Education through machines is a reality. From industry to business and to education now, it is time to lose our reservations and embrace artificial intelligence. Teachers can see the limitations that technology can't or doesn't observe. However, there is no scalable way for students to closely engage with teachers as class sizes grow. This leads to gaps of knowledge between the lessons teachers submit to their administrators and the principles students are able to absorb.

In a wide-ranging discussion today at VentureBeat's AI Transform 2019 conference in San Francisco, AWS AI VP Swami Sivasubramanian declared "Every innovation in technology is going to be driven by developers." Sivasubramanian made the statement while talking about growing demand for machine learning engineers and internal efforts at Amazon to train more employees to use machine learning. Facebook VP Jérôme Pesenti also stressed plans to make machine learning part of each employee's job at the company. And earlier today, Amazon committed $700 million to upskilling its U.S. workers. "Amazon developed what we call Machine Learning University. This is what we use to train our own engineers on machine learning even if they didn't take it as part of their own university [course work]," Sivasubramanian said.

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.

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I've been thinking a lot recently about the many ways in which artificial intelligence may change our lives. One of the biggest impacts may be on jobs, not only on the nature of work itself, but on the availability of work. Some crystal ball gazers are predicting that AI (working in concert with its older sibling, automation) will trigger massive job losses; others see AI producing a net gain in employment. Both views can be supported both logically and empirically; but they both can't be right. As Andy Kessler put it in a June 17 Wall Street Journal column, "The future happens, just not the way most people think."

As a partner in the Generation AI initiative, the Human Rights Center at UC Berkeley School of Law spent the Fall 2018 and Spring 2019 semesters researching how artificial intelligence technologies are being used in ways that impact children at home, at school, and at play. After reviewing and identifying the disparate child rights that might be disproportionately impacted, both positively and negatively, by its use, the team drafted a memo exploring the potential impacts AI is having and may have on children. This document summarizes the memo, providing a series of case studies to illustrate the various ways that artificial intelligence-based technologies are beginning to positively and negatively impact children's rights, and spotlighting critical questions that researchers, corporations, governments, educators and parents should be asking now in order to better promote children's rights and protect children from negative consequences. We hope that this memo will help a range of stakeholders better understand and begin to lay a framework for addressing the potential impact of artificial intelligence on today's children and on future generations. The authoring team of this memorandum are Mélina Cardinal-Bradette, Diana Chavez-Varela, Samapika Dash, Olivia Koshy, Pearlé Nwaezeigwe, Malhar Patel, Elif Sert, and Andrea Trewinnard, who conducted their research and writing under the supervision of Alexa Koenig of the UC Berkeley Human Rights Center.

We propose symbolic learning as extensions to standard inductive learning models such as neural nets as a means to solve few shot learning problems. We device a class of visual discrimination puzzles that calls for recognizing objects and object relationships as well learning higher-level concepts from very few images. We propose a two-phase learning framework that combines models learned from large data sets using neural nets and symbolic first-order logic formulas learned from a few shot learning instance. We develop first-order logic synthesis techniques for discriminating images by using symbolic search and logic constraint solvers. By augmenting neural nets with them, we develop and evaluate a tool that can solve few shot visual discrimination puzzles with interpretable concepts.

Robots helped build your car and pack your latest online shopping order. A chatbot might help you figure out your credit card balance. A computer program might scan and process your resume when you apply for work. What will work in America look like a decade for now? A team of economists at the McKinsey Global Institute set off to figure out in a new report out Thursday.

Years ago, MIT Professor Neil Gershenfeld had an audacious thought. Struck by the fact that all the world's living things are built out of combinations of just 20 amino acids, he wondered: Might it be possible to create a kit of just 20 fundamental parts that could be used to assemble all of the different technological products in the world? Gershenfeld and his students have been making steady progress in that direction ever since. Their latest achievement, presented this week at an international robotics conference, consists of a set of five tiny fundamental parts that can be assembled into a wide variety of functional devices, including a tiny "walking" motor that can move back and forth across a surface or turn the gears of a machine. Previously, Gershenfeld and his students showed that structures assembled from many small, identical subunits can have numerous mechanical properties.

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