2016 September

New California Law Allows Test of Autonomous Shuttle With No Driver

A bill signed into law on Thursday by California Governor Jerry Brown allows a self-driving vehicle with no operator inside to test on a public road, a key step enabling a private business park outside San Francisco to test driverless shuttles.

Self-driving cars are already allowed to test on California public roads by 15 automakers, technology companies and startups, including Alphabet’s Google, Ford, Honda and Tesla. But under current state regulations, a person must be in the driver’s seat for monitoring, and the car must have brakes and a steering wheel.

The bill introduced by Democratic Assemblywoman Susan Bonilla allows testing in Contra Costa County northeast of San Francisco of the first full-autonomous vehicle without a steering wheel, brakes, accelerator or operator.

New California Law Allows Test of Autonomous Shuttle With No Driver

 

 

FDA bans antiseptic chemicals from soaps; no proof they work

The federal government Friday banned more than a dozen chemicals long-used in antibacterial soaps, saying manufacturers failed to show they are safe and kill germs.
“We have no scientific evidence that they are any better than plain soap and water,” said Dr. Janet Woodcock, the Food and Drug Administration’ drug center director, in a statement.
Friday’s decision primarily targets two once-ubiquitous ingredients—triclosan and triclocarban—that some limited research in animals suggests can interfere with hormone levels and spur drug-resistant bacteria.
The 19 banned chemicals have long been under scrutiny, and a cleaning industry spokesman said most companies have already removed them from their soaps and washes.
The FDA said it will allow companies more time to provide data on three additional chemicals, which are used in most antibacterial soaps sold today.

http://medicalxpress.com/news/2016-09-fda-antiseptic-chemicals-soaps-proof.html

 

 

Latest IoT DDoS Attack Dwarfs Krebs Takedown At Nearly 1Tbps Driven By 150K Devices

If you thought that the massive DDoS attack earlier this month on Brian Krebs’ security blog was record-breaking, take a look at what just happened to France-based hosting provider OVH. OVH was the victim of a wide-scale DDoS attack that was carried via network of over 152,000 IoT devices.
According to OVH founder and CTO Octave Klaba, the DDoS attack reached nearly 1 Tbps at its peak. Of those IoT devices participating in the DDoS attack, they were primarily comprised of CCTV cameras and DVRs. Many of these types devices’ network settings are improperly configured, which leaves them ripe for the picking for hackers that would love to use them to carry our destructive attacks.

http://hothardware.com/news/latest-iot-ddos-attack-dwarfs-krebs-takedown-at-nearly-1-terabyte-per-second

 

 

A Neural Network for Machine Translation, at Production Scale

Ten years ago, we announced the launch of Google Translate, together with the use of Phrase-Based Machine Translation as the key algorithm behind this service. Since then, rapid advances in machine intelligence have improved our speech recognition and image recognition capabilities, but improving machine translation remains a challenging goal.

Today we announce the Google Neural Machine Translation system (GNMT), which utilizes state-of-the-art training techniques to achieve the largest improvements to date for machine translation quality. Our full research results are described in a new technical report we are releasing today: “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation” [1].

In addition to releasing this research paper today, we are announcing the launch of GNMT in production on a notoriously difficult language pair: Chinese to English. The Google Translate mobile and web apps are now using GNMT for 100% of machine translations from Chinese to English—about 18 million translations per day. The production deployment of GNMT was made possible by use of our publicly available machine learning toolkit TensorFlow and our Tensor Processing Units (TPUs), which provide sufficient computational power to deploy these powerful GNMT models while meeting the stringent latency requirements of the Google Translate product. Translating from Chinese to English is one of the more than 10,000 language pairs supported by Google Translate, and we will be working to roll out GNMT to many more of these over the coming months.

https://research.googleblog.com/2016/09/a-neural-network-for-machine.html

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