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“All that takes is just the right incentive.” Reliably identifying the mutations that matter for disease risk, drug response, and personalized medicine remains one of the hard problems in genomics. On the latest Novelty Search, Minos explained how SN107 uses Bittensor to improve variant calling, the step that turns...

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Openτensor Foundaτion

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22,315 Aufrufe • vor 20 Tagen •via X (Twitter)

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Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.
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Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.

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The Bible is the one book that dares to predict the future. Not once or twice, but hundreds times with 100% accuracy. That is because it is the very word of God. All that is happening with Israel and the Middle East right now? It was all spoken of thousands of years ago by the Hebrew Prophets. Check out this video I just did for a Biblical perspective on the headlines of today.
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158,440 Aufrufe • vor 2 Jahren

Demis Hassabis just described the moment medicine stops treating disease and starts deleting it from the source code. For all of human history, doctors have fought symptoms. The tumor. The organ failure. The collapse. Always downstream. Always after the damage has already started. The cause sits upstream. Written into the DNA. Ninety-eight percent of the human genome sits in non-coding regions. For decades, science understood the genes but couldn’t read the vast dark territory between them. That’s where most disease hides. Hassabis: “It takes the big, long genetic sequences and then it tries to predict, if you made a mutation to this particular single letter, single position in the genetic sequence, will that be a harmful mutation that might cause disease, or is it benign?” AlphaGenome reads your entire genetic sequence and identifies the exact letter that’s corrupted. Not a region. Not a probability range. A single position in a three-billion-letter sequence. That alone would be a generational breakthrough. But most diseases aren’t that clean. Hassabis: “What if they’re multigenic diseases where there’s cascades of mutations causing the problem? Those are even harder to detect, but actually perfect for sort of AI.” One mutation is hard enough to find. A cascade of mutations interacting across the genome is a problem no human researcher can hold in their head at once. Three billion data points. Compounding errors across all of them. The human brain cannot solve that. AI doesn’t solve it either. It maps it. All of it. At once. The most devastating diseases on Earth. The ones medicine has called untreatable for generations. They are not mysteries to the algorithm. They’re compute problems. But finding the error was only ever half the equation. You also need the ability to fix it. That tool already exists. CRISPR is a molecular scalpel. It cuts DNA at exact positions. The limitation was never the editing. It was knowing exactly where to cut. Hassabis: “A kind of combination of things like AlphaGenome and CRISPR could be incredibly powerful.” AI reads the code. CRISPR rewrites it. One finds the mutation. The other corrects it at the source. Not managing symptoms. Not slowing progression. Deleting the error from the genome. The implications go beyond treatment. A disease corrected at the genetic level doesn’t just disappear from one patient. It disappears from their bloodline. The read access is here. The write access exists. The merge is inevitable. The era of accepting a broken genetic hand is ending. We stopped being passengers in our own biology.
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Demis Hassabis just described the moment medicine stops treating disease and starts deleting it from the source code. For all of human history, doctors have fought symptoms. The tumor. The organ failure. The collapse. Always downstream. Always after the damage has already started. The cause sits upstream. Written into the DNA. Ninety-eight percent of the human genome sits in non-coding regions. For decades, science understood the genes but couldn’t read the vast dark territory between them. That’s where most disease hides. Hassabis: “It takes the big, long genetic sequences and then it tries to predict, if you made a mutation to this particular single letter, single position in the genetic sequence, will that be a harmful mutation that might cause disease, or is it benign?” AlphaGenome reads your entire genetic sequence and identifies the exact letter that’s corrupted. Not a region. Not a probability range. A single position in a three-billion-letter sequence. That alone would be a generational breakthrough. But most diseases aren’t that clean. Hassabis: “What if they’re multigenic diseases where there’s cascades of mutations causing the problem? Those are even harder to detect, but actually perfect for sort of AI.” One mutation is hard enough to find. A cascade of mutations interacting across the genome is a problem no human researcher can hold in their head at once. Three billion data points. Compounding errors across all of them. The human brain cannot solve that. AI doesn’t solve it either. It maps it. All of it. At once. The most devastating diseases on Earth. The ones medicine has called untreatable for generations. They are not mysteries to the algorithm. They’re compute problems. But finding the error was only ever half the equation. You also need the ability to fix it. That tool already exists. CRISPR is a molecular scalpel. It cuts DNA at exact positions. The limitation was never the editing. It was knowing exactly where to cut. Hassabis: “A kind of combination of things like AlphaGenome and CRISPR could be incredibly powerful.” AI reads the code. CRISPR rewrites it. One finds the mutation. The other corrects it at the source. Not managing symptoms. Not slowing progression. Deleting the error from the genome. The implications go beyond treatment. A disease corrected at the genetic level doesn’t just disappear from one patient. It disappears from their bloodline. The read access is here. The write access exists. The merge is inevitable. The era of accepting a broken genetic hand is ending. We stopped being passengers in our own biology.

