Although AI feels like a recent tech development, Danny reminds us that it has a long-established history. Danny worked alongside the likes of Marvin Minsky, and Claude Shannon – no, they’re not Bugsy Malone characters but are two of the team members who established the field of artificial intelligence. Working with them, Danny and the crew discovered that what they thought would be easy was much harder than expected, while what they were wary of was much easier to achieve. Pattern recognisers were developed with little effort, but creating a computer that could beat a human at Chess was much harder.

It turned out that the main barriers to success were a lack of data and, the most limiting factor of all, a lack of computational power. But that’s OK because Danny’s PhD focused on what would be required to build the biggest computer. He discussed his Thinking Machines in our Speaker Series chat which we shared last month.

Danny notes that today’s AI researchers are working on algorithms that are very close to the ones the team imagined back at the start of this area of research, however, he reminds us that we are still way off machines that can replace humans. While well-trained machines can carry out specific talks well, they are missing the critical thinking part of intelligence, however good they are becoming in mimicking intelligence, as evidenced in numerous case studies of AIs that hallucinate, or create solutions that look and sound right based on the fact that the machine has recognised patterns and attempts to apply those rules but that, without real meaning or understanding, are factually incorrect. Danny tells the story of how his granddaughter can recognise patterns in visiting contractors and become someone who sounds like an expert in moments, but scratch the surface and there is no real knowledge of the area to be able to make logical decisions. I too am reminded of the time I accidentally found myself co-piloting an island-hopper propellor plane across Belize, having curiously followed the actions of the pilot for the first two stops – but we’ll save that story for another time. The year is young, and we’ve got lots more to chat about, and many more stories to share.

Danny reflects that, while to experts it doesn’t feel like AI has moved on much since the development of supercomputational power, there is a change coming, as evidenced by the ever-increasing rate of development in the area. The difference this time around is funding, which is attracting the smartest minds in their droves, catalysing this progress by exploring the intuitive aspects of this technology.

To make this technology truly good, Danny firmly believes that a source of truth is required. One of his interests is building a knowledge graph of the provenance of information, which he further expanded on in last month’s Speaker Series. This would go some way to building technology that is as robust and trustworthy as possible, while attempting to eliminate biases or building on questionable knowledge that can bed into the foundations, creating points of future weakness and instability.

The great thing about building good technology is that it in turn starts to iteratively learn and teach itself, generating more knowledge, even about that knowledge itself. These are exciting times for AI, but public and research community engagement remains vital to ensure that developments do not double down on historically discriminatory narratives or unscientific knowledge that have no place in today’s society.

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Venki breaks his thoughts down into two categories, the first of which is around general integrity in research. Measures of robust research include whether published research is sufficiently detailed that someone could perform the experiment and observe results that reproduce the reported research outcomes. This is just one measure to help determine the quality of research being conducted and is a core tenet of peer review, by peers and competitors who would be able to expose any issues in the research. There is also the conscious or unconscious cherry-picking of observations, which can again be mitigated through community consensus and conversation. If something is proven to be wrong, this consensus can be challenged and eventually help shift our understanding of the problem.

However, Venki also discusses the much bigger and more pervasive issue of misinformation, which goes beyond the scientific community and impacts all aspects of our lives, including politics, behaviour, economics, and more. Many threads of life are susceptible to the negative impacts of misinformation, which is being accelerated further by the ease at which information can be shared, and the contribution that AI is making to the volume of misinformation available in the wild.

Venki encourages us to be alert and involved as a society in combatting misinformation through robust critical thinking, to help prevent the spread of incorrect information that could otherwise go on to misinform policy and processes that impact us every day.

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Joy acknowledges the need to have a term that refers to both the geographic region of the Southern hemisphere and also the economic Global South, especially when describing the differences in access to funding and research information, and opportunities to collaborate on a global scale due to a lack of visibility of research outputs or the inability to attend conferences half a world away.

However, using Africa as an example, Joy expresses concern about the fact that, while it is easy to use the four major commercial capitals of the continent to represent the entire region, the resulting outlook is underrepresenting vast swathes of communities across the continent, so that when solutions are proposed, they do not adequately serve the needs of the many, only of the few that were represented.

Do you have any ideas for how we can better represent our communities across the world? Get involved in the conversation on our many social media channels using the hashtag #TLDRShorts, and let us know if you’d like to suggest future contributors for our series or suggest some topics you’d like us to cover.

