The concept of autonomous vehicles is driving dynamism and innovation in the components industry. Sensors, power converters, GPS systems, wireless modules, control and communications technologies, and HMI products are all being developed with the autonomous vehicle in mind. In the meantime, Advanced Driver Assistance Systems (ADAS) are already producing revenue for the component makers. Here's a peek at some of the most interesting developments announced in the last few months. The first generation of self-driving cars used LIDAR systems to recognize terrain and obstacles.

In this project we want to design and develop a deep learning model to mimic driving behavior. The inputs are images and the outputs are control commands like steering angle. For simplicity we are only predicting steering angle. First I generated a large dataset from the simulator with my keyboard, but that dataset was not perfect and there were a lot of noisy samples. My model was not good.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. Over the past few weeks, we published a series of posts outlining expert opinions in data science, machine learning, artificial intelligence, and related fields. In an encore post of this series, we bring you the collected responses to an amalgam question -- including experts from all of the previous posts' fields -- while adding a second dimension this time around. I'd like to thank one of my researchers, Alekh Agarwal, for great input here. The way to increase the number of women in AI, ML and data science is two-fold. First, we must expand the definitions of the fields to include their interaction with the other sciences, including the biological and social sciences.

What does artificial intelligence have in common with the price of eggs? Say you're trying to decide between 9 or 10 different varieties of eggs at the store. One catches your eye: "All natural." Well, that's nice, natural is good and they're only 30 cents more -- you buy those. Now, those chickens and the eggs they produce may or may not be more natural than the others -- because there's no official or even generally agreed-upon definition of natural.

People have always had a fascination with robots – from Da Vinci's automatons in the 15th century right the way through to Channel 4's biggest drama hit in 20 years, Humans, which is back for a second season. The power and capability of robotics and computing is increasing at pace – Google Deepmind's AlphaGo programme finally beat Lee Sedol at the game'Go' earlier this year, something programmes have aimed for since IBM's Deep Blue beat chess champion Garry Kasparov in 1995. Our fascination at this progress is certainly tinged with fear. While it may make for excellent TV, we are not comfortable with the idea of machines coming for our jobs, our partners and world domination. The robot apocalypse is still (hopefully) a while off, so rather than hiding in a bunker and waiting for the end, we should instead look to the world of chess as an example of how to make the most of technology.

Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately. The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you.

At the end of December YerevaNN scientific educational foundation presented a deep learning guide, which received large recognition around the world. YerevaNN founder Hrant Khachatryan told Itel.am that Reddit, Hacker News and a number of other famous website contributed to making the guide popular. The guide was one of Google top global searches. "We had more than 40 000 visits in just two weeks, 35% of them being from USA, 7.7% from China and 3.7% from Armenia," he said. Touching on how they came up with the idea of such a guide, Hrant Khachatryan noted that after foundation of YerevaNN many people asked them how they could acquire deep learning knowledge.

Audi and Nvidia have been collaborating for some time, but at CES 2017, the companies made their biggest joint announcement yet. Using artificial intelligence and deep learning technology, the companies will bring fully automated driving to the roads by 2020. To achieve this, Audi will leverage Nvidia's expertise in artificial intelligence, the fruits of which are already being shown at CES. Audi's Q7 Piloted Driving Concept is fitted with Nvidia's Drive PX 2 processor and after only four days of "training," the vehicle is already driving itself over a complex road course. This is due to the Drive PX 2's incredible ability to learn on the go, which is a far cry from the first driverless cars that needed pre-mapped routes to function properly. "Nvidia is pioneering the use of deep learning AI to revolutionize transportation," Nvidia CEO Jen-Hsun Huang said. "Audi's adoption of our Drive computing platform will accelerate the introduction of next-generation automated vehicles, moving us closer to a future of greater driving safety and new mobility services."

Thanks to advanced new machine learning techniques, artificial intelligences are better at performing human tasks than ever. AIs can tell you what's in your photos, beat you at chess, design typefaces, dream up entirely new cities, and even tweet like Donald Trump--often better than the average person. They can't apply what they've learned from one problem to another--which is why even the best AIs are idiot savants: really smart in one arena, and dumb as sticks in all others. So how can AIs reach this elusive general intelligence? OpenAI--an artificial intelligence research nonprofit backed by Microsoft, Elon Musk, and Peter Thiel--thinks it involves AIs playing video games.

How do we unlock the true value of data? Matt Lovell, CTO of Centiq, argues why we need to be taking a different approach to data science… Nowadays, collecting data has never been easier and faster, but understanding its true meaning and value still remains a challenge. IBM estimates 90% of all the data existing in the world today has been generated in the last 24 months. For data-driven organisations such as the NHS, which has disparate trusts throughout Britain generating vast amounts of data, having the ability to harness this information and analyse it correctly to get the right insights has never been more important. In the technology profession, we may have, ironically, promoted Big Data as a panacea without truly and fully understanding the issues it can solve.