SAN JOSE: AS PART OF this year's Nvidia GTC keynote in San Jose, CEO and co-founder Jen-Hsun Huang revealed two huge pieces of new tech that lead Nvidia's charge in the fields of machine learning, artificial intelligence and autonomous auto. First up was the Tesla DGX-1, described as "the fastest supercomputer in the world" and "250 servers in a box". The DGX-1 is aimed at providing predictive intelligence and artificial learning to server rooms. The first unit will be donated to the MGH Centre of Clinical Data Science, where it will be used to analyse 10 billion medical records to search for correlations, coincidences and cures. As a result, the Nvidia DGX-1 can do the same amount of work with eight Pascal GPUs in two hours as four Maxell GPUs took 25 hours to do last year.

Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But an emerging trend promises to bring the power of machine learning to mobile devices that may lack or have only intermittent online connectivity. This will give rise to machines that sense, perceive, learn from, and respond to their environment and their users, enabling the emergence of new product categories, reshaping how businesses engage with customers, and transforming how work gets done across industries. Emerging technologies rarely get as big a publicity boost as machine learning recently saw, when Google software defeated one of the world's top players of Go, one of the most complex board games ever created, in a best-of-five series of matches.6 The international headlines confirmed that machine learning--the process by which fresh data can teach computers to better perform tasks--is one of the hottest domains within the field of artificial intelligence, and that this cognitive technology is progressing rapidly.7 Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning.

Facebook and Google are two of Silicon Valley's leaders in artificial intelligence. They have both invested heavily in AI, hiring loads of top researchers to develop projects like the Facebook Messenger "smart" assistant M, or Google's in-the-works AI-powered chatbot. While Facebook and Google are duking it out in international competitions, the two companies are far from alone in their efforts to dominate the space. Welcome to the AI Conspiracy: The'Canadian Mafia' Behind Tech's Latest Craze As for the rest of Silicon Valley, research firm CB Insights recently published a report on the AI investments the tech industry is making.

Teams from both organizations will soon move into what used to be a Stelco office building that's located in the downtown core. "We're re-inventing Hamilton through healthcare," said Dino Trevisani, president of IBM Canada, who is himself originally from Hamilton. "We're going to make this a centre of excellence in Canada and for the world." Trevisani spoke at a well-attended event, held at Hamilton Health Sciences, to publicize the announcement. Significantly, researchers at the new collaboration centre will be the first in Canada to make extensive use of IBM's Watson artificial intelligence software for healthcare.

What will Google do next? Google's London AI powerhouse has set up a new healthcare division and acquired a medical app called Hark, an article from Business Insider, tells us the latest. DeepMind, Google's artificial intelligence research group, launched a new division recently called DeepMind Health and acquired a healthcare app. The article describes DeepMind Health's new app called Hark, "Hark -- acquired by DeepMind for an undisclosed sum -- is a clinical task management smartphone app that was created by Imperial College London academics Professor Ara Darzi and Dr Dominic King. Lord Darzi, director of the Institute of Global Health Innovation at Imperial College London, said in a statement: "It is incredibly exciting to have DeepMind – the world's most exciting technology company and a true UK success story – working directly with NHS staff.

Nvidia chief executive Jen-Hsun Huang said that deep-learning artificial intelligence has become a new computing platform, and the company is dealing with hundreds of startups in the space that plan to take advantage of the platform. Speaking at the GPUTech conference in San Jose, California, Huang noted that 5 billion was invested last year in A.I. startups, and there are probably a thousand companies working on the technology for applications ranging from face recognition to self-driving cars. "Deep learning is not an industry," he said. "Deep learning is going to be in every industry. Deep learning is going to be in every application."

CHIPMAKER Intel has scooped up Yogitech, an Internet of Things (IoT) startup that focuses on boosting the security credentials of chips used in robots, self-driving cars and other autonomous devices. Yogitech was founded in Italy in 2000, and works on "functional security" for semiconductors, an unexciting yet core part of the IoT as it ensures that connected devices are secure and functioning correctly. This is particularly important in areas such as autonomous and connected cars, as robust systems where electronics can harvest data on driving performance are needed to keep the systems working accurately and in real time. Having this functionality assured creates faith in the use of IoT devices that require people to surrender some aspect of control or rely on a network of smart devices to work together without significant management needs. Yogitech's focus in the IoT arena has been on advanced driver assistance systems, and robotic and autonomous machines for industrial and automotive use where functional safety is vital.

This ANN introduction only covers the basic fundamentals. In the next chapter, we will learn about the computing strategies that enable ANN's predictive capabilities.

Learning machines are capable of working ever more autonomously on ever more complex tasks. In this article, I explore whether it would it be smart for humankind to develop an'intelligent gun'. There are an estimated 875 million civilian, law-enforcement, and military firearms in the world, of which 650 million are in the hands of civilians, either legally or illegally[1]. Given the plethora of high-profile gun attacks in recent months and years – particularly in the US but also in France, Norway, Pakistan and Tunisia to name but a few – it is disturbingly easy to imagine gunmen on the loose in a school or at a public event, shooting indiscriminately and leaving casualties in their wake. Imagine how different things could be if a gun had artificial intelligence built in to it, turning it into an intelligent gun.

The banking world, and now Goldman Sachs in particular, is getting on board with artificial intelligence. The investment bank has put millions into a US startup which uses machine learning for marketing. The 30m round for New York-based Persado also came from previous investors American Express Ventures and Citi Ventures, as well as Bain Capital Ventures and StarVest Partners. "We are increasingly excited about Persado's ability to build a thriving business around a truly unique and differentiated technology. It stands out for driving great results for an impressive roster of customers - the world's most recognisable brands - with unparalleled consistency and effectiveness," said StarVest Partners genereal partner and co-founder Laura Sachar.