Machine Learning Roundup (September 29, 2016)
Machine learning (ML) and deep learning (DL) content from the past 24 hours. ICYMI.
On the Electronic Frontier Foundation’s Deeplinks blog, Gennie Gebhart and Erica Portnoy ponder privacy issues raised by facial recognition technology.In Fortune, Jeff John Roberts enumerates what companies get wrong about ML.
Also in Fortune, Roger Parloff explains why DL is suddenly changing your life.
Katyanna Quach quotes Oren Etzioni, CEO of the Allen Institute of Artificial Intelligence, who cautions against overhyping this stuff.
In Bloomberg, Saijel Kishan quotes quant hedge fund Two Sigma’s founder David Siegel, who thinks AI is stupid. Scroll down to the bottom of this page, and you’ll see that Two Sigma is hiring.
IBM’s Ginny Rometty lectures bankers, touts Watson. Under Rometty’s leadership, IBM’s revenue and profit have declined 20% so far while bank profits soared.
Tiffany Fox profiles the Pattern Recognition Lab at the University of California, San Diego.
The Penn State Daily Collegian profiles Alayna Kennedy, who will present a paper on the use of neural networks in human prosthetics.
In the EURASIP Journal on Information Security, a paper about Hidost, which uses ML to detect malicious files.
Leslie Ellis reports on a machine learning workshop for cable and network execs.
MIT Technology Review describes how Los Alamos uses ML to track disease outbreaks around the world.
ML outperforms inexperienced radiologists differentiating malignant and benign thyroid nodules, according to a report in Diagnostic Imaging.
The Japan Times reports on AI-assisted medical diagnosis.
Adam Stone describes how ML transforms customer care.
Professional Planner describes ML in quant investing.
In Fierce Biotech, Stacy Lawrence summarizes “Surgery 4.0”, robotic surgery guided by AI.
Mohan Devie surveys potential uses of ML/DL/AI in financial services.
In Ars Technica UK, Bob Dormon describes how grocers seek to use ML/DL to sort groceries.
In RTInsights, Kai Waehner explains how to avoid the anti-pattern. Read the article to see what that is.
Facebook’s AI guru Yann LeCun touts unsupervised learning.
Software and Services
Also, Google adds ML tools to Google Analytics.
Sight Machine announces release 2.0 of its ML-driven manufacturing software.
Tessera Technologies’ FotoNation business unit announces DL-driven facial recognition, iris recognition solutions.
In ExtremeTech, Graham Templeton explains the potential of neuromorphic ‘brain’ chips.
CEVA introduces DSP-based hardware accelerator, software framework for DL on low-power embedded systems (such as smartphones, robots, drones and autonomous vehicles.)
NVIDIA announces ‘Xavier’ System-on-Chip (SoC) designed for DL on autonomous vehicles.
E4 Computer Engineering announces the availability of E4 OP206, a system for DL with the latest IBM POWER8 chip, NVIDIA NVLink and up to four Tesla P100 GPU-accelerated platforms.
Amazon, Facebook, Google, IBM and Microsoft launch an AI partnership which they describe as an initiative to “advance public understanding and formulate best practices,” but which some might describe as an oligopolistic combination to restrain competition. Linkapalooza here.
ML startup DataRobot celebrates its 110 millionth predictive model.
Security vendor Webroot acquires Cyberflow, which specializes in anomaly detection.
Skymind raises $3M to develop an open-source deep-learning library for Java. Because we don’t have enough of those already.
Chris Pash reports surging interest in ML-driven security vendor BrainChip following an announced deal with a Vegas casino.
Sales conversion specialist Apptus secures $88 million in late-stage funding.
Two Sigma is hiring.
For PhDs, some open positions in Switzerland, which doesn’t suck.
Airbus Toulouse has an internship in ML, for European youth who can’t find permanent jobs.