The winner of the 1912 Nobel Prize for Physics was, by some margin, the unlikeliest physics Nobel laureate in history. He wasn’t a physicist, for starters. He wasn’t even a chemist. He was an inventor by the name of Nils Gustaf Dalén, and the invention that won him the prize was closely connected – in more ways than one – to the industrial accident that almost cost him his life.

To understand why members of the Royal Swedish Academy of Sciences plumped for Dalén in 1912 over his more famous contemporaries (including such luminaries as Max Planck and Albert Einstein) it helps to know a bit about the man himself. Like Alfred Nobel, Dalén was Swedish, born in 1869 in the small farming community of Stengstorp. Located around 140 km north-east of Gothenburg, Stengstorp is now home to a museum in Dalén’s honour, but as a young man, he did not seem like museum material. On the contrary, he was incredibly lazy – so lazy, in fact, that he invented a machine to make coffee and turn the light on for him in the mornings.

This ingenious device brought Dalén some local notoriety, but his big break came when Sweden’s most famous inventor at the time, Gustaf de Laval, saw him demonstrate a device for measuring milk fat content. Encouraged by de Laval to attend university, Dalén sold his family’s farm and enrolled at what is now the Chalmers University of Technology. In 1896 he earned his doctorate, and after a year at ETH Zürich in Switzerland, he returned to Sweden to set up his first engineering firm.

A light in the darkness

The engineering challenge that set Dalén on the path to the Nobel was hugely important in a country like Sweden with a long, complex coastline. Years before the advent of GPS, or even reliable radio communications, lighthouses were the main way of warning ships away from danger. However, they were extremely expensive and hard to maintain. As well as needing 24-hour attention from skilled and hardy humans, they required huge amounts of propane fuel, necessitating frequent (and frequently dangerous) resupply trips.

The obvious way of reducing these costs was to make lighthouses burn something else. Acetylene was attractive because it could be manufactured in industrial quantities, and it produced a bright light when burned. Unfortunately, it was also highly explosive, meaning it couldn’t be safely bottled or shipped.

To tame the acetylene dragon, Dalén developed three separate inventions. The first was a combination of asbestos and diatomaceous earth that Dalén called “agamassan” after his company (Aktiebogalet Gasaccumulator) and the Swedish word for compound, massan. By filling a container with agamassan, wetting it with acetone and then forcing acetylene into the container under pressure, Dalén showed that the acetylene would dissolve in the acetone and become trapped within the agamassan like water in a sponge. Under these conditions, it could be shipped, stored and even dropped without exploding.

Having made acetylene safe to use, Dalén turned his hand to making it economical. His second invention was a device that automatically turned the acetylene supply on and off. This saved fuel and enabled the light to flash (distinguishing it from other light sources on the shore) without the need for cumbersome rotation mechanisms.

Dalén’s third invention enabled even greater automation. Rather than relying on human lighthouse-keepers to switch acetylene burners on at night and off in the morning, Dalén developed a valve that could do it automatically. This valve worked by means of a set of metal rods, one of which was blackened while the others were polished. When the blackened rod absorbed enough heat from the Sun, it would expand and close the valve. At dusk, or in foggy conditions, the blackened rod would return to the temperature of the others, contract, and open the valve.

Choosing a laureate

While Dalén was perfecting the use of acetylene gas for lighthouses, the Nobel Committee for Physics was getting on with its usual business of recommending candidates for the prize. In 1909 the committee suggested the radio pioneer Guglielmo Marconi and his academic counterpart Karl Ferdinand Braun. The wider Academy accepted this choice. In 1910 the committee recommended the father of modern molecular science, Johannes Diderik van der Waals, and he also won the Academy’s approval. In 1911 the quantum theorist Wilhelm Wien, whose joint nomination with Max Planck in 1908 provoked such bitter disputes that neither of them got the prize, finally got the nod from both the committee and the Academy (Planck would have to wait for his prize until 1918).

