Banks seem to hate when their customers go on holiday. Somewhere in the world, Joe Bloggs has gambled that he won't need to bring cash with him to sunny Sweden, since the Swedes seem to accept card payments more or less everywhere. As soon as he taps his plastic to the reader, however, there's still a small chance that his bank decides to block the transaction. After all, say the algorithms, what evidence is there in the corporate records that Mr Bloggs is ever likely to pay for his kladkakka in Stockholm? Billions of these types of decisions are made every day by machine learning (ML) algorithms in banks.

The coronavirus pandemic changed the way businesses of almost all types operate virtually overnight, hurting most and redefining which ones are truly essential in what quickly became the new normal for billions of people around the world. And it brought with it an unexpected kind of acceleration of trends, forcing the closure of businesses that would have struggled on for a few more years, while bringing a global spotlight to technologies that would have remained relatively obscure or experimental for years to come. Market trends that otherwise would have taken years to evolve transformed in a matter of weeks, it seemed, retiring outdated concepts while stretching emerging tech to its limits. One segment suddenly in the spotlight--and that seemingly saw years of demand and market interest explode in a matter of days--is delivery robots, which until the month of March had seen moderate interest from Silicon Valley and some skepticism from the general public. Suddenly, Amazon founder Jeff Bezos' comment in 2013 that the company was researching parcel delivery via flying drones went from a pie in the sky whimsy with seemingly few advantages to something that businesses large and small needed in 2020.

Previously I wrote about the Cyberworld that's coming – the world of extended reality, or XR, enabled by a confluence of maturing foundational technologies, with AI a central part – computer vision and graphics, 3D reconstruction, natural language processing and more. It goes without saying that this seamless overlay of digital and real worlds will be populated by digital humans and avatars, both realistic and stylistic, driven by real humans and/or artificial intelligence. Here I am going to dive deeper into one of these foundational technologies: the creation and animation of digital humans (mainly faces). The good news first – it won't take us another decade to get there. When I started the Disney Research Laboratory back in 2008, I launched a long-term research vision to find the Holy Grail of special effects in film; i.e. to create and animate digital human faces indistinguishable from reality.

Understanding and deploying the right technologies at the right time is pivotal to being successful in business today. From AI (artificial intelligence) and automation to line of business applications, there is no shortage of technologies that could have a profound, positive impact on an organization. Deciphering which ones are right for your business and when to deploy them is no easy task. Conversely, move too late and you may have missed out on a competitive edge. Few know the challenge of getting in at the right point of a technology hype cycle better than those in the IoT (Internet of Things) sector--where the buzz outpaced the reality of early applications, yet it is already plateauing in many markets.

Bank of America Chief Operations and Technology Officer Cathy Bessant lent her voice to a drum beat in Washington last week, saying that blockchain and AI have years to go before they have significant benefits for consumers and investors. Blockchain and artificial intelligence aren't ready for primetime, financial leaders were told in Washington last week. At conferences for SIFMA, the securities industry trade group, and the Commodity Futures Trading Commission, speakers were pretty much in agreement that fintech's promises will take years to become everyday realities. "[Blockchain/distributed ledger technology] is not as impactful as the hype," asserted Bank of America Chief Operations and Technology Officer Cathy Bessant at the SIFMA annual conference. She pointed to DLT as a promising technology waiting for proven benefits for consumers, businesses and regulators to cash in.

This month the McKinsey Global Institute released a discussion paper called "Notes from the AI Frontier: Insights from Hundreds of Use Cases" that measures the recent progress of artificial intelligence business applications and identifies industries and use cases where AI has the greatest potential to unlock value. There are a number of different'problem types' identified by the analysts, each of which are suited to particular deep learning or AI techniques. These problem types include classification (based on a set of training data, categorizing new inputs as belong to one of a set of categories), continuous estimation (based on a set of training data, estimate the next numeric value in a sequence), clustering (a system creates a set of categories for which individual data instances have a set of common or similar characteristics), optimization, anomaly detection, ranking, recommendations, and data generation. The two most important takeaways are that the best AI systems have consistently outperformed humans since 2015, and specifically in the transport and logistics industry, AI can improve performance over other analytics techniques by 89%. In other words, the technology is maturing and becoming commercially viable, and transportation and logistics stands to gain more from AI than almost any other industry.

Recently, I finished binge-watching the first season of HBO's popular series, Westworld. The show sheds light on machines coming of age and the extent humans can go to exploit technology. From Matrix to Ex Machina, all these sci-fi movies reveal a darker side of not only humans but also the machines. I dread the idea of machines becoming smarter than us or a day when machines lead our lifestyles. But that might or might not happen in the next century, most probably I will not live to see that time.

Machine learning and artificial intelligence are finding their way into an insane amount of products and services these days, some more conventionally useful than others. You would be hard-pressed to find a company in the tech sphere that isn't involved in AI in some form, whether by using somebody's product that leverages AI, or by developing their own solutions. This push is not a fad or just another random happening in the tech world; it's something that has been long in the making, but the technology just hasn't been up to snuff until now. Specifically, neural networking in computers and other methods of machine learning have been researched since the 1970s, though they spent a while on the shelf, since people recognized after a while that the technology just wasn't ready. Now it is, however, and the race is in full swing to find the next great AI platform for all of the services of tomorrow to run on.

Is AI ready for primetime? In a recent interview with Charlie Rose, he stated that machine learning showed great promise for cybersecurity, but that the necessary technology was probably five years out. If machine learning is currently so successful in other areas of society, why isn't it ready for cybersecurity? Machine learning is a subset of Artificial Intelligence, a field of computer science that started in 1958 when Marvin Minsky founded the Artificial Intelligence lab. Everyone, including DARPA, was pouring money into it.