AI-powered drones soon will be everywhere, monitoring crowds at major events, checking out traffic patterns on busy roads, surveying disaster sites, and inspecting airplanes. IBM is edging into this airborne safety and maintenance market early, with a deal to bring its Watson internet of things technology to unmanned aircraft systems built by Netherlands-based Aerialtronics. Data captured by high-resolution drone camera lenses will be fed into IBM's visual recognition application programming interfaces (APIs) and services on its Watson cognitive computing and Bluemix cloud-based analytics platforms. The first market for the Aerialtronics drones is expected to be for cell tower maintenance. Instead of sending humans to laboriously climb towers and report back, inspection teams can deploy drones, which quickly gain a 360-degree overview, according to IBM. The visual recognition APIs can then analyze the images captured by the drone to detect problems like damaged cabling or equipment defects.

A few months ago, we discussed the integration of machine learning into healthcare to speed review of medical data and better allocate treatment options and preventative medicine. These examples were a very brief glimpse into the possibilities within the field of healthcare. In reality, machine learning has already been in practice for some time on the web, by our email services, on our smartphones, by finance and banking companies, and beyond. Pioneered in the 1960's, machine learning employs algorithms to study data, conduct tiny experiments, observe patterns, adjust and repeat. Essentially the computer learns and evolves through processing.

Global software major Microsoft on Monday invited Indian developers to use its machine learning and data platforms for building digital products and solutions to transform businesses and drive inclusive growth. "Our ambition is to democratise access to new technologies so that software developers can build, innovate and transform the world with them," said Microsoft data group Vice President Joseph Sirosh at a conference on machine learning and data sciences here. Noting that cloud-based services for machine learning and big data, coupled with the Internet of Things (IoT) have the potential to revolutionise every aspect of life, including sports, healthcare, education and even government, he said the company's unique platforms such as the Cortana Intelligence suite were helping customers to harness the power of artificial intelligence. The two-day conference is aimed at exploring the possibilities with big data, machine learning, artificial intelligence and open source technologies in enabling platforms, intelligent apps, services and experiences to accelerate economic growth, empower people and drive real impact. Demonstrating the power of data analytics, Microsoft unveiled an Azure machine learning-based approach for calculating target scores in weather-interrupted T-20 cricket matches.

Trading algorithms continue to dominate financial markets with both good and bad affects. In predictable steady markets they take the emotion out and adhere to a machines rhythm. The bad is when it breaks from left field as we saw in the past week with increased volatility and panic. We are effectively getting repetitive fractals of varying degree, but how do we predict fractals on new unseen fractals? Algorithms are only as good as the data provided and the ability of the algorithms to consume it.

Global Equities Research is confident that NVIDIA Corporation (NASDAQ: NVDA) has the capability to win machine learning or Deep Learning due to its acquisition of Nervana and XEON Phi Deep Learning processor. Following the news, the stock advanced more than 3 percent. However, it is merely a hobby for Intel Corporation (NASDAQ: INTC). In a research note, the brokerage said, "We continue to insist that the right investment framework is based on Machine Learning Practitioners framework, as we presented it in our September 11 report." The lead analyst termed the semiconductor approach as flawed.

NVIDIA CEO Jen-Hsun Huang unveils technology that will accelerate the deep learning revolution that is sweeping across industries. "AI computing will let us create machines that can learn and behave as humans do. It's the reason why we believe this is the beginning of the age of AI." Huang was joined onstage by Andrew Ng, chief scientist of Baidu, China's largest search engine, who got a rousing reception from the GTC China attendees. With AI becoming "the new electricity," Ng described how the early bets his company has made on GPU technology and deep learning are paying off in a host of AI services that have the potential to transform industries.

Most small businesses are focused on the nuts and bolts of the IT needed to run their companies, including networks, cloud services and PCs. However, there are many emerging technologies that will likely have large impacts on how businesses are run in the years ahead. The consultancy PwC recently released a report highlighting what it calls eight tech "megatrends" that it thinks will have a large impact on businesses in the next three to seven years. The trends include artificial intelligence, augmented reality, blockchain technology, drones, Internet of Things, robotics, virtual reality and 3D printing. Some of them, like IoT, have been around for years and are already widely used, while others, like virtual reality or the use of blockchain for ledgers and transactions, are rapidly maturing.

Gartner recently published it's annual report "Hype Cycle For Emerging Technologies 2016" and for the first time included Enterprise Taxonomy and Ontology. However, it is added into Gartner's Through of Disillusionment (ToD), never fret this is the way forward.. since it is the only way to reach success i.e. Normally, hype is over sold and falls into the ToD after failing to deliver on the hype. Enterprise Taxonomy has never been a hype, It was introduced by Gartner because it is obviously gaining ground after being around for quite a while. Enterprise Taxonomy both underpins Information Infrastructure (Separate Gartner Report 2016) as well as being a"Smart machine technology that will revolutionize manufacturing and its related industries" Enterprise taxonomy has struggled through many years without professional editing and management software.

Last week, along with EPCC's Alan Gray, I attended a workshop at Donington Park held by Roborace. For those who've not heard of Roborace, it's a project to build and race autonomous cars, along the lines of Formula 1 but without any drivers or human control of the cars. Actually, it's more like Formula E but without drivers, as the plan is for the cars to be electric. The workshop was to showcase their first set of development cars, called DevBots, that they have recently constructed to test their hardware and software. The reason we were attending was that Alan saw a presentation on Roborace at the GTC conference, where it was revealed that the NVidia Drive PX 2 platform is going to be used as the computing resource for the AI controlling the cars.

While Chatbots are aimed at rattling the customer service world, humans will still be at the steer tomorrow. But, how can we ensure harmonious coexistence of humans and bots? Also, what should be the trigger point for automation (bots) to jump in? To answer the second question take a situation where the customer is spending a lot of time on the customer service page of a website. It is highly possible that they have an issue, complaint or query.