By the early autumn of 1912, there was every indication that the Academy would again accept the committee’s recommendation, which was Heike Kammerlingh Onnes, who had liquefied helium for the first time in 1908 and subsequently used it to discover superconductivity. Although Dalén had also been nominated, Mats Larsson, a physicist at Stockholm University who served on the committee between 2016 and 2023, says he wasn’t a serious contender.

“It’s clear from the report from the Nobel committee to the Academy that they recognize there is an importance to Dalén’s inventions, but it doesn’t reach the standard for a Nobel prize,” says Larsson. With only a single nomination from a member of the Academy’s technical section, Larsson adds, “Dalén is not even on the shortlist.”

An industrial accident

Then, before the Academy could vote, tragedy struck. On 27 September 1912, during an experiment so risky it was performed in a quarry rather than in Aktiebogalet Gasaccumulator’s Stockholm factory, an explosion left Dalén seriously injured. The next day, Sweden’s national paper of record, Dagens Nyheter, put the accident on its front page, describing Dalén’s face as “unrecognizable” and his right side as “horribly massacred and burned”. Though conscious and talking when taken to hospital, he was not expected to survive.

Nobel prizes cannot be awarded posthumously. If Dalén had died of his injuries, it is unlikely that his colleagues would have voted to honour him. But though Dalén’s doctors could not save his eyesight, they did save his life. By the time the Academy convened to vote on the 1912 Nobel prizes, he was recovering in the care of his family and very much on the minds of his sympathetic colleagues.

We don’t know exactly what happened next. “The material [in the Nobel archives] is very meagre,” Larsson explains. “It just says there was a vote and Dalén won the prize.”

Still, it’s easy to imagine that someone in the Academy must have pled Dalén’s cause. “This is our national hero who fought the war against ignorance and against darkness,” agrees Karl Grandin, who directs the Academy’s Center for History of Science. “And he loses his sight in the purpose of bringing light to the world. It was a symbolic thing.”

Dalén’s most enduring invention

Dalén was too unwell to attend the usual Nobel prize celebrations in Stockholm. Instead, he sent his brother, a physician, to accept the prize on his behalf. Eventually, though, he recovered well enough to resume his duties at Aktiebogalet Gasaccumulator. In time, he even returned to inventing. And herein lies the final twist in his story.

During his convalescence, the blind Dalén noticed something that had apparently escaped his attention when he could still see. His wife, Elma, worked very hard around the house, and cooking for him and their four children was especially tiresome. It would be much easier, Dalén decided, if she had a device that could cook several dishes at once, at different temperatures.

In 1922, ten years after losing his sight and winning the Nobel prize, Dalén unveiled the invention that would become his most enduring. Named, like agamassan, after the initials of his company, the AGA cooker is still sold today, bringing warmth to kitchens just as its inventor brought safe, effective and economical illumination to lighthouses. Gustaf Dalén may be the least likely physics Nobel laureate in history, but it would be facile to dismiss him as undeserving. After all, how many other physics laureates can boast of saving hundreds of thousands of lives at sea, while also relieving the drudgery of hundreds of thousands back home?

The post Nobel prizes you’ve never heard of: how a Swedish inventor was honoured for a technology that nearly killed him appeared first on Physics World.

By the time Gabriel Lippmann won the Nobel Prize for Physics, his crowning scientific achievement was already obsolete – and he probably knew it. Four days after receiving the 1908 prize “for his method of reproducing colours photographically based on the phenomenon of interference”, Lippmann, a Frenchman with a waxed moustache that would shame a silent film villain, ended his Nobel lecture with the verbal equivalent of a Gallic shrug.

After nearly 20 years of work, he admitted, the minimum exposure time for his method – one minute in full sunlight – was still “too long for the portrait”. Though further improvements were possible, he concluded, “Life is short and progress is slow.”

