Vivienne Ming is a theoretical neuroscientist, entrepreneur, and author. Named one of 10 "women to watch in technology" by Inc. Magazine, she is the co-founder and managing partner of educational technology company Socos, which focuses on using machine learning and neuroscience to improve educational outcomes and workplace development. She was previously a visiting scholar at the Redwood Center for Theoretical Neuroscience at UC Berkeley, and sits on the board for companies and nonprofits like StartOut, the Palm Center, and Cornerstone Capital. We had the opportunity to speak with Ming about artificial intelligence, workforce development, and how purpose drives performance in the run-up to her upcoming Dreamtalk on November 7 at Dreamforce. You've spoken previously about your mission to leverage AI to maximize human potential.

Conjugate gradient methods are a class of important methods for solving linear equations and nonlinear optimization. In our work, we propose a new stochastic conjugate gradient algorithm with variance reduction (CGVR) and prove its linear convergence with the Fletcher and Revves method for strongly convex and smooth functions. We experimentally demonstrate that the CGVR algorithm converges faster than its counterparts for six large-scale optimization problems that may be convex, non-convex or non-smooth, and its AUC (Area Under Curve) performance with $L2$-regularized $L2$-loss is comparable to that of LIBLINEAR but with significant improvement in computational efficiency.

We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks. Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped). Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

I finally cracked and decided to shove everything in my personal life aside for the next few months to take on Udacity's Machine Learning Nanodegree Program. On second thought, beer, you should stay. I might require a cheerleader. Jokes aside, I've had my eyes on this program for quite some time. After hearing a few strong endorsements from friends and colleagues, I seriously started to consider diving in.

Welcome to Essential Education, our daily look at education in California and beyond. L.A. Unified school board member Ref Rodriguez pled not guilty to campaign finance money laundering charges Tuesday. Rodriguez's pro-charter school allies on the board asked him to step down, but he said no. L.A. Unified school board member Ref Rodriguez pled not guilty to campaign finance money laundering charges Tuesday. Rodriguez's pro-charter school allies on the board asked him to step down, but he said no. Rodriguez won't budge, Michelle King's long leave, Caltech's new drone lab: What's new in education Ref Rodriguez's allies on the L.A. school board asked him to step down. He said no, shortly after pleading not guilty to felony and misdemeanor charges.

Hiring someone who turns out to be a bad fit can be costly: Unhappy employees cost the U.S. economy between $450 billion and $550 billion in lost productivity each year, according to research firm Gallup. And replacing a full-time worker can cost up to twice the employee's salary. While working on a project at Harvard Law School, Angela Antony found herself immersed in statistics like those. "If you look across the economy, about 46 percent of hires leave within 18 months. That's despite all the time, resources, and billions of dollars spent trying to effectively hire," Antony says.

Is there a global theory for the shapes of fish? But for most of the history of biology, it's not the kind of thing anyone would ever have asked. Stephen Wolfram is the creator of Mathematica, Wolfram Alpha and the Wolfram Language; the author of A New Kind of Science; and the founder and CEO of Wolfram Research. Sign up to get Backchannel's weekly newsletter, and follow us on Facebook, Twitter, and Instagram. And it's now 100 years since D'Arcy Thompson published the first edition of his magnum opus On Growth and Form--and tried to use ideas from mathematics and physics to discuss global questions of biological growth and form. Stretch one kind of fish, and it looks like another. Yes, without constraints on how you stretch. It's not quite clear what this is telling one, and I don't think it's much. But just to ask the question is interesting, and On Growth and Form is full of interesting questions--together with all manner of curious and interesting answers. D'Arcy Thompson was in many ways a quintessential British Victorian academic, steeped in the classics, and writing books with titles like A Glossary of Greek Fishes (i.e. But he was also a diligent natural scientist, and he became a serious enthusiast of mathematics and physics. And where Aristotle (whom Thompson had translated) used plain language, with perhaps a dash of logic, to try to describe the natural world, Thompson tried to use the language of mathematics and physics.

Robots filling orders for Amazon AMZN, 0.99% and driverless vehicles hardly spell the end of work, but the artificial intelligence driving the current wave of automation -- if Americans don't embrace it effectively -- could catapult China ahead of the United States once and for all. Those of us who were around in the 1960s remember elevator operators and bowling alley pin-setters losing their jobs. Alarmists warn that artificial intelligence now is beginning to enable machines to replace not just unskilled workers but knowledge workers too -- for example, insurance adjusters. They worry society will divide between those owning the intellectual property and indolent masses who will depend on government handouts. More compelling is the example of mid-20th century office workers.

I may have mentioned it before, but schools are big business. In the U.S. alone, some 50 million school-aged boys and girls are not only gracing our schools with wide-eyed wonder, they are also helping to drive our economy in an industry that spends a cool $700 billion on everything from piping hot breakfasts to busses to software. In fact, once you get past all the basics like salaries and utilities that eat about 80 percent of the pie, and you'll find that software represents a huge slice of what's left. Little Johnny and little Suzy are forking over between $100 and $250 per kiddo on software and technology purchases. The ability for districts to efficiently buy and manage Suzy and Johnny's massive appetite for software can drastically effect the school district's capacity to properly educate each child.

Bill Gates proclaimed in a recent graduation ceremony, that artificial intelligence (AI), energy, and bio science are three most exciting and rewarding career choices today's young college graduates can choose from. I have come to believe strongly that some of the most important questions of our generation - related to sustainability, energy generation and distribution, transportation, access to basic amenities of life etc., are dependent on how intelligently we can mix the the first two branches of knowledge Mr. Gates mentions. I am a semiconductor professional with 8 years of post-PhD experience in a top technology company. I take pride in the fact that I work in the cross-section of physical electronics which directly contributes to the energy sector. I develop power semiconductor devices.