If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

In the past half decade, artificial intelligence and machine learning have made significant leaps into the mainstream and into our daily lives. According to research firm Markets and Markets, the artificial intelligence market is set to grow to 5.05 billion by 2020 thanks to the increased applicability of various AI technologies into everything from finance to healthcare to retail. Today, doctors can diagnose Sepsis with an AI algorithm, for instance, and researchers can track endangered species through AI-enhanced photo capture systems. Clearly, these new self-learning and ever-improving technologies have limitless potential in a number of innovative industries. The U.S. Chamber of Commerce's Technology Engagement Center (C_TEC) recently hosted a panel discussion during its TecNation 2016 event that focused on where we stand with Artificial Intelligence and how it will affect our lives and unlock our potential in the long run.

According to Elon Musk, driverless car technology is a problem that's pretty much solved -- the regulators just need to catch up. And they might want to start moving faster, because Musk isn't slowing down. The chief executive of electric car maker Tesla said Wednesday that all the cars the company produces going forward will be equipped with the hardware needed to transform them into self-driving cars, as soon as the software and road rules are ready. On Thursday, the company posted a video with a Rolling Stones soundtrack that shows a Tesla Model S driving itself around highways and streets in Silicon Valley, pulling into a Tesla parking lot, searching for a spot and parking itself. It even spins its front wheels to the left so the passenger-side tire properly kisses the curb.

Verdigris, a San Francisco, CA-based provider of an artificial intelligence and IoT platform for smart buildings, raised an additional 6.7m in Series A funding. The round was led by Jabil – which also plans to roll out Verdigris' AI energy sensor platform in a number of its largest manufacturing sites – with participation from Verizon Ventures, Stanford StartX Fund and existing angels. The company, which has raised 16m in total funding to date, plans to use the funds to scale manufacturing and customer operations. Led by co-founder and CEO Mark Chung, Verdigris is an artificial intelligence and IoT platform which combines proprietary hardware sensors, machine learning, and software to make buildings smarter and more connected while reducing energy consumption and costs. The software produces reports including energy forecasts, alerts about faulty equipment, maintenance reminders, and energy usage information for each and every device and appliance.

Israeli startup Faception made headlines this year by claiming it could predict how likely people are to be terrorists, pedophiles, and more by analyzing faces with deep learning. Experts and research in the field, however, suggest that it is more fantasy than reality. Faception assigns ratings after training artificial intelligence on faces of terrorists, pedophiles, Mensa members, professional poker players, and more. Through deep learning--that emerging technique found in everything from Alpha Go to Siri to Netflix--the AI can supposedly predict how likely a new face is to belong to any given group. While this may sound believable, there's no evidence that face-based personality predictions are more than a tiny bit accurate.

TORONTO, Oct. 20, 2016 /CNW/ - RBC today announced two new initiatives in collaboration with the University of Toronto designed to ensure Canada remains a leading centre of development in machine learning and artificial intelligence. RBC Research in Machine Learning will be a state-of-the-art research practice working to push the boundaries of the science around machine learning. RBC is also partnering with the Creative Destruction Lab at the University of Toronto's Rotman School of Management, becoming a Founding Partner of the Lab's Machine Learning Initiative focused on artificial intelligence-enabled companies. "RBC Research in Machine Learning is part of our commitment to the advancement of machine learning and artificial intelligence in Canada," says Gabriel Woo, vice-president of innovation at RBC. "We are not only building our own capabilities, we're also big believers in creating jobs in this space to retain the amazing talent we have in Canada. RBC Research in Machine Learning will be housed at the Banting Institute at the University of Toronto, and will be headed up by successful inventor and entrepreneur Dr. Foteini Agrafioti. Dr. Agrafioti is the co-founder and co-inventor of Nymi, the first wearable device to authenticate users using the biometric technology HeartID. "This has really never been done before in Canada," says Dr. Agrafioti, who was named "Inventor of the Year" in 2012 by the University of Toronto for inventing HeartID. "We've lost so much talent in this country to other companies and institutions, but RBC has both the scale and commitment to ensure Canada remains a centre of excellence in machine learning." Under the leadership of Dr. Agrafioti, the RBC Research in Machine Learning team will collaborate with academics from the University of Toronto and other institutions, publishing new research in the fields of machine learning and artificial intelligence. They will also be connected to the teams within the bank working on artificial intelligence and machine learning to provide expertise and help solve business challenges. RBC also extends its close collaboration with the University of Toronto through a new partnership with the Creative Destruction Lab, a seed-stage program for massively scalable, science-based companies. The Lab employs an objectives-based mentoring process led by highly accomplished entrepreneurs and angel investors with the goal of maximizing the equity value creation of its ventures. As part of the agreement, RBC is contributing to the Creative Destructions Lab's programming fund and will assume a role on the Lab's Advisory board. "We're thrilled to partner with RBC on this initiative," says Rachel Harris, director of The Creative Destruction Lab. "With their support we are able to scale our program.

Ever since the field of artificial intelligence research was founded at a conference at Dartmouth College in 1956, it has undergone a rapid expansion of applications to a multitude of other fields and subjects. Starting off originally as a way to compute mathematical equations, artificial intelligence is now used in everyday items on a regular basis. This technology can be seen through obvious examples, such as Siri on your iPhone, characters in video games and in the currently developing technology that will power smart cars. It is also prevalent in more subliminal cases, like fraud detection on your credit card, news generation by popular information outlets like Yahoo! or Fox, purchase prediction on Amazon or recommended viewings on Netflix. Perhaps one of the most popular examples of A.I. technology today is IBM's supercomputer, otherwise known as Watson, which appeared on a special addition of Jeopardy! in 2011.

John Bassett, a British spy who worked for the agency GCHQ for nearly two decades, has told Daily Express that the U.S. was considering plans to employ thousands of robots by 2025. At a meeting with police and counter-terrorism officials in London, he said: "At some point around 2025 or thereabouts the U.S. army will actually have more combat robots than it will have human soldiers. Many of those combat robots are trucks that can drive themselves, and they will get better at not falling off cliffs. But some of them are rather more exciting than trucks. So we will see in the West combat robots outnumber human soldiers."

Traffic congestion costs the U.S. economy 121 billion a year, mostly due to lost productivity, and produces about 25 billion kilograms of carbon dioxide emissions, Carnegie Mellon University professor of robotics Stephen Smith told the audience at a White House Frontiers Conference last week. In urban areas, drivers spend 40 percent of their time idling in traffic, he added. The next step is to have traffic signals talk to cars. Pittsburgh is the test bed for Uber's self-driving cars, and Smith's work on AI-enhanced traffic signals that talk with self-driving cars is paving the way for the ultimately fluid and efficient autonomous intersections.

What goes into making a computer understand the world through senses, learning and experience, as IBM says Watson does? To build a body of knowledge for Watson to work with on Jeopardy, researchers put together 200 million pages of content, both structured and unstructured, including dictionaries and encyclopedias. When asked a question, Watson initially analyzes it using more than 100 algorithms, identifying any names, dates, geographic locations or other entities. It also examines the phrase structure and the grammar of the question to better gauge what's being asked. In all, it uses millions of logic rules to determine the best answers. Today Watson is frequently being applied to new areas, which means learning new material.