US forces have carried out new strikes on Iran, hitting an oil tanker in the Strait of Hormuz and targeting Qeshm Island, a vital military stronghold and it’s not the first time they have attacked it.
Premiered 17 hours ago #RichardDawkins#SamHarris#SecularismThe Center for Inquiry proudly presented the 2026 Richard Dawkins Award to neuroscientist, philosopher, bestselling author, and host of the Making Sense podcast, Sam Harris. Presented annually by CFI, the Richard Dawkins Award honors distinguished individuals whose work advances science, reason, secularism, and free inquiry. Past recipients include Neil deGrasse Tyson, Brian Cox, Bill Nye, Steven Pinker, Daniel Dennett, Christopher Hitchens, Ricky Gervais, Tim Minchin, and many others who have helped shape public conversations around evidence, skepticism, and humanist values. In this special online ceremony, recorded live on April 18, 2026, Dawkins and Harris engaged in a wide-ranging conversation touching on artificial intelligence, consciousness, morality, politics, mindfulness, and the rapidly changing relationship between technology and human creativity. The event concluded with an audience Q&A exploring the broader intellectual journey that has defined Harris’ career. Watch the full conversation and ceremony here. Learn more about the Richard Dawkins Award and the Center for Inquiry: CFI: https://centerforinquiry.org Richard Dawkins Award: https://centerforinquiry.org/richard-…#SamHarris#RichardDawkins#CenterForInquiry#Skepticism#Secularism#FreeInquiry
Geoffrey Hinton, Nobel Prize–winning pioneer of deep learning, joins Sana’s founder and CEO, Joel Hellermark, for a candid conversation about the future of AI, what it means to create “beings” rather than tools, and how we navigate the next great intellectual revolution. From fast‑learning neural architectures to the ethics of training data, Hinton offers a rare, unfiltered look at where this all might be heading.
What’s in this video: —How evolution, development, learning, and now AI form ever‑faster “loops” of intelligence —Why Hinton thinks AI may soon outstrip human mathematicians and reach much higher levels of intelligence —The coming “third revolution” after Copernicus and Darwin: humans no longer being the only beings around —How language‑model “self‑play” and internal inconsistencies could let AI improve with little new data —The risks of a purely profit‑driven race for smarter AI and why we must design “beings that care about us” —Hinton’s architectural bet on fast‑changing weights and brain‑like synapses beyond current transformer hardware
Hinton argues that the real stakes of AI are not only economic or technical, but civilizational. As we move from being the only intelligent beings to coexisting with artificial ones, we face a choice: build systems that are merely powerful, or build beings that genuinely care about human flourishing.
Recorded live at Sana AI Summit 2026, New York, May 21st, 2026. Subscribe for more insights on AI and the future of technology.Geoffrey Hinton and Joel Hellermark explore the evolution of artificial intelligence and its potential to surpass human cognition. They discuss the future of model architectures, the importance of training data, and the societal implications of developing autonomous systems that possess capabilities beyond traditional computing.Summary
Just over seven months from completing phase one of this mega-project, Chinese engineers have finished the build and switched on the world’s first underwater data center (UDC) powered by offshore wind turbines. What’s more, it doesn’t need freshwater and cuts land use by more than 90% compared with above-ground centers.
We reported on the big build in October 2025, when the first stage had been constructed. At the time, there was no projected timeline for it to become operational. The underwater infrastructure, off the coast of Shanghai in the Lin-hang Special Area, was officially switched on in late May, and it’s far more impressive than it may sound on paper.
Data centers don’t need freshwater to function – but it remains the simplest cooling option, as it puts fewer demands on surrounding infrastructure, thanks to its lower levels of salts, minerals and biological impurities that can corrode pipes or reduce cooling efficiency over time. Unlike many inland facilities that still rely on freshwater, UDCs instead use the surrounding ocean as a heat sink, transferring this heat through sealed cooling systems.
This center, built by a subsidiary of China Communications Construction, uses a circulating copper-pipe heat exchange system that reportedly reduces electricity consumption by 22.8%. Offshore wind farms are also estimated to generate 95% of the electricity needed to run its 192 server racks across four levels, significantly reducing reliance on existing power infrastructure.
