The self-driving car space is getting increasingly more cutthroat. The sheer number of lawsuits filed recently are a testament to that. Tesla, for example, is suing its former Autopilot director Sterling Anderson. The lawsuit claims Anderson stole data for a competing venture, Aurora Innovations, that hasn't even come out of stealth mode yet. "In their zeal to play catch-up, traditional automakers have created a get-rich-quick environment. Small teams of programmers with little more than demoware have been bought for as much as a billion dollars. Cruise Automation, a 40-person firm, was purchased by General Motors in July 2016 for nearly $1 billion. In August 2016, Uber acquired Otto, another self-driving startup that had been founded only seven months earlier, in a deal worth more than $680 million."

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

Want to see better marketing results? You might want to jump on the artificial intelligence bandwagon. A February 2017 study of 200 businesses showed that retailers plan on expanding their marketing, particularly social media and mobile marketing, and incorporating artificial intelligence to better personalize the customer's journey as well as analyze results. The study was conducted by Sailthru, a cross-channel management platform company. When discussing what marketing channels best met marketing goals, 56 percent of businesses surveyed said their websites generate the most online revenue, with email marketing and mobile coming in next at 18 percent and 7 percent.

IBM researchers have set a milestone in conversational speech recognition by achieving a new industry record of a 5.5 percent word error rate, surpassing its previous record of 6.9 percent, according to the company's blog post. The researchers conducted a difficult speech recognition task to achieve this record, where they recorded conversations between humans discussing typical everyday topics like "buying a car." This recorded corpus, titled "SWITCHBOARD", has been used for over two decades to benchmark speech recognition systems. To achieve the 5.5 percent record, the researchers focused on extending the company's application of deep learning technologies by combining LSTM (Long Short Term Memory) and WaveNet language models with three strong acoustic models. The first two models were six-layer bidirectional LSTMs, with one of the models being equipped with multiple feature inputs and the other being trained with speaker-adversarial multi-task learning.

People spend more and more time on their smartphones, but with such a limited set of apps that it's becoming harder and harder for newcomers to reach them. Chatbots are the solution, two proponents told a crowded ballroom at SXSW on Saturday, day two of the conference. That's because they play out on one of the entrenched sets of apps that people already chronically use, they argued: messaging apps. "We believe that conversations will become the new user interface," said Laura Newton, a product manager at the youth-oriented messaging app Kik. Consumers don't know what to expect from bots, but are easily disappointed.

This new form of Artificial General Intelligence runs over the top of Deep Learning and Machine Learning based systems to understand and act upon changing and unlabeled data in real-time. It's the inevitable conclusion for how all Artificial Intelligence will be built in the future. And PROME has a 6-year head start.

Self-Driving Trucks -Continual developments are making long-haul trucks that drive themselves for extended stretches on highways more attainable. Significant challenges remain, including having sensors and code match the situational awareness of a professional trucker. In the short term, this technology may free truck drivers to complete routes more efficiently, but it could also erode their pay and eventually replace many of them altogether. Key companies to watch in this area include Daimler, Otto, Peterbilt, and Volvo. Availability is prediction to be in 5 to 10 years.

This white paper discusses the advantages of using the PySpark API, which enables the use of Python to interact with the Spark programming model. It starts with a basic description of Spark and then describes PySpark, its benefits, and when it is appropriate to use instead of "pandas" open source...

Among all of the self-driving startups working towards Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. "If you look at the long-term possibilities of these algorithms and how people are going to build [self-driving cars] in the future, having a learning system just makes the most sense. There's so much complication in driving, there are so many things that are nuanced and hard, that if you have to do this in ways that aren't learned, then you're never going to get these cars out there." It's only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area--even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars. Last month, we went out to California to take a ride in one of Drive's cars, and to find out how they're using deep learning to master autonomous driving.

Machine Learning is the new buzz word and AI is the slang word these days. What does happen in this exiting field in Europe? Is AI common ground for all businesses or the exclusive territory for a few? Who has managed to validate a business model for autonomous vehicles or chatbots?Whatdoesdata-drivenor API-firstbusinessmodelslook like? With this report we want to provide a comprehensive review of investment in startups and high-growth AI and Data Analytics companies across 22 countries in Europe.