Why did Lippmann win a Nobel prize for a method that not even he seemed to believe in? It certainly wasn’t for a lack of alternatives. The early 1900s were a heady time for physics discoveries and inventions, and other Nobels of the era reflect this. In 1906 the Royal Swedish Academy of Sciences awarded the physics prize to J J Thomson for discovering the electron. In 1907 its members voted for Albert Michelson of the aether-defying Michelson–Morley experiment. So what made the Academy choose, in 1908, a version of colour photography that wouldn’t even let you take a selfie?

An elegant solution

Let’s start with the method itself. Unlike other imaging processes, Lippmann photography directly records the entire colour spectrum of an object. It does this by using standing waves of light to produce interference fringes in a light-sensitive emulsion backed by a mirrored surface. The longer the wavelength of light given off by the object, the larger the separation between the fringes. It’s an elegant application of classical wave theory. It’s easy to see why Edwardian-era physicists loved it.

Lippmann’s method also has an important practical advantage. Because his photographs don’t require pigments, they retain their colour over time. Consequently, the images Lippmann showed off in his Nobel lecture look as brilliant today as they did in 1908.

The method’s disadvantages, though, are numerous. As well as needing long exposure times, the colours in Lippmann photographs are hard to see. Because they are virtual, like a hologram, they are only accurate when viewed face-on, in perpendicular light. Lippmann’s original method also required highly toxic liquid mercury to make the mirrored back surface of each photographic plate. Though modern versions have eliminated this, it’s not surprising that Lippmann’s method is now largely the domain of hobbyists and artists.

A French connection

If technical merit can’t explain Lippmann’s Nobel, could it perhaps have been due to politics? The easiest way to answer this question is to look in the Nobel archives. Although the names of Nobel prize nominees and the people who nominated them are initially secret, this secrecy is lifted after 50 years. The nomination records for Lippmann’s era are therefore very much available, and they show that he was a popular candidate. Between 1901 and 1908, he received 23 nominations from 12 different people – including previous laureates, foreign members of the Academy, and scientists from prestigious universities invited to make nominations in specific years.

Funnily enough, though, all of them were French.

Faced with this apparent conspiracy to stamp the French tricolour on the Nobel medal, Karl Grandin, who directs the Academy’s Center for History of Science, concedes that such nationalistic campaigns were “quite common in the first years”. However, this doesn’t mean they were successful: “Sometimes when all the members of the French Academy have signed a nomination, it might be impressive at one point, but it might also be working in the opposite way,” he says.

A clash of personalities

Because Nobel Foundation statutes stipulate that discussions and vote numbers from the prize-awarding meeting of the Academy are not recorded, Grandin can’t say exactly how Lippmann came out on top in 1908. He does, however, have access to an illuminating article written in 1981 by a theoretical physicist, Bengt Nagel.

Drawing on the private letters and diaries of Academy members as well as the Nobel archives, Nagel showed that personal biases played a significant role in the awarding of the 1908 prize. It’s a complicated story, but the most important strand of it centres on Svante Arrhenius, the Swedish physical chemist who’d won the Nobel Prize for Chemistry five years earlier.

Today, Arrhenius is best known for predicting that putting carbon dioxide in the Earth’s atmosphere will affect the climate. In his own lifetime, though, Grandin says that Arrhenius was also known for having a long-running personality conflict with a wealthy Swedish mathematician called Gustaf Mittag-Leffler.

“Stockholm at the time was a small place,” Grandin explains. “Everyone knew each other, and it wasn’t big enough to host both Arrhenius and Mittag-Leffler.”

Arrhenius wasn’t the chair of the Nobel physics committee in 1908. That honour fell to Knut Angstrom, son of the Angstrom the unit is named after. Still, Arrhenius’ prestige and outsized personality gave him considerable influence. After much debate, the committee agreed to recommend his preferred choice for the prize, Max Planck, to the full Academy.

This choice, however, was not problem-free. Planck’s theory of the quantization of matter was still relatively new in 1908, and his work was not demonstrably guiding experiments. If anything, it was the other way around. In principle, the committee could have dealt with this by recommending that Planck share the prize with a quantum experimentalist. Unfortunately, no such person had been nominated.

