There is a large global divide in AI research and development, with the vast majority of research publications and funding coming from the US, China, and EU27.

AI research has become fragmented across disciplines, geography, and policy. Specialised subfields rarely collaborate, limiting spread of innovations from one area to others. Concentration in high-income countries also excludes global perspectives while policies created in AI hubs may not transfer. Government regulations remain disjointed as well. In 2022 most countries lacked AI strategies, with existing policies conflicting across jurisdictions, ranging from promoting competitiveness to ethics. Overall this disciplinary, geographic, and policy division hampers coordination across all of AI.

Well, Falling Walls certainly lived up to expectations! It’s six years since I was originally slated to attend but had to hand presentation duties over to my cofounder due to the birth of my youngest daughter, Annabelle. I was fortunate to be able to attend in person this year, and today started with a wonderful panel session on the “Implications of AI for Science: Friend or Foe?”, chaired by Cat Allman who has recently joined Digital Science (yay!) and featuring a brilliant array of panellists. Here's my notes from the session.

#FuturePub returned to London for a special AI edition, and this one was on a whole new scale...180 people attended -- a new record -- and we had a much wider mix of attendees; government representatives, researchers, research administrators, healthcare professionals, publishers, finance & technology experts, and of course, start-up founders! Here's the lowdown on what went on...

"Because of my background, I'm always interested in how technology can serve the underserved in society, and how it can empower people to live their best lives. With AI, I'm not worried about robots taking over the world. I'm more worried about people using technology to do bad things to other people, rather than the technology itself. One of the biggest issues we've got with technology is that most people in society, particularly those who aren't in tech, think that they can't understand it. I want to help change that, on a global scale."

In the second part of this three-part series on artificial intelligence, machine learning and open research, Mark Hahnel takes a look into how this technology can actually result in better, more diverse, more inclusive and ultimately FAIR-er data for the whole research community.

Could Generative AI provide a solution to the problem of predatory journals? We share the good and the bad news.

Trust. Five letters, multiple meanings, immense power. Trust arrives on foot and leaves on horseback. Trust is the basis for society, but foundations are fracturing in a world of growing divides. Trust in research has never been more important in our lifetimes. In the vast subway system of the scientific world, we must navigate through research integrity. All who create or consume science are on this journey. How do we safely traverse information and hold on to the sanctity of science. How do we mind the trust gap?

This is the story of how a school science fair inspired a passion for science communication, a PhD in microbiology, and a valuable perspective on the current AI debate. Dr Jessica Miles recently participated in the SSP2023 panel on AI and the Integrity of Scholarly Publishing, the writeup from which has just been published on the Scholarly Kitchen. I caught up with Jessica to chat about how she came to be on the panel, her background in microbiology, and her thoughts on the future of scholarly publishing in an AI world. I got a Barack Obama impression for free 😊

The effects of new technology are seldom either solely positive or solely negative and, as such, there is a responsibility that sits with those that develop technology to consider how it will be used.  Underlying that responsibility sits a need to deeply understand technology.  The recent rise of Large Language Models (LLMs) and its rapid adoption begs many questions about whether we understand the technology and whether we understand how it will impact us at a cultural, societal, or economic level. When I have written before on this topic I have noted that it is not the large biases that are concerning to me, rather it is the subtle biases that are difficult to detect that I think should be of more concern. I found recently what I regard to be an excellent illustration of the phrase that I have chosen to describe the output of AI. I call this the ‘Lone Banana Problem’.