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In our first Speaker Series chat of 2025, and in a month that heralds much political interest in the role of truth and trust in society, Deborah delves into the challenges and opportunities facing science communication today. She explores the importance of storytelling, the necessity of improving scientific literacy in all, and the steps needed to build a future where science journalism drives meaningful dialogue and action. Her insights offer a vision of how journalists and scientists can work together to showcase the human side of science and ensure it serves all communities fairly and effectively.

Deborah Blum is an acclaimed science journalist, Pulitzer Prize-winning author, and Director of the Knight Science Journalism program at MIT. With a career spanning decades, she has worked tirelessly to bridge the gap between science and the public through her compelling storytelling and her commitment to advancing science literacy. Her influential books, which include The Poisoner’s Handbook and The Poison Squad, explore the intersection of science, history, and societal impact. At the Knight Science Journalism program, Deborah leads efforts to train and support journalists worldwide, fostering a global community dedicated to improving the quality of science communication and addressing pressing challenges like misinformation and declining public trust in science.

The role of science journalism

Science journalism plays an important role in making connections between scientific discoveries and people’s everyday lives. Deborah describes the role that science journalists play in helping to translate complex scientific ideas into stories that resonate with readers. Good storytelling can make even the most abstract research feel relevant and engaging.

In a world increasingly driven by scientific and technological progress, this connection is more important than ever. Deborah highlights that science journalism not only informs but also inspires public interest and action. By showing how science impacts issues like health, climate change, and technology, journalists help communities see the relevance of research in shaping our future. As she puts it, “We need to write about science and its impacts, right? We need to acknowledge that it has these social and cultural impacts. We need to illuminate those in all of their social justice issues.”

The impact of the KSJ program

At the heart of Deborah’s work is her leadership of the Knight Science Journalism (KSJ) program at MIT, where she has been Director for a decade, a role which she will be stepping down from in July 2025. The KSJ program is somewhat of a global engine for excellence in science communication. The program provides resources, fellowships, and a thriving community for journalists to deepen their expertise and broaden their perspectives.

But the KSJ program goes beyond training – it builds a community of professionals who share a commitment to thoughtful, accurate reporting. Deborah believes this community approach is critical in a world where misinformation spreads rapidly. When journalists feel supported and connected, they are better equipped to tackle tough stories and elevate public understanding. This global impact is reflected in the program’s alumni, who are shaping conversations about science across continents.

Challenges in science communication

Communicating science effectively isn’t without its hurdles. Deborah points out that misinformation is casting an ever-growing shadow, compounded by public scepticism and limited access to scientific education and critical thinking skills. We now live in an age where everyone has a platform from which they can share their thoughts, but not everyone values the accuracy of those thoughts. Deborah emphasised the need for journalists to cut through the noise with credible, engaging stories.

Another challenge is the disconnect between scientists and the public. Deborah argues that many researchers struggle to communicate their work in accessible ways, leaving gaps that can be filled by misunderstanding or fear. “The more people can see scientists as actual human beings next door, the better off we all are,” she insists. Building bridges between these groups is crucial to fostering a more informed and engaged society.

The importance of science literacy

Science literacy is the foundation of informed decision-making, yet many people lack the tools to critically evaluate scientific information, with many people even being fearful and actively disengaged with science. Deborah shares her thoughts about the shortcomings of educational systems. She believes that by not teaching people how to think critically about science, we are doing a disservice to society. Without this foundation, the public is more vulnerable to the potentially negative consequences of pseudoscience and misinformation.

Deborah believes that improving science literacy requires a collective effort. Journalists, educators, and policymakers must work together to ensure that everyone has access to clear and reliable information. It is not only about teaching facts but also about teaching people how to think, and how to evaluate those facts for any inherent bias. “We don’t want everyone to be a scientist, but we do want everyone to know something about science and how to make decisions about science and to recognize that every time you fry an egg or make a cup of tea or peel a banana, you’re engaging with chemistry, right, in everyday science. And it makes the world that much more interesting.” By developing people’s ability to understand and question scientific claims, society can make better choices for the future, and hold bad actors accountable for bad science.

