Microsoft’s AGI Lab, Google’s Gradient Ventures, and the State of AI and ML in 2017

Issue 59

This week we check in Microsoft’s artificial general intelligence lab, Google’s new AI venture fund, their new people and AI research initiative, and take a high-level view of the state of ML and AI in 2017.

Plus our favorite reads and some things to try at home!

Microsoft’s AGI Lab

Microsoft is launching an AI research lab to challenge Google and DeepMind.

More than 100 scientists from across various disciplines of artificial intelligence research will work toward building a more general artificial intelligence — a single system that can solve a range of problems compared to “narrow” AI, which can only solve the specific problem it was trained to do.

Google is attempting to tackle the same challenge of generalized AI via both its own Google Brain project and through efforts at DeepMind.

The new lab, called Microsoft Research AI, will be based in Redmond, Washington and will focus on “building AI advances that amplify human ingenuity, and also that reflect our shared societal values and expectations. The AI tools and services we create must assist humanity and augment our capabilities.”

Gradient Ventures

You might have heard… Google launched Gradient Ventures, an AI-focused venture fund to provide startups with funding and connect them with Google’s AI-related resources in an effort to help founders navigate the challenges in developing AI-based products.

Gradient has made four investments so far, including leading Algorithmia’s Series A investment round.

But did you know… Google also launched PAIR — the People + AI Research Initiative — that will focus on the relationship between users and technology. They’ll be open sourcing tools and research based on learnings from AI practitioners from across Google.

State Of Machine Learning And AI

McKinsey interviewed more than 3,000 senior executives on the use of AI technologies and here’s what they found:

  • Tech companies like Baidu and Google spent between $20B to $30B on AI in 2016. 90% was spent on R&D and 10% on AI-related acquisitions.
  • AI investment has turned into a race for patents and intellectual property among the world’s leading tech companies.
  • 66% of all AI investments in 2016 were to US-based companies. China was second with 17%.

Download the PDF report here.

What We’re Reading

  • Three different sources of bias in AI and how to fix them. We’ve figured out how to represent our culture as AI, but all algorithms that affect people’s lives should be subject to audit. (Joanna Bryson)
  • Under the Hood of a Self-Driving Taxi. A look at compute and other core self-driving car systems. (Voyage)
  • 1000x Faster Deep Learning Queries over Video. Recent advances in deep learning enable automated analysis of video data, however, these deep learning methods are extremely computationally expensive. (Stanford DAWN)
  • The Confluence of Geometry and Learning. Researchers present methods for building 3D prediction systems that can learn in a similar manner to how humans can effortlessly infer the rich 3D structure of single 2D image. (Berkeley Artificial Intelligence Research)
  • A Blueprint for Coexistence with Artificial Intelligence. Your future AI diagnostic tool may well be 10 times more accurate than human doctors, but patients will not want a cold pronouncement from the tool. (WIRED)

Things to Try at home🛠

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