Bernd Greifeneder is the CTO and founder of Dynatrace, a software intelligence company that helps to simplify enterprise cloud complexity. Continuous digital transformation now defines modern, competitive organizations. Yet, the infrastructure that supports this transformation--powering everything from mobile banking to personalized, omnichannel retail experiences and "smart" healthcare--is built on complex multicloud architectures. The scale and complexity of these data and application environments are increasing relentlessly, and many companies already use five different cloud service platforms on average, according to research conducted by Coleman Parkes and commissioned by Dynatrace. This complexity exceeds humans' ability to manage.

Tech Mahindra and Mahindra University have signed a memorandum of understanding (MoU) to set up a new'Makers Lab' for research and development in quantum computing, explainable artificial intelligence, and Metaverse. Tech Mahindra already has 10 Makers Lab across the world and the new unit at Mahindra University will be the 11th facility globally and second in Hyderabad. Emphasising the need to focus on development of quantum computing, Tech Mahindra MD and CEO CP Gurnani said, the industry is looking at data explosion with growth in cloud computing, data centres, and 5G driving the change in the present computing system. "I think the basics of quantum computing is quantum physics. Quantum physics clearly shows there is always this inflection point and then after that, either the current hardware or the quant developers will be able to suddenly create magic. My only personal belief is that the pressure on the systems will come in because of the data explosion," he said.

David Lareau is CEO of Medicomp Systems, a provider of physician-driven point-of care solutions that fix EHRs. In 2020, the amount of healthcare data created globally was an estimated 2,314 exabytes -- which is an unfathomable amount when you consider a single exabyte is equivalent to one billion gigabytes. While it may be hard to wrap one's head around such a figure, this much is clear: To make sense of such healthcare's ever-growing volumes of data, we need advanced technologies, such as artificial intelligence (AI)-based tools, to enhance user productivity and minimize burdensome searches. One of the most promising AI technologies to help manage huge volumes of data is natural language processing (NLP). NLP is a branch of linguistics, computer science and AI that enables computers to read, understand and structure large volumes of human prose (i.e., natural language).

Jiawei Han, a professor of computer science at the University of Illinois at Urbana-Champaign, was recently named a Michael Aiken Chair, one of the University's highest awards. The endowed chair is the latest honor in Han's distinguished and pioneering career, with notable accomplishments including creating core data mining algorithms and co-authoring the textbook that is considered by many to have defined the field. Professor Han is also a busy and successful teacher with a love for "train[ing] the younger generation, whether at UIUC or all over the world on Coursera." Professor Han had three PhD students graduate in May, with one becoming a professor at Georgia Tech, one joining Google, and one joining Facebook. Students taking his classes as part of the Online Master of Computer Science in Data Science degree have an opportunity to learn from him through videos and can ask him questions directly during live office hours.

Artificial intelligence can help us cope with the growth of work. Whether its Amazon drones putting couriers out of business, AI-powered health checkers diagnosing patients in hospitals, or algorithms at Microsoft providing the perfect recipe for whisky, the growing threat of artificial intelligence (AI) as a global job killer has been a prevailing media story that seems just too good to be false. But the rhetoric is not supported by most recent studies, which suggest that while skill shifts across all industries will certainly be considerable, net job loss over the next 15 years is likely to be negligible. Well, many assumptions imbedded in the "automageddon" narrative are highly questionable: that automation creates few jobs whether short or long term, that whole jobs can be automated, that the technology is perfectible, that organizations can seamlessly and quickly deploy AI, that human thought and action can be replicated, and that it is politically, socially and economically feasible to apply these technologies. Then there are the macro factors.

Artificial Intelligence (AI) is getting a lot of attention these days, particularly in the technology industry and in corporate boardrooms. AI is also becoming prevalent in consumers everyday lives. Consumers don't always recognize it as such, as corporate marketing experts prefer to avoid technical jargon and instead use consumer friendly names like Siri and Alexa -- but for people that are more technically inclined, the ubiquitous presence of AI is hard to miss. AI is not a new concept. In fact, its roots go back several decades. Is this just another technology hype that is going to fade, or does it truly have the potential to bring about transformations, either good or bad, of epic proportions? Let's take a look at how we got here and why AI is suddenly capturing so much attention.

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.

Circa 1950: Alan Turing creates the "Turing Test" to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human. Circa 2016: Google's Artificial Intelligence algorithm beats a professional player at the world's most complex board game, "Go". The AlphaGo algorithm, developed by Google DeepMind, manages to win five games out of five in the competition. The history of Machine Learning is, literally, encapsulated between these two developments.