deeplearning

And again… if you read one Artificial Intelligence article this month, make it this one. I highly value Erik Brynjolfsson since I saw his TED talk (here is also a great recap and interview with him in the NPR TED Radio Hour “The Digital Industrial Revolution”). Together with with fellow MIT principal research scientist Andrew McAfee he draws a sharp and easy to grasp picture of what machine learning is really capable of today and what the outlook for the future is. This is really worth your time, providing first class insights into “no bullshit” artificial intelligence state of the nation. Here’s an intro… follow the link at the end of this post for the full article on Harvard Business Review:

“For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available…”

via The Business of Artificial Intelligence (@Harvard Business Review)

Really insightful Long-Read: Alien Knowledge – When Machines justify Knowledge (David Weinberger)

The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

So wrote Wired’s Chris Anderson in 2008. It kicked up a little storm at the time, as Anderson, the magazine’s editor, undoubtedly intended. For example, an article in a journal of molecular biology asked, “…if we stop looking for models and hypotheses, are we still really doing science?” The answer clearly was supposed to be: “No.”

But today — not even a decade since Anderson’s article — the controversy sounds quaint. Advances in computer software, enabled by our newly capacious, networked hardware, are enabling computers not only to start without models — rule sets that express how the elements of a system affect one another — but to generate their own, albeit ones that may not look much like what humans would create. It’s even becoming a standard method, as any self-respecting tech company has now adopted a “machine-learning first” ethic…

via Our Machines Now Have Knowledge We’ll Never Understand

May 4, 2017 0

Awesome & insightful Q&A Session with Yann LeCun, Director of AI Research at Facebook and Professor at NYU (Quora)

What are some recent and potentially upcoming breakthroughs in deep learning?

There are many interesting recent development in deep learning, probably too many for me to describe them all here. But there are a few ideas that caught my attention enough for me to get personally involved in research projects.

The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This is an idea that was originally proposed by Ian Goodfellow when he was a student with Yoshua Bengio at the University of Montreal (he since moved to Google Brain and recently to OpenAI)

This, and the variations that are now being proposed is the …

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Source: Yann LeCun – Session on Jul 28, 2016 – Quora

August 16, 2016 0

Great Piece on how and why Google gets Corporate Innovation Right: Go Inside Google Brain (Greg Satell)

Apple fuses technology with design. IBM invests in research that is often a decade ahead of its time. Facebook “moves fast and maintains a stable infrastructure” (but apparently doesn’t break things anymore).

Each of these companies, in its own way, is a superior innovator. But what makes Google (now officially known as Alphabet) different is that it doesn’t rely on any one innovation strategy, but deploys a number of them to create an intricate — but powerful — innovation ecosystem that seems to roll out innovations by the dozens…

Source: Want to Do Corporate Innovation Right? Go Inside Google Brain

June 2, 2016 0

Awesome Compilation: Getting Up to Speed on Deep Learning – 20+ Resources (Isaac Madan, David Dindi)

For good reason, deep learning is increasingly capturing mainstream attention. Just recently, on March 15th, Google DeepMind’s AlphaGo AI — technology based on deep neural networks — beat Lee Sedol, one of the world’s best Go players, in a professional Go match.
Behind the scenes, deep learning is an active, fast-paced research area that’s proliferating quickly among some of the world’s most innovative companies. We are asked frequently about our favorite resources to get up to speed on deep learning and follow its rapid developments. As such, we’ve outlined below some of our favorite resources. While certainly not comprehensive, there’s a lot here…

Source: Getting Up to Speed on Deep Learning: 20+ Resources — Life Learning — Medium

May 1, 2016 0

ANDRÉ CRAMER

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