Building trust in science

Trust in science has been eroded in recent years, but Deborah sees this as an opportunity for change. Trust isn’t automatically granted – it is something that must be earned. Scientists and journalists both have roles to play in this process. Deborah encourages researchers to embrace transparency and share not just their successes but also their uncertainties and failures, to humanise their motivations and actions, and to showcase the more realistic side of the scientific process.

She also highlights the importance of equity in building trust. Science needs to serve all communities, not just a select few, so addressing social justice issues in science such as unequal access to education and healthcare can help rebuild trust in science’s potential to improve lives. Through honest and inclusive communication, science can regain its role as a trusted guide for society.

The future of science journalism

Looking ahead, Deborah envisions a future where science journalism is more valuable than ever. She sees the next generation of journalists as not only storytellers but also advocates for social justice. She believes that the future of science journalism lies in showcasing the human side of science, and how it impacts people and communities.

Deborah encourages young journalists to be fearless in tackling big issues, from climate change to misinformation. We need journalists who are smarter, braver, and more curious. By embracing innovation and collaboration, science journalism can continue to be a powerful force for good, shaping public understanding and inspiring meaningful action in an increasingly complex world.

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Though Amgen’s core business priority is to serve patients, Scott talks about a handful of the many ways in which the organisation also helps make a difference within communities beyond those directly impacted by their research outputs. The Amgen Foundation has given over $450 million since its inception in 1990. Scott mentions some of the ways in which these occur on both an organisational and an individual level.

We had a longer chat with Scott and spent a few days at the Amgen biotech campus in Massachusetts, and we’ll be sharing that Speaker Series interview and another exciting new feature in 2025, so watch this space to find out much more about the very tangible ways in which Amgen’s collaboration with the Amgen Foundation helps them to meet their goals of making a difference to everyone’s lives and empowers the next generation of biotech scientists.

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Danny is a renowned computer scientist, engineer, and entrepreneur. As the co-founder of Applied Invention, and before that of Thinking Machines, Danny is also a pioneer in parallel computing, which helped shape the trajectory of technology and its applications across diverse fields. His CV includes a long list of outputs and achievements, including designing the Connection Machine, a supercomputer that was way ahead of its time, and his work on the Long Now Foundation’s 10,000-Year Clock, an example of how art and science can promote long-term thinking. In this chat, Danny and I discuss the exciting inventions that thrive at the intersections of technology, research, and innovation, and what we can expect regarding the future of science and its role in solving humanity’s greatest challenges for everyone.

The goal of Thinking Machines was to build computers with a lot of power that could develop AI, large language models, and so on. According to Amdahl’s Law – a problem doesn’t become easier to solve when you apply more processing power, rather it becomes less efficient – many people presumed that the problem couldn’t be overcome, and parallel computers got little to no interest. However, Danny wouldn’t let it go. He understood that brains are parallel computers – they may consist of slow components, but they still do things quickly. Danny wanted to build a machine that proved this rule wrong and set about building something capable of doing so. He proved that Amdahl’s Law can be broken, and made some very fast machines in the process. These machines were responsible for creating the first-ever global climate models and also three-dimensional seismic models of the Earth. Whichever problem you brought the supercomputer, it found a way to apply its power usefully. More importantly, the supercomputer and its varied associated projects were a magnet for very smart people who liked solving a range of problems – including someone called Richard Feynman who apparently got very excited about physics.

Because of its varied applications and capabilities, a very interdisciplinary community of users developed around the Connection Machine. Most other research endeavours reward specialists with deep knowledge of one small area, but the supercomputer set the tone for Thinking Machines as being a place where being a generalist pays off.

I was thrilled to learn in our chat that one of my favourite authors Neal Stephenson was also inspired by Danny’s invention. In the original idea for his first published SciFi book, Snowcrash was a computer program. However, he pivoted slightly after he tried and failed to program the Connection Machine. Sergey Brin was another member of the Connection Machine’s research community – he already had an idea for search engines, which turned into Google. By using this new and powerful technology, these researchers were able to stretch the possibility of what they could do, only just beyond the realms of what they were doing, but enough to fuel further exploration.

Knowledge graphs for humanity

Danny realised that having a computer-readable graphic representation of general knowledge – people, places, things, schedules, etc – would become important. It would be a tool to help computers navigate questions posed to them. His company Metaweb therefore set about building a knowledge graph and, as predicted, people started using it. It became a critical piece of technology that enabled search engines, especially once Google bought it. That same technology became the Google Knowledge Graph, and its legacy is seen in Google Maps for example. The connected, related information presented in response to a search query is coming out of the knowledge graph even now.

