An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. These technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. In sizing and forecasting the total global AI market, Tractica has created a taxonomy of 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies. Some use cases - such as image recognition, algorithmic securities trading, and healthcare patient data management - have huge scale potential, while others are niche applications. Likewise, a few key industry sectors including consumer products, business services, advertising, and defense applications will drive significant revenue for AI software implementations in addition to AI-driven hardware and service sales, but during the coming decade the technologies will have an effect on almost every conceivable industry sector.

NVIDIA Tuesday unveiled the NVIDIA Jetson TX2, a credit card-sized platform that puts AI computing to work in the world all around us. Over the past five years, the mobile revolution has brought more and more devices online, Deepu Talla, vice president and general manager of the Tegra business at NVIDIA, told an audience of press, analysts and robotics enthusiasts at an event in San Francisco. At the same time, GPU-based deep learning has given computers the ability to understand -- and react to -- the data streaming in from all these devices in uncanny new ways. Both through training -- which creates smart systems -- and through inference -- which creates systems that are able to react intelligently to the world around them in real time. "We're seeing a lot of this inference not just in the cloud, but also moving towards the edge, whether it's a robot, or a drone or a security camera," Talla said.

Pattern Based Heuristics 2.4 Machine Learning and Response Generation in Chatbots 2.5 Chatbot Ecosystem 2.5.1 Native Chatbots 2.5.2

A new artificial intelligence system can spot the tell-tale signs of skin cancer just as accurately as human doctors, say researchers, and the next step is to get the tech on a smartphone, so anyone can run a self-diagnosis. Once the system is refined further and becomes portable, it could give many more people the chance to get screened with minimal cost, and without having to wait for an appointment with a doctor to confirm the symptoms. The Stanford University researchers behind the deep learning system say the key to its success is an algorithm that enables it to apply what it knows from its existing database of skin cancer samples to pictures it hasn't seen before. "We made a very powerful machine learning algorithm that learns from data," says one of the team, Andre Esteva. "Instead of writing into computer code exactly what to look for, you let the algorithm figure it out."

China has pledged to prioritise the development of artificial intelligence for the first time within the government's latest annual work report, underlining its ambition to lead what has fast become one of the hottest areas of global technological innovation. One analyst is now projecting the industry in China to grow by more than 50 per cent in value to 38 billion yuan (US$5.5 billion) by 2018. "We will implement a comprehensive plan to boost strategic emerging industries," said Premier Li Keqiang in his delivery at the annual parliamentary session in Beijing over the weekend. "We will accelerate research & development (R&D) on, and the commercialisation of new materials, artificial intelligence (AI), integrated circuits, bio-pharmacy, 5G mobile communications, and other technologies." Artificial intelligence, which focusses on creating machines that work and react like humans, will create the next industrial revolution and China and "should grab the opportunity to overtake other global competitors" in the field, added Zhou Hanmin, a member of the Standing Committee of the Chinese People's Political Consultative Conference, the nation's top political advisory body, during the "two sessions".

Chatbots & messaging are the future of marketing. MIT's ELIZA pioneered the revolution in the 1960s and Siri took the trend mainstream in 2011. Now chatbots are used by brands in every industry to go beyond passive advertisements and engage customers with interactive conversations. A chatbot is a conversational computer program that customers can interact with via a messaging interface. Instead of pushing buttons on a website or mobile app, you can get things done simply by chatting with a chatbot and asking questions naturally like "What movies are playing tonight?" or "What's the latest score in the NBA finals?".

So how might automation in the Fourth Industrial Revolution differ fundamentally from that in the past, preventing technological progress from being labour augmenting, at least in the short to medium term? Perhaps the main difference is the speed of technological progress and its adoption. The technologist Hermann Hauser argues there were nine new General Purpose Technologies (GPTs) with mass applications in the first 19 centuries AD, including the printing press, the factory system, the steam engine, railways, the combustion engine and electricity. GPTs by definition disrupt existing business models and often result in mass job losses in the industries directly affected. For example, railways initiated the replacement of the horse and carriage, with resultant job losses for coachmen, stable lads, farriers and coach builders.

With the broad release of Google Assistant last week, the voice-assistant wars are in full swing, with Apple Inc, Amazon.com Inc, Microsoft Corp and now Alphabet Inc's Google all offering electronic assistants to take your commands. Siri is the oldest of the bunch, and researchers including Oren Etzioni, chief executive officer of the Allen Institute for Artificial Intelligence in Seattle, said Apple has squandered its lead when it comes to understanding speech and answering questions. But there is at least one thing Siri can do that the other assistants cannot: speak 21 languages localized for 36 countries, a very important capability in a smartphone market where most sales are outside the United States. With the broad release of Google Assistant last week, the voice-assistant wars are in full swing, with Apple Inc, Amazon.com

China has pledged billions of dollars to boost the development of artificial intelligence in the country's first technology research drive of its kind, highlighting Beijing's commitment to expanding the horizons of the nascent field. During an annual meeting of parliament, Chinese Premier Li Keqiang told legislators Sunday that the nation would invest in new technologies and their associated markets. The new initiative was set to devote up to $5.5 billion by 2018, according to an analyst with the South China Post, which reported the story Thursday. In his speech, Li outlined where the funds would likely be appropriated. "We will implement a comprehensive plan to boost strategic emerging industries," Keqiang said.

How is poker harder than chess or Go for AI? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. How is poker harder than chess or Go for AI? It is much easier to build a defensive poker algorithm that makes a lot of money from average players and is difficult for even an expert to win from, than it is to build even the most basic chess or Go program. Poker is a far simpler game than either of those, with far fewer choices and much simpler resolution. Expert poker, however, requires an ability to learn from a player that is attempting to mislead you.