Searching DNA databases: cold hits and hot-button issues

Many criminal investigations, including “cold cases,” do not have a suspect but do have DNA evidence. In these cases, a genetic profile can be obtained from the forensic specimens at the crime scene and electronically compared to profiles listed in criminal DNA databases. If the genetic profile of a forensic specimen matches the profile of someone in the database, depending on other kinds of evidence, that individual may become the prime suspect in what was heretofore a suspect-less crime.

Searching DNA databases to identify potential suspects has become a critical part of criminal investigations ever since the FBI reported its first “cold hit” in July 1999, linking six sexual assault cases in Washington, D.C., with three sexual assault cases in Jacksonville, Florida. The match of the genetic profiles from the evidence samples with an individual in the national criminal database ultimately led to the identification and conviction of Leon Dundas.

How the statistical significance of a match obtained with a database search is presented to the jury should, in my view, be straightforward but, given the adversarial nature of our criminal justice system, remains contentious. One view is that if the profiles of the evidence and a suspect who had been identified by the database search match, then the estimated population frequency of that particular genetic profile (equivalent to the Random Match Probability in a non-database search case) is still the relevant statistic to be presented to the jury. The Random Match Probability (RMP) is an estimate of the probability that a randomly chosen individual in a given population would also match the evidence profile. The RMP is estimated as the population frequency of the specific genetic profile, which is calculated by multiplying the probabilities of a match at each individual genetic marker (the “Product Rule”).

An alternative view, often invoked by the defense, is that the size of the database should be multiplied by the RMP. For example, if the RMP is 1/100 million and the database that was searched is 1 million, this perspective argues that the number 1/100 is the one that should be presented to the jury. This calculation, however, represents the probability of getting a “hit” (match) with the database and not the probability of a coincidental match between the evidence and suspect (1/100 million), the more relevant metric for interpreting the probative significance of a DNA match. Although these arguments may seem arcane, the estimates that result from these different statistical metrics could be the difference between conviction and acquittal.

There are many different kinds of DNA databases. Ethnically defined population databases are used to calculate genotype frequencies and, thus, to estimate RMPs but are not useful for searching. The first DNA searches were of databases of convicted felons. In some jurisdictions, databases of arrestees have also been established and searched. These searches have recently been expanded to include “partial matches,” potentially implicating relatives of the individuals in the database. This strategy, known as “familial searching,” has been very effective but contentious, with discussions typically focused on the “trade-offs” between civil liberties and law enforcement. In some jurisdictions, the “trade-off” has been between two different controversial criminal database programs. In Maryland, for example, an arrestee database (albeit one specifying arraignment) was allowed but familial searching was outlawed. Familial searching has been critiqued as turning relatives of people in the database into “suspects.” A more accurate description is that these partial matches revealed by familial searching identify “persons of interest” and that they provide potential leads for investigation.

Recently, searching for partial matches in the investigation of suspect-less crimes has expanded from criminal databases to genealogy databases, as applied in the Golden State Killer case in 2018. These databases consist of genetic profiles from people seeking information about their ancestry or trying to find relatives. Genetic genealogy involves constructing a large family tree going back several generations based on the individuals identified in the database search and on genealogical records. Identifying several different individuals in the database whose profile shares a region of DNA with the evidence profile allows a family tree to be constructed. The shorter the shared region between two individuals or between the evidence and someone in the database, the more distant the relationship. This is because genetic recombination, the shuffling of DNA regions that occurs in each generation, reduces the length of shared DNA segments over time. So, in the construction of a family tree, the length of the shared region indicates how far back in time you have to go to locate the common ancestor. Tracing the descendants in this family tree who were in the area when the crime was committed identifies a set of potential suspects.

The DNA technologies used in investigative genetic genealogy (IGG) are different from those typically used in analyzing the evidence samples or the criminal database samples, which are based on around 25 short tandem repeat markers (STRs). The genotyping technology used to generate profiles in genealogy databases is based on analyzing thousands of single nucleotide polymorphisms (SNPs). With the recent implementation of Next Generation Sequencing technology to sequence the whole genome, even more informative searching for shared DNA regions can be accomplished. (Next Generation Sequencing of the whole genome is so powerful that it can now distinguish identical (monozygotic) twins!)

Investigative genetic genealogy (IGG) has completely upended the trade-offs and guidelines proposed for familial searching as well as many of the arguments. Many of the rationales justifying familial searching of criminal databases, such as the recidivism rate, and the presumed relinquishing by convicts of certain rights do not apply to genealogical databases. Also, the concerns about racial disparities in criminal databases don’t apply to these non-criminal databases either. In general, it’s very hard to draw lines in the sand when the sands are shifting so rapidly and the technology is evolving so quickly. And it is particularly difficult when dramatic successes in identifying the perpetrators of truly heinous unsolved crimes are lauded in the media, making celebrities of the forensic scientists who carried out the complex genealogical analyses that finally led to the arrest of the Golden State Killer and, shortly thereafter, to many others.

It’s still possible and desirable to set some guidelines for IGG, a complex and expensive procedure. It should be restricted to serious crimes. The profiles in the database should be restricted to those individuals who have consented to have their personal genomic data searched for law enforcement purposes. With the appropriate guidelines, the promise of DNA database searching to solve suspect-less crimes can truly transform our criminal justice system.

_{Featured image by TanyaJoy via iStock.}

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Artificial <em>Intelligence</em>? I think not!

“The machine demands that Man assume its image; but man, created to the image and likeness of God, cannot become such an image, for to do so would be equivalent to his extermination”

(Nicolai Berdyaev, “Man and Machine” 1934)

These days, the first thing people discuss when the question of technology comes up is AI. Equally predictable is that conversations about AI often focus on the “rise of the machines,” that is, on how computers might become sentient, or at least possess an intelligence that will outthink, outlearn, and thus ultimately outlast humanity.

Some computer scientists deny the very possibility of so-called Artificial General Intelligence (AGI). They argue that Artificial Narrow Intelligence (ANI) is alone achievable. ANI focusses on accomplishing specific tasks set by the human programmer, and on executing well-defined tasks within changing environments, and thus rejects any claim to actual independent or human-like intelligence. Self-driving cars, for example, rely on ANI.

Yet as AI researcher and historian Nils J. Nilsson makes clear, the real ‘prize’ for AI researchers is to develop artifacts that can do most of the things that humans can do—specifically those things thought to require ‘intelligence.’” Thus the real impetus of AI research remains AGI, or what some now call “Human Level Artificial Intelligence (HLAI).

To achieve this goal, AI researchers attempt to replicate the human brain on digital platforms, so that computers will mimic its functions. With increasing computational power, it will then be possible first to build machines that have the object-recognition, language capabilities, manual dexterity, and social understanding of small children, and then, second, to achieve adult levels of intelligence through machine learning. Once such intelligence is achieved, many fear the nightmare scenario of 2001: A Space Odyssey’s self-preserving computer HAL 9000, who eliminates human beings because of their inefficiency. What if these putative superintelligent machines disdain humans for their much inferior intellect and enslave or even eliminate them? This vision has been put forward by the likes of Max Tegmark (not to mention the posthuman sensationalist Yuval Harari), and has enlivened the mushrooming discipline of machine ethics, which is dedicated to exploring how humans will deal with sentient machines, how we will integrate them into the human economy, and so on. Machine ethics researchers ask questions like: “Will HLAI machines have rights, own property, and thus acquire legal powers? Will they have emotions, create art, or write literature and thus need copyrights?”

The central problem with such discussions about AI, however, is the simple fact that Artificial Intelligence does not exist. There is an essential misunderstanding of human intelligence that undergirds all of these concerns and questions—a misunderstanding not of degree but of kind, for no machine is or ever will be “intelligent.”

Before the advent of modernity, human intelligence and understanding (deriving from the Latin intellectus, itself rooted in the ancient Greek concepts of nous and logos) indicated the human mind’s participation in an invisible spiritual order which permeated reality. Tellingly, the Greek term logos denotes law, an ordering principle and also language or discourse. Originally, human intelligence did not imply mere logic, or mathematical calculus, but the kind of wisdom that comes only from the experiential knowledge of embodied spirits. Human beings, as premodern philosophers insisted, are ensouled or living organisms, or animals, that also possess the distinguishing gift of logos. Logos, translated as ratio or reason, is the capacity for objectifying, self-reflexive thought.

Moreover, as rooted in a universal logos, human intelligence was intrinsically connected to language. In this pre-modern world, symbols are not arbitrary cyphers assigned to things, as AI researchers have always assumed; rather, language derives from and remains inseparably linked to the human experience of a meaningful world. As the German philosopher Hans-Georg Gadamer explains, “we are always already at home in language, just as much as we are in the world.” We live, think, move, and have our being in language. As the very matrix that renders the world intelligible to us, language is not merely an instrument by which a detached mind masters the world. Instead, we only think and speak on the basis of the linguistic traditions that make human experience intelligible. And let’s not forget that human experience is embodied.

No wonder, then, that human understanding, to use the English equivalent of the Latinate ‘intellect,’ has a far deeper meaning than what computer scientists usually attribute to the term. Intelligence is not shuffling around symbols, recognizing patterns, or conveying bytes of information. Rather, human intelligence refers to the intuitive grasp of meaningful relations within the world, an activity that relies on embodied experience and language-dependent thought. The critic of AI, Hubert Dreyfus summed up this meaning of intelligence as “knowing one’s way around in the world.” Algorithms, however, have no body, have no world, and therefore have no intelligence or understanding.

The only way we can even conceive of computers attaining human understanding is a radical redefinition of this term in functionalist terms. As Erik Larson has shown, we owe this redefinition in part to Alan Turing, who, after initial hesitations, reduced intelligence to mathematical problem solving. Turing and AI researcher after him thus aided a fundamental mechanization of nature and human nature. We first turn reality into a gigantic biological-material mechanism, then reconceive human persons as complex machines powered by a computer-like brain, and thus find it relatively easy to envision machines with human intelligence. In short, we dehumanize the person in order to humanize the machine. We have in fact, as Berdyaev prophesied, exterminated the human in order to create machines in the image of our de-spirited, mechanized corpses.

In sum, our problem for a proper assessment of so-called AI is not an imminent threat of actual machine intelligence, but our misguided imagination that wrongly invests computing processes with a human quality like intelligence. Not the machines, but we are to blame for this. Algorithms are code, and the increasing speed and complexity of computation certainly harbors potential dangers. But these dangers arise from neither sentience nor intelligence. To attribute human thought or understanding to computational programs is simply a category mistake. Increasing computational power makes no difference. No amount of computing power can jump the ontological barrier from computational code to intelligence. Machines cannot be intelligent, have no language, won’t “learn” in a human educational sense, and they don’t think.

As computer scientist Jaron Lanier pithily sums up the reality of AI: “there is no A.I.” The computing industry should return to the common sense of those AI researchers who initially disliked the label AI and called their work “complex information processing.” As Berdyaev reminds us with the epigram above, the true danger of AI is not that machines might become like us, but that we might become like machines and thereby forfeit our true birthright.

_{Featured image by Geralt (Gerd Altmann) from Pixabay.}

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Is humanity a passing phase in evolution of intelligence and civilisation?

“The History of every major Galactic Civilization tends to pass through three distinct and recognizable phases, those of Survival, Inquiry and Sophistication…”

Douglas Adams, The Hitchhiker’s Guide to The Galaxy (1979)

“I think it’s quite conceivable that humanity is just a passing phase in the evolution of intelligence.”

Geoffrey Hinton (2023)

In light of the recent spectacular developments in artificial intelligence (AI), questions are now being asked about whether AI could present a danger to humanity. Can AI take over from us? Is humanity a passing phase in the evolution of intelligence and civilisation? Let’s look at these questions from the long-term evolutionary perspective.

Life has existed on Earth for more than three billion years, humanity for less than 0.01% of this time, and civilisation for even less. A billion years from now, our Sun will start expanding and the Earth will soon become too hot for life. Thus, evolutionarily, life on our planet is already reaching old age, while human civilisation has just been born. Can AI help our civilisation to outlast the habitable Solar system and, possibly, life itself, as we know it presently?

Defining life is not easy, but few will disagree that an essential feature of life is its ability to process information. Every animal brain does this, every living cell does this, and even more fundamentally, evolution is continuously processing information residing in the entire collection of genomes on Earth, via the genetic algorithm of Darwin’s survival of the fittest. There is no life without information.

It can be argued that until very recently on the evolutionary timescale, i.e. until human language evolved, most information that existed on Earth and was durable enough to last for more than a generation, was recorded in DNA or in some other polymer molecules. The emergence of human language changed this; with language, information started accumulating in other media, such as clay tablets, paper, or computer memory chips. Most likely, information is now growing faster in the world’s libraries and computer clouds than in the DNA of all genomes of all species.

We can refer to this “new” information as cultural information as opposed to the genetic information of DNA. Cultural information is the basis of a civilisation; genetic information is the basis of life underpinning it. Thus, if genetic information got too damaged, life, cultural information, and civilisation itself would disappear soon. But could this change in the future? There is no civilisation without cultural information, but can there be a civilisation without genetic information? Can our civilisation outlast the Solar system in the form of AI? Or will genetic information always be needed to underpin any civilisation?

For now, AI exists only as information in computer hardware, built and maintained by humans. For AI to exist autonomously, it would need to “break out” of the “information world” of bits and bytes into the physical world of atoms and molecules. AI would need robots maintaining and repairing the hardware on which it is run, recycling the materials from which this hardware is built, and mining for replacement ones. Moreover, this artificial robot/computer “ecosystem” would not only have to maintain itself, but as the environment changes, would also have to change and adapt.