That was awkward, and it gave Mittag-Leffler the ammunition he needed. When the matter went to the Academy for a vote, he used members’ doubts about quantum theory to argue against Arrhenius’ choice. It worked. In Mittag-Leffler’s telling, Planck got only 13 votes. Lippmann, the committee’s second choice, got 46.

A consensus winner

Afterwards, Mittag-Leffler boasted about his victory. “Arrhenius wanted to give it to Planck…but his report, which he had nevertheless managed to have unanimously accepted by the committee, was so stupid that I could easily have crushed it,” he wrote to a French colleague. “Two members even declared that after hearing me, they changed their opinion and voted for Lippmann. I would have had nothing against sharing the prize between [quantum theorist Wilhelm] Wien and Planck,” Mittag-Leffler added, “but to give it to Planck alone would have been to reward ideas that are still very obscure and require verification by mathematics and experimentation.”

Lippmann’s work posed no such difficulties, and that seems to have swung it for him. In a letter to a colleague after the dust had settled, Angstrom called Lippmann “obviously a prizeworthy candidate who did not give rise to any objections”. However, Angstrom added, he “could not deny that the radiation laws constitute a more important advance in physical science than Lippmann’s colour photography”.

Much has been written about excellent scientists getting overlooked for prizes because of biases against them. The flip side of this – that merely good scientists sometimes win prizes because of biases in their favour – is usually left unacknowledged. Nevertheless, it happens, and in 1908 it happened to Gabriel Lippmann – a good scientist who won a Nobel prize not because he did the most important work, but because his friends clubbed together to support him; because Academy members were wary of his quantum rivals; and above all because a grudge-holding mathematician and an egotistical chemist had a massive beef with each other.

And then, four years later, it happened again, to someone else.

The post Nobel prizes you’ve never heard of: how an obscure version of colour photography beat quantum theory to the most prestigious prize in physics appeared first on Physics World.

What skills do you use every day in your job?

As a planetary scientist, I use mathematics, physics, geology and atmospheric science. But as the principal investigator of Juno, I also have to manage the Juno team, and interface with politicians, people at NASA headquarters and other administrators. In that capacity, I need to be able to talk about topics at various technical levels, because many of the people I’m speaking with are not actively researching planetary science. I need a broad range of skills, but one of the most important is to be able to recognize when I don’t have the right expertise and need to find someone who can help.

What do you like best and least about your job?

I really love being part of a mission that’s discovering new information and new ideas about how the universe works. It’s exciting to be at the edge of something, where you are part of a team that’s seeing an image or an aspect of how nature works for the first time. The discovery element is truly inspirational. I also love seeing how a mixture of scientists with different expertise, skills and backgrounds can come together to understand something new. Watching that process unfold is very exciting to me.

Some tasks I like least are related to budget exercises, administrative tasks and documentation. Some government rules and regulations can be quite taxing and require a lot of time to ensure forms and documents are completed correctly. Occasionally, an urgent action item will appear requiring an immediate response and having to drop current work to fit in a new task. As a result, my normal work gets delayed, and this can be frustrating. I consider one of my main jobs to shelter the team from these extraneous tasks so they can get their work done.

What do you know today that you wish you’d known at the start of your career?

The most important thing I know now is that if you really believe in something, you should stick to it. You should not give up. You should keep trying, keep working at it, and find people who can collaborate with you to make it happen. Early on, I didn’t realize how important it was to combine forces with people who complemented my skills in order to achieve goals.

The other thing I wish I had known is that taking time to figure out the best way to approach a challenge, question or problem is beneficial to achieving one’s goals.  That was a very valuable lesson to learn. We should resist the temptation to rush into finding the answer – instead, it’s worthwhile to take the time to think about the question and develop an approach.

The post Ask me anything: Scott Bolton – ‘It’s exciting to be part of a team that’s seeing how nature works for the first time’ appeared first on Physics World.

A few weeks ago, I experienced a classic annoyance of modern life: one of my computer games stopped working. The cause? An “update” to the emulator that translates old games into programs that today’s machines can execute. In my case, this update broke the translation process, and the tenuous thread of hardware and software connecting my laptop to the game’s 30-year-old code was severed.

