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AI companies are looking beyond simply scaling up AI models, and the competition between OpenAI and Google is fiercer than ever. Large language models can answer many questions remarkably well, but they often stumble when asked to solve puzzles that require basic math or logic. OpenAI announced an improved version of its most capable artificial intelligence model to date—one that takes even more time to deliberate over questions—just a day after Google announced its first model of this type. OpenAI’s new model, called o3, replaces o1, which the company introduced in September. The o3 model scores much higher on several measures than its predecessor, OpenAI says, including ones that measure complex coding-related skills and advanced math and science competency. It is three times better than o1 at answering questions posed by ARC-AGI, a benchmark designed to test an AI models’ ability to reason over problems they’re encountering for the first time. Original article: Will Knight | Wired Featured on This Week’s Awesome Tech Stories From Around the Web (Through December 21)

Do you want to live past 100? A new study, published in Aging Cell, leveraged blood draws from the New England Centenarian Study—the largest and most comprehensive database of centenarians—to transform blood cells into induced-pluripotent stem cells (iPSCs). Centenarians don’t just live longer. They’re also healthier, even in extreme old age, and less likely to suffer age-related diseases, such as dementia, Type 2 diabetes, cancer, or stroke. Some evade these dangerous health problems altogether until the very end. What makes them special? In the last decade, several studies have begun digging into their genes to see which are active (or not) and how this relates to healthy aging. Others have developed aging clocks, which use myriad biomarkers to determine a person’s biological age—that is, how well their bodies are working. Centenarians frequently stood out, with a genetic landscape and bodily functions resembling people far younger than expected for their chronological age. Because of their age, they initially expected that turning back the clock might not work on old blood cells. Luckily, they were wrong. Several proteins showed the iPSCs were healthy and capable of making other cells. They also mostly maintained their genomic integrity—although surprisingly, cells from three male centenarians showed a slight loss of the Y chromosome. If the secrets to longevity are, even only partially, hidden in the genes, these super-aging stem cells could help researchers figure out what’s protective or damaging, in turn prompting new ideas that slow the ticking of the clock.

The description of neurons you've come to know and love in your textbooks may be missing a crucial detail. A team from Johns Hopkins University found tiny “bubbles” dotted along the long tail—called the axon. Normally depicted as a mostly smooth, cylindrical cable, axons may instead look like “pearls on a string.” Why care? Axons transmit electrical signals connecting the neural networks that give rise to our thoughts, memories, and emotions. Small changes in their shape could alter these signals and potentially the brain’s output—that is, our behavior. The study also included testing the axon's reaction to sugar, being stripped of of cholesterol in their membranes, electrical signaling, and duress. “Understanding the structure of axons is important for understanding brain cell signaling,” Shigeki Watanabe at the Johns Hopkins University School of Medicine, who led the study, said in a press release.

As we stand on the precipice of a new year, we're feeling more charged up than a quantum computer on espresso! 2024 has been a whirlwind of innovation, impact, and moments that remind us why we do what we do. We'd like to take a moment to geek out (because, well, that's who we are). This year has been nothing short of extraordinary. 💫 Back to the Future: In 2024, we've focused our energy on reviving and expanding what made Singularity great - our mission to educate, inspire, and empower leaders to imagine and create breakthroughs powered by exponential technologies. As an ode to those Futuremakers who came before us, we've returned to the original shield logo - with a modern new take that we believe represents this new journey ahead. 👥 5,199 New Futuremakers: We’re proud to welcome our 2024 alumni, from the Executive Program, Abundance360, International Summits, Enterprise Programs, and other programs into this special community of change-makers. 🌐 Global Impact Challenges Return: We partnered with Sync Ithra to reignite our activity in the Startup Ecosystem. After a competitive live pitch event in Silicon Valley with our 10 finalists, ChatLicense took home the $250,000 prize for the Digital Wellbeing Challenge. The success of the first GIC since 2019 once again proves that our network is packed with world-changing problem solvers and we are thrilled to announce future challenges in 2025! 🎓 New Singularity Fellows: : Singularity welcomed 15 Fellows in the learning areas of AI, BioTech, Computing, Economies, EnergyTech, Ethics, Governance, Impact, Innovation, Leadership, Mixed Reality, Purpose, Robotics, SpaceTech, and Transformation. These 15 Fellows will drive thought leadership and community initiatives in collaboration with Singularity throughout 2025 and 2026. 🔔 New, More Accessible Programs: In May, we launched the new Future of X series - a three-day program aimed to provide a deep dive into how one technology area can, and will, impact everything else. With no application process, multiple pricing tiers and virtual attendance options, this program is a more accessible way for our global community to engage with Singularity and learn from our experts. Building on the success of our 2024 Future of AI program, we're bringing this transformative approach to biotech in 2025. 🌍 Return to the Global Stage: We are incredibly grateful to our international partners who are working to help spread Singularity’s message to the world with Summits, Global Impact Challenges, and local programs in Singularity Brazil, Singularity Chile, SingularityU Czech Summit, Singularity India, SingularityU Mexico Summit, SingularityU Portugal, Singularity South Africa , and Singularity Spain Thank you for joining us on this journey. Our offices will be closed from December 23 to January 6th to let the team rest and recharge. We hope you can do the same. We look forward to seeing you in 2025!

