The hire marks the start of the Alphabet ( GOOG)-owned, Art Levinson-helmed biotech's drive to build out a computational biology and machine learning team. Calico will tap into that expertise--and that of the machine learning team it has tasked Koller with building--to advance its drive to understand aging and, in doing so, enable people to live longer, healthier lives. "Daphne and her team will work in close collaboration with the basic and translational scientists at Calico and partner with other machine learning experts, including the team at Google, to derive novel insights and effective interventions," Calico R&D President Hal Barron said. The plan now is to add more staff to work with Koller, including a machine learning engineer and someone capable of developing algorithms to analyze biological images and videos.

The hire marks the start of the Alphabet ( GOOG)-owned, Art Levinson-helmed biotech's drive to build out a computational biology and machine learning team. Calico will tap into that expertise--and that of the machine learning team it has tasked Koller with building--to advance its drive to understand aging and, in doing so, enable people to live longer, healthier lives. "Daphne and her team will work in close collaboration with the basic and translational scientists at Calico and partner with other machine learning experts, including the team at Google, to derive novel insights and effective interventions," Calico R&D President Hal Barron said. The plan now is to add more staff to work with Koller, including a machine learning engineer and someone capable of developing algorithms to analyze biological images and videos.

On the first day of "camp," two dozen rising high school sophomores arrive at the Stanford Artificial Intelligence Laboratory's Outreach Summer program (SAILORS) giddy and ready to get started. The rigorous, two-week program is designed to encourage young women from underrepresented populations to get more involved in the field of science. High school students Ishla Zareef-Mustafa and Genaro Pamatz participate in an anatomy lab as part of the Stanford Medical Youth Science Program. On the last day of the summer residential Stanford Medical Youth Science Program (SMYSP), 24 high school students, surrounded by family members, friends and mentors, present the research they have been working on during the five-week summer program.These programs, which fall under the umbrella of Stanford Pre-Collegiate Studies, are designed to provide teenagers from underrepresented populations with an opportunity to explore careers in science, but also to build new relationships, while taking what they've learned back to their home communities. The SAILORS curriculum includes lectures, hands-on research projects and mentoring activities that are intended to educate and excite young women about artificial intelligence.

If you passed high school math and can hack around in Python, I want to teach you Deep Learning. Edit: 50% Coupon Code: "mltrask" (expires August 26) I've decided to write a Deep Learning book in the same style as my blog, teaching Deep Learning from an intuitive perspective, all in Python, using only numpy. I wanted to make the lowest possible barrier to entry to learn Deep Learning. The Problem with most entry level Deep Learning resources these days is that they either assume advanced knowledge of Calculus, Linear Algebra, Differential Equations, and perhaps even Convex Optimization, or they just teach a "black box" framework like Torch, Keras, or TensorFlow (where you just hit "train" but you don't actually know what's going on under the hood). Both have their appropriate audience, but I don't believe that either are appropriate for your average python hacker looking for a 101 on the fundamentals.

Facial recognition makes sense as a method for your computer to recognize you. After all, humans already use a powerful version of it to tell each other apart. But people can be fooled (disguises! Now researchers have demonstrated a particularly disturbing new method of stealing a face: one that's based on 3-D rendering and some light Internet stalking. Earlier this month at the Usenix security conference, security and computer vision specialists from the University of North Carolina presented a system that uses digital 3-D facial models based on publicly available photos and displayed with mobile virtual reality technology to defeat facial recognition systems.

Schools today look almost nothing like they did 50 years ago. According to Jonathan Rochelle, head of product management for Google Apps for Education, the next 50 years might see even crazier advances. By 2066, Rochelle says, schools are poised to become highly collaborative spaces, thanks to the advent of virtual and augmented reality. Instead of needing to meet in the same physical space, kids could work on long-term projects remotely and interact through online platforms. Rochelle has a unique perspective on the value of teamwork: In 2006, he co-founded the Google Docs suite.

In the year 1820, a person could expect to live less than 35 years, 94% of the global population lived in extreme poverty, and less that 20% of the population was literate. Today, human life expectancy is over 70 years, less that 10% of the global population lives in extreme poverty, and over 80% of people are literate. These improvements are due mainly to advances in technology, beginning in the industrial age and continuing today in the information age. There are many exciting new technologies that will continue to transform the world and improve human welfare. Here are eleven of them.

It seems like machine learning and artificial intelligence are topics at the top of everyone's mind in tech. Be it autonomous cars, robots, or machine intelligence in general, everyone's talking about machines getting smarter and being able to do more. At the same time, for many developers, machine learning and artificial intelligence are nebulous terms representing complex mathematical and data problems they just don't have the time to explore and learn. As I've spoken with lots of developers and CTOs about Fuzzy.io and our mission to make it easy for developers to start bringing intelligent decision-making to their software without needing huge amounts of data or AI expertise, some were curious to learn more about the greater landscape of machine learning. Here are some of the links to articles, podcasts and courses discussing some of the basics of machine learning that I've shared with them.

There's been quite the spate of discussion of late about Artificial Intelligence (AI) and adaptive learning. You've no doubt seen the commercials where Watson conducts conversations with talents from Bob Dylan to teacher Ashley Bryant, the latter in which great learning outcomes are proposed. And I think it's important to know what is real, where we are, and where we are going, if we're to plan accordingly. We've previously touched on AI, but it's worth going deeper. To start, we need to clarify what AI really is.

The NVIDIA Deep Learning Institute offers self-paced classes for deep learning that feature interactive lectures, hands-on exercises, and live Q&A with instructors. You'll learn everything you need to design, train, and integrate neural network-powered artificial intelligence into your applications with widely used open-source frameworks and NVIDIA software. During the hands-on exercises, you will use GPUs and deep learning software in the cloud. This is an introductory course, so previous experience with deep learning and GPU programming is not required. Please send your questions to DeepLearningInstitute@nvidia.com.