Data is useless unless you can make sense of it. By using knowledge graphs, information can be associated through connection and meaning, presenting results that don’t contain the words in your keyword search query but are closely related to it in meaning and semantics. This is a sentiment that Sebastian Schmidt echoed in our chat back in May. Danny says that right now there are more than 100s of billion relationships between entities means lots of connected information. However, despite its continued widespread use, Danny feels that for him the project was still a failure in some senses – he wanted to create this resource for the world. Instead, every company makes their own knowledge graph which feels like a waste of human effort, and also biases progress towards wealthier companies.

Danny also believes there is scope to make knowledge graphs even better and more fit for purpose in today’s society. He says that there is not a rich enough representation of the provenance of information that is included, and humanity is facing a crisis over what they can and can’t believe. There is so much incorrect information out there, and AI is supercharging the capacity to create what sound like plausible truths but are instead contributing to misinformation and disinformation. One tool to help people determine the truth in information could be a graph of public assertions of knowledge. By including a range of cultural lenses through which to view this knowledge, some information could be contradictory, but we are giving people the power to decide what to believe by also including information about how that knowledge came to be created. In an age where truth is a complicated concept, there are more tools available to us than ever before to help us remain as scientific as possible.

Interdisciplinarity for innovation

After his time at Thinking Machines, Danny wanted a break from computers. He had wanted to be a Disney Imagineer ever since he heard the term, and was offered a role at the company. However, he needed a new title to reflect the varied work he would be doing. Thus Danny and some of his key collaborators became the first cohort of Disney Fellows, with Danny also taking up the position of VP of Imagineering. This role opened Danny up to thinking about storytelling, art and audience engagement in a way he had never done before.

Danny says it was fun to turn make-believe into a magical reality, but he missed pushing the boundaries of truth-oriented work, and so took what he had learned from problem-solving during his time at Thinking Machines and combined it with Disney’s studio approach whereby they would call on a network of experts to solve different problems and created Applied Invention, one of the few interdisciplinary innovation organisations around, alongside the likes of Google’s moonshot factory X.

Applied Invention exists as a “company of last resort”, If a problem can’t be solved in any other way, Danny and his interdisciplinary team will give it a good go. He says that they do this because rather than use the same hammer to hit the same nail, they have a range of different tools with which to hit the nail instead, potentially giving rise to a different solution. For the team at Applied Invention, each project they take on has to fulfil three criteria – someone has to be excited by the project, someone else has to determine that it won’t lose the company too much money, and finally, someone determines whether the team will be able to create a solution that is better than one anyone else could come up with. If all three criteria are met, the project is live.

When I asked Danny about how this way of working differs from academia, he said that while a PhD will train you on how to do something well with a deep knowledge of a very small area, organisations like applied minds work across all disciplines, and that taking a more generalistic approach means that while each person’s knowledge may be broad but not quite expert-level, the teams know of many other experts with that depth of knowledge that they can bring in to work with them. Danny says that academics may still have that experimental curiosity but because of how research is rewarded in academia, most researchers are unable to pursue such blue skies ideas unless or until they have a free pass in the form of a Nobel Prize or other such ticket to academic adventure.

Art and science

Danny reflects on the inner beauty of the workings of technology and the outer beauty of how it looks and what it can do. Danny believes that scientists have long understood that you need to care about how something looks. If we go back to Faraday’s time in the lab (Michael Faraday, and not my cat Faraday, creative though he also is) we are reminded that even then people knew that societal solutions had to look good in order to improve their chances of being adopted. This is something that Danny learned more about during his time at Disney. Inventions and interventions succeed and fail based on whether or not they have the right story behind them. There is scope to use art for more public engagement with science too – in fact one of Danny’s other projects, the Long Now Clock, is a piece of technology designed to remind people of the human story and our place in this universe. 

