Prof. Pieter Abbeel is featured in a New York Times article titled "Building A.I. That Can Build A.I.," about how Google and others, in competition for a small pool of qualified researchers, are looking for automated ways to deal with a shortage of artificial intelligence experts.   The key might be to build algorithms that analyze the development of other algorithms, learning which methods are successful and which are not--leading to  more effective machine learning.  This could help companies build systems with artificial intelligence even if they don’t have extensive expertise in that area.  Abbeel and his team demonstrate techniques that could allow robots to learn new tasks based on what they have learned in the past. “Computers are going to invent the algorithms for us, essentially,” said  Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.”

A UC Berkeley/UC Riverside research group that includes Prof. Jeffrey Bokor, Prof. Sayeef Salahuddin, postdoc Charles-Henri Lambert, postdoctoral fellow Jon Gorchon, and EE graduate student Akshay Pattabi have developed a new, ultrafast method for electrically controlling magnetism in certain metals, a breakthrough that could lead to greatly increased performance and more energy-efficient computer memory and processing technologies.  Their findings are published in both Science Advances (Vol. 3, No. 49, Nov. 3, 2017) under the title Ultrafast magnetization reversal by picosecond electrical pulses and Applied Physics Letters (Vol. III, No. 4, July 24, 2017) under the title Single shot ultrafast all optical magnetization switching of ferromagnetic Co/Pt multilayers.  “The development of a non-volatile memory that is as fast as charge-based random-access memories could dramatically improve performance and energy efficiency of computing devices,” says Bokor. “That motivated us to look for new ways to control magnetism in materials at much higher speeds than in today’s MRAM.”

A paper co-authored by postdoc Pramod Subramanyan, grad student Rohit Sinha, alumnus Ilia Lebedev (B.S. '10), alumnus and MIT Prof. Srinivas Devadas (M.S. '86/Ph.D. '88), and EECS Prof. Sanjit A. Seshia has won Best Paper Award at the 2017 ACM Conference on Computer and Communications Security (CCS).  The paper, A Formal Foundation for Secure Remote Execution of Enclaves, introduces a formal modeling and verification methodology for secure remote execution based on the notion of a trusted abstract platform.  CCS is the flagship annual conference of the Special Interest Group on Security, Audit and Control (SIGSAC) of the Association for Computing Machinery (ACM).

EECS undergraduate Rohan Phadte and Interdisciplinary Studies major Ash Bhat are the subjects of a Wired article titled "The College Kids Doing What Twitter Won't," about their creation of a Google Chrome browser extension that checks whether Twitter profiles are bots.  It describes the genesis of their partnership, which they call RoBhat Labs, and their efforts to stop the proliferation of fake Twitter accounts from flooding the internet with propaganda. It also highlights the roles played by CS Assistant Prof. Joseph Gonzalez and the class Data Science 100.

Three papers with Berkeley authors received the Helmholtz Prize at the International Conference on Computer Vision (ICCV) 2017 in Venice, Italy.  This award honors  papers that have stood the test of time (more than ten years after first publication) and is bestowed by the IEEE technical committee on Pattern Analysis and Machine Intelligence (PAMI).    Seven papers won this year, among them: "Recognizing action at a distance," by A Efros, A Berg, G Mori and J Malik, ICCV 2003; "Discovering objects and their location in images," by J Sivic, B Russell, A Efros, A Zisserman and W Freeman, ICCV 2005; and "The pyramid match kernel: Discriminative classification with sets of image features," by K Grauman and T Darrell, ICCV 2005."

Mattel has launched a line of biologically inspired foldable robot bugs designed in collaboration with Dash Robots, a spin-off of the Biomimetic Millisystems Lab (BML).  The researchers at BML, under the direction of Prof. Ron Fearing, draw inspiration from nature to build more efficient robotics.  The new toys, called  Kamigami, let kids build their own robotic bugs, like mantises, ladybugs and scorpions.  Each $50 kit contains parts of a six-legged robot (with an accelerometer, gyroscope, and IR transmitter/receiver) and foldable plastic origami sheets to transform each robot into a different creature.

The experiences of EECS freshman Rebecca Chery, a participant in the Pre-Engineering Program (PREP), are described in a Berkeley Engineering article titled "PREP by design."  PREP is a three-week program that gives incoming engineering majors a head start on academics, networking and professional development. Chery's team used equipment at the Jacobs Institute makerspace to create a phone case with a keyfob inside that would trigger a door to open once the phone case detected a sensor in close proximity. The prototype was chosen by the PREP students as their favorite project from the design challenge.

Berkeley DeepDrive (BDD) and Nexar announced the release of 36,000 high frame-rate videos of driving, in addition to 5,000 pixel-level semantics-segmented labeled images, and invited public and private institution researchers to join the effort to develop accurate automotive perception and motion prediction models.  The BDD Industry Consortium, led by EE Prof. Trevor Darrell , investigates state-of-the-art technologies in computer vision and machine learning for automotive applications. Nexar, as a member of the consortium, contributes video and images captured by its road safety AI camera application deployed in over 100 countries worldwide.  The Nexar driving data will be used for academic research (for activities like training and validating models for real world applications) as well as open crowdsourced research challenges based on parts of the driving data.

EECS alumnus Bryan Catanzaro (Ph.D. '11) is interviewd by Byron Reese for episode 13 of his series Voices in AI.  Catanzaro, who is the head of Applied AI Research at NVIDIA, discusses sentience, transfer learning, speech recognition, autonomous vehicles, and economic growth.  "I like to think about artificial intelligence as making tools that can perform intellectual work.  Hopefully, those are useful tools that can help people be more productive in things that they need to do," he says.