Life, as we know it, has been evolving for billions of years. It has evolved to process information and materials by zillions of nano-scale molecular “machines” all working in parallel, competing as well as backing each other up, maintaining themselves and the ecosystem supporting them. The total complexity of this machinery, also called the biosphere, is mindboggling. In DNA, one bit of information takes less than 50 atoms. Given the atomic nature of physical matter, every part in life’s machinery is as miniature as possible in principle. Can AI achieve such a complexity, robustness, and adaptability by alternative means and without DNA?

Although this is hard to imagine, cultural evolution has produced tools not known to biological evolution. We can now record information as electron density distribution in a silicon crystal at 3 nm scale. Information can be processed much faster in a computer chip than in a living cell. Human brains contain about 10¹¹ neurons each, which probably is close to the limit how many neurons a single biological brain can contain. Though this is more than computer hardware currently offers to AI, for future AI systems, this is not a limit. Moreover, humans have to communicate information among each other via the bottleneck of language; computers do not have such a limitation.

Where does this all leave us? Will the first two phases in the evolution of life—information mostly confined to DNA, and then information “breaking out” of the DNA harness but still underpinned by information in DNA, be followed by the third phase? Will information and its processing outside living organisms become robust enough to survive and thrive without the underpinning DNA? Will our civilisation be able to outlast the Solar system, and if so, will this happen with or without DNA?

To get to that point, our civilisation first needs to survive its infancy. For now, AI cannot exist without humans. For now, AI can only take over from us if we help it to do so. And indeed, among all the envisioned threats of AI, the most realistic one seems to be deception and spread of misinformation. In other words, corrupting information. Stopping this trend is our biggest near-term challenge.

_{Feature image by Daniel Falcão via Unsplash.}

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Aleph-AI: an organizing force or creative destruction in the artificial era?

The Aleph is a blazing space of about an inch diameter containing the cosmos, Jorge Luis Borges told us in 1945, after being invited to see it in the basement of a house. The Aleph deeply disrupted him, revealing millions of delightful and awful scenes simultaneously. He clearly saw everything from all points of the universe, including the sea, the day and the night, every single ant and grain of sand, animals, symbols, sickness and war, the earth, the universe, the stunning alteration of death, scrutinizing eyes, mirrors, precise letters from his beloved, the mechanisms of love, his blood, my face, yours, and apparently that of every single person. Borges felt endless veneration and pity. He feared that nothing else would surprise him after this experience.

Amelia Valcárcel proposes that technological progress equated us with an Aleph with a display of about six inches. She compares Borges’ Aleph with mobile phones, which are accessible to most people. Now, I think we have at our fingertips the third generation of Aleph, what we could call Aleph-AI. We use this new Aleph not only to see everything, but to work from everywhere, communicate whenever we want, get travel directions, play games, search for specific information, trade without borders, conduct banking, take pictures and notes, get instant translations or suggestions for completing a message, get product recommendations that we are likely to buy, listen to a personalized list of music, and perform an endless list of activities powered by AI. Has the 6 inches Aleph-AI become creative destruction, or an organizing force in the artificial era?

Some workers feel threatened with being replaced by AI applications and losing their jobs. Fear has knocked on the doors of several unions, including operators, customer service representatives, artists, manual workers, or office workers. For example, Hollywood artists protest because they fear losing control of their image by being AI simulated and replaced by computational creativity. Customer service representatives fear being replaced by chatbots, manual workers by robots, and office workers by a variety of AI innovations.

On the other hand, economists argue that AI can make us about 40 percent more productive and drive economic growth through the use of a virtual workforce. AI innovations could even generate new revenue streams. In recent years, startups have been growing in record time, thanks to internet connectivity that almost everyone can get on highly accessible devices. Trading products and services hasn’t been as seamless as it is now, thanks to AI innovations in the palm of our hands. Governments can potentially easily apply regulations and taxes through a mixed bag of applications that serve millions over their mobile phones, says Thomas Lembong, who optimistically views this as more of an organizing force than a force of creative destruction.

But we can not deny that some people will suffer from the impacts of this unstoppable force. History shows that every time innovations occur, some people face negative effects and react with fear. When Jacquard looms appeared at the beginning of 1800, artisans united under the name of Luddites. They protested, destroyed, and burned textile fabrics. Skilled textile artisans were frightened by the introduction of a technology operated by workers, who could finish their job at a much faster speed, easily operating Jacquard looms. Some intellectuals supported the Luddite protests. Among them was a well-known poet of the time, Lord George Gordon Byron, Ada Lovelace’s father. Today, Ada Lovelace is recognized as the world’s first programmer and AI forerunner, who could have been fascinated by Jacquard looms’ binary technology, suggests Hannah Fry. Indeed, Ada Lovelace pioneeringly noticed the great potential of computers to perform any complex task, like composing music. Luddite protests lasted two years and died out after the English government enacted the death penalty for those caught destroying fabrics.

Technological progress will follow its path, and we are going to adapt. Did painters disappear when photography appeared? No, they re-invented themselves. Many techniques appeared: impressionism, expressionism, cubism, and abstract art, to mention a few. Previously, painters made a living from portraits. Today, we take selfies. How many of you have hired a painter for a portrait? With Aleph-AIs in our hands, we retouch pictures taken on our own. Will actors be entirely replaced by AI? Not happening. The changes in the film industry will be led by a group of professionals, including artists, computer scientists, entrepreneurs, lawyers, and policymakers. This will be the case in every sector and industry. Will there be suffering? Change is suffering. Change is new possibilities. Change could be re-thinking what we want for the future during the artificial era.

_{Feature image by Anna Bliokh via iStock.}

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A librarian’s reflections on 2023

What did 2023 hold for academic libraries? What progress have we seen in the library sector? What challenges have academic libraries faced?

At OUP, we’re eager to hear about the experience of academic librarians and to foster conversation and reflection within the academic librarian community. We’re conscious of the changing landscape in which academic libraries operate. So, as 2024 gets underway, we took the opportunity to ask Anna França, Head of Collections and Archives at Edge Hill University, to share her impressions of the library sector and her experiences throughout the past year.

Tell us about one thing you’ve been surprised by in the library sector this year?

I’m continually surprised and impressed by how quick the library sector is to respond and adapt to wider trends and challenges. The sector’s response to developments in generative AI is one obvious example from the past year (and one that I’ll speak more on later), but I think academic libraries have navigated some difficult years remarkably well and are continuing to demonstrate their role as a vital cornerstone of their academic institutions. I have worked in academic libraries for over 18 years and in that time I have seen the library’s role shift from being primarily a support service to becoming an active partner across a range of important areas.

What have you found most challenging in your role over the past year?

In recent years, libraries have been at the forefront of conversations on wide-ranging and complex topics, including generative AI and machine learning, learner analytics and Open Research, while also placing an emphasis on support for Equality, Diversity, and Inclusion initiatives and the role of libraries in promoting social justice. These developments make libraries interesting spaces in which to work and provide opportunities for innovation and collaboration, but keeping up to speed as a professional with the most current information on a topic can be challenging. There is always something new to read or learn about!

There has been a lot of debate this year about the place of AI in academia. How has the progression of AI affected your library or role thus far?

Supporting students to develop digital literacy skills has always been an integral role of the library at Edge Hill, but with advancements in generative AI and the increased risk that students will be exposed to erroneous or biased information, we know this is more important than ever.

As a library we have recently established a group tasked with looking at the potential impacts of AI on our services. I’m excited by the opportunities that AI might offer to deliver enhanced services for our users—for example, supporting intelligent resource discovery, improving the accessibility of content, and enabling our users to carry out their research more efficiently. I certainly think that libraries are uniquely positioned within their institutions to help drive and influence the conversation around AI.

What’s an initiative your library took in 2023 that you’re proud of?

I am very proud of the work that has taken place around our archive service. Our archive is at the center of a new research group, Research Catalyst, which brings together library and archive professionals, academic staff, and students who are interested in how we can use innovative and interactive methods to research items in our collections. Research Catalyst has a focus on engagement and using the archive to connect with new audiences. One initiative involved us developing an online Archive Showcase and an associated competition which asked local school students and adults to create an original work inspired by the archive. This work led us to be shortlisted for a 2023 Times Higher Education Outstanding Library Team award—it was wonderful to have our initiative recognized nationally in this way.

Feature image by Mathias Reding via Unsplash, public domain.

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How can business leaders add value with intuition in the age of AI? [Long Read]

In a speech to the Economic Club of Washington in 2018, Jeff Bezos described how Amazon made sense of the challenge of if and how to design and implement a loyalty scheme for its customers. This was a highly consequential decision for the business; for some time, Amazon had been searching for an answer to the question: “what would loyalty program for Amazon look like?”

A junior software engineer came up with the idea of fast, free shipping. But a big problem was that shipping is expensive. Also, customers like free shipping, so much so that the big eaters at Amazon’s “buffet” would take advantage by free shipping low-cost items which would not be good for Amazon’s bottom-line. When the Amazon finance team modelled the idea of fast, free shipping the results “didn’t look pretty.” In fact, they were nothing short of “horrifying.”

But Bezos is experienced enough to know that some of his best decisions have been made with “guts… not analysis.” In deciding whether to go with Amazon Prime, the analysts’ data could only take the problem so far towards being solved. Bezos decided to go with his gut. Prime was launched in 2005. It has become one of the world’s most popular subscription services with over 100 million members who spend on average $1400 per year compared to $600 for non-prime members.

As a seasoned executive and experienced entrepreneur Bezos sensed that the Prime idea could work. And in his speech he reminded his audience that “if you can make a decision with analysis, you should do so. But it turns out in life that your most important decisions are always made with instinct and intuition, taste, heart.”

The launch of Amazon Prime is a prime example of a CEO’s informed and intelligent use of intuition paying off in decision-making under uncertainty (where outcomes are unknown and their likelihood of occurrence cannot be estimated) rather than under risk (where outcomes are known and probabilities can be estimated). The customer loyalty problem for Amazon was uncertain because probabilities and consequences could not be known at the time. No amount of analysis could reduce the fast, free shipping solution to the odds of success or failure.

Under these uncertain circumstances Bezos chose to go with this gut. This is not an uncommon CEO predicament or response. In business, decision-makers often have to act “instinctively” even though they have no way of knowing what the outcome is likely to be. The world is becoming more, not less uncertain, and “radical uncertainty” seems to have become the norm for strategic decision-making both in business and in politics. The informed and intelligent use of intuition on the part of those who have the nous and experience to be able to go with their gut is one way forward.

Human intuition meets AI

Turning to the uncertainties posed by artificial intelligence and winding the clock back to over half-a-century ago, the psychologist Paul Meehl in his book Clinical Versus Statistical Prediction (1954) compared how well the subjective predictions of trained clinicians such as physicians, psychologists, and counsellors fared when compared with predictions based on simple statistical algorithms. To many people’s surprise, Meehl found that experts’ accuracy of prediction, for example trained counsellors’ predictions of college grades, was either matched or exceeded by the algorithm.

The decision-making landscape that Meehl studied all those years ago has been transformed radically by the technological revolutions of the “Information Age” (see Jay Liebowitz, Bursting the Big Data Bubble, 2014). Computers have exceeded immeasurably the human brain’s computational capacity. Big data, data analytics, machine learning, and artificial intelligence (AI) have been described as “the new oil” (see Eugene Sadler-Smith, “Researching Intuition: A Curious Passion” in Bursting the Big Data Bubble, 2014). They have opened-up possibilities for outsourcing to machines many of the tasks that were until recently the exclusive preserve of humans. The influence of AI and machine learning is extending beyond relatively routine and sometimes mundane tasks such as cashiering in supermarkets. AI now figures prominently behind the scenes in things as diverse as social media feeds, the design of smart cars, and on-line advertising. It has extended its reach into complex professional areas such as medical diagnoses, investment banking, business consulting, script writing for advertisements, and management education (see Marcus du Sautoy, The Creativity Code, 2019).

There is nothing new in machines replacing humans: they did so in the mechanisations of the agricultural and industrial revolutions when they replaced dirty and dangerous work; dull work and decision-making work might be next. Daniel Suskind, author of World without Work thinks the current technological revolution is on a scale which is hitherto unheard of. The power with which robots and computers are able to perform tasks at high speed, with high accuracy, at scale using computational capabilities are orders of magnitude greater than those of any human or previous technology. This one reason this revolution is different and is why it has been referred to as nothing less than the “biggest event in human history” by Stuart Russell, founder of the Centre for Human-Compatible Artificial Intelligence at the University of California, Berkeley.

The widespread availability of data, along with cheap, scalable computational power, and rapid and on-going developments of new AI techniques such as machine learning and deep learning have meant that AI has become a powerful tool in business management (see Gijs Overgoor, et al). For example, the financial services industry deals with high-stakes, complex problems involving large numbers of interacting variables. It has developed AI that can be used to identify cybercrime schemes such as money laundering, fraud and ATM hacking. By using complex algorithms, the latest generation of AI can uncover fraudulent activity that is hidden amongst millions of innocent transactions and alert human analysts with easily digestible, traceable, and logged data to help them to decide, using human intuition based on their “feet on the ground” experiences, on whether activity is suspicious or not and take the appropriate action. This is just one example, and there are very few areas of business which are likely to be exempt from AI’s influence. Taking this to its ultimate conclusion Elon Musk said at the recent UK “AI Safety Summit” held at Bletchley Park (where Alan Turing worked as code breaker in World War 2) that: “There will come a point where no job is needed—you can have a job if you want one for personal satisfaction but AI will do everything.  It’s both good and bad—one of the challenges in the future will be how do we find meaning in life.”