Science is a new way of deciding what to believe

As our conversation came to a close, Danny revisited the idea of using technology to determine the best truth in an age of mis- and disinformation, when you don’t know what to believe, whether you work within research or are impacted by it. While linking information has gone a huge way to democratising access to information about all of human knowledge, we are also muddying the waters by allowing the technology we have developed to further contribute to mistruths. However, where there is challenge, there is opportunity, and Danny encourages researchers to embrace the opportunity to build better infrastructures to give people the tools to determine what to believe. Danny believes this will have the single biggest impact on the human condition. He says that knowledge is the best thing humans have created. And, while human-made technology can make it bad, we can also use those very tools to strengthen the robustness and integrity of this knowledge to rescue ourselves out of this alternative truth crisis we now face.

You can watch the full interview with Danny on our YouTube channel, and check out our Speaker Series playlist on YouTube which includes chats with some of our previous speakers, as well as our TL;DR Shorts playlist with short, snappy insights from a range of experts on the topics that matter to the research community.

With thanks to Huw James from Science Story Lab for filming and co-producing this interview. Filmed at the Applied Invention offices in Cambridge, Massachusetts, USA in April 2024.

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The power of AI in research

Fresh-faced after the previous evening’s reception at The Royal Society, James Manyika, Senior Vice President at Google Alphabet, set the tone for the conference with a powerful opening session on the tangible benefits of AI. From AlphaFold’s protein-folding revolution to AI’s role in flood forecasting in Bangladesh which has already impacted millions of people, his talk was a staggering reminder that AI is already being used in various innovative and impactful ways beyond those we are more familiar with. He also discussed AI-enabled solutions through the lens of a public health research focus, reminding attendees of advancements including diabetic retinopathy detection in low-resource settings and the groundbreaking atmospheric simulations helping better prepare agricultural workers with advanced warning of what interventions may be required. This reminded me of our recent TL;DR Shorts episode with Dr Danny Hillis of Applied Invention who talked about the potential impact that automated research could have in helping us help non-traditional researchers. But James’s outlook for AI wasn’t all rosy as he underscored the limitations of this tool and emphasised the need for responsible approaches and equitable access to AI-powered tools, echoing his colleague Dr Astro Teller’s thoughts on this.

CRISPR meets AI

Nobel Prize-winning Chemist Professor Jennifer Doudna, Professor of Biochemistry, Biophysics and Structural Biology at UC Berkeley, and James Manyika picked up on this theme as they explored the synergy between CRISPR and AI. They discussed how CRISPR’s one-and-done gene therapies are accelerating in application thanks to AI’s ability to identify the genetic changes that drive outcomes, and democratising treatment options by providing a range of more affordable therapeutic options. Beyond healthcare, they also chatted about the impressive potential impact that AI will have on climate-related research, from drought-resistant crops to better carbon storage systems. What resonated most with me was their call to reduce the barriers – financial, technical, and geographic – to accessing the outcomes of this technology, making it truly global, and reflecting the recent thought we shared from Professor Lord Martin Rees.

Collaborating across disciplines

The focus shifted to the future of collaboration in a panel led by Eric Topol, author and Executive Vice President of Scripps Research, and featuring Fiona Marshall, President of Biomedical Research at Novartis, Alison Noble, Oxford University Technikos Professor of Biomedical Engineering, and Vice President & Foreign Secretary, The Royal Society, and Pushmeet Kohli, Vice President of Science, Google DeepMind. From AI revealing 2.2 million new stable inorganic crystals for potential use in everything from energy to electronics, to revolutionising biomedical imaging through natural image processing, the discussion highlighted how AI forces us to rethink and redefine what collaboration and trust look like. Alison’s comment on the importance of training scientists to understand AI’s errors stood out for me. The panel emphasised the shifting hierarchies and power dynamics in research, with data scientists increasingly leading labs – a significant cultural change, given that they were often seen as collaborators and service providers who were rarely even named on papers that couldn’t have been published without their expertise.

Climate, complexity, and community

Thomas Friedman gave an evocative talk on “climate weirding”, highlighting how we’re hitting many tipping points all at once – AI’s massive breakthroughs, climate change chaos, and global instability – while also linking AI capabilities to potentially help us with carbon emissions and societal disorder. His call for politics to embrace science felt especially urgent amid discussions of mass migration and global conflict. His optimism about how AI can solve big problems, like making farming more efficient or cutting healthcare costs, came with a warning that we also need solid ethics and politics to go hand in hand with these developments to keep things on track, something that our recent Speaker Series guest Professor Jenny Reardon touched upon.

