How many people could self-driving cars kill before we would no longer tolerate them? This once-hypothetical question is now taking on greater urgency, particularly among policymakers in Washington. The promise of autonomous vehicles is that they will make our roads safer and more efficient, but no technology is without its shortcomings and unintended consequences -- in this instance, potentially fatal consequences. "What if we can build a car that's 10 times as safe, which means 3,500 people die on the roads each year. Would we accept that?" asks John Hanson, a spokesman for the Toyota Research Institute, which is developing the automaker's self-driving technology.

The digital revolution is faster and more comprehensive than the Industrial Revolution at the beginning of the last century. It requires less capital, and focuses on intellectual and digital innovation, which is affordable to many. The innovations produced have therefore had a much more immediate impact on the market. A wide variety of services are now offered by the leaders of this digital revolution, including Google, Amazon, Facebook and Apple. This has resulted in a rush to set up data-oriented cultures and predictive algorithms, to increase company knowledge and automate more business processes.

Amazon Lex is the tech that powers Alexa, which is used in the Amazon Echo (above) and Fire TV. With the rise of personal assistant chatbots, the tools required for organizations to build their own assistants are being moved to the cloud to give developers the ability to leverage these technologies in their products. Foremost among these tools is Amazon Lex, the speech recognition and natural language processing technology that powers the Alexa virtual assistant, used in the Amazon Echo speaker and Fire TV digital media player. TechRepublic's smart person's guide to Amazon Lex is an introduction to the language processing technology. This "living" guide will be updated periodically as further integrations are released.

IT professionals today have more demands (and more acronyms) on their time than ever. Application development and delivery are changing rapidly and increasing in complexity, revealing the limitations of traditional DNS approaches in achieving modern network goals. The IT department has made use of open source platforms for decades to provide DNS and traffic management in their internal networks and for their public, internet-facing services. The majority of DNS servers, both on the internet and in enterprise intranets, are open source-based solutions such as BIND, djbdns, PowerDNS, gdnsd and NSD. It can feel like shoving a square peg into a round hole to try to adapt these solutions to perform the functions needed in today's environments.

Imagine a world where nurses and doctors use remote monitoring solutions not only to analyze a patient's vitals such as blood pressure, weight and heart-rate, but also to interpret a patient's mood based on speech and tone analysis during a scheduled phone conversation,

SAM Seamless Network protects network equipment at the application level. For example, its software will enable ISPs to protect home routers by providing a security hub for devices in the home.

Google has launched its Fact Check tag in three more countries, as it steps up its efforts to combat fake news. The label will now appear in the expand story box in Google News search results and the Google News and Weather app in Argentina, Brazil and Mexico. The feature first launched in the US last October, ahead of the Presidential election, and has since been rolled out in France and Germany. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

Artificial intelligence, machine learning, and neural networks-based deep learning are concepts that have recently come to dominate venture capital funding, startup formation, promotion and exits, and policy discussions. The highly-publicized triumphs over humans in Go and Poker, rapid progress in speech recognition, image identification, and language translation, and the proliferation of talking and texting virtual assistants and chatbots, have helped inflate the market cap of Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these companies dominate the headlines--and the war for the relevant talent--other companies that have been analyzing data or providing tools for analysis for years are also capitalizing on recent AI advances. A case in point are Equifax and SAS: The former developing deep learning tools to improve credit scoring and the latter adding new deep learning functionality to its data mining tools and offering a deep learning API. Neural network created in SAS Visual Data Mining and Machine Learning 8.1 Both companies have a lot of experience in what they do.

Scientists know that the first signs of autism can appear in early childhood, but reliably predicting that at very young ages is difficult. A behavior questionnaire is a crapshoot at 12 months. However, artificial intelligence might just be the key to making an accurate call. University of North Carolina researchers have developed a deep learning algorithm that can predict autism in babies with a relatively high 81 percent accuracy and 88 percent sensitivity. The team trained the algorithm to recognize early hints of autism by feeding it brain scans and asking it to watch for three common factors: the brain's surface area, its volume and the child's gender (as boys are more likely to have autism).

Building neural networks models and implementing learning consist of lots of math this might be boring. Herein, some tools help researchers to build network easily. Thus, a researcher who knows the basic concept of neural networks can build a model without applying any math formula. So, Weka is one of the most common machine learning tool for machine learning studies. It is a java-based API developed by Waikato University, New Zealand.