For individuals, failures like this are irritating. But for the wider digital ecosystem, they’re a real problem – so much so, in fact, that Vint Cerf, who’s known as one of the “fathers of the Internet”, made them the subject of his talk at last week’s Heidelberg Laureate Forum (HLF) in Heidelberg, Germany.

“My big worry is that all this digital stuff won’t be there when we would like it to be there, or when our descendants would like to have it,” Cerf said.

How it used to work

Historically, the best ways of preserving information involved writing it on durable materials such as clay tablets, high-quality paper, or a form of animal skin known as vellum. These media, Cerf observed, “have one thing in common: they don’t require electricity to be stored and preserved.”

Digital media, in contrast, are much less robust. “Many of them are magnetic, and the magnetic material wears away after a while,” Cerf explained. Consequently, some old tapes are now so fragile that attempting to read them can actually lift the magnetic material off the surface: “You read it once and that’s it. It’s now transparent tape,” he said.

Being able to read data is just the beginning, though. As my broken computer game shows, you also need programs and equipment that can persuade those data to do things. “That’s often the thing that goes first,” Cerf told me in a press conference after his talk. For example, when Cerf recently tried to retrieve data from an old three-and-a-half-inch floppy disk, he discovered that doing so would require three additional components: a drive that could read the disk, a program that could open the files stored on the disk and an old computer that could run the program. “I needed a whole lot of software help and several stages in order to make that digital content useful,” Cerf said.

Creating ‘digital vellum’

As for how to fix this problem and create a digital version of vellum, Cerf, who has been the “Chief Internet Evangelist” at Google since 2005, listed three ideas that he finds interesting. The first involves a New Jersey, US-based company called SPhotonix that does research and development work in the UK and Switzerland. It’s using lasers to write bits of data into chunks of quartz crystal, which is a very long-lasting medium. However, each crystal is roughly the size of a hockey puck, and Cerf thinks that “real work” still needs to be done to organize the information the material holds.

The second idea is partly inspired by the clay tablets that proved so successful at preserving cuneiform writing from ancient Mesopotamia. Cerabyte, a start-up with facilities in Austria, Germany and the US, has developed a ceramic material that its founders claim could “store all data virtually forever”.

The third idea, and the one that seems to appeal most to Cerf, is to write digital information into DNA. That might sound like an inherently fragile medium, but as Cerf pointed out, “It’s actually a very robust molecule – otherwise, life wouldn’t have persisted for several billion years.” Provided you dehydrate the DNA first, he added, it lasts for “quite a long time”.

The question of how to read such information is not an easy one, and Cerf doesn’t have an answer to it. He is, however, hopeful that someone will find one. At the HLF, where he is such a revered figure that even the journalists want to take photos with him, he issued a call to arms for the young researchers in the audience. “I want you to appreciate the scope of the work that is required to preserve digital things,” Cerf told them. Without that work, he added, “recreating a digital environment in 100 years is not going to be a trivial matter.”

The post ‘Father of the Internet’ Vint Cerf expresses concern about the longevity of digital information appeared first on Physics World.

Today’s artificial intelligence (AI) systems are built on data generated by humans. They’re trained on huge repositories of writing, images and videos, most of which have been scraped from the Internet without the knowledge or consent of their creators. It’s a vast and sometimes ill-gotten treasure trove of information – but for machine-learning pioneer David Silver, it’s nowhere near enough.

“I think if you provide the knowledge that humans already have, it doesn’t really answer the deepest question for AI, which is how it can learn for itself to solve problems,” Silver told an audience at the 12th Heidelberg Laureate Forum (HLF) in Heidelberg, Germany, on Monday.

Silver’s proposed solution is to move from the “era of human data”, in which AI passively ingests information like a student cramming for an exam, into what he calls the “era of experience” in which it learns like a baby exploring its world. In his HLF talk on Monday, Silver played a sped-up video of a baby repeatedly picking up toys, manipulating them and putting them down while crawling and rolling around a room. To murmurs of appreciation from the audience, he declared, “I think that provides a different perspective of how a system might learn.”