Creativity and AI

Creativity is increasingly and vitally important in many aspects of business management. It is perhaps one area in which we might assume that humans will always have the edge. However, creative industries, such as advertising, are using AI for idea generation. The car manufacturer Lexus used IBM’s Watson AI to write the “world’s most intuitive car ad” for a new model, the strap line for which is “The new Lexus ES. Driven by intuition.” The aim was to use a computer to write the ad script for what Lexus claimed to be “the most intuitive car in the world”. To do so Watson was programmed to analyse 15 years-worth of award-winning footage from the prestigious Cannes Lions international award for creativity using its “visual recognition” (which uses deep learning to analyse images of scenes, objects, faces, and other visual content), “tone analyser” (which interprets emotions and communication style in text), and “personality insights” (using data to make inferences about consumers’ personalities) applications. Watson AI helped to “re-write car advertising” by identifying the core elements of award-winning content that was both “emotionally intelligent” and “entertaining.” Watson literally wrote the script outline. It was then used by the creative agency, producers, and directors to build an emotionally gripping advertisement.

Even though the Lexus-IBM collaboration reflects a breakthrough application of AI in the creative industries, IBM’s stated aim is not to attempt to “recreate the human mind but to inspire creativity and free-up time to spend thinking about the creative process.” The question of whether Watson’s advertisement is truly creative in the sense of being both novel and useful is open to question (it was based on rules derived from human works that were judged to be outstandingly creative by human judges at the Cannes festival). In a recent collaborative study between Harvard Business School and Boston Consulting Group, “humans plus AI” has been found to produce superior results compared to “humans without AI” when used to generate ideas by following rules created by humans. However, “creativity makes new rules, rules do not make creativity” (to paraphrase the French composer Claude Debussy). The use of generative AI which is rule-following rather than rule-making is likely to result in “creative” outputs which are homogeneous and which may ultimately fail the test of true creativity, i.e. both novel (in the actual sense of the word) and useful. Human creative intuition on the other hand adds value by:

1. going beyond conventional design processes and rules
2. drawing on human beings’ ability to think outside the box, produce innovative solutions
3. sensing what will or won’t work
4. yielding products and services that stand out in market, capture the attention of consumers, and drive business success.

—as based on suggestions offered by Chat GPT in response to the question: “how does creative intuition add value to organizations?”

Emotion intelligence and AI

Another example of area in which fourth generation AI is making in-roads is in the emotional and inter-personal domains. The US-based start-up Luka has developed the artificially intelligent journaling chatbot “Replika” which is designed to encourage people to “open-up and talk about their day.” Whilst Siri and Alexa are an emotionally “cold” digital assistants, Replika is designed to be more like your “best friend.” It injects emotion into conversations and learns from the user’s questions and answers. It’s early days, and despite the hype rigorous research is required to evaluate the claims being made on behalf of such applications.

The fact that computers are making inroads into areas that were once considered uniquely human is nothing new. Perhaps intuition is next. The roots of modern intuition research are in chess, an area of human expertise in which grand masters intuit “the good move straight away.” Nobel laureate and one of the founding figures of AI, Herbert Simon, based his classic definition of intuition (“analyses frozen into habit and the capacity for rapid response through recognition”) on his research into expertise in chess. He estimated that grandmasters have stored of the order of 50,000 “familiar patterns” in their long-term memories, the recognition and recall of which enables them to play chess intuitively at the chess board.

In 1997, the chess establishment was astonished when IBM’s Deep Blue beat Russian chess grand master and world champion Garry Kasparov. Does this mean that IBM’s AI is able to out-intuit a human chess master? Kasparov thinks not. The strategy that Deep Blue used to beat Kasparov was fundamentally different from how another human being might have attempted to do so. Deep Blue did not beat Kasparov by replicating or mimicking his thinking processes, in Kasparov’s own words:

“instead of a computer that thought and played like a chess champion, with human creativity and intuition, they [the ‘AI crowd’] got one that played like a machine, systematically, evaluating 200million chess moves on the chess board per second and winning with brute number-crunching force.”

Nobel laureate in physics, Richard Feynman, commented presciently in 1985 that it will be possible to develop a machine which can surpass nature’s abilities but without imitating nature. If a computer ever becomes capable of out-intuiting a human it is likely that the rules that the computer relies on will be fundamentally different to those used by humans and the mode of reasoning will be very different to that which evolved in the human organism over many hundreds of millennia (see Gerd Gigerenzer, Gut Feelings, 2007).

AI’s limitations

In spite of the current hype, AI can also be surprisingly ineffective. Significant problems with autonomous driving vehicles have been encountered and are well documented, as in the recent case which came to court in Arizona involving a fatality allegedly caused by an Uber self-driving car. In medical diagnoses, even though the freckle-analysing system developed at Stanford University does not replicate how doctors exercise their intuitive judgement through “gut feel” for skin diseases, it can nonetheless through its prodigious number-crunching power diagnose skin cancer without knowing anything at all about dermatology (see Daniel Susskind, A World Without Work, 2020). But as the eminent computer scientist Stuart Russell remarked, the deep learning that such AI systems rely on can be quite difficult to get right, for example some of the “algorithms that have learned to recognise cancerous skin lesions, turn out to completely fail if you rotate the photograph by 45 degrees [which] doesn’t instil a lot of confidence in this technology.”

Is the balance of how we comprehend situations and take business decisions shifting inexorably away from humans and in favour of machines?  Is “artificial intuition” inevitable and will it herald the demise of “human intuition”? If an artificial intuition is realized eventually that can match that of a human, it will be one of the pivotal outcomes of the fourth industrial revolution―perhaps the ultimate form of AI.

Chat GPT appears to be “aware” of its own limitations in this regard. In response to the question “Dear ChatGPT: What happens when you intuit?” it replied:

“As a language model I don’t have the ability to intuit. I am a machine learning based algorithm that is designed to understand and generate human language. I can understand and process information provided to me, but I don’t have the ability to have intuition or feelings.”

More apocalyptically, could the creation of an artificial intuition be the “canary in the coalmine,” signalling the emergence of Vernor Vinge’s “technological singularity” where large computer networks and their users suddenly “wake up” as “superhumanly intelligent entities” as Musk and others are warning of? Could such a development turn out to be a Frankenstein’s monster with unknown but potentially negative, unintended consequences for its makers? The potential and the pitfalls of AI are firmly in the domain of the radically uncertain and identifying the potential outcomes and how to manage them is likely to involve a judicious mix of rational analysis and informed intuition on the part of political and business leaders.

Human intuition, AI, and business management

Making any predictions about what computers will or will not be able to do in the future is a hostage to fortune. For the foreseeable future most managers will continue rely on their own rather than a computer’s intuitive judgements when taking both day-to-day and strategic decisions. Therefore, until a viable “artificial intuition” arrives that is capable of out-intuiting a human, the more pressing and practical question is “what value does human intuition add in business?” The technological advancements of the information age have endowed machines with the hard skill of “solving,” which far outstrips this capability in the human mind. The evolved capacities of the intuitive mind have endowed managers with the arguably hard-to-automate, or perhaps even impossible-to-automate, soft skill of “sensing.” This is the essence of human intuition.

Perhaps the answer lies in an “Augmented Intelligence Model (AIM),” which marries gut instinct with data and analytics. Such a model might combine three elements:

1. human analytical intelligence, which is adept in communicating, diagnosing, evaluating, interpreting, etc.
2. human intuitive intelligence, which is adept in creating, empathising, feeling, judging, relating, sensing, etc.
3. artificial intelligence, which is adept in analysing, correlating, optimising, predicting, recognizing, text-mining, etc.

The most interesting spaces in this model are in the overlaps between the three intelligences, for example when human analytical intelligence augments artificial intelligence in a chatbot with human intervention. Similar overlaps exist for human analytical and human intuitive intelligences, and for human intuitive intelligence and artificial intelligence. The most interesting space is where all three overlap and it is here that most value stands to be added by leveraging the combined strengths of human intuitive intelligence, human analytical intelligence, and artificial intelligence in an Augmented Intelligence Model which can drive success.

This blog post is adapted from Chapter 1 of Intuition in Business by Eugene Sadler-Smith.

_{Feature image by Tara Winstead via Pexels.}

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Science in the time of war: voices from Ukraine

On 23 February 2022, I drove back to Michigan after giving a talk at the University of Kentucky on genome diversity in Ukraine. My niece Zlata Bilanin, a recent college graduate from Ukraine, was with me. She was calling her friends in Kyiv, worried. A single question was on everyone’s mind: will there be a war tomorrow? The thought of invasion, though, seemed unimaginable, illogical, even absurd.

At 2am, Zlata woke me up. “They are coming,” she said. I remember the color of her face–pale green. The world would never be the same again.

Indeed, the war has changed everything; priorities are no longer the same. Many researchers enlisted and went to fight. Others, their homes destroyed, fled. Many packed and crossed the border in the hope of a better life in the West.

Nearly 600 days later, the war continues, each day amplifying the human tragedy, of lives and futures lost—lives that could have otherwise been dedicated to better and more meaningful purposes.

As a researcher, my colleagues and I could not help but think about the crushing blow the war delivered to the vibrant Ukrainian scientific community. Ukraine is a country with incredible resources, unique human genetics given the land once served as a human migration crossroads, and a large dedicated, community of researchers working on numerous and varied projects. Now, however, research centers have been destroyed, and universities have few new students, as they now go to study abroad where there are opportunities, and they cannot be drafted.

Through all this, although my laboratory is at Oakland University, I continue to work with my colleagues back home, building a research program in genomics at my alma mater, Uzhhorod National University (UzhNU). Several years ago, my colleagues and I dreamed up a project to sequence a hundred Ukrainian genomes to provide data for researchers to have tools to study the history of migration, admixture, and distribution of medically relevant variation in the local population. This collaboration started with President of UzhNU, Prof Volodymyr Smolanka, a neurosurgeon by training, an effective administrator, and an active scientist.

Given his work and his position, for this blog post, I wanted a comment from him on the state of Ukrainian science since the start of the war. I called and asked, simply: “Is it harder or easier?” His reply was one that matches the current thoughts of those now involved in retaining and rebuilding Ukrainian scientific programs, “One thing I can say is that there is a lot less government funding. That’s clearly a negative. On the other hand, there seem to be more grant opportunities from international sources, and this helps us to stay afloat.”

“What about the people,” I ask, “How do they feel about science?”

“I would not say that they were optimistic. I am not sure that pessimistic would be the right word either. You know, those scientists that did not leave, they are working, they really want to work in science.”

Thinking about those who are not leaving, I contacted an old colleague who has stayed: Dr Serghey Gashchak, a legendary field biologist, who, among many things, worked in the Chornobyl Exclusion Zone and knew everything there was to know about animals in Chornobyl. We used to call him “Stalker” in reference to a 1979 Soviet science fiction art film about a post-apocalyptic wasteland called “The Zone.”

Given his research background and work in a disaster zone, I emailed Serghey about his thoughts on the current situation. “It’s impossible to work in the Zone these days,” he said. “The barbarians are not at the gates anymore, but there are no research projects, and if there were, there’s no one to work on them. Many of the research staff are fighting in the war. Perhaps it is time to close.”

I was stunned to hear that, knowing Serghey’s inquisitive nature, it was hard for me to believe he would just stop doing research, Worse, I realized, this was likely felt by many. While my head said this might be true, my heart felt there must be a way forward. But, with the war’s destruction of institutions and financial mechanisms, such a mechanism couldn’t rely on expensive infrastructure and top-down government funding schemes. That would take decades to rebuild. What was needed was a way to integrate Ukrainian research into the worldwide research community: to bring opportunity and virtual infrastructure to Ukraine. In fact, the basic mechanisms for bringing research to places all around the world have been in place for decades in the form of international courses and conferences, remote learning, and worldwide collaboration — quite simply we could take the current international infrastructure and modify it to empower researchers in disaster zones.

A case in point is a summer research program developed in 2022—during the war—that takes place at Uzhhorod National University, which, although it is in Ukraine, is a safe distance from the war zone. This research program is led by an international team: Drs Fyodor Kondrashov (OIST, Japan), Roderic Guigo (CRG, Spain), Serghei Mangul (USC), and Wolfgang Huber (EMBL, Germany). Here, international faculty come to Ukrainian students and continue to train them and engage them in work around the globe.

I called Dr Kondrashov at his home in Okinawa and asked what research area he thought would be most useful to bring to a devastated Ukraine. He replied immediately: “Bioinformatics is a good choice because you could accomplish a lot more with the same amounts of resources than in other disciplines, such as molecular biology.”

He was right. The hybrid nature of bioinformatics—combining biology, computer science, mathematics, and statistics—encourages cross-disciplinary collaborations essential for solving complex biological problems—that can easily be carried out across borders. More, skills in these areas are highly transferable, can involve people who work remotely, and can serve as a catalyst for revitalizing war-affected regions.

This is just one example of how already in-place international infrastructure can be brought to Ukrainian research, and it is now one of many ongoing projects to allow Ukrainian researchers to continue their work. Many more examples are presented in the recent review, Scientists without Borders in GigaScience. In fact, we have come to realize, and have described in the review, that these mechanisms can be expanded: taking suitable and already existing international mechanisms and infrastructure to areas anywhere in the world that have been destroyed by political strife and natural disasters.

For Ukraine, and personal involvement, I teach and train Ukrainian students remotely. It is well worth it: an example of the passion of young researchers to continue their training, to embrace new opportunities is Valerii Pokrytiuk. He was admitted to my graduate program in bioinformatics at Oakland University in Michigan, but before he could come, the war broke out. Valerii volunteered to fight and is doing so somewhere in Eastern Ukraine. Periodically, when conditions allow, Valerii still joins us online for book club discussions, lab meetings, and to listen to courses I teach.

The war continues. And so does our fight.

_{Featured image: “Bucha, Ukraine, June 2022” by U.S. Embassy Kyiv Ukraine, Wikimedia Commons (public domain)}

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Tuning in to the cosmic symphony: restarting LIGO

In 2015 history was made when LIGO (Laser Interferometer Gravitational-Wave Observatory) detected the first ever gravitational wave signal. This was an incredible technological achievement and the beginning of a completely new way of investigating the cosmos.

