A quiet race is going on to set up the infrastructure needed for the industrial Internet of Things (IoT). It is generally agreed that the cloud model won't work to manage sensor data in real time, so instead hardware and network providers are rushing to evolve their technologies and sign up industrial customers to pilot and early implementation initiatives in edge processing. Stage one of the race is well underway, with the current focus on enabling edge processing on hardware gateways located in the field (factories, workplaces, cities, farms and buildings). To do that, many are leveraging Dockerized containers (and Moore's Law) to do more powerful data processing. Once this infrastructure has a little more robustness behind it, introducing machine learning (ML) at the edge will spark a second wave of the race.

It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers.

If you're a business executive (rather than a data scientist or machine learning expert), you've probably been exposed to the mainstream media coverage of artificial intelligence or AI. You've seen articles in The Economist and Vanity Fair, you've seen emotional stories about Tesla Autopilot and the threat of AI to mankind by such luminaries as Stephen Hawking, and you might even have seen Dilbert make jokes about Artificial Intelligence and Human Intelligence. So if you're an executive who cares about growing your business, all this AI media coverage may prompt two nagging questions. First, is the business potential of AI real or not? The answer to the first question is that the business potential of AI is real.

Sound is an incredibly valuable means of communicating information. Most motorists are familiar with the alarming noise of a slipping belt drive. And many other experts can detect problems with common machines in their respective fields just by listening to the sounds they make. If we can find a way to automate listening itself, we would be able to more intelligently monitor our world and its machines day and night. We could predict the failure of engines, rail infrastructure, oil drills and power plants in real time -- notifying humans the moment of an acoustical anomaly.

There is tremendous potential for facial recognition technology, such as informing visually impaired persons if someone they know is approaching them. I find it difficult to believe just how fast things are moving with regard to using artificial neural networks (ANNs) and deep learning techniques (for example, see Deep learning machine vision system aids blind and visually impaired, Deep learning hits a sweet note, Machine learning platform speeds optimization of vision systems, Unlocking the power of AI for all developers, and Push-button generation of deep neural networks). Of course, one really interesting application is to perform object detection and identification, including the really tricky task of recognizing and identifying faces in images and videos. This sort of task benefits from the extreme parallelism offered by FPGAs. Of particular interest are Intel's current generation of FPGAs, whose hard-core DSP slices offer both fixed-point and floating-point capabilities, making them suitable for a wide range of artificial intelligence (AI) and embedded vision applications.

A kind of ethics fever has taken hold of the AI community. As smart machines displace human jobs and seem poised to make life-or-death decisions in self-driving cars and health care, concerns about where AI is taking us are gaining increasing urgency. Earlier this month, the MIT Media Lab joined with the Harvard Berkman Klein Center for Internet & Society to anchor a $27 million Ethics and Governance of Artificial Intelligence initiative. The fund joins a growing array of AI ethics initiatives crisscrossing the corporate world and academia. In July 2016, leading AI researchers discussed the technologies' social and economic implications at the AI Now symposium in New York City.

Cleo, the London-based FinTech startup, got a recent boost in its angel investing round from Skype Founder Niklas Zennström. Cleo has created an artificial intelligence-powered chatbot program that works with users to manage their finances. To date, Cleo has received $700,000 in funding from eight angel investors, including the recent contribution from Zennström. Users seeking financial advice can currently access Cleo via multiple channels, including the chatbot's mobile app, Facebook Messenger, as well as through AI-assistant integrations with Amazon's Alexa and Google Home. Barney Hussey-Yeo, Cleo's cofounder and CEO, was quoted by TechCrunch as saying: "We're trying to reduce the complexity and increase the transparency of financial services for our generation. Cleo's an AI financial assistant that makes managing your money incredibly simple. Using artificial intelligence and machine learning, you can ask Cleo nearly anything about your finances."

When the government provides a basic income to all citizens of the country without any conditions attached, it is termed as universal basic income. It is a form of social security. There is increasing debate in the developed countries about the introduction of Universal Basic Income. The combination of four factors, globalization, outsourcing, automaton, and the increasing adaptation and use of artificial intelligence is taking a growing toll on the low-income and middle-class sections of the society in developed countries, which is prompting the debate for the introduction of universal basic income. In Canada, manufacturing employment has decreased.

Attributes of Participatory Culture @TransformSoc (Henry Jenkins) • Affiliations: online communities • Expressions: new creative forms • Collaborations: Problem-solving in teams • Circulations: Shaping media flow Source: Confronting The Challenges Of Participatory Culture, by Henry Jenkins, MIT Press, 2009 31.

In 1872 a British general named Alexander Cunningham, excavating an area in what was then British-controlled northern India, came across something peculiar. Buried in some ruins, he uncovered a small, one inch by one inch square piece of what he described as smooth, black, unpolished stone engraved with strange symbols -- lines, interlocking ovals, something resembling a fish -- and what looked like a bull etched underneath. The general, not recognizing the symbols and finding the bull to be unlike other Indian animals, assumed the artifact wasn't Indian at all but some misplaced foreign token. The stone, along with similar ones found over the next few years, ended up in the British Museum. In the 1920s many more of these artifacts, by then known as seals, were found and identified as evidence of a 4,000-year-old culture now known as the Indus Valley Civilization, the oldest known Indian civilization to date. Since then, thousands more of these tiny seals have been uncovered.