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🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance
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🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

Jan Jekielek

96,002 Aufrufe • vor 1 Jahr

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

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Professor Jones, whose work sits at the center of the climate change modeling apparatus, stated plainly that he would not share his data even if the World Meteorological Organization requested it. His reasoning was that 25 years of work had gone into the research, and he saw no obligation to hand it over to people whose aim was to find something wrong with it. That statement is worth reading slowly, because it is a precise inversion of how science is supposed to work. The whole point of publishing a scientific claim is that other researchers can examine the underlying data, attempt to replicate the result, and either confirm or challenge the conclusion. That is not a hostile process. That is the process. Replication is the mechanism by which a scientific claim earns the right to be called scientific. When a researcher withholds the data behind conclusions that are being used to drive policy affecting hundreds of millions of people, the enterprise has left the domain of science and entered the domain of politics. The 25 years invested in the work is not a justification for withholding it. If anything, the scale of that investment makes independent scrutiny more important, not less. You do not get to claim scientific authority while simultaneously blocking the one process that confers it.
0:47

Professor Jones, whose work sits at the center of the climate change modeling apparatus, stated plainly that he would not share his data even if the World Meteorological Organization requested it. His reasoning was that 25 years of work had gone into the research, and he saw no obligation to hand it over to people whose aim was to find something wrong with it. That statement is worth reading slowly, because it is a precise inversion of how science is supposed to work. The whole point of publishing a scientific claim is that other researchers can examine the underlying data, attempt to replicate the result, and either confirm or challenge the conclusion. That is not a hostile process. That is the process. Replication is the mechanism by which a scientific claim earns the right to be called scientific. When a researcher withholds the data behind conclusions that are being used to drive policy affecting hundreds of millions of people, the enterprise has left the domain of science and entered the domain of politics. The 25 years invested in the work is not a justification for withholding it. If anything, the scale of that investment makes independent scrutiny more important, not less. You do not get to claim scientific authority while simultaneously blocking the one process that confers it.

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UAE’s🇦🇪 Habib Al Mulla: ‘I don’t believe the news that is spreading, that UAE is going to be part of the attacks on Iran and the Iranian people, that is not going to happen. At the end of the day, we are bound by geography. Iran is a neighbouring country. We have been living together for years and years, and for years and years to come we always wanted to have a peaceful, normal relationship with Iran, irrespective of the regime that is there.’ -حبيب الملا, one of the UAE’s most respected legal authorities on the latest episode of Going Underground Watch the full interview in the quoted post, or watch it on Rumble, link below in the replies👇

Going Underground

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the future of POLITICS is clipping. Kevin O'Leary explained the clipping business indirectly. the people he pays most now take content, cut it into a 59-second clip, and bring in customers, because the work is measurable. swap customers for voters and that's a campaign. clipping isn't one ad buy with a ceiling. it's hundreds of creators turning one piece of content into thousands of clips that compound across every feed, and you only pay for the views that land. it's already everywhere. Netflix clips comedy specials. countries clip themselves for tourism. Zohran Mamdani streams for an hour as mayor of NYC and that hour turns into a week of clips across every platform. politics is just the next vertical, and the highest-stakes one. every future campaign will run this, local to federal. the ones that don't get outpaced by the ones that do.
0:27

