Computers are solving problems no human could ever decode -- and in ways that feel distinctly nonhuman to us. Should we embrace or rethink the strange intelligence of machines?In 2019, five of the top poker players in the world sat down in a casino to play poker against a computer. Over the course of the game they lost big -- some $1.7 million (E1.77 million) -- to a poker bot called Pluribus. It was the first time an artificial-intelligence (AI) program beat elite human players at a game of more than two players. In a post-game interview, the players were asked how they felt about losing to a computer.

In 2019, five of the top poker players in the world sat down in a casino to play poker against a computer. Over the course of the game they lost big -- some $1.7 million (€1.77 million) -- to a poker bot called Pluribus. It was the first time an artificial-intelligence (AI) program beat elite human players at a game of more than two players. In a post-game interview, the players were asked how they felt about losing to a computer. Pluribus, they said, ʺbluffed really well.

The fight against fraud has always been a messy business, but it's especially grisly in the digital age. To keep ahead of the cybercriminals, investment in technology – particularly artificial intelligence – is paramount, says Ajay Bhalla, president of cyber and intelligence solutions at Mastercard. Since the opening salvo of the coronavirus crisis, cybercriminals have launched increasingly sophisticated attacks across a multitude of channels, taking advantage of heightened emotions and poor online security. Some £1.26 billion was lost to financial fraud in the UK in 2020, according to UK Finance, a trade association, while there was a 43% year-on-year explosion in internet banking fraud losses. The banking industry managed to stop some £1.6 billion of fraud over the course of the year, equivalent to £6.73 in every £10 of attempted fraud.

LIKU baby humanoid robots are demonstrated on the Torooc Inc. stand on the opening day of the MWC... [ ] Barcelona in Barcelona, Spain, on Monday, Feb. 25, 2019. At the wireless industry's biggest conference, over 100,000 people are set to see the latest innovations in smartphones, artificial intelligence devices and autonomous drones exhibited by more than 2,400 companies. On February 11, 2019, President Trump signed an executive order on Maintaining American Leadership in Artificial Intelligence and in February 2019, a survey by Protiviti called Artificial Intelligence and Machine Learning indicated that only 16% of business leaders surveyed are getting significant value from advanced artificial intelligence (AI) in their companies. The report also found that companies of all sizes and across industries are investing heavily in advanced AI with an average of $36M spent in the fiscal year 2018. Of those same companies surveyed, 10% plan to increase their budgets over the next two years.

Can you imagine a world without the kind of voice assistant technology provided by Amazon Alexa, Google Assistant, Siri on the iPhone or Cortana for Windows? Probably not, as we tend to take such technological leaps forward pretty much for granted. But behind the scenes there's a whole new world of machine-learning that drives their collective ability to seemingly answer any question put to them. It's not so much knowing the answer that's the technological miracle – because, well, the internet – but rather that these virtual assistants are able to understand the question in the first place. Machine-learning is, in the broadest possible terms, what you might expect in that computer algorithms can be trained to understand how to correctly respond to an input by way of a human telling it what that response should be.

We are now living in a digital economy. Organizations all over the globe are turning their much of their focus away from producing physical assets, and towards designing and developing digital products and services that either complement or completely replace their physical predecessors. Digital transformation initiatives are at the very top of business agendas across all industries, resulting in big changes and big demands being placed on enterprise IT operations. For many years, high demands have been placed on IT operations teams – and DevOps in particular – to become more agile and proactive so that their businesses can quickly embrace new technologies and practices in order to remain competitive. However, to meet these demands, IT operations has faced the challenge of keeping costs down on the one hand, while dealing with the increasing complexity of operations on the other.

LIKU baby humanoid robots are demonstrated on the Torooc Inc. stand on the opening day of the MWC Barcelona in Barcelona, Spain, on Monday, Feb. 25, 2019. At the wireless industry's biggest conference, over 100,000 people are set to see the latest innovations in smartphones, artificial intelligence devices and autonomous drones exhibited by more than 2,400 companies. On February 11, 2019, President Trump signed an executive order on Maintaining American Leadership in Artificial Intelligence and in February 2019, a survey by Protiviti called Artificial Intelligence and Machine Learning indicated that only 16% of business leaders surveyed are getting significant value from advanced artificial intelligence (AI) in their companies. The report also found that companies of all sizes and across industries are investing heavily in advanced AI with an average of $36M spent in the fiscal year 2018. Of those same companies surveyed, 10% plan to increase their budgets over the next two years.

It's an experience many shoppers have had: You're queued in line at the store prepared to make a purchase only to have your credit card declined for no apparent reason. A perfectly legitimate charge has been flagged as fraudulent, and the result is an agitated customer and a retailer with unsold merchandise. MasterCard has now turned to artificial intelligence to better differentiate between real and mistaken fraud, hoping to tamp down on the former while allowing the latter to go through. It's the latest financial services company to see the potential for the burgeoning field of machine learning to improve security on its network and enhance the customer experience. Financial institutions have for years collected data on customers' habits and routines, and used the information to pinpoint cards that may have been compromised.

MasterCard Inc. says new machine-learning technology has helped it quickly control three separate cyberattacks that targeted automated bank tellers, limiting the damage to about 100,000 each. The transaction-monitoring system, which also employs data visualization tools, caught the three attacks during the first two months of 2016, according to MasterCard. The company declined to identify the banks. The Safety Net system, rolled out globally late last year, analyzes more than 1.3 billion transactions per day involving MasterCard debit and credit accounts at banks, merchants and ATMs, using algorithms that assess customer behavior in real-time. In the three attacks this year, directed against two U.S. banks and one bank in South America, Safety Net identified anomalies such as large cash withdrawals or transactions outside the usual geographic area for a given account.

Published by AlP Press, New York, N.Y (1988).