The collision of two massive objects shakes the fabric of space, making it ring like a bell and producing ripples that travel unhindered through space. For several decades astronomers and physicists worked on the construction of LIGO with the goal of detecting these ripples. LIGO is the most sensitive instrument ever devised. It consists of two laboratories, one located in Hanford, Washington, and the other in Livingston, Louisiana. Each houses an L-shaped interferometer whose arms extend for 4 kilometres (2.5 miles). Within these arms, a powerful laser beam travels back and forth, bouncing between mirrors before recombining to form an interference pattern. As a gravitational wave passes by, the fabric of space is pulled and pushed and this alters the distance between the mirrors and these tiny disturbances change the interference pattern. LIGO’s sensitivity is truly astonishing. It can detect changes in distance of around one billionth of the size of an atom. Having two observatories is important; like listening in stereo, it helps to determine the direction from which the waves arrive. It also ensures that a signal came from deep space and not a local disturbance.

By comparing the data captured by LIGO to computer models, physicists can determine how each gravitational wave signal was created. It is possible to deduce the masses of the colliding bodies, the rate at which they were spinning, the energy released in the collision and how far away they are. LIGO’s first signal arrived from the collision and merger of two black holes located around 1.3 billion light years away. In the subsequent five years, LIGO received close to one hundred signals. Almost all of them came from collisions between pairs of black holes. The most epic was the collision and merger of black holes with 85 and 66 times the mass of the Sun that produced a black hole of 142 solar masses. During this collision, a mind-boggling nine solar masses were converted into pure energy in the form of gravitational waves.

In 2017, the Italian gravitational wave observatory Virgo also achieved the exquisite sensitivity necessary for the detection of gravitational waves and joined the LIGO observatories in their quest for distant cosmic dramas. Later that year on 17 August one of the most spectacular duly arrived. This event, named GW170817, was the first detected signal to come from the merger of two neutron stars rather than two black holes. Neutron stars are bizarre objects formed from the collapsed cores of stars that have run out of nuclear fuel. They are just 20 kilometres in diameter but contain at least one and a half times the mass of the Sun. In many ways they are like gigantic atomic nuclei. This was the first time and, so far, the only time that the source of a gravitational wave signal has been located with optical instruments, heralding the dawn of multi-messenger astronomy. The combination of optical and gravitational data has greatly advanced our understanding of what happens when two neutron stars collide. It is like being able to both see the lightning and hear the thunderclap. These observations lent support to the idea that many of the heavier chemical elements such as gold are created and dispersed in neutron star collisions.

In 2020, LIGO’s operations were suspended to allow for a major upgrade of the system. Now, after a three-year hiatus, LIGO is back up and running. On 24 May LIGO started a new observing run with refined instruments. With its enhanced sensitivity, it is expected to detect a gravitational wave signal every two to three days. LIGO is the lynchpin of the LIGO-Virgo-KAGRA collaboration—a partnership with the world’s other two gravitational wave observatories: Virgo in Italy and KAGRA in Japan. The construction of a third LIGO detector in India has also recently been approved. This expansion of the global network of gravitational wave observatories will help to pinpoint the location of gravitational wave sources so that they can also be studied optically.

LIGO has provided the most direct evidence that we have for black holes and their properties, and offers wonderful new tests of our best theory of gravity, Einstein’s theory of general relativity. By observing and studying the mergers of black holes and neutron stars, scientists are gaining new insights into fundamental physics, the nature of gravity, and the evolution of the universe itself. The restart of LIGO and the global gravitational wave research network launches a new phase of deep space exploration. We can look forward to more incredible discoveries in the near future.

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Supporting researchers at every career stage

Academia is a complex ecosystem with researchers at various stages of their careers striving to make meaningful contributions to their fields. In support of furthering knowledge, academic journals work with researchers to disseminate findings, engage with the scholarly community, and share academic advances.

Oxford University Press (OUP) publishes more than 500 high-quality trusted journals, two-thirds of which are published in partnership with societies, organizations, or institutions. The remaining third is a list of journals owned and operated by the Press. Fundamental to this list of owned journals is our mission to create world-class academic and educational resources and make them available as widely as possible, including expanding our fully open access options for authors. As a not-for-profit university press, our financial surplus is reinvested for the purpose of educational and scholarly objectives of the University and the Press, thereby fostering the continued growth of open access initiatives and supporting the scholarly community.

How do we support researchers in different career stages through our journals?

Early Career Researchers: nurturing talent

For early career researchers (ECRs), having their work published in a reputable journal is a crucial step in establishing their academic reputation. OUP journals provide several avenues of support including:

• Mentoring and guidance: Some journals provide mentorship programs or editorial support to help young researchers navigate the publishing process.

Featuring Oxford Open Immunology and Oxford Open Energy:

Two of our Oxford Open series journals, Oxford Open Immunology and Oxford Open Energy run dedicated ECR boards, which provide a key channel for direct engagement between ECR participants and our high profile academic senior editorial teams. Activities are planned throughout the year and may include assisting with facilitating journal webinars, joining ECR board meetings to discuss journal strategy and direction, suggesting and coordinating special collections or commissioned pieces on highly topical areas of research.

• Open access initiatives: 120 of the journals we publish are fully open access and the vast majority of the remaining journals offer authors open access options, making research freely available for a global audience to read, share, cite, and reuse. This helps early career researchers, and researchers of all stages in their career, gain visibility of their work and reach a wider readership.

Featuring our Oxford Open series:

The Oxford Open series is underpinned by a set of guiding principles, which include an emphasis on open research, with each journal having been developed in a bespoke way to best serve the needs of its own research community.

Hear more about OUP’s approach to OA published and the Oxford Open series in The Oxford Comment podcast.

Many of our Oxford Open journals offer article types that are specifically developed for ECRs to start their publication journey, these may take the form of a Rapid Report, Short Communication, or Perspective article, for example. We regularly invite ECRs to submit their work to the journal, often in collaboration with their mentors or supervisors as appropriate.

Mid-career researchers: advancing expertise

As researchers progress in their careers, they require journals that can help them deepen their expertise and broaden their impact. OUP journals provide several avenues of support including:

• Cutting-edge research: OUP journals prioritise publishing high-impact, innovative research, allowing mid-career researchers to stay updated with the latest advancements in their fields.

Featuring Exposome:

Exposome is the home of cutting-edge research from the emerging field of exposomics. The journal sits at the systematic intersections of environmental science, toxicology, chemistry, and public health and policy, and it calls on daring science from a broad community of investigators to provide a forum for engagement, redefine our understanding of the human exposome, and critically advance the field.

Editor-in-Chief Gary W Miller outlines the need for this new field in the inaugural editorial.

• Editorial and reviewer roles: Many researchers at this stage are invited to serve as peer reviewers or editorial board members to further contribute their knowledge to the academic community and enhance their own expertise.

Featuring STEM CELLS Translational Medicine:

For over 10 years, STEM CELLS Translational Medicine has served as a home for timely and important research to advance the utilization of cells for clinical therapy. The journal’s peer reviewers play a critical role in ensuring that the research published in the journal serves the needs of this research community by helping move applications of these critical investigations closer to accepted best patient practices and ultimately improve outcomes.

STEM CELLS Translational Medicine is proud to work with mid-career researchers, and reviewers of all career stages and encourages researchers to join the journal’s network of expert peer reviewers where researchers can get a first-hand look at the quality of research that is required and preview cutting-edge scientific work that helps them stay atop their field.

Established researchers: global recognition

For established researchers, maintaining a high level of visibility and recognition in the academic world is paramount. OUP journals provide several avenues of support including:

• Prestige and impact in the field: OUP journals are known for their prestige and rankings in their relevant fields. Publishing in our journals can bolster an established researcher’s reputation.

Featuring Nucleic Acids Research:

For almost 50 years, Nucleic Acids Research (NAR) has provided the scientific community with detailed and constructive editorial feedback resulting in publications of the very highest standard. The quality of content has been demonstrated in this year’s Nobel Prize in Physiology or Medicine, which cited this article from NAR as one of three publications fundamental to the research recognized by the award.

Edited by a fully independent team of leading academic researchers, the journal serves as a beacon of trusted and high-quality research in a rapidly advancing field. Having flipped to fully OA in 2005, NAR has opened the doors to rigorous, impactful research, sharing knowledge globally and it remains at the cutting edge of molecular biology science.

• Leadership opportunities: As a partner to academic research, all of OUP’s journals are edited by members of the academic community, longstanding experts in their own fields. Our journals therefore offer established researchers the opportunity to take on leadership roles within journal editorial boards as associate editors or editors-in-chief, helping to shape the direction of the journal and their fields.

Featuring Oxford Open Neuroscience:

Oxford Open Neuroscience is run by a representative group of five active scientists who are subject specialists, rather than a single editor-in-chief. Representing the needs of that community and making science-based decisions, the journal’s senior editors act as ambassadors for their individual fields.

As a researcher-led publication with a focus on diversity, transparency and innovation, Oxford Open Neuroscience is a fully open access alternative to more traditional neuroscience journals and enables researchers themselves to propel the field into a new publishing era.

OUP’s owned journals are more than just platforms for publishing research, they are invaluable partners in the academic journey of researchers at every career stage. From nurturing early career talent to supporting mid-career researchers in advancing their expertise and providing global recognition for established scholars, our journals contribute to the growth and success of the academic community. As the world of research continues to evolve, our journals will remain dedicated to supporting researchers around the world, ensuring knowledge is disseminated, shared, and celebrated.

_{Featured image by Pexels on Pixabay (public domain)}

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What can Large Language Models offer to linguists?

It is fair to say that the field of linguistics is hardly ever in the news. That is not the case for language itself and all things to do with language—from word of the year announcements to countless discussions about grammar peeves, correct spelling, or writing style. This has changed somewhat recently with the proliferation of Large Language Models (LLMs), and in particular since the release of OpenAI’s ChatGPT, the best-known language model. But does the recent, impressive performance of LLMs have any repercussions for the way in which linguists carry out their work? And what is a Language Model anyway?

 At heart, all an LLM does is predict the next word given a string of words as a context —that is, it predicts the next, most likely word. This is of course not what a user experiences when dealing with language models such as ChatGPT. This is on account of the fact that ChatGPT is more properly described as a “dialogue management system”, an AI “assistant” or chatbot that translates a user’s questions (or “prompts”) into inputs that the underlying LLM can understand (the latest version of OpenAI’s LLM is a fine-tuned version of GPT-4).  

An LLM, after all, is nothing more than a mathematical model in terms of a neural network with input layers, output layers, and many deep layers in between, plus a set of trained “parameters.” As the computer scientist Murray Shanahan has put it in a recent paper, when one asks a chatbot such as ChatGPT who was the first person to walk on the moon, what the LLM is fed is something along the lines of:

Given the statistical distribution of words in the vast public corpus of (English) text, what word is most likely to follow the sequence “The first person to walk on the Moon was”?

That is, given an input such as the first person to walk on the Moon was, the LLM returns the most likely word to follow this string. How have LLMs learned to do this? As mentioned, LLMs calculate the probability of the next word given a string of words, and it does so by representing these words as vectors of values from which to calculate the probability of each word, and where sentences can also be represented as vectors of values. Since 2017, most LLMs have been using “transformers,” which allow the models to carry out matrix calculations over these vectors, and the more transformers are employed, the more accurate the predictions are—GPT-3 has some 96 layers of such transformers.

The illusion that one is having a conversation with a rational agent, for it is an illusion, after all, is the result of embedding an LLM in a larger computer system that includes background “prefixes” to coax the system into producing behaviour that feels like a conversation (the prefixes include templates of what a conversation looks like). But what the LLM itself does is generate sequences of words that are statistically likely to follow from a specific prompt.

It is through the use of prompt prefixes that LLMs can be coaxed into “performing” various tasks beyond dialoguing, such as reasoning or, according to some linguists and cognitive scientists, learn the hierarchical structures of a language (this literature is ever increasing). But the model itself remains a sequence predictor, as it does not manipulate the typical structured representations of a language directly, and it has no understanding of what a word or a sentence means—and meaning is a crucial property of language.

An LLM seems to produce sentences and text like a human does—it seems to have mastered the rules of the grammar of English—but at the same time it produces sentences based on probabilities rather on the meanings and thoughts to express, which is how a human person produces language. So, what is language so that an LLM could learn it?

A typical characterisation of language is as a system of communication (or, for some linguists, as a system for having thoughts), and such a system would include a vocabulary (the words of a language) and a grammar. By a “grammar,” most linguists have in mind various components, at the very least syntax, semantics, and phonetics/phonology. In fact, a classic way to describe a language in linguistics is as a system that connects sound (or in terms of other ways to produce language, such as hand gestures or signs) and meaning, the connection between sound and meaning mediated by syntax. As such, every sentence of a language is the result of all these components—phonology, semantics, and syntax—aligning with each other appropriately, and I do not know of any linguistic theory for which this is not true, regardless of differences in focus or else.

What this means for the question of what LLMs can offer linguistics, and linguists, revolves around the issue of what exactly LLMs have learned to begin with. They haven’t, as a matter of fact, learned a natural language at all, for they know nothing about phonology or meaning; what they have learned is the statistical distribution of the words of the large texts they have been fed during training, and this is a rather different matter.

As has been the case in the past with other approaches in computational linguistics and natural language processing, LLMs will certainly flourish within these subdisciplines of linguistics, but the daily work of a regular linguist is not going to change much any time soon. Some linguists do study the properties of texts, but this is not the most common undertaking in linguistics. Having said that, how about the opposite question: does a run-of-the-mill linguist have much to offer to LLMs and chatbots at all?   

_{Featured image: Google Deepmind via Unsplash (public domain)}

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Elon Musk, Mars, and bioethics: is sending astronauts into space ethical?