“For an undersea data center of the same scale, the electricity used for cooling would only account for about one-tenth of total power consumption,” Tsinghua University Professor Li Zhen told China Daily. “If data centers of the same scale were placed underwater, even allowing extra margins, cooling consumption could fall to around 30-billion kW. That would save about 50 billion kWh of electricity each year.”
According to state media, the center is currently operating at 2.3 MW – but has a planned capacity of 24 MW (enough to power 20,000 households). This “room to move” is essentially future-proofing the UDC’s usefulness, as companies turn their attention from initial builds to longevity when it comes to hardware upgrades and compute capacity.
Nonetheless, while UDCs may reduce freshwater demands and land use, underwater computing is still a largely unknown at commercial scale. Questions remain around how these facilities will endure – and what the ecological effects of continuously releasing heat into local marine environments might be.
But considering tech companies are racing to put data centers in space to meet rising demand, real-world projects like China’s UDC could serve as valuable test cases in the AI age, revealing whether moving computing infrastructure into new environments can offset existing land-based issues – or reveal entirely new ones.
Young people are paying the price for the remote work options employees fought for.Hristo Rusev/Getty Images)
Just a few years ago, remote work was something like a matter of life or death. In the pandemic-stricken early years of the 2020s, most white-collar workers who fled to the country or even changed their living situations counted their blessings as bosses seemed inclined to let home offices be even as lockdown orders expired. Almost half of full-time U.S. employees were working from home by fall 2021, of which some 90% said they wanted to stay remote in some shape or form.
Cognizant’s CEO is hiring over 20,000 entry-level graduates this year
They may have gotten their wish. Of jobs that could be done remotely, 78% of U.S. work locations are currently either remote or hybrid, according to Gallup data, up from 40% in 2019. Meanwhile, fully on-site roles went from 60% of placements in 2019 to 22% this year.
But for every millennial or Gen Xer happily able to take calls in sweatpants Mondays and Fridays, mounting evidence suggests that working in your sweatpants is the real reason, not AI adoption, behind the plunge in entry-level hiring halfway through the decade.
New research from the Federal Reserve Bank of New York puts numbers on the dynamic. Researchers found the unemployment rate for college graduates younger than 29 climbed from 3.1% to 3.7% over the past nine years. Over the same period, unemployment among more experienced college graduates older than 29 actually ticked down, from 1.9% to 1.8%.
The divergence traces back to “remotable” fields—software engineering, financial analysis, and other white-collar roles. In jobs that require physical presence, like nursing, the age gap spiked briefly in 2020 then normalized. In remote-eligible work, it never did. Remote work could account for as much as 64% of the overall rise in youth unemployment since the pandemic, the researchers found.
Recent graduates aged 22 to 27 are currently dealing with an unemployment rate of 5.6% as of March. It’s higher than the general unemployment rate (4.2%), and well above the share of degree-holders of all ages without a job (3.1%). Many would-be white-collar workers have designated generative AI adoption across U.S. firms as the scapegoat for a lack of entry-level work opportunities.
The same economists behind the new Fed research recently published another paper for the National Bureau of Economic Research, focusing on productivity among software engineers at a large U.S. firm. The researchers found that while remote work can boost output among experienced workers, it can be to the detriment of younger engineers.
Feedback on coding work increased 18.3% when workers were in the office, improving the quality of output, according to the paper. Younger workers disproportionately benefited from in-person mentorship and feedback sessions, while periods of flexible work at their company had “scarring effects” on young graduates’ development.
Separate researchers are increasingly aligning on this thesis—that the same privilege workers staged company walk-outs to protect only a few years ago—is at least partially responsible for the wave of youth unemployment and underemployment sweeping the U.S. Firms that operate on distributed teams have become less willing to hire young workers requiring mentorship, economists say, and are happy to keep hiring older and safer staff instead.