the future of POLITICS is clipping. Kevin O'Leary explained the clipping business indirectly. the people he pays most now take content, cut it into a 59-second clip, and bring in customers, because the work is measurable. swap customers for voters and that's a campaign. clipping isn't one ad buy with a ceiling. it's hundreds of creators turning one piece of content into thousands of clips that compound across every feed, and you only pay for the views that land. it's already everywhere. Netflix clips comedy specials. countries clip themselves for tourism. Zohran Mamdani streams for an hour as mayor of NYC and that hour turns into a week of clips across every platform. politics is just the next vertical, and the highest-stakes one. every future campaign will run this, local to federal. the ones that don't get outpaced by the ones that do.

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Mainnet is on the horizon! And that is only possible by having partners that can build the foundation of libraries, standards, and utilities of the network. In this episode of #Unshielded, what stands out in this conversation is how much of OpenZeppelin efforts goes into the unglamorous work; the essential resources for builders. That work is informed by years of lessons across different ecosystems. If you want to know how to build safer smart contracts at scale, listen to Luca share his insights with Midnight's Lauren Lee . Full discussion in the latest episode of Unshielded. 👇 YouTube:
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Mainnet is on the horizon! And that is only possible by having partners that can build the foundation of libraries, standards, and utilities of the network. In this episode of #Unshielded, what stands out in this conversation is how much of OpenZeppelin efforts goes into the unglamorous work; the essential resources for builders. That work is informed by years of lessons across different ecosystems. If you want to know how to build safer smart contracts at scale, listen to Luca share his insights with Midnight's Lauren Lee . Full discussion in the latest episode of Unshielded. 👇 YouTube:

Midnight

13,334 Aufrufe • vor 5 Monaten

Ali Ghodsi, the cofounder and CEO of Databricks, says Zoom has a massive chance to build an AI-first product, that could seriously disrupt the traditional enterprise SAAS. Because it sits on the largest datasets of meeting videos and transcripts. The big pain in enterprise software is data entry and coordination. Zoom already sits on the raw input: every customer call and internal meeting, plus the video, audio, and transcript. If Zoom can reliably pull out decisions, context, and action items, then write them back into the right system of record automatically, as an AI-first workflow layer, it becomes the front door for work. That would replace lots of separate SAAS tools that exist mainly to collect notes and updates. --- Video from 'Bg2 Pod' YT channel (link in comment)
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Ali Ghodsi, the cofounder and CEO of Databricks, says Zoom has a massive chance to build an AI-first product, that could seriously disrupt the traditional enterprise SAAS. Because it sits on the largest datasets of meeting videos and transcripts. The big pain in enterprise software is data entry and coordination. Zoom already sits on the raw input: every customer call and internal meeting, plus the video, audio, and transcript. If Zoom can reliably pull out decisions, context, and action items, then write them back into the right system of record automatically, as an AI-first workflow layer, it becomes the front door for work. That would replace lots of separate SAAS tools that exist mainly to collect notes and updates. --- Video from 'Bg2 Pod' YT channel (link in comment)

Rohan Paul

84,823 Aufrufe • vor 4 Monaten

Venezuela’s🇻🇪 Former Deputy Foreign Minister for North America: ‘Right-wing Latin American governments are sustained by repression, money and support from the US🇺🇸.’ ‘There’s this angry tide throughout the region that is the result of years of conservative movements trying to get hold of power in Latin America. And there’s a need for the left to look into how it could work together to put a hold on this neoconservative wave that is going to take Latin America into a deeper inequality than it has already been for the last several years.’ -Carlos Ron on the latest episode of Going Underground FULL INTERVIEW BELOW IN THE REPLIES👇
1:10

Venezuela’s🇻🇪 Former Deputy Foreign Minister for North America: ‘Right-wing Latin American governments are sustained by repression, money and support from the US🇺🇸.’ ‘There’s this angry tide throughout the region that is the result of years of conservative movements trying to get hold of power in Latin America. And there’s a need for the left to look into how it could work together to put a hold on this neoconservative wave that is going to take Latin America into a deeper inequality than it has already been for the last several years.’ -Carlos Ron on the latest episode of Going Underground FULL INTERVIEW BELOW IN THE REPLIES👇

