Uber Self-Driving Crash Calls Safety, Rules Into Question

U.S. News

Herzberg's death occurs at time when eagerness to put autonomous vehicles on public roads is accelerating in Silicon Valley, the auto industry and state and federal governments. More than 100 auto manufacturers and industry associations in early March sent a letter urging Congress to expedite passage of a proposal from Sen. John Thune, R-South Dakota, that aims to provide regulatory oversight and make it easier to deploy the technology.

Why Deep Learning over Traditional Machine Learning?


Artificial Intelligence is on a rage! All of a sudden every one, whether understands or not, is talking about it. Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts Machine Learning and Deep Learning. But lately, Deep Learning is gaining much popularity due to it's supremacy in terms of accuracy when trained with huge amount of data. Machine Learning has become necessary in every sector as a way of making machines intelligent.

A Gentle Introduction to Neural Networks for Machine Learning Codementor


We need machine learning for tasks that are too complex for humans to code directly, i.e. tasks that are so complex that it is impractical, if not impossible, for us to work out all of the nuances and code for them explicitly. So instead, we provide a machine learning algorithm with a large amount of data and let it explore and search for a model that will work out what the programmers have set out to achieve. Let's look at these two examples: Then comes the Machine Learning Approach: instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical hand-written program -- it may contain millions of numbers. If we do it right, the program works for new cases, as well as the ones we trained it on. If the data changes, the program can change too by training from the new data.

Moore's Law is fizzling. One chipmaker thinks its flexible Everest design offers an answer


Moore's Law, the principle that chips are supposed to get smaller and faster every few years, is faltering. But one company, Xilinx, thinks that's actually good news for a new type of flexible processors it expects to sell next year. You've heard of Intel, Apple and Samsung, some of the biggest chipmakers around. But you probably don't know Xilinx unless you're building things like high-end network equipment or self-driving cars. Xilinx's new chief executive, Victor Peng, hopes to change that.

7 examples of how AI and machine learning are changing the arts


We read these words constantly. We also hear they will leave no area or industry untouched. How have they influenced or aided highly creative areas? Let's take a look at some ways people have started to pair these technologies with art. At Toronto's 2017 Expo for Design, Innovation, and Technology (EDIT), architects suspended in the air a large, intricate glasswork, inspired by the shape of a nerve cell, in an old, abandoned soap factory.

Design Challenge: Rapid Prototyping a functioning Augmented Reality App


During one of our weekly design team meetings at Marino Software we discussed use cases for Augmented Reality (AR). One use case that stood out was a way for people with specific dietary requirements to find suitable products in a supermarket. Discovering what products are suitable can be a real pain if you have any specific dietary needs not consistent with the mainstream. What if there was a quick and easy way to see if something is suitable? What if your phone could highlight products to make finding and choosing easier?

Global Healthcare Fraud Detection Market 2018 Forecast to 2027


The application machine learning for fraud detection is evolving 3.2 Drivers 3.3 Constraints 3.4 Industry Attractiveness 3.4.1 Bargaining power of suppliers 3.4.2

Researchers use AI, big data and machine learning to find best place in the world to live


Researchers at analytics firm SAS claim to have created an artificial intelligence (AI) program that can rank the best places to live in the world using a range of publicly available data sources. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Affectiva Automotive AI helps cars monitor your emotions


The Robotics Summit and Showcase is just a couple months away. Find out all about our agenda here and register by April 20 for a 20% discount to learn from the best in the robotics industry. Affectiva Automotive AI hopes to improve driver safety. Artificial intelligence (AI), to date, has helped autonomous vehicles mainly by monitoring the world around them. As we learned from the fatal Uber self-driving car crash, unfortunately, the technology is not perfect.