Silver, a computer scientist at University College London, UK, has been instrumental in making this experiential learning happen in the virtual worlds of computer science and mathematics. As head of reinforcement learning at Google DeepMind, he was instrumental in developing AlphaZero, an AI system that taught itself to play the ancient stones-and-grid game of Go. It did this via a so-called “reward function” that pushed it to improve over many iterations, without ever being taught the game’s rules or strategy.

More recently, Silver coordinated a follow-up project called AlphaProof that treats formal mathematics as a game. In this case, AlphaZero’s reward is based on getting correct proofs. While it isn’t yet outperforming the best human mathematicians, in 2024 it achieved silver-medal standard on problems at the International Mathematical Olympiad.

Learning in the physics playroom

Could a similar experiential learning approach work in the physical sciences? At an HLF panel discussion on Tuesday afternoon, particle physicist Thea Klaeboe Åarrestad began by outlining one possible application. Whenever CERN’s Large Hadron Collider (LHC) is running, Åarrestad explained, she and her colleagues in the CMS experiment must control the magnets that keep protons on the right path as they zoom around the collider. Currently, this task is performed by a person, working in real time.

In principle, Åarrestad continued, a reinforcement-learning AI could take over that job after learning by experience what works and what doesn’t. There’s just one problem: if it got anything wrong, the protons would smash into a wall and melt the beam pipe. “You don’t really want to do that mistake twice,” Åarrestad deadpanned.

For Åarrestad’s fellow panellist Kyle Cranmer, a particle physicist who works on data science and machine learning at the University of Wisconsin-Madison, US, this nightmare scenario symbolizes the challenge with using reinforcement learning in physical sciences. In situations where you’re able to do many experiments very quickly and essentially for free – as is the case with AlphaGo and its descendants – you can expect reinforcement learning to work well, Cranmer explained. But once you’re interacting with a real, physical system, even non-destructive experiments require finite amounts of time and money.

Another challenge, Cranmer continued, is that particle physics already has good theories that predict some quantities to multiple decimal places. “It’s not low-hanging fruit for getting an AI to come up with a replacement framework de novo,” Cranmer said. A better option, he suggested, might be to put AI to work on modelling atmospheric fluid dynamics, which are emergent phenomena without first-principles descriptions. “Those are super-exciting places to use ideas from machine learning,” he said.

Not for nuclear arsenals

Silver, who was also on Tuesday’s panel, agreed that reinforcement learning isn’t always the right solution. “We should do this in areas where mistakes are small and it can learn from those small mistakes to avoid making big mistakes,” he said. To general laughter, he added that he would not recommend “letting an AI loose on nuclear arsenals”, either.

Reinforcement learning aside, both Åarrestad and Cranmer are highly enthusiastic about AI. For Cranmer, one of the most exciting aspects of the technology is the way it gets scientists from different disciplines talking to each other. The HLF, which aims to connect early-career researchers with senior figures in mathematics and computer science, is itself a good example, with many talks in the weeklong schedule devoted to AI in one form or another.

For Åarrestad, though, AI’s most exciting possibility relates to physics itself. Because the LHC produces far more data than humans and present-day algorithms can handle, Åarrestad explained, much of it is currently discarded. The idea that, as a result, she and her colleagues could be throwing away major discoveries sometimes keeps her up at night. “Is there new physics below 1 TeV?” Åarrestad wondered.

Someday, maybe, an AI might be able to tell us.

The post The pros and cons of reinforcement learning in physical science appeared first on Physics World.

When researchers at Microsoft released a list of the 40 jobs most likely to be affected by generative artificial intelligence (gen AI), few outsiders would have expected to see “mathematician” among them. Yet according to speakers at this year’s Heidelberg Laureate Forum (HLF), which connects early-career researchers with distinguished figures in mathematics and computer science, computers are already taking over many tasks formerly performed by human mathematicians – and the humans have mixed feelings about it.