The recent crash of the largest-ever space rocket, Starship, developed by Elon Musk’s SpaceX company, has certainly somewhat disrupted optimism about the human mission to Mars that is being prepared for the next few years. It is worth raising the issue of the safety of future participants in long-term space missions, especially missions to Mars, on the background of this disaster. And it is not just about safety from disasters like the one that happened to Musk. Protection from the negative effects of prolonged flight in zero gravity, protection from cosmic radiation, as well as guaranteeing sufficiently high crew productivity over the course of a multi-year mission also play an important role.

Fortunately, no one was killed in the aforementioned crash, as it was a test rocket alone without a crew. However, past disasters in which astronauts died, such as the Space Shuttle Challenger and Space Shuttle Columbia disasters, remind us that it is the seemingly very small details that determine life and death. So far, 15 astronauts and 4 cosmonauts have died in space flights. 11 more have died during testing and training on Earth. It is worth mentioning that space flights are peacekeeping missions, not military operations. They are carried out relatively infrequently and by a relatively small number of people. 

It is also worth noting the upcoming longer and more complex human missions in the near future, such as the mission to Mars. The flight itself, which is expected to last several months, is quite a challenge, and disaster can happen both during takeoff on Earth, landing on Mars, and then on the way back to Earth. And then there are further risks that await astronauts in space. 

The first is exposure to galactic cosmic radiation and solar energetic particles events, especially during interplanetary flight, when the crew is no longer protected by both Earth’s magnetic field and a possible shelter on Mars. Protection from cosmic radiation for travel to Mars is a major challenge, and 100% effective protective measures are still lacking. Another challenge remains being in long-term zero-gravity conditions during the flight, followed by altered gravity on Mars. Bone loss and muscle atrophy are the main, but not only, negative effects of being in these states. Finally, it is impossible to ignore the importance of psychological factors related to stress, isolation, being in an enclosed small space, distance from Earth.

A human mission to Mars, which could take about three years, brings with it a new type of danger not known from the previous history of human space exploration. In addition to the aforementioned amplified impact of factors already known—namely microgravity, cosmic radiation, and isolation—entirely new risk factors are emerging. One of them is the impossibility of evacuating astronauts in need back to Earth, which is possible in missions carried out at the International Space Station. It seems that even the best-equipped and trained crew may not be able to guarantee adequate assistance to an injured or ill astronaut, which could lead to her death—assuming that care on Earth would guarantee her survival and recovery. Another problem is the delay in communication, which will reach tens of minutes between Earth and Mars. This situation will affect the degree of autonomy of the crew, but also their responsibility. Wrong decisions, made under conditions of uncertainty, can have not only negative consequences for health and life, but also for the entire mission.

Thus, we can see that a future human mission to Mars will be very dangerous, both as a result of factors already known but intensified, as well as new risk factors. It is worth raising the question of the ethicality of the decision to send humans into such a dangerous environment. The ethical assessment will depend both on the effectiveness of available countermeasures against harmful factors in space and also on the desirability and justification for the space missions themselves. 

Military ethics and bioethics may provide some analogy here. In civilian ethics and bioethics, we do not accept a way of thinking and acting that would mandate the subordination of the welfare, rights, and health of the individual to the interests of the group. In military ethics, however, this way of thinking is accepted, formally in the name of the higher good. Thus, if the mission to Mars is a civilian mission, carried out on the basis of values inherent in civilian ethics and bioethics rather than military ethics, it may be difficult to justify exposing astronauts to serious risks of death, accident, and disease.

One alternative may be to significantly postpone the mission until breakthrough advances in space technology and medicine can eliminate or significantly reduce the aforementioned risk factors. Another alternative may be to try to improve astronauts through biomedical human enhancements. Just as in the army there are known methods of improving the performance of soldiers through pharmacological means, analogous methods could be applied to future participants in a mission to Mars. Perhaps more radical, and thus controversial, methods such as gene editing would be effective, assuming that gene editing of selected genes can enhance resistance to selected risk factors in space. 

But the idea of genetically modifying astronauts, otherwise quite commonsensical, given also the cost of such a mission, as well as the fact that future astronauts sent to Mars would likely be considered representative of the great effort of all humanity, raises questions about the justification for such a mission. What do the organizers of a mission to Mars expect to achieve? Among the goals traditionally mentioned are the scientific merits of such a mission, followed by possible commercial applications for the future. Philosophers, as well as researchers of global and existential catastrophes, often discuss the concept of space refuge, in which the salvation of the human species in the event of a global catastrophe on Earth would be possible only by settling somewhere beyond Earth. However, it seems that the real goals in our non-ideal society will be political and military.

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Digital dilemmas: feminism, ethics, and the cultural implications of AI [podcast]

Skynet. HAL 9000. Ultron. The Matrix. Fictional depictions of artificial intelligences have played a major role in Western pop culture for decades. While nowhere near that nefarious or powerful, real AI has been making incredible strides and, in 2023, has been a big topic of conversation in the news with the rapid development of new technologies, the use of AI generated images, and AI chatbots such as ChatGPT becoming freely accessible to the general public.

On today’s episode, we welcomed Dr Kerry McInerney and Dr Eleanor Drage, editors of Feminist AI: Critical Perspectives on Data, Algorithms and Intelligent Machines, and then Dr Kanta Dihal, co-editor of Imagining AI: How the World Sees Intelligent Machines, to discuss how AI can be influenced by culture, feminism, and Western narratives defined by popular TV shows and films. Should AI be accessible to all? How does gender influence the way AI is made? And most importantly, what are the hopes and fears for the future of AI?

Check out Episode 82 of The Oxford Comment and subscribe to The Oxford Comment podcast through your favourite podcast app to listen to the latest insights from our expert authors.

Recommended reading

Look out for Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines, edited by Jude Browne, Stephen Cave, Eleanor Drage, and Kerry McInerney, which publishes in the UK in August 2023 and in the US in October 2023. 

If you want to hear more from Dr Eleanor Drage and Dr Kerry McInerney, you can listen to their podcast: The Good Robot Podcast on Gender, Feminism and Technology.

In May 2023, the Open Access title, Imagining AI: How the World Sees Intelligent Machines, edited by Stephen Cave and Kanta Dihal publishes in the UK; it publishes in the US in July 2023.

You may also be interested in AI Narratives: A History of Imaginative Thinking about Intelligent Machines, edited by Stephen Cave, Kanta Dihal, and Sarah Dillon, which looks both at classic AI to the modern age, and contemporary narratives.

You can read the following two chapters from AI Narratives for free until 31 May:

• Chapter 8: “Enslaved Minds: Artificial Intelligence, Slavery, and Revolt” by Kanta Dihal
• Chapter 9: “Machine Visions: Artificial Intelligence, Society, and Control” by Will Slocombe

Other relevant book titles include: 

• Relationships 5.0: How AI, VR, and Robots Will Reshape Our Emotional Lives by Elyakim Kislev
• Human-Centered AI by Ben Shneiderman (Read Chapter 16: “Social Robots and Active Appliances” for free until 31 May)

You may also be interested in the following journal articles: 

• “AI ethical bias: a case for AI vigilantism (AIlantism) in shaping the regulation of AI” by Ifeoma Elizabeth Nwafor from the Autumn 2021 International Journal of Law and Information Technology
• “My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship” by Petter Bae Brandtzaeg, Marita Skjuve, and Asbjørn Følstad from the July 2022 Human Communication Research (Open Access)
• “Persuasion in the Age of Artificial Intelligence (AI): Theories and Complications of AI-Based Persuasion” by Marco Dehnert and Paul A Mongeau from the July 2022 Human Communication Research

_{Featured image: ChatGPT homepage by Jonathan Kemper, CC0 via Unsplash.}

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Five books to celebrate British Science Week 2023

British Science Week is a ten-day celebration of science, technology, engineering and math’s, taking place between 10-19 March 2023. To celebrate, join in the conversation, and keep abreast of the latest in science, delve into our reading list. It contains five of our latest books on plant forensics, the magic of mathematics, women in science, and more.

1. Planting Clues: How Plants Solve Crimes

Discover the extraordinary role of plants in modern forensics, from their use as evidence in the trials of high-profile murderers such as Ted Bundy to high value botanical trafficking and poaching.

In Planting Clues, David Gibson explores how plants can help to solve crimes, as well as how plant crimes are themselves solved. He discusses the botanical evidence that proved important in bringing a number of high-profile murderers such as Ian Huntley (the 2002 Soham Murders), and Bruno Hauptman (the 1932 Baby Lindbergh kidnapping) to trial, from leaf fragments and wood anatomy to pollen and spores. Throughout he traces the evolution of forensic botany, and shares the fascinating stories that advanced its progress.

Buy Planting Clues, How Plants Solve Crimes

Take a look at Gibson’s blog on Environmental DNA, as well as John Parrington’s (author of ‘Mind Shift’) blog on what neuroscience can tell us about the mind of a serial killer.

2. The Spirit of Mathematics: Algebra and all that

 What makes mathematics so special? Whether you have anxious memories of the subject from school, or solve quadratic equations for fun, David Acheson’s book will make you look at mathematics afresh.

Following on from his previous bestsellers, The Calculus Story and The Wonder Book of Geometry, here Acheson highlights the power of algebra, combining it with arithmetic and geometry to capture the spirit of mathematics. This short book encompasses an astonishing array of ideas and concepts, from number tricks and magic squares to infinite series and imaginary numbers. Acheson’s enthusiasm is infectious, and, as ever, a sense of quirkiness and fun pervades the book.

Buy The Spirit of Mathematics, Algebra and all that

To learn more, discover our Very Short Introductions series, including editions about Geometry, Algebra, Symmetry, and Numbers.

3. Not Just for the Boys: Why We Need More Women in Science

Why are girls discouraged from doing science? Why do so many promising women leave science in early and mid-career? Why do women not prosper in the scientific workforce?

Not Just For the Boys looks back at how society has historically excluded women from the scientific sphere and discourse, what progress has been made, and how more is still needed. Athene Donald, herself a distinguished physicist, explores societal expectations during both childhood and working life using evidence of the systemic disadvantages women operate under, from the developing science of how our brains are—and more importantly aren’t—gendered, to social science evidence around attitudes towards girls and women doing science.

Buy Not Just for the Boys, Why We Need More Women in Science

Make sure not to miss Athene Donald’s limited 4-part podcast series featuring Donald in conversation with fellow female scientists and allies about the issues women face in the scientific world. 

4. Distrust: Big Data, Data-Torturing, and the Assault on Science

Using a wide range of entertaining examples, this fascinating book examines the impacts of society’s growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community.

This thought-provoking book argues that, ironically, science’s credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fueled by the technological advances in Big Data and the development of ever-increasingly powerful computers.

Buy Distrust, Big Data, Data-Torturing, and the Assault on Science

Check out Gary Smith’s previous titles, including: The Phantom Pattern Problem, The 9 Pitfalls of Data Science, and The AI Delusion.

5. Sentience: The Invention of Consciousness

What is consciousness and why has it evolved? Conscious sensations are essential to our idea of ourselves but is it only humans who feel this way? Do animals? Will future machines?

To answer these questions we need a scientific understanding of consciousness: what it is and why it has evolved. Nicholas Humphrey has been researching these issues for fifty years. In this extraordinary book, weaving together intellectual adventure, cutting-edge science, and his own breakthrough experiences, he tells the story of his quest to uncover the evolutionary history of consciousness: from his discovery of blindsight after brain damage in monkeys, to hanging out with mountain gorillas in Rwanda, to becoming a leading philosopher of mind. Out of this, he has come up with an explanation of conscious feeling—”phenomenal consciousness”—that he presents here in full for the first time.  

Buy Sentience, The Invention of Consciousness (UK Only)

As an added bonus, you can also read more on the topics of evolutionary biology, the magic of mathematics, and artificial intelligence with the Oxford Landmark Science series. Including “must-read” modern science and big ideas that have shaped the way we think, here are a selection of titles from the series to get your started.

You can also explore more titles via our extended reading list via Bookshop UK.

OUPblog - Academic insights for the thinking world.

A new OUP journal connecting health and infrastructure

This week sees the launch of our new journal, Infrastructure and Health: Big Connections for Wellbeing, or OOIH for short. 

Humanity strives to and achieves progress through infrastructure. Infrastructure provides the hardware, tools, and services for a connected and functioning planet. Those connections are not just for humans but whole ecosystems. But the world faces challenges that require infrastructure to be bold in scope and ambition, dynamic and adaptive, and full of vision and aspiration. Ecological devastation, climate justice, health inequity, compounded by popular distrust in politics and institutions, has cemented the need for infrastructure that covers all dynamics, human and environmental, between the planetary and nano-particle. Above all, the new era of the Anthropocene requires infrastructure that emphasises big connections for wellbeing. 

OOIH provides a new unique scholarly and policy platform and outlet that has never been more urgent. Connections between big boundary spanning ideas like the Sustainable Development Goals are foundational. Links across sectors and disciplines is the journal’s necessary function. And above all, our aim for OOIH is to challenge the status quo to be bolder, better, innovative, adaptive and accountable. 

Supporting the launch are three foundational articles. An introduction to the journal by us as Editors in Chief, a response to our introduction by editorial board member Prof Phil McManus, and reflections on the political nature of infrastructure from editorial board member, Toks Omishakin (the Secretary of the California State Transport Agency). 

Read the OOIH articles:

• “Infrastructure and Health: laying down the big connections for wellbeing” by Dr Patrick Harris and Prof Evelyne De Leeuw
• “Infrastructure, health and urban planning: Rethinking the past and exploring future possibilities as a response to Harris and De Leeuw” by Prof Phil McManus
• “The Built Environment, People at Its Axis” by Toks Omishakin

The journal is accepting submissions year-round. We expect to have four editions per year made up of a mix of article types. As per instructions to authors, original research, reviews, reflections and visually based “narrative” pieces are welcome. If you have a novel idea to progress the field, we are keen to hear from you before commissioning Editorials.

_{Featured image: © Rawpixel.com/Shutterstock}

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Environmental DNA: the future of forensic testing?