The next panel discussion on building research infrastructure echoed these sentiments. Chaired by Paul Hofheinz, President and Co-Founder, Lisbon Council, and featuring Asmeret Asefaw Berhe, Professor of Soil Biogeochemistry and Falasco Chair in Earth Sciences, University of California, Merced, Bosun Tijani, Minister of Communications, Innovation & Digital Economy, The Federal Republic of Nigeria, and Fabian J. Theis, Director of the Institute of Computational Biology and Professor at TUM Mathematics & Life Sciences, both Paul and Asmeret stressed the need for equity and inclusion to be at the forefront of people’s agendas as they develop solutions using AI, to ensure that advancements don’t deepen the digital divide. Bosun Tijani’s discussion of Africa’s talent acceleration programs was inspiring – and proof that we can nurture talent globally if we commit to the cause. We recently heard from Joy Owango about how important it is to build infrastructure that ensures the persistence and visibility of research contributions from all across the globe, and how impactful this has already been in Africa and other parts of the Global South.

This theme continued as Lila Ibrahim, Chief Operating Officer at Google DeepMind chaired a conversation with Dame Angela McLean, UK Government Chief Scientific Adviser, Ilan Gur, CEO, Advanced Research and Invention Agency (ARIA), and Sir Paul Nurse, Director of the Francis Crick Institute, Nobel laureate, and returning President of the Royal Society chatted about collaborating for impact. The panellists discussed the importance of thinking big and including diverse perspectives through better community engagement in science, something that X’s Dr Astro Teller talked about in a previous TL;DR Shorts episode. Dame Angela talked about how the government needs to aim higher, pushing for more thoughtful use of AI and predictive models, while Sir Paul stressed the need for mixing disciplines to boost innovation. Ilan shared his excitement about creating spaces where scientists from different fields can cross paths, sparking unexpected ideas. Recorded at Sci Foo, a perfect example of a catalyst for collaboration, Dr Etosha Cave echoes this sentiment and the need for interdisciplinarity for innovation. The panel also discussed building trust, with Angela and Ilan both emphasising the importance of transparency in science and technology. All panellists highlighted the role of public engagement in encouraging people to engage with and trust these cutting-edge advances.

Public engagement and trust

The conference ended with a final discussion featuring recent Nobel Prize winners in Chemistry, Sir Demis Hassabis and John Jumper, as well as former winners Professor Jennifer Doudna, and Sir Paul Nurse. Their reflections on public engagement were poignant: how do we bridge the gap between experts and the public? Sir Paul’s call for deliberate public dialogue reminded me how crucial it is to address fears and misconceptions about AI before they grow into barriers. However, one issue that continually cropped up, and one I may have mentioned once or a million times in the past, is that as it stands, the framework within which we reward research success does not make space for valued and impactful public engagement, or even innovation and entrepreneurship. Mariette DiChristina had a few thoughts on this, and we’ll be hearing more from her in 2025 about the value of effective communication of, and engagement with research in the age of open research, research integrity, and novel technology such as AI.

Some key takeaways

• AI isn’t just transforming research; it’s reshaping the cultures around it. We’re seeing shifts in leadership, collaboration, and the ethical frameworks underpinning research.
• Accessibility remains a challenge. Whether it’s CRISPR or AI infrastructure, we need to ensure the benefits reach everyone, not just the privileged few.
• Collaboration is more vital than ever. From breaking disciplinary silos to engaging the public, success hinges on our ability to connect diverse voices.

In a world increasingly shaped by AI, this conference left me both hopeful and reflective. Science thrives when it’s inclusive, transparent, and collaborative – and AI could give us a chance to embrace those ideals like never before, provided we build research methods and applications in thoughtful, considerate, trustworthy and community-minded ways. My teammates recently authored a report that, in true Digital Science style, was informed by reflections from our own research community. The report looks at the changing research landscape in the age of AI and echoes the many challenges and opportunities discussed at this conference. AI is an exciting development that is already changing the way we do research. However, we must hold each other accountable to ensure that its development and application are open to all.

With thanks to Google DeepMind and The Royal Society for hosting this event. You can watch all sessions on Google DeepMind’s YouTube channel.