Going Underground

86,610 Aufrufe • vor 7 Monaten

.David Deutsch: “There is an inherent conflict in the human condition—both individually and in society—between the need to preserve existing knowledge and the need to create new knowledge. For most of human history, preserving existing knowledge trumped any kind of attempt to improve knowledge. Because attempts to improve knowledge risk error. Or rather, to be exact, it risks error that has never occurred before. There were plenty of errors that had occurred before. Plenty of kings got overthrown because they couldn’t solve the problem of how to fend off the enemy. But they were in familiar territory conceptually, and they were afraid of changing culture. This fear of changing culture was built into culture itself. And this conflict exists to this day. The most extreme example known to me in the West, anyway, is the conflict in education between preserving existing knowledge and creating new knowledge. Even today, education—education theory—is conceived as the theory of how to decant existing knowledge from the brains that already know it to the brains that do not yet know it. And that’s what Popper calls the ‘bucket theory of the mind’—that the mind is a bucket and knowledge is a fluid. What he stressed, and what I always also want to stress, is that not only is that a mischaracterization of knowledge and of the use of knowledge, but it is the wrong way around. As a practical matter, even right back two million years ago, the transmission of cultural knowledge was not something done by the transmitter; it was something done by the receiver.”
2:29

.David Deutsch: “There is an inherent conflict in the human condition—both individually and in society—between the need to preserve existing knowledge and the need to create new knowledge. For most of human history, preserving existing knowledge trumped any kind of attempt to improve knowledge. Because attempts to improve knowledge risk error. Or rather, to be exact, it risks error that has never occurred before. There were plenty of errors that had occurred before. Plenty of kings got overthrown because they couldn’t solve the problem of how to fend off the enemy. But they were in familiar territory conceptually, and they were afraid of changing culture. This fear of changing culture was built into culture itself. And this conflict exists to this day. The most extreme example known to me in the West, anyway, is the conflict in education between preserving existing knowledge and creating new knowledge. Even today, education—education theory—is conceived as the theory of how to decant existing knowledge from the brains that already know it to the brains that do not yet know it. And that’s what Popper calls the ‘bucket theory of the mind’—that the mind is a bucket and knowledge is a fluid. What he stressed, and what I always also want to stress, is that not only is that a mischaracterization of knowledge and of the use of knowledge, but it is the wrong way around. As a practical matter, even right back two million years ago, the transmission of cultural knowledge was not something done by the transmitter; it was something done by the receiver.”

Arjun Khemani

10,523 Aufrufe • vor 1 Jahr

🚨 Pep Guardiola on racism in football: "I said the week before. Not for where you were born or for the colour of your skin make us better or worse. There is a lot of work still to do. It is in the society, it is not [just] in football. Racism is everywhere. You pretend that racism is just for the colour of the skin? How you behave is racism. It's how you pretend you are better than the other one just for many reasons." How to improve the situation?: "The schools. Pay a lot to the teachers. That's how you deal with it. In the schools, not in football. The teachers and the doctors have to be the best, the most important people in society, by far. Not the managers."
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🚨 Pep Guardiola on racism in football: "I said the week before. Not for where you were born or for the colour of your skin make us better or worse. There is a lot of work still to do. It is in the society, it is not [just] in football. Racism is everywhere. You pretend that racism is just for the colour of the skin? How you behave is racism. It's how you pretend you are better than the other one just for many reasons." How to improve the situation?: "The schools. Pay a lot to the teachers. That's how you deal with it. In the schools, not in football. The teachers and the doctors have to be the best, the most important people in society, by far. Not the managers."