Can plants solve crimes? It’s been known for a long time that botanical evidence has forensic value. Back in the 1930s, Edmond Locard, the “father of forensics,” reported how a single seed of a rare species of dandelion caught up on the jacket of a murder suspect placed him at the scene of the crime. Locard had observed the plant growing next to the body. This sort of plant trace evidence is more valuable than ever today in forensic investigations and can include microscopic materials from plant seeds, pollen, spores, and their DNA.  

Indeed, exciting recent advances allowing the detection and sequencing of minute amounts of DNA are providing new tools for conservation biologists and forensic scientists. Specifically, the detection of environmental DNA (eDNA) in water, air, dust, or soil samples can be used to identify the presence of plants, animals, and microbes that can’t be otherwise easily observed. Organisms shed their DNA into the environment as skin particles, cells, and liquids, even while still alive. When these DNA fragments are sequenced they can be matched to DNA sequences from known organisms in eDNA reference libraries providing an identification. Even when comparison sequences from named organisms are unavailable, as they are for many microbes, the number of unique eDNA sequences can provide a biodiversity estimate. As noted by environmental scientist Eric Larson at the University of Illinois, this approach has value in the detection of hard to observe or cryptic organisms, such as rare species. Levels of biodiversity can be estimated with important conservation implications, especially in light of climate change that is threatening natural ecosystems.

eDNA technology allows improved identification of traditionally surveyed organisms used in forensic examination of trace evidence (e.g., pollen and fungal spores, or even fragments of plant tissues). The method provides opportunities to characterize the microbial fungal and bacterial components of samples. One exciting avenue that has been proposed by Sarah Ishak at the Université de Sherbrooke is the characterization of the leaf phyllosphere (microbial fungi that grow on plants surfaces such as leaves) associated with trace evidence samples. These fungi may be unique to the plants from the location and environment that they grew in providing an “assignment capacity,” i.e., crucial links to crime scenes. Amaury Frank at Ghent University takes this idea a step further proposing that plant eDNA in soil samples can be used to identify specific vegetation communities from where a sample originated.

The promise of forensic eDNA

Does eDNA sampling have forensic value in criminal or civil cases? Forensic botanist Mark Spencer certainly thinks so suggesting its value in the geolocation of crime scenes, clandestine burials, and providing a means to improve “traditional” linking of suspects to victims. Proof-of-concept tests by Fabian Roger at Zurich University and James Comolli at MIT of dust samples collected from the air have shown that eDNA screening from objects of evidentiary value, such as clothing, vehicles, or instruments, could be used to estimate their geographic point of origin. Plant eDNA in the soil on the sole of a suspect’s shoe could be traced back to unique vegetation at a crime scene.

The identification of microbes, determined via their eDNA, has forensic applications in minimum post-mortem interval (PMI) estimation, human suspect identification (though transfer of bacteria to surfaces), and toxicology cases (by identifying microbes involved with toxin degradation).

Forensic eDNA applications are few at present but are likely to increase as the approach becomes more widespread and is introduced into the court system. Standardisation of the methodology and wider development of DNA databases is needed to ensure that results of eDNA applications are consistent, scientifically reliable, and valid so that they can accepted into evidence.

A pioneering eDNA court case

A pioneering forensic eDNA involved Asian carp in 2010. Biologists in Chicago were concerned that the opening of dams and weirs in the Chicago Area Waterways System (CAWS) would allow Asian carp to migrate up from the Mississippi River Basin into Lake Michigan. Asian carp are a hugely problematic exotic fish that reduce biodiversity of native fish in waterways. These large fish jump out of the water into boats injuring people (imagine being hit in the head by a 100 lb fish!). eDNA testing carried out by the US Army Corps of Engineers supported anecdotal sightings that live Asian carp were likely present in the CAWS indicating that at least some fish were close to Lake Michigan.

The US State of Michigan and five partner plaintiffs brought action against the Army Corps of Engineers to require the building of a hydrological barrier between the lake and the basin to stop the spread of Asian Carp. The eDNA evidence was a critical part of the forensic evidence considered by the court even though the case was dismissed as the plaintiffs did not demonstrate the Corps failure to abate a public nuisance (i.e. presence of Asian carp in the Great Lakes).

So, can we use plants to solve crimes? When Edmond Locard introduced his Exchange Principle in 1920, he emphasized the forensic value of materials identified in “les poussières organiques” (organic dust) to link suspects to victims and crime scenes. Over 100 years later, the examination of forensic trace evidence through eDNA testing is poised to take a step forward that he likely never imagined.

OUPblog - Academic insights for the thinking world.

The Ukraine invasion: wrestling at the edge of the nuclear cliff

The paradoxical combination of loud saber-rattling and cautious military strategy on both sides of the Ukraine war follows the new rules of conflict involving nuclear powers. At the beginning of the invasion, Russia threatened nuclear attack in order to deter NATO and the United States from intervening in Ukraine’s defense. The US and NATO called Russia’s bluff, sending weapons, ammunition, and intelligence information to the Ukrainian defenders. The war then morphed into a “wrestling match at the edge of a cliff,” with both sides accepting limits on their strategy and tactics in order to avoid escalation to nuclear war: the US and NATO by not allowing their troops to engage directly with the Russians, the Russians by not attacking supply chains outside of Ukraine. Both the threats and the restraint fall within the new rules of engagement.

Here’s the wider background. At the beginning of its invasion of Ukraine, Russia followed its counter-intuitive military doctrine of “escalate to de-escalate.” In keeping with this doctrine, it tried to deter any outside power from intervening in the regional conflict by threatening to be the first to explode a nuclear weapon. Now that the invasion has gone badly, it threatened to explode a nuclear weapon and, in this way, to give NATO and the US the choice either to withdraw or else to retaliate and risk escalation to all-out nuclear war.

Experience with numerous wargames has led to the conclusion that it would be difficult to prevent such escalation once the first nuclear weapon has been exploded in anger. This means that the doctrine of escalate to de-escalate leads directly to the teenagers’ game of chicken, in which cars are driven toward each other at high speed and the “loser” is the one who turns aside to avoid a head-on collision. The game of chicken favors the adversary that is more willing to accept the risk of collision in order to achieve its objective of “winning”—the more so if the risk-accepting driver is thought to be crazy, or worse, has thrown away the steering wheel.

Applying the model of the game of chicken to the conflict in Ukraine, either the US would “turn aside” (“lose”) by abandoning its ally, or Russia would “turn aside” (“lose”) by not carrying out its nuclear threat. If neither side “turned aside,” the resulting “head-on collision” would be nuclear war. This high-stakes “game” could also apply to possible future invasions of Estonia, Latvia, Lithuania, or even Taiwan.

So far, the US and NATO have accepted the risk of nuclear war by continuing to send weapons and other support to Ukraine. The Russians have not carried through on their threat. Even so, both sides are treading carefully. We have avoided direct conflict between US and Russian forces. We have not tried to break the Russian blockade of grain export from Odessa by military action, despite the impact of that blockade on the world’s food supply. The Russians, for their part, have also held back, refraining from attacking the cross-border logistical facilities by which weapons are conveyed into Ukraine. For now, instead of a game of chicken, we have a wrestling match at the edge of a nuclear cliff, both sides trying at the same time to win the contest and to avoid falling off the cliff together to mutual destruction. But the more the Russians repeat their threats without acting on them, the more they come to resemble the boy who cried wolf, and the greater the danger that they will be moved to carry them out.

There is yet another danger: that of accidental nuclear war. The risk increases as the hostility and the sense of crisis between Russia and NATO grows. It’s worth remembering here that we owe our lives to two Soviet military officers: first, Colonel Stanislav Petrov, who realized that the signal he had received of a US missile attack in 1983 was a false alarm; and second, submarine chief of staff Vasili Arkhipov, who refused permission to a Soviet submarine commander who wanted to launch a nuclear missile at the US at the height of the Cuban Missile Crisis in 1962. Both Petrov and Vasiliev probably saved the world from all-out nuclear war. The nuclear arms control and non-proliferation regimes of treaties, hotlines, and regular consultations among militaries, painfully built up after World War II, have so far protected us from nuclear war, aided by a good bit of luck. These regimes have been allowed to deteriorate. They need strengthening and updating, in large part because of the need to control new technology. This calls for collaborative research among natural and social scientists from both allies and potential enemies, as well as urgent, frank discussions about nuclear stability among hostile and distrustful governments and militaries.

The good news is that the nuclear taboo still holds. There have been many conventional wars since World War II, but no one has exploded a nuclear weapon in anger. We’re down a long way from the 68,000 nuclear weapons that the US and the USSR maintained at the height of the Cold War. The bad news is that many hundreds of missiles in the US and in Russia are still on hair-trigger alert, ready to be launched on warning, whether true or false, of an incoming attack. This is dangerous and unnecessary, given that both we and the Russians could ride out a strategic nuclear attack and still be able to retaliate, so that we each have sufficient “second-strike capability” to deter the other from attacking.

Meanwhile, China is watching the Ukraine situation carefully. Unlike the US and the Soviets during the Cold War, China does not have the experience with near misses that forced the US and the USSR to come to a shared understanding of the requirements for nuclear stability and avoidance of accidental nuclear war. To make matters still more dangerous, several powers, including the US, Russia, and China, are developing hypersonic missiles that decrease the time available to respond to a warning of possible strategic nuclear attack, and hence greatly increase the risk of accidental nuclear war—a classic case of failure to control the march of technology.

This is the most dangerous situation the world has faced since the Cuban Missile Crisis. To defend Ukrainian democracy requires us to do our best to avoid nuclear war and at the same time to accept the risk that it may happen—a tricky, high-stakes navigation that attracts criticism from all sides. We have every right to debate the importance and the dangers involved in intervening in regional conflicts like Ukraine in order to defend democratic values. Considering the stakes involved, there has been relatively little public discussion regarding the risks in intervening in regional conflicts like Ukraine in order to defend democratic values. The public is not accustomed to thinking about nuclear war. It may not appreciate the seriousness of the repeated Russian threats to use nuclear weapons, the limits imposed on both Russian and NATO strategy by the need to avoid a nuclear cataclysm, or the speed and ease with which a single use of nuclear weapons can escalate into full-scale nuclear war.

We face an unstable situation where the Ukraine war—or any future confrontation between nuclear powers—can quickly get out of hand. In the longer run, and assuming that the current situation in Ukraine can somehow be resolved, we urgently need strengthened and updated agreements on arms control and the avoidance of accidental nuclear war that take into account newly empowered nuclear powers, new technology and new weapons systems. We don’t have a lot of time for trial and error.

_{Featured image by Max Kukurudziak on Unsplash, public domain}

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Four ways machine learning is set to revolutionize breast surgery

Machine learning has grown to become quite the buzzword in clinical research. Across recent years, we’ve seen an almost exponential increase in the number of successful machine learning trials conducted, with the technology now hailed as a torchbearer for healthcare’s artificial intelligence revolution. Yet, this begs an obvious question for doctors and healthcare professionals alike—what actually is “machine learning”? 

Machine learning refers to the concept of using large amounts of data to build elaborate algorithms that aim to mimic the way the human brain thinks. Whilst the ground-breaking technology has had an undeniable impact across many aspects of surgery, its implementation in breast surgery is one crucially yet to be established.

Below, we distil down four ways we see machine learning revolutionizing the day-to-day operations that a breast surgeon undertakes. 

1. Planning before surgery

Imaging plays a phenomenal role in planning for any major breast operation—whether it be from pinpointing the location of a breast tumour to helping a surgeon navigate complex breast anatomy. Radiologists now have access to swarms of imaging data to aid the former, thanks in part due to modern imaging techniques. Nonetheless, it can be time-consuming to process this information before a surgery is planned.

New machine learning technology aims to bring greater efficiency and accuracy to this process. Initial trials suggest that machine learning performs to the same level if not better than a radiologist in detecting cancer, and also shows a higher sensitivity (i.e. a better ability to detect cancer in an individual that actually has cancer). Not only will this provide surgeons with prerequisite knowledge to make smarter treatment decisions but will lead to reduced workloads, a reduced burden on resources, and reduced chance of error.

2. Making long-term predictions

Clinicians always want to ensure what they do is backed up by strong evidence—one of the reasons why so much time is spent applying traditional statistical ideas to monitor and predict what might happen to patients after their breast surgery operations.

Whilst a relatively new phenomenon, machine learning looks promising as a gamechanger within this field. New trials suggest it predicts five-year mortality after breast cancer operations more accurately than statistical models and have even gone on to suggest it can predict a patient’s chance of developing a complication like lymphoedema (a long-term swelling in the tissues of the body after an operation).

All of this has been down to the creation of a specific type of machine learning dubbed an “artificial neural network”—a type of machine learning modelled after a human brain cell called a neurone.

 3. Holistic care

With the growing recognition of how important it is that we apply a holistic attitude to treating anyone, machine learning holds an important key. Pain following breast operations can be a debilitating experience and is often something we don’t investigate as much as we ought to. Research has suggested machine learning can allow us to predict neuropathic pain, a type of pain that results from nerve damage, more quickly after an operation. This could allow surgeons and doctors to provide more optimised support to patients in the recovery period.

4. Beyond predicting and planning

The potential applications of machine learning to breast surgery are vast and boundless, and some ideas have applied machine learning concepts in exciting and innovative ways. With advances in medical research, come a wealth of new treatments. Conceptual studies suggest that machine learning can be integrated in decision-making support systems for breast surgeons, including examples such as the “DESIREE Project”, that aim to simplify the process of choosing specific therapeutic options.

Alternatively, machine learning has predicted that the more times a breast surgeon has carried out a specific operation before improves the long-term success of their patients after surgery. It is now theorised that machine learning can be used to study the technique of more experienced surgeons and use this information in the training of inexperienced surgeons.

