The Chairman of Nokia on Ensuring Every Employee Has a Basic Understanding of Machine Learning — Including Him

I’ve long been both paranoid and optimistic about the promise and potential of artificial intelligence to disrupt — well, almost everything. Last year, I was struck by how fast machine learning was developing and I was concerned that both Nokia and I had been a little slow on the uptake. What could I do to educate myself and help the company along?

As chairman of Nokia, I was fortunate to be able to worm my way onto the calendars of several of the world’s top AI researchers. But I only understood bits and pieces of what they told me, and I became frustrated when some of my discussion partners seemed more intent on showing off their own advanced understanding of the topic than truly wanting me to get a handle on “how does it really work.”

I spent some time complaining. Then I realized that as a long-time CEO and Chairman, I had fallen into the trap of being defined by my role: I had grown accustomed to having things explained to me. Instead of trying to figure out the nuts and bolts of a seemingly complicated technology, I had gotten used to someone else doing the heavy lifting.

Why not study machine learning myself and then explain what I learned to others who were struggling with the same questions? That might help them and raise the profile of machine learning in Nokia at the same time.
Going back to school



Read More at:    https://hbr.org/2018/10/the-chairman-of-nokia-on-ensuring-every-employee-has-a-basic-understanding-of-machine-learning-including-him



Posted by:    Jayne Merdith, Tendron Systems Ltd,

Astronomers report success with machine deep learning

Astronomers report success with machine deep learning

Machine learning continues its successes in astronomy.

Classifying galaxies. On April 23, 2018, astronomers at UC Santa Cruz reported using machine deep learning techniques to analyze images of galaxies, with the goal of understanding how galaxies form and evolve. This new study has been accepted for publication in the peer-reviewed Astrophysical Journal and is available online. In the study, researchers used computer simulations of galaxy formation to train a deep learning algorithm, which then:

… proved surprisingly good at analyzing images of galaxies from the Hubble Space Telescope.

The researchers said they used output from their simulations to generate mock images of simulated galaxies as they would look in ordinary Hubble observations. The mock images were used to train the deep learning system to recognize three key phases of galaxy evolution. The researchers then gave their artificial neutral network a large set of actual Hubble images to classify.
The results showed a remarkable level of consistency, the astronomers said, in the classifications of simulated and real galaxies. Joel Primack of UC Santa Cruz said:

We were not expecting it to be all that successful. I’m amazed at how powerful this is. We know the simulations have limitations, so we don’t want to make too strong a claim. But we don’t think this is just a lucky fluke.

Jane Merdith, Tendron Systems Ltd, London, UK.