As one of the so-called "big four" U.S. banks, Chase needs little in the way of introduction. And like many age-old institutions, including its direct rivals, the New York-based financial powerhouse has had to move with the times, with Chase now investing more than $11 billion each year on the technology side of its business. This includes software development, cybersecurity, and -- increasingly -- artificial intelligence (AI) and machine learning (ML). Talking at Transform 2020 today, Sandra Nudelman, chief data and analytics officer at Chase for the past two years, outlined some of the main ways the company is harnessing AI and ML across its business, including helping streamline internal processes such as managing PPP applications, improving marketing efforts, increasing credit lines, and preventing fraud. In response to the COVID-19 crisis, the U.S. government launched the Paycheck Protection Program (PPP) a couple of months back to ensure money continues to roll into the workforce -- this, in turn, led to significant paperwork for banks, which have had to deal with a mountain of applications.

Most large banks in the US are pursuing AI fairly assiduously, but JPMorgan Chase stands out for the depth of its commitment to the technology, the breadth of projects it has adopted, and the focus on driving actual business change from its AI initiatives. The Bank, the largest in the US and 6th largest in the world in terms of total assets, has AI projects or production applications in all the usual areas of banking: risk, fraud prevention, marketing, investment banking, wealth management advice, trading, back office automation, and customer engagement (particularly in the corporate banking area thus far). But JPMorgan Chase distinguishes itself from other banking firms in its level of investment, its hiring of AI academic stars, and its coordinated approach to the management of AI and analytics. JPMorgan Chase spends $11 billion a year on technology, and about half of that amount is devoted to research on new and emerging technologies. Its research investments cover a wide variety of domains, including investments in AI startups and AI-based hedge funds.

Artificial intelligence is creeping into our smartphones in small, subtle ways. Google's Pixel 3, announced Tuesday, can answer robocalls on your behalf thanks to Google's Duplex technology and Google Assistant. Meanwhile, Android P, the latest operating system for Google's phones, can learn from how you interact with phone alerts to suggest stopping notifications for particular apps, reducing the amount of unnecessary intrusions your phone makes into your daily life. But there's another new phone in the pipeline that takes these kinds of developments further. By pairing them with more robust voice control, it may help fill in the picture of how we'll talk to the next generation of smartphones--and what they'll learn about us in order to talk back.

We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent space in which affinity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task characteristics. The approach allows us to use a principled decision theoretic framework for expert selection, allowing the user to choose a utility function that best suits their objectives. The model component for taking into account the performance feedback data is pluggable, allowing flexibility. We apply the model to manage a portfolio of algorithms to solve hard combinatorial problems. This is a well studied area and we demonstrate a large improvement on the state of the art in one domain (constraint solving) and in a second domain (combinatorial auctions) created a portfolio that performed significantly better than any single algorithm.