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...

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.

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.

Summary: Whether you are a startup person or data science-minded executive in a larger organization what logic can you apply to spot the most compelling opportunities for AI in your organization. In 2014 Kevin Kelly, founder of Wired magazine and prolific futurist famously said, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Kevin you were clearly right. The Silicon Valley and every other tech startup haven are awash in companies trying to fulfill his vision. However, whether you are a startup person or data science-minded executive in a larger organization that's not enough information to spot compelling opportunities. What are the rules or guidelines you can apply to identify these transformative applications?

Eno is the first natural language SMS chatbot from a U.S. bank. And we launched Eno at SXSW on March 10, open as a pilot to Capital One customers. When it came to giving this new SMS chatbot a name, we looked for a gender-neutral name with human-like qualities consistent with Capital One's mission of bringing humanity to banking. We chose the name Eno because it's "one" (as in Capital One) spelled backwards. And we also liked that when we asked Google for the definition of Eno, the first result returned said "Awesome, cool." After deciding to create a gender-neutral character, we worked to define Eno's character traits and develop its backstory.

The basic Dexter interface isn't dissimilar to Wordpress or any other blogging platform you might have tried. There's a large main composing window with some toolbars up top and off to the sides. To get started, you type in an example of what the user says to the bot -- to keep it simple, you can just start with "hello," or a variety of salutations (hello, hi, sup). Then, you just decide what the bot will say in response -- this case, I went with the Lionel Ritchie classic "is it me you're looking for?" There's a window on the right that you can test the code in and make sure you're getting the responses you wanted, and then it's just a matter of hitting publish.

As artificial intelligence increasingly takes center stage in computer technology, large-scale orders are heading to Japan's supercomputer makers from research entities in need of the massive processing power those machines provide. Fujitsu is assembling a dedicated AI-use supercomputer for the Institute of Physical and Chemical Research, better known as Riken, which plans to start using it in April. Tokyo Institute of Technology, also known as Tokyo Tech, has ordered a large-scale system for AI-education purposes from SGI Japan, another supercomputer maker. The institute plans to introduce it in August. And industry insiders are looking forward to an even bigger opportunity: a huge AI-use supercomputer the National Institute of Advanced Industrial Science and Technology, or AIST, wants to start using in 2018.

Many high-performance organizations remain passionate about Vilfredo Pareto, the incisive Italian engineer and economist. They continue to be inspired by his 80/20 principle, the idea that 80% of effects (sales, revenue, etc.) come from 20% of causes (products, employees, etc). As machine learning and AI algorithmic innovation transform analytics, I'm betting that next-generation algorithms will supercharge Pareto's empirically provocative paradigm. Here are three important ways that AI and machine learning will redefine how organizations use the Pareto principle to digitally drive profitable innovation to levels beyond conventional analytics. First, greater volumes and variety of data guarantee that algorithms get the training they need to get smarter.

The advent of easily accessible, mondo computing power could open up lots of possibilities in the arts, not just in the startup and tech world. Brooklyn startup Paperspace has a new post out about how to use its product (which is sort of like AWS for the computational power required of machine learning) to make art. The post centers on "style transfer," which is taking the style of one image and imparting it onto the content of another. "The central problem of style transfer revolves around our ability to come up with a clear way of computing the'content' of an image as distinct from computing the'style' of an image," the post reads. "Before deep learning arrived at the scene, researchers had been handcrafting methods to extract the content and texture of images, merge them and see if the results were interesting or garbage."