One of those expressing disquiet is Yang-Hui He, a mathematical physicist at the London Institute for Mathematical Sciences. In general, He is extremely keen on AI. He’s written a textbook about the use of AI in mathematics, and he told the audience at an HLF panel discussion that he’s been peddling machine-learning techniques to his mathematical physics colleagues since 2017.

More recently, though, He has developed concerns about gen AI specifically. “It is doing mathematics so well without any understanding of mathematics,” he said, a note of wonder creeping into his voice. Then, more plaintively, he added, “Where is our place?”

AI advantages

Some of the things that make today’s gen AI so good at mathematics are the same as the ones that made Google’s DeepMind so good at the game of Go. As the theoretical computer scientist Sanjeev Arora pointed out in his HLF talk, “The reason it’s better than humans is that it’s basically tireless.” Put another way, if the 20th-century mathematician Alfréd Rényi once described his colleagues as “machines for turning coffee into theorems”, one advantage of 21st-century AI is that it does away with the coffee.

Arora, however, sees even greater benefits. In his view, AI’s ability to use feedback to improve its own performance – a technique known as reinforcement learning – is particularly well-suited to mathematics.

In the standard version of reinforcement learning, Arora explains, the AI model is given a large bank of questions, asked to generate many solutions and told to use the most correct ones (as labelled by humans) to refine its model. But because mathematics is so formalized, with answers that are so verifiably true or false, Arora thinks it will soon be possible to replace human correctness checkers with AI “proof assistants”. Indeed, he’s developing one such assistant himself, called Lean, with his colleagues at Princeton University in the US.

Humans in the loop?

But why stop there? Why not use AI to generate mathematical questions as well as producing and checking their solutions? Indeed, why not get it to write a paper, peer review it and publish it for its fellow AI mathematicians – which are, presumably, busy combing the literature for information to help them define new questions?

Arora clearly thinks that’s where things are heading, and many of his colleagues seem to agree, at least in part. His fellow HLF panellist Javier Gómez-Serrano, a mathematician at Brown University in the US, noted that AI is already generating results in a day or two that would previously have taken a human mathematician months. “Progress has been quite quick,” he said.

The panel’s final member, Maia Fraser of the University of Ottawa, Canada, likewise paid tribute to the “incredible things that are possible with AI now”.  But Fraser, who works on mathematical problems related to neuroscience, also sounded a note of caution. “My concern is the speed of the changes,” she told the HLF audience.

The risk, Fraser continued, is that some of these changes may end up happening by default, without first considering whether humans want or need them. While we can’t un-invent AI, “we do have agency” over what we want, she said.

So, do we want a world in which AI mathematicians take humans “out of the loop” entirely? For He, the benefits may outweigh the disadvantages. “I really want to see a proof of the Riemann hypothesis,” he said,  to ripples of laughter. If that means that human mathematicians “become priests to oracles”, He added, so be it.

The post Are we heading for a future of superintelligent AI mathematicians? appeared first on Physics World.

This episode of the Physics World Weekly podcast features Scott Bolton, who is principal investigator on NASA’s Juno mission to Jupiter. Launched in 2011, the mission has delivered important insights into the nature of the gas-giant planet. In this conversation with Physics World’s Margaret Harris, Bolton explains how Juno continues to change our understanding of Jupiter and other gas giants.

Bolton and Harris chat about the mission’s JunoCam, which has produced some gorgeous images of Jupiter and it moons.

Although the Juno mission was expected to last only a few years, the spacecraft is still going strong despite operating in Jupiter’s intense radiation belts. Bolton explains how the Juno team has rejuvenated radiation-damaged components, which has provided important insights for those designing future missions to space.

However Juno’s future is uncertain. Despite its great success, the mission is currently scheduled to end at the end of September, which is something that Bolton also addresses in the conversation.

The post Juno: the spacecraft that is revolutionizing our understanding of Jupiter appeared first on Physics World.