For example: “If you’re worried about immigration, you should be way more concerned about AI” — for the impact on jobs, cultural stability, and social predictability.
Ex-Google Insider and AI Expert TRISTAN HARRIS reveals how ChatGPT, China, and Elon Musk are racing to build uncontrollable AI, and warns it will blackmail humans, hack democracy, and threaten jobs…by 2027.
Tristan Harris is a former Google design ethicist and leading voice from Netflix’s The Social Dilemma. He is also co-founder of the Center for Humane Technology, where he advises policymakers, tech leaders, and the public on the risks of AI, algorithmic manipulation, and the global race toward AGI.
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He explains:
With its largest advancement since the NVIDIA CUDA platform was invented in 2006, CUDA 13.1 is launching NVIDIA CUDA Tile. This exciting innovation introduces a virtual instruction set for tile-based parallel programming, focusing on the ability to write algorithms at a higher level and abstract away the details of specialized hardware, such as tensor cores.
CUDA exposes a single-instruction, multiple-thread (SIMT) hardware and programming model for developers. This requires (and enables) you to exhibit fine-grained control over how your code is executed with maximum flexibility and specificity. However, it can also require considerable effort to write code that performs well, especially across multiple GPU architectures.
There are many libraries to help developers extract performance, such as NVIDIA CUDA-X and NVIDIA CUTLASS. CUDA Tile introduces a new way to program GPUs at a higher level than SIMT.
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*GUEST BIO:*
Michael Levin is a biologist at Tufts University working on novel ways to understand and control complex pattern formation in biological systems.
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- Biological Robots: https://arxiv.org/abs/2207.00880
- Classical Sorting Algorithms: https://arxiv.org/abs/2401.05375
- Aging as a Morphostasis Defect: https://pubmed.ncbi.nlm.nih.gov/38636560/
- TAME: https://arxiv.org/abs/2201.10346
- Synthetic Living Machines: https://www.science.org/doi/10.1126/scirobotics.abf1571
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Chemical rockets have taken us to the moon and back, but traveling to the stars demands something more powerful. Space X’s Starship can lift extraordinary masses to orbit and send payloads throughout the solar system using its chemical rockets, but it cannot fly to nearby stars at 30% of light speed and land. For missions beyond our local region of space, we need something fundamentally more energetic than chemical combustion, and physics offers, or, in other words, antimatter.
When antimatter encounters ordinary matter, they annihilate completely, converting mass directly into energy according to Einstein’s equation E=mc². That c² term is approximately 10¹⁷, an almost incomprehensibly large number. This makes antimatter roughly 1,000 times more energetic than nuclear fission, the most powerful energy source currently in practical use.
As a source of energy, antimatter can potentially enable spacecraft to reach nearby stars at significant fractions of the speed of light. A detailed technical analysis by Casey Handmer, CEO of Terraform Industries, outlines how humanity could develop practical antimatter propulsion within existing spaceflight budgets, requiring breakthroughs in three critical areas; production efficiency, reliable storage systems, and engine designs that can safely harness the most energetic fuel physically possible.
An international team of researchers has unveiled a spacecraft attitude control system that can guarantee precise stabilization and maneuvering within a predefined time, even under extreme and unpredictable space disturbances.
Published in IEEE Transactions on Industrial Electronics, the study titled “Predefined-Time Disturbance Observer-Based Attitude Tracking Control for Spacecraft: A Solution for Arbitrary Disturbances” was led by Dr. Nguyen Xuan-Mung of Sejong University (South Korea), alongside colleagues from China and Taiwan.
As artificial intelligence devours electricity, a quiet nuclear revolution is taking shape deep below future data centers.
Across Europe, tech firms are staring at an uncomfortable equation: soaring digital demand, power grids near saturation, and climate goals that leave little room for more fossil fuels. A young French company now claims it can rewrite that equation with a compact reactor that hides underground and feeds on nuclear waste.
A Japanese research team from Keio University demonstrated that a quantum algorithm can solve a core metabolic-modeling problem, marking one of the earliest applications of quantum computing to a biological system. The study shows quantum methods can map how cells use energy and resources.
Flux balance analysis is a method widely used in systems biology to estimate how a cell moves material through metabolic pathways. It treats the cell as a network of reactions constrained by mass balance laws, finding reaction rates that maximize biological objectives like growth or ATP production.
No. The demonstration ran on a simulator rather than physical hardware, though the model followed the structure of quantum machines expected in the first wave of fault-tolerant systems. The simulation used only six qubits.