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Joy believes that open infrastructure can tackle both of these challenges and has already gone some way to proving its value in connecting people to underexplored areas of research and underrepresented researchers. Open infrastructures can also help us better analyse and understand the networks forming within and beyond research to drive research, discovery and innovation.

Reflecting on the impact that open research infrastructures have already had on the visibility and the collaborative discovery potential of African research outputs, Joy knows first-hand that these new ways of supporting research are already working – but she encourages us to create an even stronger framework within which everyone’s research contributions can be seen and built upon.

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Martin acknowledges that AI is an exciting development, for research and society as a whole. The attention it receives speaks to the tremendous potential of AI-enabled tools. When pondering the more worrying aspects of this novel technology, Martin says that he feels the hype is exaggerated. Rather than worry about a superintelligence taking over, Martin believes that we would be better off worrying about the downsides of things going wrong.

Martin reflects on the pace of progress in AI and how it mirrors previous transformative innovations; progress doesn’t go uniformly and exponentially, rather it goes up quickly and then levels off. Comparing the rise of AI to space travel, Martin reminds us that there were only 12 years between Sputnik and the first Moon Landing, but since then there has been very little progress in space flight. There were also 50 years between the first transatlantic flight and the development jumbo jet. However,x since then commercial flight has barely changed. While AI is surging now, Martin reminds us that we shouldn’t presume that this trend will continue exponentially.

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Danny uses agriculture as one example of an area of research vital to the survival of humanity where we aren’t doing enough research. Any fellow BBC Countryfile fan will know that farmers work incredibly hard tending to their agricultural land and responding to the dynamic needs placed on them by the changing climate and other factors. Though they may like to, they often don’t have time to do experiments and contribute to the corpus of research information in this space in a way they would like to.

However, if we start to collect data from the automation of the mechanisation farmers used to work the land, we can allow these “robots” to conduct a series of experiments that humans don’t have the time to do.

Danny believes that in the future these machines will also contribute to planning future experiments to explore such research spaces. He believes that the future of automated science will be done by AI – allowing humans to increase the number of experiments they can conduct, increase the amount of data gathered, and increase the number of hypotheses being tested.

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Jenny has a research background in genomics, however she moved into examining the social issues raised by this area of research. In our chat she recalls being optimistic around the time of the fall of the Berlin Wall, and how this new field of science was a beacon of hope for bringing people of all backgrounds together through commonality. Tempted by the opportunity to combine her love for science, politics and justice, Jenny almost worked on the Human Genome Diversity Project – a project that intended on representing genetic variation across populations. Though the purpose of the project was to showcase how people across the world share fundamental similarities, the project sadly ended up being described as a so-called “vampire project”, sucking the blood of indigenous peoples in the name of research instead of working towards their continued survival.

This project and others like it inspired Jenny to make space for scientists that want to do good in the world without inadvertently contributing to the existing extractive nature of some areas of research, however this is tricky when the entire endeavour of research is more complicated. Current research culture is based on an archaic way of exploring thoughts and ideas, the framework of which was crafted by a fairly homogeneous group of wealthy people who shared similar thoughts and lived experiences hundreds of years ago. These reinforced the informal ways in which power was being distributed across the research profession. What place does that framework hold in today’s research ecosystem? I asked Jenny whether it is time for a research revolution. Naturally she also thinks we should be grabbing our flags and flocking to the barricades. She argues that if we don’t address the politics of research, we are at risk of research being deemed to be an untrustworthy pursuit. She discusses the concept of macropolitics vs micropolitics, reminding us that while we should absolutely be concerned by how the Donald Trump and others will impact research, we should also consider the impact of our own actions have on research culture and whether we are contributing to upholding aspects of a culture that are not conducive to inclusive and impactful research.

Jenny recommends that we stop “othering” the politicisation problem and take a level of individual responsibility and accountability. Most people go into research with idealistic interests, but research is an institution and we must be honest about the fact that, while many research transformations have led to better ways of doing work, there is still a lot to do to unpick the imbalanced power relationships that persist in the scientific method. Reflecting on her own experiences, and resonating with those we have heard from across the research community, Jenny finds it challenging that we don’t equip early career researchers with the tools to navigate the social aspects of the research space. If you are lucky, you may end up in a research group that passes down pearls of wisdom around the bunsen burner, much like our ancestors shared survival tactics around the campfire. However this level of support is not universal, and very much comes down to the luck of the draw. This results in many people feeling that they do not belong in research, and leaving to pursue other paths of interest. Their loss is felt in the absence of their ideas as we innovate to overcome the huge challenges we face as a global society. It also takes a lot of learning, listening and empathy to ensure that the ways we do this are free of racism, colonialism, and other factors that fly in the face of inclusion of all.