Remote work villain
A closer look at who is and who isn’t finding jobs points to remote work as a powerful variable. Comparing unemployment trends across “remotable” occupations, such as software engineering or financial analysis, with occupations that rely on physical presence like nursing, the Fed research found the entire increase in relative youth unemployment boils down to remotable fields. Hiring rates have mostly normalized for physical roles. Nursing, for example, has been one of the labor market’s bright spots lately.
Other research is coming to a similar conclusion. A working paper by economists at the London School of Economics and the University of Oxford, published last month, scanned hundreds of millions of hiring records and job postings in the U.S., U.K., Canada, and Australia between 2017 and 2025. They found that while entry-level hiring has indeed plummeted—between 14% and 29%, depending on which country—senior hiring has risen 5% to 21%.
Remote work appeared to be a key culprit behind the discrepancy. Companies that publicly announced strategy shifts towards working from home or hybrid models early in the pandemic are now more likely to staff senior roles and older workers, with fewer entry-level roles available.
The authors do note that the companies most adapted to offer flexible work options are also more likely to employ for roles that are more easily automated by AI, suggesting some correlation might be at play.
But both the working paper and the new Fed research point out that the age gap in hiring predates the mass diffusion of AI tools. Over the past few months, companies that have pinned layoffs and muted hiring on automation have been accused of so-called AI-washing, blaming technology for headcount decisions that were likely to happen anyway. Economists have so far been at pains to find evidence that AI adoption is directly responsible for unprecedented shifts in the labor market, finding that its impact so far has been mostly similar to that of the Internet or computers: disruptive, but not apocalyptic.
“There is zero evidence of job losses because of AI,” Torsten Slok, chief economist at investment firm Apollo, wrote in a blog post last week. Citing employment data that has held steady in recent months, Slok said the push for AI adoption might actually be raising demand for jobs as firms hire more engineers and AI experts.
Employers might see AI as a convenient justification for their hiring decisions, though from an economics’ standpoint, the remote work factor might have more to stand on.
That AI has taken the brunt of blame is likely small reassurance to new graduates struggling to advance in their career. Young workers in general are likely aware that the popularity of remote work is mostly to their disadvantage, as a Gallup poll last year found Gen Z to be the age group least likely to prefer a fully remote workplace setup, citing in part the lack of interaction with coworkers.
The Fortune 500 Innovation Forum will convene Fortune 500 executives, U.S. policy officials, top founders, and thought leaders to help define what’s next for the American economy, Nov. 16-17 in Detroit. Apply here.
The atmosphere at the SPIEF in St Petersburg: humanoid robots, novelties of the Russian auto industry, and numerous stands from representatives of more than 140 countries.
🇺🇸🇮🇱 Special Envoy Kushner is using economic incentives to expand the Abraham Accords, with Morocco and Syria in his sights.
– Kushner says he will create "economic packages" for countries that join – "We are looking at opportunities to invest in countries that have joined the… https://t.co/CbWuqrRtKd
So, Ivanka Trump is bragging about her new, off-grid island in the Mediterranean that her and Jared are developing into a resort. She’s even pretending to care about the environmental impact in her propaganda. What she isn’t saying is that the island, Sazan island, is in Albania, in the Adriatic Sea, not the Med. And Albania is one of the most corrupt states in Europe. The island is a 1,400-acre decommissioned Cold War weapons base with underground bunkers and tunnels, which was restricted until the Trump’s bought it to develop a monstrous $1.4 billion resort on the island, when the Albanian govt gave preliminary approval for development. So once they’ve finished bribing Albanian officials and de-mined the island, they’ll be able to host the ultra wealthy pedophile Epstein class again, under the Trump brand. In secret, with no accountability, and with bunkers they can use for their sick sexual fantasies and experiments on kids.
