We may have just seen a major player in the drive towards autonomous cars apply screeching brakes. Apple has reportedly abandoned its plans to build its own self-driving electric vehicle and is instead going to focus on the underlying autonomous software. A similar initiative to produce a fully autonomous car by Google also appeared to run out of steam. Building self-driving cars clearly poses a challenge that even the world's top technology giants can't yet meet. So what is it about building autonomous cars that is proving to be such a challenge? The high-value consumer electronics and software industry is used to very different margins than the cut-throat automotive sector, which has tough market entry conditions and tribal supply-chain relationships.

Heart of the Tesla's new autonomous driving hardware, that some day will enable fully self-driving cars, is the latest NVIDIA DRIVE PX 2 AI computing platform (see live presentation of it in action below). NVIDIA DRIVE PX 2 is the open AI car computing platform that enables automakers and their tier 1 suppliers to accelerate production of automated and autonomous vehicles. For NVIDIA, DRIVE PX 2 is now in full production as Tesla requires thousands of units each month for manufacturing of the Model S and Model X, soon that number could be tens of thousands per month when the Model 3 assembly starts later next year. Tesla Motors has announced that all Tesla vehicles -- Model S, Model X, and the upcoming Model 3 -- will now be equipped with an on-board "supercomputer" that can provide full self-driving capability. The computer delivers more than 40 times the processing power of the previous system. It runs a Tesla-developed neural net for vision, sonar, and radar processing.

"As digitization and machine intelligence continues to proliferate and get pervasive, the need for customer intimacy, transparency, and security of data are becoming essential to next generation business networks," said Nij Chawla, Chief Product Officer of CognitiveScale. "CognitiveScale is one of the first companies to combine Blockchain, big data, and machine learning for industry-specific outcomes that power the next-generation Internet of Trust." CognitiveScale provides two products called ENGAGE and AMPLIFY that use machine intelligence to transform customer experience at the edge of the business and deploy self-learning, self-assuring business processes at the core. The company's product portfolio has been enhanced in the past year to address an evolving market, where trust, relevance, and assurance are becoming increasingly integrated and business critical. CognitiveScale will use Blockchain technology to underpin its products to put additional "smarts" in Blockchain smart contracts for multiple industries and processes, including financial services, healthcare, and procurement.

"THE development of full artificial intelligence could spell the end of the human race," Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it. Dread that the abominations people create will become their masters, or their executioners, is hardly new. But voiced by a renowned cosmologist, a Silicon Valley entrepreneur and the founder of Microsoft--hardly Luddites--and set against the vast investment in AI by big firms like Google and Microsoft, such fears have taken on new weight.

In the last twelve months, we've witnessed a huge surge in the development and adoption of chatbots, artificial intelligence (A.I.), and machine learning. Many startups, including my own (ReplyYes), are utilizing A.I. and chatbots to help consumers engage with brands through their mobile devices in interesting and creative ways. Examples include the Domino's chatbot, which enables customers to order a pizza through Facebook Messenger, the Burberry chatbot for London Fashion Week that helps customers order products they see on the runway, and Lowercase Alpha that helps founders and friends of Chris Sacca's venture capital firm Lowercase discover some of the best new apps in the world. Given the increasing interest and venture capital dollars being spent to build creative chatbot and A.I. solutions, we've developed eight predictions that outline where we think things will evolve in the next 18-24 months. We're sure there are many additional trends that we're not predicting that will come to fruition.

Right now, these systems are fairly superficial, but they are becoming more complex and life-like. Could we consider a system to be suffering when its reward functions give it negative input? What's more, so-called genetic algorithms work by creating many instances of a system at once, of which only the most successful "survive" and combine to form the next generation of instances. This happens over many generations and is a way of improving a system. The unsuccessful instances are deleted.

It says "we are working on providing a GPU build as well." If GPU is enabled you need to install the CUDA 8.0 Toolkit and CUDNN 5.1. Thanks, they should probably update the readme then, it's a bit misleading when they say "CPU support only" under current known limitations:) Anyway, great news! It is the same but as I've got the link from the forked readme there is the TF repo

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

This dataset was used to train a system to automatically extract process models from paragraphs that describe processes. The dataset consists of 200 paragraphs that describe biological processes. Each paragraph is annotated with its process structure, and accompanied by a few multiple-choice questions about the process. Each question has two possible answers of which exactly one is correct. The dataset contains three files: 1. bioprocess-bank-questions.tar.gz:

A new report from the Obama Administration focuses on the opportunities, considerations, and challenges of Artificial Intelligence (AI). Today, to ready the United States for a future in which Artificial Intelligence (AI) plays a growing role, the White House is releasing a report on future directions and considerations for AI called Preparing for the Future of Artificial Intelligence. This report surveys the current state of AI, its existing and potential applications, and the questions that progress in AI raise for society and public policy. The report also makes recommendations for specific further actions. A companion National Artificial Intelligence Research and Development Strategic Plan is also being released, laying out a strategic plan for Federally-funded research and development in AI.