CaughtOffside

39,038 Aufrufe • vor 5 Monaten

Arthur Mensch confirmed on CNBC this week that OpenAI and Anthropic are calling Mistral asking for compute. The two companies racing to build the most powerful AI in the world are dependent on a European competitor they have been trying to outrun. Let that land for a second. The narrative you have been sold is that the US is running away with AI. That OpenAI and Anthropic have such a commanding lead that the rest of the world is playing catch-up. That narrative is missing something physical. The International Energy Agency projected this year that US data center electricity demand will double by 2030. The US grid is already at capacity. Europe has the power. Europe has the land. And right now the company fielding calls from the most funded AI labs on earth is a four-year-old French startup. Mensch said Mistral is prioritizing its own customers first. The surge in agentic usage is generating far more tokens than anyone planned for and existing capacity is spoken for. The US labs are in the queue. But they are in the queue. Most people following AI are watching benchmark scores and funding rounds. The actual constraint right now is electricity and physical space. And on that dimension, the scoreboard looks nothing like the one most people are reading. Watch the full podcast on YouTube at CNBC International
1:06

Arthur Mensch confirmed on CNBC this week that OpenAI and Anthropic are calling Mistral asking for compute. The two companies racing to build the most powerful AI in the world are dependent on a European competitor they have been trying to outrun. Let that land for a second. The narrative you have been sold is that the US is running away with AI. That OpenAI and Anthropic have such a commanding lead that the rest of the world is playing catch-up. That narrative is missing something physical. The International Energy Agency projected this year that US data center electricity demand will double by 2030. The US grid is already at capacity. Europe has the power. Europe has the land. And right now the company fielding calls from the most funded AI labs on earth is a four-year-old French startup. Mensch said Mistral is prioritizing its own customers first. The surge in agentic usage is generating far more tokens than anyone planned for and existing capacity is spoken for. The US labs are in the queue. But they are in the queue. Most people following AI are watching benchmark scores and funding rounds. The actual constraint right now is electricity and physical space. And on that dimension, the scoreboard looks nothing like the one most people are reading. Watch the full podcast on YouTube at CNBC International

Ihtesham Ali

47,092 Aufrufe • vor 1 Monat

Today is a glorious day. Not only for the Cubans on the island, but for every Cuban in exile, for every family torn apart, for every neighbor, every friend, every loved one who spent their life waiting to go home. For 67 years, a group of gangsters seized a nation and turned it into their business. But today, is the beginning of the end. The forces of freedom are coming, and the Cuba that was born free in 1902 is finding its way back.
4:03

Today is a glorious day. Not only for the Cubans on the island, but for every Cuban in exile, for every family torn apart, for every neighbor, every friend, every loved one who spent their life waiting to go home. For 67 years, a group of gangsters seized a nation and turned it into their business. But today, is the beginning of the end. The forces of freedom are coming, and the Cuba that was born free in 1902 is finding its way back.

Rep. María Elvira Salazar

364,196 Aufrufe • vor 2 Monaten

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.
5:50

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,607 Aufrufe • vor 7 Monaten

“I don’t think it’s exaggerating to say that this is the future of medicine.” For years, Dr. Rebecca Ahrens-Nicklas treated babies with rare genetic diseases knowing palliative care was often the only option. In 2023, she partnered with gene-editing researcher Dr. Kiran Musunuru to try something new: using CRISPR to correct disease-causing mutations before the damage became irreversible. In 2025, doctors delivered the first successful personalized CRISPR therapy, pointing to a future where gene therapies are custom-built for patients who once had no options. See the full TIME100 Health list of leaders, scientists, doctors, and more here:
1:23

“I don’t think it’s exaggerating to say that this is the future of medicine.” For years, Dr. Rebecca Ahrens-Nicklas treated babies with rare genetic diseases knowing palliative care was often the only option. In 2023, she partnered with gene-editing researcher Dr. Kiran Musunuru to try something new: using CRISPR to correct disease-causing mutations before the damage became irreversible. In 2025, doctors delivered the first successful personalized CRISPR therapy, pointing to a future where gene therapies are custom-built for patients who once had no options. See the full TIME100 Health list of leaders, scientists, doctors, and more here:

TIME

19,885 Aufrufe • vor 5 Monaten