Revolutionary technology is rarely spoken of in terms of real-world returns. Rather, it is heralded for the opportunities/possibilities it opens for businesses that can perfect it in business cases and leverage for higher returns. One of those technologies – AI – is finally showing some ROI (not the first time, but nonetheless). Denmark’s largest bank sees returns as it tears apart the playbook and brings machines into operations.
Pick #1. Denmark’s largest bank is using AI and machine learning to ‘tear everything apart’ – and it’s starting to pay off
Danske Bank, the largest bank in Denmark, lead the way in 2013 by introducing a new and convenient way to transfer money called MobilePay. Today, MobilePay is the Nordic region’s leading money transfer app with 3.8 million users in its core market, Denmark.
Now, Danske Bank is applying the same approach as with MobilePay – but instead of payments, it focuses on customer behavior and analytics. The bank has created an in-house startup called Advanced Analytics, whose sole purpose is to shake things up with bleeding-edge AI and machine learning technology.
“Based on online behavior we identified customers in a specific situation where financial advice is needed – for instance, when a person changes jobs with a new salary and pension plan.”
Danske Bank used these situations to contact customers proactively and achieved 62% better results than in their traditional campaigns. Danske Bank also wants to empower customers on their own terms – by using peer-based models á la TripAdvisor.
“We introduced a concept called ‘Others Like You,’ which enables us to show a customer the choice of people with a similar profile, [for instance when choosing] pension plans,” says Bjørn Büchmann-Slorup, Head of Advanced Analytics at Danske Bank.
Read more on BI.
A recent survey of CIOs found that CIOs downvote blockchain in favor of AI – not really surprisingly. Does that mean AI is much closer to finding a widespread business application with positive ROI than the much-hyped blockchain?
Pick #2. CIOs prefer AI over blockchain for banking and investment services: Report
Artificial intelligence (AI) overpowers blockchain and emerges as the most preferred option for bank honchos as far as investments are concerned, said a report by Gartner.
CIOs in the banking and investment services have started believing that the old practices and models are not going to sustain in future. “Digital transformation and its related technologies such as APIs are more important for banking than for other industries,” said Pete Redshaw, Managing Vice President at Gartner.
The survey revealed that digital transformation is of high priority and matter the most for the respondents in the banking domain. About 26% of respondents from the banking sector marked digitization as a matter of first priority in comparison to 17% respondents from other industries.
Read more on The Banking and Finance Post.
Another interesting piece for today explores how AI evolves, suggesting that “within not so many years, we’ll have an AI that incrementally learns to become as smart as a little animal – curiously, creatively, and continually learning to plan, reason, and decompose a wide variety of problems into quickly solvable (or already solved) sub-problems. Soon after we develop monkey-level AI, we may have human-level AI with truly limitless applications.”
Pick #3. Falling Walls: The Past, Present & Future of Artificial Intelligence
A particular focus of mine since the year 1990 has been on unsupervised AIs that exhibit what I have called “artificial curiosity” and creativity. They invent their own goals and experiments to figure out how the world works, and what can be done in it. Such AIs may use LSTM as a sub-module that learns to predict consequences of actions. They do not slavishly imitate human teachers, but derive rewards from continually creating and solving their own, new, previously unsolvable problems – a bit like playing kids, to become more and more general problem-solvers in the process (buzzword: PowerPlay, 2011). We have already built simple “artificial scientists” based on this.
Extrapolating from this work, I think that within not so many years, we’ll have an AI that incrementally learns to become as smart as a little animal – curiously, creatively, and continually learning to plan, reason, and decompose a wide variety of problems into quickly solvable (or already solved) sub-problems. Soon after we develop monkey-level AI, we may have human-level AI, with truly limitless applications.
And it won’t stop there. Many curious AIs that invent their own goals will quickly improve themselves, restricted only by the fundamental limits of computability and physics. What will they do? Space is hostile to humans but friendly to appropriately designed robots and offers many more resources than our thin film of a biosphere, which receives less than a billionth of the sun’s light. While some AIs will remain fascinated with life – at least as long as they don’t fully understand it – most will be more interested in the incredible new opportunities for robots and software out there in space. Through innumerable self-replicating robot factories in the asteroid belt and beyond, they will transform the solar system, and then within a few hundred thousand years, the entire galaxy; and within billions of years, the rest of the reachable universe, held back only by the light-speed limit. (AIs or parts thereof are likely to travel by radio from transmitters to receivers – although putting these in place will take considerable time.)
Read more on Scientific American.
As with any groundbreaking technology, specific applications of AI will go through a learning curve and evolution. As scientists and developers explore the depth of opportunities AI presents, they also inevitably discover fault lines that require attention. Nonetheless, this is a natural process, and the more potential challenges and ‘bugs’ are discovered, the brighter the future of improved systems will be.
Pick #4. There’s a glaring mistake in the way AI looks at the world
The patterns AI looks for in images can be reverse-engineered and exploited, by using what they call an “adversarial example.” By changing an image of a school bus just 3%, one Google team was able to fool an AI into seeing an ostrich. The implications of this attack mean that any automated computer vision system – whether it be facial recognition, self-driving cars, or even airport security – can be tricked into “seeing” something that’s not actually there.
Read more on Quartz.