Ann Dooms, mathematician: ‘In the real world, human intuition remains irreplaceable’
She is one of the world’s experts in what she and others in her field call ‘digital mathematics’: a term of their own to distinguish it from classical signal processing or more conventional data analysis
Mathematician Ann Dooms in Madrid.Laura Moreno Iraola (ICMAT)
After two days in Madrid, Ann Dooms, 47, still hadn’t managed much sightseeing: only a quick visit to the Santiago Bernabéu stadium with her daughter. She was staying at the Residencia de Estudiantes, where she gave a talk at the invitation of the Institute of Mathematical Sciences and the Spanish National Research Council (CSIC). Public outreach is only one of the many tasks that occupy Dooms. She also leads the Mathematics and Data Science research group at the Free University of Brussels (VUB, by its Dutch initials), where she is a full professor, and chairs both the Belgian Defence Scientific Council and the Education Committee of the European Mathematical Society.
Across all these roles, one conviction appears again and again — something she repeats several times during the conversation: understanding, and making understandable, the ways in which mathematical patterns help us read the world. The teaching of mathematics is another of Dooms’s concerns, as she also chairs the Education Committee of the European Mathematical Society.
Her vocation began in high school when she saw a BBC program featuring Michael Barnsley, a pioneer of fractal geometry who became a multimillionaire after developing a revolutionary image‑compression technique based on fractals that Microsoft incorporated into its Encarta encyclopedia. After completing a PhD in noncommutative algebra, Dooms shifted her career back toward her childhood idol: she moved to an engineering faculty to develop algebraic watermarking techniques to authenticate digital images.
That move toward the applied end of the spectrum would make her one of the world’s leading experts in what she and her group call “digital mathematics”: a term coined at the Free University of Brussels — the alma mater of Ingrid Daubechies, a pioneer in the field and recipient of the Princess of Asturias Award and the BBVA Foundation Award — to distinguish this approach from classical signal processing or more conventional data analysis. It involves developing mathematical tools, largely from functional analysis, capable of extracting structure and meaning from different types of digital information.
Question. Your contributions in this field — in which you began working alongside Daubechies — include collaborations with major museums to analyze high‑resolution digital scans of masterpieces in order to characterize a painter’s style or detect hidden restorations. Can these be considered one of the early major successes of machine learning?
Answer.Yes. On a computer, an image is a numerical structure made up of pixels: we examine it through that structure, manipulate the numbers mathematically and translate the result into information a human can interpret. We were doing this in the 2000s, although we didn’t call it machine learning then, because it was still frowned upon to say we were making a machine learn.
Q. What information can these tools extract that could not be perceived otherwise?
A.We develop transforms that decompose images into different kinds of building blocks depending on the goal: simplify information to compress files; characterize with great detail the brushstrokes of a painting to mathematically distinguish one painter’s style from another’s; identify cracks in a work so they can be digitally removed... Recently, together with the Reproductive Medicine Center at UZ Brussel University Hospital, we are applying these ideas to select oocytes to be frozen for women undergoing cancer treatments who want to preserve their fertility.
Q. You also say this approach can improve artificial intelligence models.
A.I think right now we are exploiting the power of neural networks in a very naive way. If we can add mathematical structure to data processing, the results will be much better. The key is to look for the best representation for the problem you want to solve, transform the data into constructive building blocks, and only then apply machine learning. It is proven that neural networks can learn to do what wavelets [the mathematical transforms with which Daubechies revolutionized image processing from the 1980s] do; there is a theorem that guarantees any continuous task can be approximated by a neural network with a single hidden layer. But that means spending enormous computational resources to rediscover something we already know. Why not start from it directly?
Q.What else would we gain from that mathematical approach?
A.Reliability and transparency. The next generation of artificial intelligence models should not consist only of larger models, but of models that are better understood mathematically and more tightly linked to the structure of the problem they aim to solve.
Q. Have you been able to test it?
A. I’ve run some tests, but even with a high-performance computer, I have to wait 48 hours to run very small examples. Academia does not have the computational power of the large tech companies.We need to collaborate with them, and I think private capital will have to play a central role in research, as it did in the early days of computing.