So, where does this leave us? As exciting as this technology sounds, it is still limited by its relative infancy and hence shrouded in challenges.

It comes with no surprise that machine learning is complicated. Before any of this technology can move forward, we need to ensure breast surgeons are equipped and trained with extensive knowledge on how to utilise it. Stakeholder collaboration will form a major part of designing a practical machine learning platform ready for clinical use. In addition, machine learning is inherently driven by data and hence it is our shared responsibility to ensure any data fed into it is as reliable and representative of our target population as possible. 

Nonetheless, it’s certainly an exciting time to be a breast surgeon and we can’t wait to watch machine learning’s trajectory in overhauling patient care in the not-so-distant future.

_{Featured image by Irwan iwe, via Unsplash.com, public domain.}

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The need for affordable and clean energy [podcast]

High gas prices. Nuclear reactors closed forever. The growth of the electric car industry. Record-breaking temperatures, and Europe’s Dependence on Russian Natural Gas. There has been no shortage in energy-related news stories this summer, and we know that they are not going to go away any time soon.

On today’s episode of The Oxford Comment, we spoke with Martin J. Pasqualetti, Professor of Geography at Arizona State University, and the author of The Thread of Energy, and Paul F. Meier, an independent clean fuels consultant and author of The Changing Energy Mix: A Systematic Comparison of Renewable and Nonrenewable Energy, on the need for affordable and clean energy (which is one of the UN’s sustainable development goals), the history of energy in the United States, and the dire implications of not changing our energy habits.

Check out Episode 75 of The Oxford Comment and subscribe to The Oxford Comment podcast through your favourite podcast app to listen to the latest insights from our expert authors.

Recommended reading

To learn more about the themes raised in this podcast, we’re pleased to share a selection of free-to-read chapters and articles:

Read the first chapter of Martin Pasqualetti’s The Thread of Energy, entitled “Discovery”.

The introduction to Paul Meier’s The Changing Energy Mix can be found here. Meier has also written numerous blog posts for the OUPblog, including “The versatility of hydrogen: storable, portable, and renewable,” “Renewable solar energy: how does it work and can it meet demand?,” and “Electric vehicles: a shift in the resource landscape for the transportation market.”

The Oxford Handbook of Energy Politics is a great resource on the energy issues relating to international relations and comparative politics. In particular, check out the chapter titled “Renewable Energy: A Technical Overview” by Kyle Bahr, Nora Szarka, and Erika Boeing.

What are the environmental consequences of natural resources and energy politics? This bibliography by Markus Kröger gathers sources relating to this topic.

_{Featured image: Karsten Würth, CC0 via Unsplash.}

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The versatility of hydrogen: storable, portable, and renewable

Hydrogen has long been a mainstay in industry and is an important component in refining, ammonia production, and methanol production. Of the 10 million metric tonnes (MMT) produced in the US in 2020, about 70% was used by refineries and 22% for ammonia production. For the same year, the world produced 90 MMT, with 44% going to refineries and 38% for ammonia production. The remaining amounts were used in methanol production, steel production, feed processing, and treating metals.

Hydrogen is becoming a more versatile fuel, however, and is receiving interest in energy storage, as a transportation fuel, as a fuel for electricity generation, and as a fuel for heating. Renewables such as solar photovoltaics (PV) and wind have variable output, so hydrogen is one option for storing electrical energy from renewables to better match production and demand. In addition, the hydrogen made from renewables could be transported from regions with abundant solar PV and wind to large cities for subsequent use. Such means of transportation include sending it as a gas via pipeline or converting it to liquid form and transporting by truck or ship.

The transportation market

The US government started the Freedom Car Fuel Partnership in 2006 to study the use of hydrogen as a transportation fuel. The goal was to transform the transportation market to one using hydrogen fuel cell vehicles (FCVs). If the hydrogen is produced from renewable energy, the overall CO₂ emissions will be quite small. An FCV is as much as three times more efficient than a gasoline vehicle, so a higher percentage of the energy in hydrogen is used relative to gasoline. Compared to battery electric vehicles (BEVs), hydrogen FCVs allow a refueling time similar to gasoline, thus avoiding the long charging times common with BEVs.

The US program has not rapidly advanced, however, as there are less than 50 hydrogen fueling stations in the US (mostly in California) which, along vehicle costs, have limited the transition to a hydrogen transportation economy. At the end of 2021, the US had less than 13,000 hydrogen FCVs in service. In spite of the slow development in the US, Denmark has an energy plan to establish a nationwide hydrogen infrastructure with a goal of being independent of fossil fuels by 2050. The plan is to produce hydrogen using wind energy, both onshore and offshore, and solar PV. Time will tell whether Europe is more committed to hydrogen FCVs than the US, and how BEVs and FCVs will compete in a transition from fossil fuels.

Electricity and heating

As a fuel for electricity generation, hydrogen can be used with oxygen in a fuel cell to produce electricity and water. Hydrogen can also be blended with natural gas, thereby using a blended fuel mixture in a natural gas turbine, with an overall a reduction of CO₂ emissions. Hydrogen can also be used for heating. While the use of pure hydrogen would require considerable alteration or replacement of existing home and industrial heating infrastructure that uses natural gas, hydrogen can be blended with natural gas up to concentrations around 20% without changes to the infrastructure.

Producing hydrogen

Demand for hydrogen, which has grown more than threefold since 1975, is still mostly produced from fossil fuels. Of the 10 MMT produced in the US, 95% is from natural gas (methane), 4% from coal gasification, and 1% from electrolysis. For worldwide production of hydrogen, 76% is from natural gas, 22% from coal gasification, and 2% from electrolysis.

Significant amounts of hydrogen do not exist in pure form but there are great quantities available in different chemical compounds, especially natural gas, coal, biomass, and water. Therefore, hydrogen, unlike fossil fuels, is not an energy source but rather an energy carrier. It can be produced in a variety of ways and it is now common to assign a color code based on the source of the hydrogen and the relative amount of CO₂ made during production. For example, the majority of hydrogen in the US is produced via natural gas reforming, a reaction of natural gas and steam to make synthesis gas, a mixture of hydrogen, carbon monoxide (CO), and CO₂. In this case the hydrogen is referred to as “grey” hydrogen. If the hydrogen is produced from coal or biomass via gasification, such that oxygen is used to convert the fossil fuel into CO, hydrogen, and CO₂, it is referred to as “black” hydrogen. Reforming and gasification produce large amounts of CO₂ and if these processes are coupled with carbon capture and sequestration (CCS), such that the CO₂ is captured and transported to a geological formation for permanent storage, it is referred to as “blue” hydrogen. “Green” hydrogen is produced from electrolysis of water using electricity from only renewable energy types, such as wind, solar, and hydroelectric. If, instead, nuclear energy is used to provide energy for the electrolysis it is referred to as “pink” hydrogen.

In conclusion, the use of hydrogen for storing energy, as a transportation fuel, to generate electricity, and for heating could greatly expand hydrogen demand, adding to the existing industrial uses in refining and ammonia production, as well as a variety of other applications.

_{Featured image by Jeremy Bishop on Unsplash, public domain}

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Renewable solar energy: how does it work and can it meet demand?

For years, solar energy has indirectly provided us with different sources of energy. The energy in fossil fuels originally comes from solar energy, trapped in the dead organisms such as algae, prehistoric plants and animals, and plankton that eventually transition to fuels. Also, bioethanol is produced from different crops, such as corn and sugar cane. Hydroelectric power would not be possible without solar energy to create rain and snow to grow rivers and wind energy relies on solar energy to heat the earth and create air movement.

What are the main solar energy technologies?

The direct use of solar energy to produce electricity on a utility scale is more recent and falls under two main categories, namely solar photovoltaic (solar PV) and concentrating solar power (CSP). Solar PV is the production of electricity from sunlight via the photovoltaic effect, which is both a physical and chemical phenomenon. When a photon particle from sunlight strikes a photovoltaic cell, some is absorbed by a semiconductor in the cell to create electron current and, hence, electricity. These semiconductors are chemical compounds with limited ability to conduct electrical current and are neither good insulators nor good conductors. CSP, also known as solar thermal, concentrates sunlight using mirrors and then uses this heat to produce steam. This steam, once made, will power a turbine to generate electricity. In this respect, CSP generates electricity in the same way as conventional technologies such as natural gas, coal, and nuclear power.

CSP technologies are divided into two groups, based on whether the solar collectors concentrate sunlight along a focal line or a single focal point. The most common technology used is a focal line technology called the parabolic trough. Here, parabolic mirrors concentrate sunlight and heat a fluid in a central receiver tube. After heating, this hot fluid is used to boil water to make steam and power the steam turbine, which in turn powers the generator to make electricity. An example of the single focal point technology is the solar tower, which uses a field of mirrors to focus sunlight onto a receiver mounted high on a central tower.  The mirrors in this field are computer-controlled to track the sun.

The growth of solar energy technologies

Utility scale electricity generation using these technologies has grown rapidly. Before 2005, there was very little solar PV, only 0.5 GW in the US and about 5 GW worldwide. For reference, a 1 GW power plant will provide electricity to around 180,000 homes. By 2020, solar PV had grown to 710 GW worldwide, a growth of around 900% over the last 10 years. Likewise, in the US, solar PV has grown to 73.8 GW, a growth of 1,300% over the last 10 years. In contrast, CSP has not seen the same growth, and in 2020 there was only 6.4 GW installed worldwide with most in the US and Spain.

The growth in solar PV can be attributed in large part to decreasing capital cost. In 2013, a solar PV plant cost $3,700/kW, but decreased to $1,300/kW in 2019, or 35% of the 2013 value. This makes it more competitive with a natural gas plant, with a capital cost of around $900/kW. By comparison, CSP has seen little change in capital cost and languishes around $7,000/kW.

Can solar energy meet global demand?

Unlike fossil fuels or nuclear power, where it is possible to assess proven reserves, the assessment of solar energy potential is more difficult. Worldwide potential can, however, be estimated using a broad mathematical approach:

Taking into account the surface area of the earth and recognizing that the average solar flux striking the atmosphere of the earth is 342.5 W/m² (watts/meter²), about 175,000 TW (terawatt = 10¹² watt) reach the surface of the earth. Since 30% is reflected back to space and 19% is absorbed by the atmosphere and clouds, this leaves 89,300 TW reaching the earth to be absorbed by land and oceans. Ignoring the 71% of the earth that is ocean and the 3.5% of land that are in frigid zones as areas impractical for solar development, a potential of 2,500 TW can be calculated by assuming a solar PV cell efficiency of 10%. Solar panel efficiency is the percent of incoming sunlight that is converted to electricity, a value typically ranging from 10 to 15%. Assuming a capacity factor of only 10%, the 2,500 TW would generate 2,190,000 TWh in one year. As the world consumed 22,015 TWh in 2017, this means world energy consumption was only 1% of the potential electricity generation from solar energy!

The capacity factor is the fraction of electricity production compared to the maximum possible output, and this factor will vary based on latitude, the time of year, and cloud cover. While it is impractical to have solar panels dotting virtually every available surface of the earth, it does show the awesome potential of solar energy as a renewable energy to meet our needs for generations to come.

_{Featured image by Andreas Gucklhorn via Unsplash (public domain)}

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The role of DNA research in society [podcast]

Since its discovery in 1953, DNA has revolutionized our world in many ways. From medical research to paternity tests to solving crimes, thanks to DNA, we now have a better understanding of who we are, how we have developed, and how we can heal.  

For today’s episode of The Oxford Comment, we’re commemorating National DNA Day in the United States by considering the role that DNA plays in our society. First, we welcome Amber Hartman Scholz, co-author of the article “Myth-busting the provider-user relationship for digital sequence information”, looking at how genetic resources are actually used and shared across the globe. We discuss the surprising findings of this research as well as the important implications for policy makers. We then interview Dee Denver, the author of The Dharma in DNA: Insights at the Intersection of Biology and Buddhism, to talk about the significance of DNA research and what the lay person should know about the uses and findings of DNA. We also talk about another aspect that is much less well known: the role that DNA plays at the intersection of spirituality and science. Underlying both interviews is the question of open science and why it matters, specifically, for DNA research.

Check out Episode 71 of The Oxford Comment and subscribe to The Oxford Comment podcast through your favourite podcast app to listen to the latest insights from our expert authors.

Recommended reading

Amber Hartman Scholz’s aforementioned GigaScience article can be found here. For further context on the provider-user relationship for digital sequence information, please see the companion article “Quantitative monitoring of nucleotide sequence data from genetic resources in context of their citation in the scientific literature“.

To learn more about Dee Denver’s work, please enjoy Chapter 1: Water from The Dharma in DNA. He is also the author of numerous journal articles, such as “Sex and Mitonuclear Adaptation in Experimental Caenorhabditis elegans Populations” in Genetics and “Adaptive Evolution under Extreme Genetic Drift in Oxidatively Stressed Caenorhabditis elegans” and “Comparative genomics of a plant-parasitic nematode endosymbiont suggest a role in nutritional symbiosis” in Genome Biology and Evolution.

_{Featured image: @rabbit_in_blue, CC0 via Unsplash.}

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Electric vehicles: a shift in the resource landscape for the transportation market

The massive US and worldwide transportation sector are fueled mostly by crude oil. Although ethanol and compressed natural gas (CNG) have grown to 6% and 2%, respectively of the US total, gasoline, diesel, and jet fuel account for more than 90% with very little from electric vehicles (EVs) using electricity or fuel cell vehicles (FCVs) using hydrogen. Before the worldwide pandemic, worldwide crude oil consumption was more than 100 million barrels per day (MM BPD), and the US consumed 20.5 MM BPD, with 9.3 MM BPD of gasoline and 3.0 MM BPD of diesel. These fuels support a vehicle market of 278 million serving a population of 331 million. Worldwide, the number of vehicles exceeds one billion.

