Hungarian company AImotive is working towards developing an affordable autonomous driving system for $6000. The company has applied for permission to test the technology on the local roads near its California office, located near Google's HQ. While companies like Waymo and Uber use an expensive radar-like system called LIDAR (Light Detection and Ranging) in their self-driving car programs, AImotive claims to achieve the same using regular cameras combined with artificial intelligence. The company claims that this brings down the cost of converting a regular car into a driverless one to around $6,000 as opposed to $70,000-$100,000. "The whole traffic system is based on the visual system. Drivers don't have bat ears and sonars, you just look around and drive," said Laszlo Kishonti, CEO and Founder, AImotive.

Artificial intelligence is coming to banking. Here are five steps that can help you plan a strong AI strategy for your bank or credit union. Artificial intelligence (AI) might sound like something out of a science fiction novel, but it's becoming a reality faster than you might think. Owing to recent advancements in big data, computational hardware, and machine learning, artificial intelligence is becoming increasingly powerful and useful by the day. Andrew Ng, founder of the Google Brain project, describes artificial intelligence as the " new electricity."

We have seen a machine master the complex game of Go, previously thought to be one of the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks. All of this has given rise to a flurry of people claiming that the AI revolution is already upon us. Understanding the growth in the functional and technological capability of AI is crucial for understanding the real world advances we have seen.

Competitive advantage in business today means having the computing power to analyze oceans of data on the back end while providing a seamless customer experience up front. Artificial intelligence, machine learning and cloud computing are the new tools of the trade, and companies everywhere are striving to adopt them--with varying degrees of success. Wall Street Journal reporter Steven Norton discussed the challenges with Dheeraj Pandey, co-founder of Nutanix, a cloud-based platform that helps companies transform their IT infrastructure, and Lise Buyer, a partner at the IPO consultancy Class V Group. Edited excerpts of their conversation follow. NORTON: What are some common obstacles to getting companies to transform their IT architecture to artificial intelligence, machine learning and the cloud?

Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

What is the next wave of emerging technologies going to look like? The ones that chief information officers should be paying attention to? The Wall Street Journal's Rolfe Winkler spoke with Steve Herrod, managing director of General Catalyst, Peter Levine, general partner of Andreessen Horowitz, and Rich Wong, partner at Accel Partners. The conversation touched on machine learning and the future of big data and cloud computing. WINKLER: VCs are supposed to predict the future.

For those of you who have an interest, and or involvement in "Deep Learning" or want to learn more I've created this matrix. It's by no means all inclusive. It will provide you with a landscape of some Deep Learning resources to get you started or complement resources you might already have. The original version is available here as a 5-page PDF document. You can click on the 5 images below to zoom in.

This is programming assignment 3 from the course T81-855: Applications of Deep Learning at Washington University in St. Louis. All students must create a Kaggle account and submit a solution. Once you have submitted your solution entry log into Blackboard (at WUSTL) and submit a single file telling me your Kaggle name on the leaderboard (you do not need to register to Kaggle with your real name). This competition will be visible to the public, so there may be non-student submissions as well as student. The data set for this competition consists of 7 input columns that should be used to predict an outcome.

As Matt Asay observed last week, AI appears to be reaching "peak ludicrous mode," with almost every software vendor laying claim to today's most hyped technology. Hang on -- see what I did there? I used "AI" and "machine learning" interchangeably, which should get me busted by the artificial thought police. The first thing you need to know about AI (and machine learning) is that it's full of confusing, overlapping terminology, not to mention algorithms with functions that are opaque to all but a select few. This combination of hype and nearly impenetrable nomenclature can get pretty irritating.

Imaging and understanding the brain is getting so good we are on the cusp of truly enhancing it. This article originally appeared on SOSV.) As I start to look at the emerging trends of 2017 from the vantage of IndieBio, where we see hundreds of biotech startup applications and technologies per year, a few key themes are already emerging. Even as political landscapes change, science and technology continue to push forward. Most of us have seen science fiction shows that show future doctors regrowing and replacing entire organs.