The Artificial Intelligence and Global Security Summit will bring together technology leaders and top policymakers to explore the state of artificial intelligence and discuss the implications of the AI revolution on global security. Past industrial revolutions led to changes in the balance of power between nations and even the fundamental building blocks of power, with coal- and steel-producing nations benefitting and oil becoming a global strategic resource. The AI revolution has similar transformative potential to alter power dynamics, the character of conflict, and strategic stability among nations and private actors. The United States must anticipate these changes and capitalize on opportunities to stay ahead of competitors. To anticipate these challenges, CNAS' all-day summit will explore technology trends, uncertainties, and possible trajectories for how AI may affect global security.

Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online Thursday is different: Its lead author is still in high school. The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net--the kind of system that tech giants use to recognize your voice or face--two years ago, at the age of 15. Inspired by reports of software mastering Atari games and the board game Go, he has since been reading research papers and building pieces of what they described.

Some people are afraid that heavily armed artificially intelligent robots might take over the world, enslaving humanity -- or perhaps exterminating us. These people, including tech-industry billionaire Elon Musk and eminent physicist Stephen Hawking, say artificial intelligence technology needs to be regulated to manage the risks. But Microsoft founder Bill Gates and Facebook's Mark Zuckerberg disagree, saying the technology is not nearly advanced enough for those worries to be realistic. As someone who researches how AI works in robotic decision-making, drones and self-driving vehicles, I've seen how beneficial it can be. I've developed AI software that lets robots working in teams make individual decisions, as part of collective efforts to explore and solve problems.

As a new sub-discipline of Data Science, I notice that SYSTEMS Analytics is starting to get some traction! There are a couple of Analytics graduate level programs with *Systems* in its title (Stevens Institute of Technology and University of North Carolina are the only ones I know). Web search brings up NO books on *Systems* Analytics. With the publication of my book with *Systems* in the title, that gap has been filled now! "SYSTEMS Analytics: Adaptive Machine Learning workbook". My last Analytics startup launched in 2013 explicitly used SYSTEMS Analytics in our Retail Recommendation and Uplift SaaS product; my initial bias for the Systems approach was confirmed by the success of our product.

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.