Q. How is this ecosystem, dominated by big tech and their AI models, changing the way mathematics is done?
A.To me, they are an extraordinarily powerful tool.For example, you no longer need years to explore an abstract link: you can give the model certain rules and ask it to check whether an intuition you have might be true, or even to search for new properties on its own. Something similar happened with the arrival of the first computers: mathematicians working on the space race did calculations by hand, and suddenly that was automated and changed the whole way of working. But the problems didn’t disappear; a different kind of understanding was required to apply the new tools. The same happens with AI: you have to understand what is relevant and what is not. And when you work with real-world phenomena, where everything is enormously complex, human intuition remains irreplaceable. These networks have no sense of reality. Even if they process images or video, it is still not our world. We are three-dimensional beings working in a four-dimensional space. That will remain our advantage.
Q. What, then, happens to young researchers who are just starting out and have not yet developed that intuition?
A. It’s a question the whole community is asking. Just a few days ago, Timothy Gowers [Fields Medal, one of the world’s most respected mathematicians] wrote on his blog that ChatGPT 5.5 Pro had produced, in just over two hours and with almost no mathematical guidance from him, a result that, in his view, would have made “a perfectly reasonable chapter in a doctoral thesis in combinatorics,” and he concluded that we urgently need to rethink what a doctorate in mathematics is. It’s advancing our field a lot, but I think these models, for now, do things that generalize what already exists in the data. And of course there are theses that consist of that, which is not easy either: it involves a lot of reading, identifying relevant information, connecting ideas. But many other theses are something else. I have never given my PhD students tasks that are simply “connect the dots.”
Q. Beyond doctorates, what would you say is Europe’s main challenge in mathematics education?
A.It’s not exclusive to Europe. The difficulties in the United States and Canada are very similar. Rejection of mathematics is growing fast, and I think it has a lot to do with how it is taught in primary school: as a purely computational tool, never explaining why things work. Of course. there are questions — even among the most basic ones about the natural numbers, like why two times three is the same as three times two — that are too sophisticated to prove to children. But the problem is that currently nothing is explained. When you reach the formulas for area or circumference, they’re just presented as: this is the formula.
However, geometry is precisely the discipline where mathematical proof was born; there you can prove things, and in an accessible way. Then, suddenly in secondary school, formal and unintuitive proofs appear. That produces a very strange feeling in students: some things must be memorized, others must be proved rigorously. It’s never clear why. Hung-Hsi Wu, an American mathematician of Chinese origin, sums it up well: what we call school mathematics is a very young discipline, and in fact we still do not fully know how to teach it.
Q. Do you have any concrete proposals for improvement?
A. For me, the challenge is to convey, from the start, that mathematics is the only science in which you can be truly certain about something, and to show how that is so with very carefully chosen examples. But also to acknowledge the limits: natural numbers are among humanity’s oldest technologies, we know they work very well, but explaining why is extraordinarily difficult. The goal should be to teach children to look at the world structurally: identify what things behave similarly if you remove the details.
That requires drastic changes in what we teach, to whom and how, and a fundamental rule: never teach something that is incorrect, but choose the level of detail carefully. This is extremely costly. That is why my dream would be for European education ministers to launch an international project specifically for this: bring mathematicians together, jointly design the content and approach, and produce a curriculum that makes children think about the world. In Flanders, this idea provokes criticism because people say it undermines freedom of teaching. But that is not freedom. Mathematics is about truth.
Q. You hold another striking post: you chair the Scientific Council of Belgium Defence. How does a mathematician come to advise the military, and what is the extent of that advice?
A.We advise on research projects required by the Royal Academy for military training and we also work with research carried out by intelligence services. There are calls in which academics participate alongside the Academy and Defence. The results are sometimes secret; not everything can be published.
Q. Do you consider yourself a pacifist?
A.Yes, but I believe defence is important and one should not be naive. One of my role models is Alan Turing, who also worked for Defence, but not to attack — rather to save lives. That was also my mission when I joined the Council: not to produce weapons of war, but to contribute on security matters and, increasingly, on everything related to AI. How can we use AI to protect ourselves, but also how to defend against attacks using it, which is already happening. I can’t say much more.
Q. Do you think the mathematical community is handing over its responsibility in debates about the military uses of AI to lawyers and philosophers?
A.Yes, absolutely. Mathematics is the basis of these technologies, and mathematicians should be talking about them. But that is not common practice.
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