Data center emissions have tripled since 2018. As more complex AI models like OpenAI’s Sora see broad release, those figures will likely go through the roof. A new paper, from teams at the Harvard T.H. Chan School of Public Health and UCLA Fielding School of Public Health, examined 2,132 data centers operating in the United States (78% of all facilities in the country). These facilities—essentially buildings filled to the brim with rows of servers—are where AI models get trained, and they also get “pinged” every time we send a request through models like ChatGPT. They require huge amounts of energy both to power the servers and to keep them cool. Since 2018, carbon emissions from data centers in the US have tripled. AI models are rapidly moving from fairly simple text generators like ChatGPT toward highly complex image, video, and music generators. Until now, many of these “multimodal” models have been stuck in the research phase, but that’s changing.

Kaustav Banerjee, co-founder and CTO of Destination 2D, claims that they are the only company to demonstrate graphene deposition directly on top of transistor chips, rather than growing the interconnects separately and attaching them to the chip after the fact. Why is this important? The semiconductor industry’s long held imperative—Moore’s Law, which dictates that transistor densities on a chip should double roughly every two years—is getting more and more difficult to maintain. The ability to shrink down transistors, and the interconnects between them, is hitting some basic physical limitations. In particular, when copper interconnects are scaled down, their resistivity skyrockets, which decreases how much information they can carry and increases their energy draw. The industry has been looking for alternative interconnect materials to prolong the march of Moore’s Law a bit longer. Graphene is a very attractive option in many ways: The sheet-thin carbon material offers excellent electrical and thermal conductivity, and is stronger than diamond. However, researchers have struggled to incorporate graphene into mainstream computing applications for two main reasons. First, depositing graphene requires high temperatures that are incompatible with traditional CMOS manufacturing. And second, the charge carrier density of undoped, macroscopic graphene sheets is relatively low. Now, Destination 2D, a startup based in Milpitas, Calif., claims to have solved both of those problems.

Progress has been made in the Neuralink-sphere. The idea is that while the neurons remain housed in the implant, their axons—long strands that carry nerve signals away from the cell body—and their dendrites—the branched structures that form synapses with other cells—will be free to integrate with the host’s brain cells. Max Hodak, former president of Neuralink, is the CEO of the California-based startup Science Corporation. To see if the idea works in practice they installed the device in mice, using neurons genetically modified to react to light. Three weeks after implantation, they carried out a series of experiments where they trained the mice to respond whenever a light was shone on the device. The mice were able to detect when this happened, suggesting the light-sensitive neurons had merged with their native brain cells. You can squeeze a lot more neurons into a millimeter-scale chip than electrodes and each of those neurons can form many connections. That means the potential bandwidth of a biohybrid device could be much more than a conventional neural implant. The approach is also much less damaging to the patient’s brain.

They carried out a computation that would take the world’s second fastest supercomputer, Frontier, 10 septillion years to complete. How long do you think it took for Google's quantum processor? (answer down below) The test they used is a contrived one with little practical use, but the new error-correction result is a big step in the right direction. Google’s demonstration simply involved storing information in its logical qubits rather than using them to carry out calculations. Speaking to MIT Technology Review in September, when a preprint of the research was posted to arXiv, Kenneth Brown from Duke University noted that carrying out practical calculations would likely require a quantum computer to perform roughly a billion logical operations. So, despite the impressive results, there’s still a long road ahead to large-scale quantum computers that can do anything useful. However, Google appears to have reached an important inflection point that suggests this vision is now within reach. (answer: 5 minutes!)

📈 #exponentialmindset

Why do some people lose memory or cognitive problems as they age, while others seem to stay sharp till the very end? A new study published in Nature Aging analyzed brain imaging data from nearly 11,000 healthy adults, middle-aged and older, using AI to gauge their “brain age.” Roughly half of participants had their blood proteins analyzed to fish out those related to aging. Thirteen proteins popped up—eight associated with faster brain aging and five that slowed down the clock. Most alter the brain’s ability to handle inflammation or are involved in cells’ ability to form connections. The protein markers could also help us learn to prevent age-related brain disorders, such as dementia, Alzheimer’s disease, stroke, or problems with movement. Early diagnosis is key.