CS Prof. Stuart Russell gave an engaging TED talk in April describing some of the problems in, and possible solutions for, creating a species that is smarter than humans.  He argues that building provably altruistic,  humble, and humanitarian machines might help us avoid some of the pitfalls inevitable in a future with superintelligent AI.

Computer vision pioneer Prof. Avideh Zakhor is the subject of a Mercury News profile titled "Avideh Zakhor: the brains behind Google Earth and Street View,"  which touches on her emigration from Iran,  the creation of the 3-D city modeling technology for a Defense Department-funded start-up which she ultimately sold to Google, and her current research on indoor mapping.  She also discusses the value of encouraging skilled immigrant workers to come to the U.S and the importance of getting more women into STEM fields.  "Maybe then we wouldn’t in Silicon Valley have a shortage of STEM workers — it makes it very hard for tech companies to operate; the labor market is very tight." she says.

Prof. Emeritus David Patterson is profiled in a CNBC article which describes how he postponed retirement to conduct research at Google into the Tensor Processing Unit (TPU), an ambitious new chip that's designed to run at least 10 times faster than today's processors and is sophisticated enough to handle the intensive computations required for artificial intelligence.  Without it, it is estimated that Google would have to double its data centers to support even a limited amount of voice processing.  Prof. Patterson described his work on the TPU when he returned to Berkeley as a Colloquium speaker on May 3rd.

U.C. Berkeley has the top ten most AMiner Most Influential Scholar Award winners across all fields of computer science in 2016 and the top five most award winners in the fields of Computer Vision, Database, Machine Learning, Multimedia, Security, Computer Networking, and System.  The 28 CS faculty members included in the rankings were among the 100 most-cited authors in 12 of the 15 research areas evaluated. Two were among the 100 most-cited authors in 3 different areas each: Scott Shenker ranked #1 in Computer Networking, #51 in System, and #99 in Theory; and Trevor Darrell ranked #8 in Mulitmedia, #18 in Computer Vision, and #100 in Machine Learning.  Out of the 700,000 researchers indexed, only 16 appeared on three or more area top 100 lists.  See a more detailed breakdown of our influential faculty scholars.

Computer pioneer Harry Huskey, who designed the G15--which might be called the first "personal computer"--at Berkeley in 1954, has died.  He taught and conducted research into computer language in the EE department from 1954 to 1967, when he left to found and direct the computer center at U.C. Santa Cruz.  Starting in the 1940s, he worked on the Eniac (the country’s first general-purpose programmable electronic computer), the Automatic Computing Engine, and the SWAC, before designing the G-15.  It was manufactured and sold by Bendix Aviation Corporation as the first computer designed to be used by a single person without the intervention of other operators.  At 950 lbs and the size of a refrigerator, it was much smaller than the other room-sized computers at the time, and cost just under $50,000 (or could be rented for about $1,500 a month),  a fraction of the millions of dollars that other systems cost.  At 101 years old, he was one of the last surviving scientists in the vanguard of the computer revolution.

CS undergraduates Tomás Vega Gálvez and Corten Singer have been chosen the $10,000 Lemelson-MIT “Drive it!” Undergraduate Team Winner for an open-source smart add-on system for wheelchairs. Vega and Singer created WheelSense, a modular, customizable add-on system for wheelchairs that provides spatial awareness for visually impaired users to identify obstacles and ease their navigation. It has three features: frontal staircase detection through auditory feedback, backward obstacle-avoidance assistance through auditory feedback, and lateral ramp-edge detection through haptic feedback. They hope to disrupt the expensive market for assistive technologies for the disabled community by making their technology open source.  The “Drive it!” Lemelson-MIT Student Prize rewards students working on technology-based inventions that can improve transportation.

EECS Chair and CS Prof. Jitendra Malik will discuss Deep Visual Understanding from Deep Learning as one of the keynote speakers at the Embedded Vision Summit on May 2, 2017.  The summit is the only event focused exclusively on the technologies, hardware, and software that bring visual intelligence to products.  This year, "It's all about deployable computer vision and deep learning" and will feature more than 90 expert presenters in 4 conference tracks over three days.

CS Prof. Emeritus David Patterson co-authored and presented a report on Tensor Processing Units (TPUs) at a regional seminar of the National Academy of Engineering, held at the Computer History Museum in Menlo Park on April 5, 2017.   TPUs, which have been deployed in Google datacenters since 2015, are printed-circuit cards which are inserted into existing servers and act as co-processors tailored for neural-network calculations.  Prof. Patterson says that TPUs are "an order of magnitude faster than contemporary CPUs and GPUs" with an even larger relative performance per watt.  According to an article for the IEEE Spectrum, TPUs are "built for doing inference," having hardware that operates on 8-bit integers rather than the higher-precision floating-point numbers used in CPUs and GPUs.

CS Prof. Michael Jordan, graduate students Philipp Moritz and Robert Nishihara, and research in the RISELab are featured in a Datanami article titled "Meet Ray, the Real-Time Machine-Learning Replacement for Spark."  Ray is one of the first technologies to emerge from RISELab, the successor to AMPLab and its host of influential distributed technologies including Spark, Mesos, and Tachyon. Ray is a new distributed framework designed to enable Python-based machine learning and deep learning workloads to execute in real-time with MPI-like power and granularity. This framework is ostensibly a replacement for Spark, which is seen as too slow for some real-world AI applications.

The paper “X-Trace: A Pervasive Network Tracing Framework”, authored by EECS alumi Rodrigo Fonseca (Ph.D. ’08) and George Porter (Ph.D. ’08) and Professors Randy Katz, Scott Shenker, and Ion Stoica, has received the 2017 Networked Systems Design and Implementation (NSDI) Test-of-Time Award. X-Trace was not the first tracing framework, but it was influential given that it was effectively the first framework for end-to-end tracing to focus on generality and pervasiveness. The researchers implemented X-Trace in protocols and software systems, and in their prize-winning paper, they set out to explain three different use scenarios: domain name system (DNS) resolution; a three-tiered photo-hosting website; and a service accessed through an overlay network.