Whether or not an artificial intelligence (AI) ought to be granted patent rights is a timely dilemma given the increasing proliferation of AI in the workplace. AI technology has been applied effectively in medical advancements, psycholinguistics, and tourism and food preparation. Even a film written by an AI recently debuted online, and AI has sneaked into the legal profession. One might argue that Intellectual Property (IP) laws and IP Rights were designed to excusively benefit human creators and inventors, and thus would exclude non-humans from holding IP rights. However, many IP laws were drafted well before the emergence of AI, and in any case do not explicitly require that a creator or inventor be'human.'

Artificial Intelligence (AI) may have a big role in Africa's eHealth strategies. An article in Wired says it's hugely successful at poker too. From the 1940s, chess was the test of AI. It's a game of structured rules that computers could learn. To succeed, it has to outmanoeuvre other players' bluffs and intuition and deal with only partial information about the state of each game too, which aren't structured or rule-based. Libratus, built by Brown and Sandholm, two computer science researchers at Carnegie Mellon University, did it.

We are at the historic moment, where we have to decide on the right path--a path that allows us all to benefit from the digital revolution. Will Democracy Survive Big Data and Artificial Intelligence?

This is a particularly tough time for broadcasters and service providers. There's more competition than ever before thanks to instant streaming and on-demand viewing, and each company is in a battle for the best content. Pay TV operators have built enormous on-demand catalogues, and broadcasters are expanding their online services with more library content and short-form video. The ultimate goal for each company is to draw in as many viewers as possible, and ultimately to keep them engaged for as long as possible, too. But, with an ever-expanding sea of content in front of them, it's getting more difficult for viewers to choose what they want to watch.

Toyota (NYSE:TM) has a brand-new self-driving test vehicle -- and it's designed to help the company develop two different approaches to advanced safety technology, simultaneously. Like most of its big global rivals, Toyota is aggressively pursuing full self-driving technology. But Toyota has also put a lot of work into a second research path, toward a driver-assist system that goes well beyond anything currently on the market. The new test vehicle was created by the Toyota Research Institute (TRI), a research facility based in Silicon Valley that Toyota opened early last year. It's based on a current-generation Lexus LS 600hL, a big hybrid luxury sedan.

In the past year or so, a new marketing channel has emerged around bots and intelligent agents. This includes voice-based intelligent agents like Google Home's Assistant or Amazon's Alexa, and chatbots that interact largely through text conversations. Marketers are beginning to plan their conversational strategies and logic for this channel. But another channel -- maybe it should be considered a sub-channel -- is about to emerge. It's when the bot or agent, fulfilling the needs of the human user, is interacting with another bot or agent instead of searching the web, or a knowledge base, or a profile.

Uber and Lyft might be the leading ride-hailing companies in the U.S. currently, but it seems that other companies are also making a foray into the market. Didi Chuxing, the Chinese ride-hailing company is reportedly setting up a new lab at Mountain View, California, which will focus on intelligent driving systems, artificial-intelligence-based transport security and self-driving. According to Recode, Didi wants to move its AI operations to California since it is gradually becoming a "Mecca of the self-driving industry" and has the talent pool available for the same. Didi first gained recognition outside China when it acquired Uber's China assets in August 2016. The company seems to be hot on Uber's trail since it has also employed former Uber engineer Charlie Miller.

Google's WaveNet can also synthesize realistic human speech, but it's quite computationally demanding and hard to use for real-world applications at this point. Baidu says it solved WaveNet's problem by using deep-learning techniques to convert text to phenomes, the smallest unit of speech. It then turns those phonemes into sounds using its speech synthesis network. The system converts the word "hello," for instance, into "(silence HH), (HH, EH), (EH, L), (L, OW), (OW, silence)" before the speech network pronounces it. Both steps rely on deep learning and don't need human input.

NEW YORK; Mar. 8, 2017 – Rentokil, a global pest control company based in the UK, is piloting an Artificial Intelligence app developed by Accenture to help its almost 5,000 pest control technicians fight bugs faster for customers. The solution utilizes a partnership announced last year, when Accenture and Google joined forces to bring to market industry-specific solutions that help clients use cloud, mobility, machine learning and analytics to advance their digital transformation agenda and improve business performance. Rentokil technicians are some of the best trained in the industry but even they come across unusual insects that they can't identify. As part of a larger rollout of new Android mobile devices and apps to its field service workers, Rentokil took advantage of Accenture's alliance with Google to solve their business challenge through an innovative mobile app that identifies pests and automatically presents information, customer recommendations, and treatment plans. The Android app, called PestID and built by Accenture Mobility, part of Accenture Digital, uses Google's CloudML image classification technology to help field technicians identify and treat pest problems quickly and efficiently.

SANTA CLARA, CA--(Marketwired - Mar 8, 2017) - NVIDIA (NASDAQ: NVDA) with Microsoft today unveiled blueprints for a new hyperscale GPU accelerator to drive AI cloud computing. Providing hyperscale data centers with a fast, flexible path for AI, the new HGX-1 hyperscale GPU accelerator is an open-source design released in conjunction with Microsoft's Project Olympus. HGX-1 does for cloud-based AI workloads what ATX -- Advanced Technology eXtended -- did for PC motherboards when it was introduced more than two decades ago. It establishes an industry standard that can be rapidly and efficiently embraced to help meet surging market demand. The new architecture is designed to meet the exploding demand for AI computing in the cloud -- in fields such as autonomous driving, personalized healthcare, superhuman voice recognition, data and video analytics, and molecular simulations.