Margo Seltzer, the Canada 150 Research Chair in Computer Systems at the University of British Columbia and 2023–2024 ACM Athena Lecturer, is the kind of researcher who stands out not just for her accomplishments, but for her tirelessness. After building a database software library that underpinned many first-generation Internet services, she worked on topics that range from file systems and storage to capturing and accessing data provenance. Here, she speaks with Leah Hoffmann about finding impactful research projects--and keeping up with everything that's going on in the field. The story of Berkeley DB, the database software library that you built with Keith Bostic and Mike Olson, has been told before at greater length, but let me see if I can summarize. Your work on packages such as hash and B-tree was released with Berkeley Unix as the DB 1.85 library.

At Biogen Digital Health (BDH), we aspire to transform Biogen and patients' lives by making personalized & digital medicine in neuroscience a reality. Powered by data-science and digital technologies, we drive solutions to advance research, clinical care, and patient empowerment. Our team strives for real impact through excellence, innovation, and collaboration. This role is of key importance to achieve the strategic vision and objective to make Biogen a recognized leader in digital health sciences, hence contributing to our corporate vision & strategy. At Biogen Digital Health (BDH), we aspire to transform Biogen and patients' lives by making personalized & digital medicine in neuroscience a reality.

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." As organizations across the globe realize the value of artificial intelligence, there is also a growing need to acknowledge the roadblocks and make efforts to remedy them to maximize the impact of the technology. AI experts share their thoughts.

However, the technology seems to be lagging when it comes to other areas, including translation into drug discovery. Despite a huge amount of media attention for its potential to accelerate this field, AI is yet to be proven as an effective solution. What needs to change for AI to advance drug discovery? AI could make the strongest impact on drug discovery by reducing the number of drugs failing in clinical trials. Currently AI is largely focused on method development using preclinical data – data from research that takes place before human clinical trials – rather than focusing on applying and generating the clinical data we need to make a real impact on drug discovery.

Overstating the capabilities of AI is a well-known problem in AI research and machine learning, and it's led to a complacency toward understanding the actual problems they've been designed to solve, as well as identifying potential problems downstream. The belief that incompetent and immature AI system once deployed can be remedied by a human on the loop or assumption that an antidote exists, especially compatible with cybersecurity, is an erroneous and potentially dangerous illusion.

Few would dispute the idea that artificial intelligence will be a transformative technology for financial services. Yet the view of how that transformation will shake out may be evolving significantly. A report from Deloitte and the World Economic Forum contends that in the near future, technology expertise will grow so commonly available that raw AI and multiple technologies built around that hub will not be what separates the winners from the other players. Instead, as envisioned by the report, the transformative technologies that excite so many today will become as basic to the industry as the longstanding payments rails they all share today. What institutions do with that transformative technology will mean much more and that will hinge on some surprisingly basic ideas.

Organizations that want to get started quickly with machine learning may be interested in investigating emerging low-code options for AI. While low-code techniques will never completely replace hand-coded systems, they can help accelerate smaller, less experienced data science teams, as well as help with prototyping for professional data scientists. First of all, what is low-code? Well, the phrase can mean different things to different people, and its applicability to AI is not entirely nailed down. Mainstream developers have been using low-code (or no-code) approaches to creating business and consumer applications for years, and that largely forms the basis for low-code approaches in AI.

In 2020, the nature of customer engagement will change as personalisation - how marketing and customer value management actually engage with customers - rapidly matures. This means a change will be required in legacy campaign and loyalty programme management solution architectures (i.e. a move from relational databases of static customer data and batch processes to a real-time online customer profiling and engagement triggering). Those Communications Service Providers (CSPs) who lead the way will tap the real benefits that can be achieved by moving to CE 3.0. Net Promoter Scores in the telecoms industry are low; yet to date there's been relatively little analysis of why. One change lies in clearer answers to the question "Does my operator give me value for my money?".

In the past two or three years artificial intelligence has felt like rocket science. Companies such as DeepMind have captivated our attention. We have been wowed by developments in areas such as computer vision, machine translation and speech recognition. In 2020, AI will begin to live up to the hype by starting to generate real economic value through its application across industries. According to consulting firm PricewaterhouseCoopers, the widespread adoption of AI will add about $15.7 trillion (£12.8

The role of a data scientist is often referred to as the sexiest job of the 21st century. Perhaps you were drawn toward the career because you love math, programming, and everything technical. But I'm willing to bet many of you were also interested in using data to make a real impact. At the end of a long day of tweaking data and building machine learning models, you're the ones who want to say, "Today I created something that will positively influence somebody's life." In other words, you want to see your work unfolding in the real world.