

Cool Tech Releases

Uber how its Piper framework can be used to automate machine learning workflows.

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

Y-combinator incubated company Skymind raised $11.5 million to make deep learning more accessible to enterprises.

>Read more in this coverage from TechCrunch

In this new presentation, Jeremy Hermann discusses Michelangelo, the core platform that powers machine learning at Uber.

>Watch the presentation at InfoQ

AI in the Real World

Microsoft partners with Stanford University in the new Institute for Human-Centered AI.

>Read more in this blog post from Microsoft Research

The White House launched a new website dedicated to artificial intelligence

>Read more in this coverage from VentureBeat

The Massachusetts Institute of Technology unveiled a new machine learning models that learns the role of individual amino acids to improve protein design.

>Read more in this coverage from MIT News

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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