New entrants to the legal profession will be competing head on against Kim, the virtual assistant from Riverview Law, and Ross, IBM Watson's'super-intelligent' attorney, in delivering services to clients. Ross, unlike most of us, has the ability to research every resource of legal knowledge in seconds, and, even more impressive to the older ones among us, remember it. There's no doubt that clients will always value negotiating skills, judgement, ethical standards and reassurance from their lawyers but if the apprentice style of learning at the expert's knee is going to be overtaken by Kim and Ross, how will the profession generate the experienced advisers that clients seek to consult? Clients seek good value for money from their law firms and those expectations change over time. In the past, certain tasks were seen as good value for money. Now, as tasks become more familiar, technology enables a more efficient delivery.

Machine learning is, to keep it simple, an algorithm developed to note changes in data and evolve in it's design to accommodate the new findings. As applied to predictive analytics, this feature has wide ranging impact on the activities normally undertaken to develop, test, and refine an algorithm for a given purpose. Sophisticated pattern recognition – Along with noting relationships, the Yottamine Predictive Platform can determine the type and quantify as well. This is not just happening with key, or even secondary variables, but on every relationship that takes part in the pattern. This feature delineates irrelevant data as well, which provides the benefits of mitigating pre-processing requirements and accelerating processing.

Autism spectrum disorder (ASD) is a major developmental disorder characterized by repetitive, restricted behaviour as well as deficits in communication and reciprocal social interactions1. ASD has attracted a great deal of attention of basic and clinical scientists in the hope that clarification of its underlying mechanisms will lead to the development of remedies for ASD as well as a better understanding of the neural substrates of important cognitive functions, including social behaviour2. Despite the significance of the disorder, no effective biomarker has been developed. The medical diagnosis for ASD has been made largely based on narrative interactions between individuals and clinical professionals. With the exception of'clear and typical' cases, such diagnostic methods without any biological grounds could run the risk of producing a high variance in diagnosis3 and delaying the detection of abnormalities4.

Last November, Google opened up its in-house machine learning software TensorFlow, making the program that powers its translation services and photo analytics (among many other things) open-source and free to download. Now, the company is giving TensorFlow the machine learning equivalent of smart pills, releasing a distributed version of the software that allows it to run across multiple machines -- up to hundreds at a time. This sounds like an obvious way to improve TensorFlow, and, well, it is. Machine learning software only gets to be clever by analyzing large amounts of data; looking for common properties and trends like facial features in photographs, for example. Letting TensorFlow run these sorts of operations on networks of computers simultaneously rather than individual machines means users can make smarter systems, and improve them faster.

Salesforce's recent acquisitions of AI startups including MetaMind will enable it to keep pace with the functions that consumer-focused companies … From Dick Tracy's watch to Wonder Woman's Bracelets to Bat Girls total recall and technopathy, fiction has a intriguing dialog with invention. Enable natural interaction with your app using a conversational interface powered by the Watson Developer Cloud APIs. KPMG LLP and IBM today announced plans to apply IBM's Watson cognitive computing technology to KPMG's professional services offerings. It can answer guests' questions about the hotel and tourist destinations. The hotel has recently welcomed a new concierge named "Connie," you see, and it's actually a Nao robot powered by IBM's AI.

After a long winter frozen in the technological permafrost, it's springtime again in the field of artificial intelligence. A.I. is poised to take off in 2016 as enterprises begin figuring some element of it into their application portfolios. By 2018, artificial intelligence will be incorporated into about half of all apps developed, according to research firm IDC, and by 2020, savings fueled by A.I. -- in reduced people costs and increased workflow efficiencies, for example -- are expected to total an estimated 60 billion for U.S. enterprises. Why A.I. now, when it was considered dead and buried a decade ago? The answer is cheap computer processing power and the allure of secrets buried amid a torrent of data that didn't exist in such voluminous quantities then.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Microsoft is taking no chances with its latest artificial intelligence (AI) experiment. After its last AI chatbot turned into a genocide-advocating, misogynistic, holocaust-denying racist, the company's latest project -- a bot that tells you what's in photos -- refuses to even look at photos of Adolf Hitler. CaptionBot is the latest in a series of periodic releases from Microsoft's AI division to show off its technical prowess in novel ways. You can upload photos to it, and it will tell you what it thinks is in them using natural language. "I think it's a baseball player holding a bat on a field," it says in response to one example photo.

Artificial Intelligence seems to be everywhere at the moment. Massive tech companies are spending vast amounts of money on research and development of ever more sophisticated AI-focused tools, services and other assorted products. News outlets give a huge amount of coverage to events in which AI is the star of the show. Perhaps most importantly, AI is being discussed more by the public at large. Yet for the vast majority of people, AI is a nebulous area.

DigitalGenius is announcing a Human AI customer service platform today with a 4.1 million seed investment. The platform integrates with existing customer service software suites -- like Salesforce, Zendesk, and Oracle -- to automate the most repetitive parts of customer service through AI and machine learning-powered chat bots. The work to augment the process, while still keeping the human element decidedly at the center of things. It's interesting to note that Salesforce was part of the deal, as that could conceivably help the startup scale quickly in this space, thanks to Salesforce's massive distribution network and its suite of automation products ripe for AI. I talked to DigitalGenius' chief strategy officer Mikhail Naumov to clarify what AI is and isn't.