During the 33rd International Joint Conference on Artificial Intelligence (IJCAI), held in Jeju, I had the opportunity to meet with one of the keynote speakers, and winner of the 2024 IJCAI Computers and Thought Award, Professor Nisarg Shah. I asked him about his research, the role of theory in machine learning research, fairness and safety guarantees, regulation, conference reviews, and advice for those just starting out on their research journey. Could you start by telling us about yourself, your career, and your education? Nisarg Shah (NS): I grew up in India and went to IIT Bombay for my undergraduate. Ever since then, I knew that I wanted to go into higher education and academia. I actually did do an industrial placement after my undergrad, and I got a job offer that was very lucrative and would have been more lucrative than doing a PhD. However, that [money] is not why I wanted to do my PhD. I wanted to do my PhD because I was genuinely curious about different questions in this field, and I wanted to study more about them and have fun while doing it.

Most MLOps teams have people with extensive software development skills who love to build things. But the continuous build of new AI/ML tools must also be balanced with risk efficiency, governance, and risk mitigation. Many engineers today are experimenting with new generative AI capabilities. It's exciting to think about the possibilities that something like code generation can unlock for efficiency and standardization, but auto-generated code also requires sophisticated risk management and governance processes before it can be accepted into any production environment. Furthermore, a one-size-fits-all approach to things like generating code won't work for most companies, which have industry, business, and customer-specific circumstances to account for.

More companies in every industry are adopting artificial intelligence to transform business processes. But the success of their AI initiatives depends on more than just data and technology -- it's also about having the right people on board. An effective enterprise AI team is a diverse group that encompasses far more than a handful of data scientists and engineers. Successful AI teams also include a range of people who understand the business and the problems it's trying to solve, says Bradley Shimmin, chief analyst for AI platforms, analytics, and data management at consulting firm Omdia. "The technologies and the tooling that we have available is skewing more and more toward enabling and empowering domain professionals, the business users, or the analytics professionals to take direct ownership of AI within companies," he says.

There is a crisis in machine learning that is preventing the field from progressing as fast as it could. It stems from a broader predicament surrounding reproducibility that impacts scientific research in general. A Nature survey of 1,500 scientists revealed that 70% of researchers have tried and failed to reproduce another scientist's experiments, and over 50% have failed to reproduce their own work. Reproducibility, also called replicability, is a core principle of the scientific method and helps ensure the results of a given study aren't a one-off occurrence but instead represent a replicable observation. In computer science, reproducibility has a more narrow definition: Any results should be documented by making all data and code available so that the computations can be executed again with the same results.

Many organizations have come around to seeing the business imperative of an AI ethical risk program. Countless news reports -- from faulty and discriminatory facial recognition to privacy violations to black box algorithms with life-altering consequences -- have put it on the agendas of boards, CEOs, and Chief Data and Analytics Officers. What most leaders don't understand, however, is that addressing these risks requires raising awareness of them across their entire organization. Those that do understand this often don't know how to proceed. For companies that use AI, this needs to be a top priority.

Artificial intelligence is transforming the asset management industry by enabling fundamental analysts to research and extract more information faster so they can uncover accurate investment insights. Analysts spend hours and sometimes even days manually researching hundreds of sources. This process is extremely labor-intensive, and it's easy for analysts to miss critical pieces of information. Analysts can use AI and natural language processing (NLP) to detect and extract the most relevant facts from unstructured datasets. One of the ways AI has evolved is with its accessibility.

Google Cloud has launched Vertex AI, a fully managed cloud platform that simplifies the deployment and maintenance of machine learning models. Vertex was announced during this year's virtual I/O developer conference and somewhat breaks from Google's tradition of using its keynote to focus more on updates to its mobile and web development solutions. Google announcing the platform during the keynote shows how important the company believes it to be for a wide range of developers. Google claims that using Vertex enables models to be trained with up to 80 percent fewer lines of code when compared to competing platforms. "Data science practitioners hoping to put AI to work across the enterprise aren't looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order. It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process -- all while encouraging the flexible adoption of diverse technologies."

At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. It's a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesn't traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers. The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. "Machine learning in the enterprise is in crisis, in my view," Craig Wiley, the director of product management for Google Cloud's AI Platform, told me. "As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you -- every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it.

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However, for many IT organizations, AI is not just on the IT leader's radar as a business enabler: It's having fundamental impacts on the function itself – from automating some longstanding functions to demanding greater involvement and newer approaches from IT teams. AI is beginning to reshape IT in a number of ways that forward-looking IT leaders will want to follow. Let's consider five worth watching: Tools to automate traditional break-fix and other IT service desk processes are not new, but they're getting significant traction these days, says Wayne Butterfield, director of cognitive automation and innovation at ISG. "An IT Service Desk is as prone to repetition (and therefore automation) as a customer service operation," he says. That's not the only area of hyper AI-enabled automation coming for the IT function. "IT has quickly become not just a partner but a consumer as well, leveraging AI for security and system management to automate processes and move at the speed of an AI-driven enterprise," says Shawn Rogers, vice president of analytic strategy at TIBCO.