Scientists aren’t to blame for this. We work in a culture where very few scientific researchers are trained in social and political issues. If they had a better understanding of these, they have a better chance of doing good in the world through their research. Part of the challenges we need to overcome centres around the value we place on these aspects of research. Much like public engagement and science communication, an awareness of the social and political space is not valued in the same way as other research activities and outputs. By changing the way in which we reward successful research and looking beyond simply publications and grants won to include such skills, we will move another step forward in making research more robust and more inclusive. By changing how we value participation in these activities, we go beyond seeing ethics and the social justice of our research as tick box exercises and move towards understanding how they make the research we conduct better for more groups in society. This is why Jenny and her team focus more on justice than ethics, a term that is now associated with mandatory online courses and workshops that instil fear in researchers instead of empowerment. Justice is about a world where more people are included and their needs and aspirations are being addressed. It is a fundamental facet of the work we do as researchers.

Jenny goes on to talk about how this is a journey that social scientists need to take too. For a long time social science has been presented as a space of purity and neutrality, free of politics, but research is riddled with the politicisation of science, so this is an impossible claim to make and we need to be honest in recognising that, especially as we talk about the differential distribution of power and access to resources. Who is able to ask questions about our world, who can participate in this discourse, and who is really represented? If we continue to present a world in which we claim that research is apolitical, we risk further eroding trust in research.

However we need a massive culture change and global buy-in to move the needle. We need a deeper appreciation for how social issues are impacted by science. Jenny believes that the best research will reflect this more considered way of working. It will be more inclusive, more impactful, and ultimately more trustworthy. Until we change this culture, we aren’t doing the most innovative research as we are simply not making the most of the wealth of ideas out there.

When we talk about science and social justice, conversations about inequitable access to research information spring to mind. However there is also the issue of inequitable access to the production of knowledge too. Red tape and financial hurdles make overcoming these barriers challenging, and working across funding councils based in different continents is almost impossible. How can we solve global challenges when these barriers to impactful collaboration exist? Funding must be available to benefit our entire planet, and not just for the countries from which that funding originates. Overcoming these challenges requires time and resource to build communities and work together to iron out processes that work for everyone. Jenny’s work includes investigating how we can build truly global networks, working out who will fund them, and how we can ensure that funding sources are trusted. She believes that we need a shift in how funders measure their impact and return on investment. Funders need to park their local or national agendas and think bigger and more collaboratively to enable a democratic ethos of how funding is distributed.

So what does this all have to do with politics, and why does the outcome of today’s election matter for research, to the USA and to the world? Where there is politics, there is policy, and this impacts everyone. Policy makers are one tool we have to fundamentally change the culture of how we do research. When we look back at the UK’s own open research journey, the impact of national funding mandates and research assessment exercises have had on the adoption of open science principles. In the USA under the Biden administration, the Office of Science and Technology Policy (OSTP) created an Office of Science in Society that focuses on the societal impact of research. Headed up by the amazing Alondra Nelson who is trained in science and technology studies and cultural studies, the Office is one way in which policy makers and other stakeholders can think deeply on this issue, and look at research policy through a DEIA lens to determine whether it is truly serving everyone in society, or whether we are still leaving some people out of the discourse.

Whatever the outcome of today’s election, its impact will be felt across the world, from the allocation of funding to the way in which we measure research success and impact. Through Jenny’s work and that of her research centre, while we may have overlooked the inclusion of everyone in the impact of research for a long time, we have an opportunity to take stock and collectively contribute to a more inclusive, trustworthy and impactful research culture.

You can watch the full interview with Jenny on our YouTube channel, and check out our Speaker Series playlist on YouTube which includes chats with some of our previous speakers, as well as our TL;DR Shorts playlist with short, snappy insights from a range of experts on the topics that matter to the research community.

With thanks to Huw James from Science Story Lab for filming and co-producing this interview. Filmed at the Møller Institute, Churchill College at the University of Cambridge in July 2024.

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