The original founding members of OPEC (Organization of Petroleum Exporting Countries), including Iran, Iraq, Kuwait, Saudi Arabia, and Venezuela, currently control 58% of the worldwide crude oil reserves, estimated to be about 1.7 trillion barrels. Canada is also a large player, controlling 10% of worldwide reserves but the US has less than 3%.

Therefore, at this time the critical resource for the transportation industry is crude oil, the energy source needed to power vehicles. This could change, however, as some parts of the world move away from fossil fuel driven vehicles and towards battery electric vehicles (BEV). For example, the European Union is targeting at least 30 million zero-emission vehicles on its roads by 2030. Also, China, currently the world’s largest auto market, plans to have BEVs make up 20% of new car sales by 2025 and 50% by 2035. In addition, even if there was no growing trend to move away from fossil fuels, proven reserves and worldwide consumption for crude oil suggest we have enough left for 47 years. Certainly, this number could decrease if developing countries increase consumption, or increase if more reserves are discovered from deep-water oil and fracking technologies. Nevertheless, these factors would increase crude oil production costs and potentially shift the market towards BEVs.

If the world moves to a transportation market dominated by BEVs, the big change is that the key resource will shift from an energy source (i.e., crude oil) to the metal resources needed to make batteries. Certainly, energy in the form of electricity will still be needed but a rough estimate shows the entire 278 million US vehicle fleet would represent 25% of the amount of electricity currently generated. The transition would be slow, and the basic infrastructure of electric plants and electric grid already exist, so the main infrastructure needed would be charging stations.

How does a battery work?

A detailed discussion of how a battery works is beyond the scope of this blog post, but it is important to understand the basics in order to understand the resources needed for making batteries. At this time, most BEVs use lithium (Li) ion batteries consisting of three major parts including a cathode and an anode which are separated by an electrolyte. When charging, Li ions transfer from the cathode to the anode and this process is reversed during discharging. The most common cathodes used today are lithium manganese oxide (LMO), lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC) and lithium nickel cobalt aluminum (NCA). Also, the most common anode used is graphite, a crystalline form of carbon arranged in a hexagonal structure. 

The resources needed for a BEV depends on the choice for the cathode and anode, but driving range is an important consideration. At this time, BEVs come in a variety of ranges from a low end of less than 100 miles to about 300 miles, depending on the size of the battery system. If the BEV is to become more than a commuter vehicle, 300 miles seems like a minimum value for road trips and this will require a battery with an energy content of around 100 kWh.

The types and compositions of batteries for BEVs is still in flux but typically batteries use 160 g Li/kWh, so a 100-kWh battery would use about 16 kg Li. On this basis, some of the other metals can be estimated by composition. For one version of the NMC battery, NMC333, the battery would need 45 kg nickel (Ni), 42 kg manganese (Mn), and 45 kg cobalt (Co). Co has some issues, to be discussed later, so there is another version of this battery, NMC532, with a lower Co content. For this composition, Ni is now 68 kg, Mn is 38 kg, and Co is 27 kg. Similarly, for a typical composition of the NCA battery, the number of metals would be 114 kg of Ni, 16 kg of Co, and about 3 kg of aluminum (Al). For the anode, the amount of graphite will be more than 60 kg for a 100-kWh battery pack.

Which countries are rich in resources for batteries?

Which countries are rich in these metal resources? First, it is important to note that there are both “proven reserves,” reserves that can be economically mined, and “probable reserves,” reserves which are less likely to be recovered. Also, current production may not necessarily match a country’s standing with proven reserves. For this blog post, proven reserves will be used based on United States Geological Survey (USGS) data. When it comes to Li, Chile, China, Argentina, and Australia control most of the proven reserves. Chile, Argentina and Bolivia (which has probable reserves) make up the so-called Lithium Triangle, an area in the Andes bordering these three countries. Co is controlled by the Democratic Republic of the Congo (DRC) and Australia, and the DRC has nearly 50% of worldwide reserves. Unfortunately, much of the Co comes from artisanal mining, and this mining is done in hazardous conditions and even with child labor. Mn is mostly found in South Africa, Ukraine, and Australia and these three countries control 70% of the reserves. And for Ni, almost 50% is controlled by Australia, New Caledonia, Cuba, and Indonesia with Australia at 24% of worldwide reserves. Finally, China is by far the biggest producer of graphite for the anode, producing around two-thirds of worldwide supplies.

Therefore, if the world shifts to a transportation fleet of BEVs, the control of critical resources will change from OPEC countries to countries in South America as well as China, Australia, and the DRC. This could be an economic opportunity for these countries, but external exploitation, social issues, and environmental concerns will have to be managed to reap the full benefits of production.

_{Featured image by Ather Energy on Unsplash. Public domain.}

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Celebrating women in STEM [timeline]

March is Women’s History Month, an annual occurrence dedicated to commemorating and highlighting the contributions that women have made throughout history. Many of these contributions have gone unsung or undervalued, particularly in the fields of science, technology, engineering, and medicine, where women have historically been underrepresented. Celebrating and recognizing the work of women in these field remains a priority for Oxford University Press, and this month and every month we are proud to support diverse voices across our publishing. We seek to create an inclusive space to highlight the work of women in STEM, and celebrate the contributions of trans and cis women, and women of all races, ethnicities, and sexual orientations.

The timeline—first published in 2018, now updated in 2022—provides a curated selection of achievements, discoveries, and innovations made by women in STEM, from the foundation of modern nursing to critical contributions in the effort to fight the COVID-19 pandemic. This is just a small picture of the countless women making an impact in STEM. For more on this subject we also offer two collections featuring the work of women in STEM: women in medicine and women in science.

_{Featured image by Dmytro Zinkevych via Shutterstock.} 

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Behind the cover: <em>Human-Centered AI</em>

Images have long shaped thinking. Graphic designers, especially for advertising companies, are well-versed in how to convey the luxury of new cars, the strength of professional sports teams, or the attraction of tourist destinations. These images create interest, set expectations, and shape behavior. 

The metaphor of thinking machines has a long history, which was reinforced by images, which adorned books, magazines, corporate advertisements, and websites. A key image, showing a human finger reaching out to touch a robot finger, is based on Michelangelo’s painting, The Creation of Adam, showing God’s finger reaching out to Adam’s finger. That image, created during 1508-1512 in the Vatican’s Sistine Chapel, inspired many artists for 500 years, leading to the familiar AI and robotic portrayal of a human finger touching a robot finger, and then variations, such as human and robot handshakes. 

Other robotic images depict humanoid robots with electronic wiring and human brains with chip circuitry. These compelling images support the misguided notion that future robots will be made in the form of humans, suggesting other human-like capabilities such as two-legged mobility, five-fingered dexterity, and dominance of voice interaction. These images also suggest that computers could or should be emotional, have beliefs, and possibly be conscious. 

I’ve long felt that these images were misleading, thereby slowing research on technologies that ensured human control, which would be designed to amplify, augment, empower, and enhance human performance. My expectation for the future is that computers will become supertools, such as the AI-infused cellphone camera or navigation apps, or active appliances like Google’s Nest for home control and iRobot’s Roomba for house cleaning. These innovations give people super-powers by ensuring human control, while increasing the level of automation. They boost human capabilities.

Finding alternative images that overcome the strongly entrenched robotic presence proved to be a serious challenge. Sociologist Lewis Mumford’s writings helped me to understand that the “obstacle of animism”, the belief that new technologies would be modeled on human forms, had to be overcome in images as well as in metaphors, terminology, guidelines, and prototypes. My metaphors describe AI-infused supertools, tele-bots, active appliances, control centers, but I struggled to find visual representations.

In working on the cover for my book Human-Centered AI, I developed these guidelines for what I wanted:

1. images of people: individuals and teams working together to do something meaningful, supported by technology
2. diverse people: men/women, old/young, white/black/brown, abled/disabled
3. enough human features such as eyes, nose, mouth, to appear expressive: happy/sad, playing/working (silhouettes can work)
4. technology connecting them, empowering them: people using tools, standing on technology platforms, typing on keyboards, looking at screens, tapping on mobile devices, networked together
5. natural world representations such as plants, birds, animals, butterflies, clouds, sunshine, rainbows.

The challenge of finding other ways to present future technologies is still a wide open one. Fresh strategies are needed to make iconic images, compact logos, appealing animations, and compelling Hollywood films that provide helpful visions of what future technologies should do to empower people. The film Minority Report showed a wall-sized user interface that gave Tom Cruise ample room for physical sweeps and swipes across the screen. Howard Rheingold called computers “tools for thinking” in his book Mind Amplifier. Steve Jobs triggered interest when he called computers a “bicycle for the mind,” which led to appealing images in Apple ads.

Bolder visions of amplified humans as superheroes with superpowers such as Marvel Comics’ Iron Man, in which wealthy inventor Tony Stark creates a magical body-covering suit that enables him to fly, fight, and save the world from evil forces. The move from comic books to action films gave special effects animators ample opportunities to explore visual representations of superpowers. Similarly, other comic book superheroes, from Superman to Wonder Woman, with superpowers showed how technology could enhance human performance. Wonder Woman, from DC Comics, was a founding member for the Justice League, which presumably was the inspiration for the Joy Buolamwini’s Algorithmic Justice League that fights against biased machine learning algorithms. Their story was told in the 2016 Netflix documentary Coded Bias.

Additionally, a team of seven authors wrote a serious technical paper titled “Superpowers as Inspiration for Visualization,” which offers delightful mind-expanding ideas. They used the Superpower Wiki as a guide to thousands of enhanced human abilities from fiction to shape their ideas of how to build superpowers into new technologies.

Due to this, my guidelines may be useful to others who seek to show how technology amplifies, augments, enhances, and empowers people, but I hope creative artists will make bold images that open our minds to new possibilities.

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Five books to celebrate British Science Week

British Science Week is a ten-day celebration of science, technology, engineering and maths, taking place between 11-20 March 2022. To celebrate, join in the conversation, and keep abreast of the latest in science, delve into our reading list. It contains five of our latest books on evolutionary biology, the magic of mathematics, artificial intelligence, and more.

1. The Parrot in the Mirror: How evolving to be like birds made us human

How similar are your choices, behaviours, and lifestyle to those of a parrot?

Discover how many of our defining human traits are far more similar to birds than to our fellow mammals in The Parrot in the Mirror, by Antone Martinho-Truswell. From our lifespans to our intelligence, our relationships and our language, we can learn a great deal about ourselves by thinking of the human species as “the bird without feathers.” In this insightful read, learn more about how parrots, specifically, are our biological mirror image; an evolutionary parallel to ourselves. And how they are the only species to share one particular human trait: spite.

Read The Parrot in the Mirror: How evolving to be like birds made us human.

To learn more about how, much like humans, the senses of animals are key to their survival, discover Secret Worlds: The extraordinary senses of animals, by Martin Stevens.

2. Mind Shift: How culture transformed the human brain

The mental capacities of the human mind far outstrip those of other animals. Our imaginations and creativity have produced art, music, and literature; built bridges and cathedrals; enabled us to probe distant galaxies, and to ponder the meaning of our existence. What makes the human brain unique, and able to generate such a rich mental life? In this book, John Parrington draws on the latest research on the human brain to show how it differs strikingly from those of other animals in its structure and function at a molecular and cellular level. And he argues that this “shift,” was driven by tool use, but especially by the development of one remarkable tool—language.

Read Mind Shift: How culture transformed the human brain.

You can also read Parrington blog on what neuroscience can tell us about the mind of a serial killer, as well as listening to his podcast on culture and the human brain.

3. Colliding Worlds: How cosmic encounters shaped planets and life

In Colliding Worlds, Simone Marchi explores the key role that collisions in space have played in the formation and evolution of our solar system, the development of planets, and possibly even the origin of life on Earth. Analysing our latest understanding of the surfaces of Mars and Venus, gleaned from recent space missions, Marchi presents the dramatic story of cosmic collisions and their legacies. You can also read his blog’s on the Earth’s wild years and the creative destruction of cosmic encounters, as well as his response to Netflix’s “Don’t Look Up!” satire, Do Look Up! Could a comet really kill us all?

Read Colliding Worlds: How cosmic encounters shaped planets and life.

To learn more, discover our Very Short Introductions series, including Planetary Systems, Climate Change, Evolution, Human Evolution, and The Animal Kingdom.

4. The Wonderful Book of Geometry: A mathematical story

How can we be sure that Pythagoras’s theorem is really true? Why is the “angle in a semicircle” always 90 degrees? And how can tangents help determine the speed of a bullet?

David Acheson takes the reader on a highly illustrated tour through the history of geometry, from ancient Greece to the present day. He emphasizes throughout elegant deduction and practical applications, and argues that geometry can offer the quickest route to the whole spirit of mathematics at its best. Along the way, we encounter the quirky and the unexpected, meet the great personalities involved, and uncover some of the loveliest surprises in mathematics.

Read The Wonderful Book of Geometry: A mathematical story.

Take a sneak peek inside, and listen to Acheson explain the magic of geometry.

5. Human-centered AI

Focusing not on the risks of AI, but on the opportunities it presents and how to capitalize on them, Ben Shneiderman puts forward 15 recommendations about how programmers, business leaders, educators, professionals, and policy makers can implement human-centered AI. Bridging the gap between ethical considerations and practical realities to make successful, reliable systems, Schneiderman provides a range of human-centered AI design metaphors to show ways to get beyond current limitations and see new design possibilities that empower people, giving humans control.

Read Human-centered AI.

To learn more, discover our What Everyone Needs to Know® series, including titles on Artificial Intelligence (and a blog post on What is Artificial Intelligence?), and Evolution.

As an added bonus, you can also read more on the topics of evolutionary biology, the magic of mathematics, and artificial intelligence with the Oxford Landmark Science series. Including “must-read” modern science and big ideas that have shaped the way we think, browse the series here:

You can also explore more titles via our extended reading list via Bookshop UK.

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