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Category: supercomputing

Elon’s Cryptic Post Sparks Big Questions

Questions to inspire discussion.

🌐 Q: What distinguishes embedded AI from language models like ChatGPT? A: Embedded AI interacts with the real world, while LLMs (Large Language Models) primarily answer questions based on trained information.

Chip Production and Supply.

💻 Q: What are Samsung’s plans for chip production in Texas? A: Samsung’s new Texas chip plant will produce 2nm chips with 16,000 wafers/month by the end of 2024, boosted by a $16B Tesla deal.

🔧 Q: How will the Samsung-Tesla deal impact Tesla’s chip supply? A: The deal will significantly boost Tesla’s chip supply, producing 17,000 wafers per month of 2 nanometer chips reserved solely for Tesla.

AI Infrastructure and Applications.

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Elon Musk Pushes Tesla Forward

Questions to inspire discussion.

📷 Q: What camera technology does the Optimus bot use? A: Optimus uses car cameras with macro modes for reading small text, supplied by Simco (a Samsung division), featuring a miniaturized camera assembly with internal movement mechanisms.

Tesla AI and Chip Development.

🧠 Q: How does Tesla’s AI5 chip compare to competitors? A: The AI5 chip is potentially the best inference chip for models under 250 billion parameters, offering the lowest cost, best performance per watt, and is milliseconds faster than competitors.

💻 Q: What advantages does Tesla have in chip development? A: Tesla controls the chip design, silicon talent, and has vertical integration, giving them a significant edge over competitors in AI chip development.

Tesla Product and Business Updates.

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Elon Musk’s Drops Hints About His Next Master Plan

Questions to inspire discussion.

AI and Supercomputing Developments.

🖥️ Q: What is XAI’s Colossus 2 and its significance? A: XAI’s Colossus 2 is planned to be the world’s first gigawatt-plus AI training supercomputer, with a non-trivial chance of achieving AGI (Artificial General Intelligence).

⚡ Q: How does Tesla plan to support the power needs of Colossus 2? A: Elon Musk plans to build power plants and battery storage in America to support the massive power requirements of the AI training supercomputer.

💰 Q: What is Musk’s prediction for universal income by 2030? A: Musk believes universal high income will be achieved, providing everyone with the best medical care, food, home, transport, and other necessities.

🏭 Q: How does Musk plan to simulate entire companies with AI? A: Musk aims to simulate entire companies like Microsoft with AI, representing a major jump in AI capabilities but limited to software replication, not complex physical products.

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What came before the Big Bang? Supercomputers may hold the answer

Scientists are rethinking the universe’s deepest mysteries using numerical relativity, complex computer simulations of Einstein’s equations in extreme conditions. This method could help explore what happened before the Big Bang, test theories of cosmic inflation, investigate multiverse collisions, and even model cyclic universes that endlessly bounce through creation and destruction.

Simulations reveal pion’s interaction with Higgs field with unprecedented precision

With the help of innovative large-scale simulations on various supercomputers, physicists at Johannes Gutenberg University Mainz (JGU) have succeeded in gaining new insights into previously elusive aspects of the physics of strong interaction.

Associate Professor Dr. Georg von Hippel and Dr. Konstantin Ottnad from the Institute of Nuclear Physics and the PRISMA+ Cluster of Excellence have calculated the interaction of the pion with the Higgs field with unprecedented precision based on . Their findings were recently published in Physical Review Letters.

Mitsui Works With Quantinuum and QSimulate to Launch Quantum-Integrated Chemistry Platform

Mitsui & Co. has formally launched a new quantum-enabled chemistry platform, QIDO, in collaboration with U.S.-based Quantinuum and QSimulate. The system, designed to accelerate the discovery of new materials and pharmaceuticals, blends classical and quantum computing resources to streamline complex chemical calculation, according to a story in Nikkei and a Quantinuum blog post.

Quantum computers hold promise for modeling chemical reactions beyond the reach of traditional supercomputers. But fully fault-tolerant systems remain years away, leaving companies searching for ways to extract value from today’s noisy, early-stage machines. QIDO, short for Quantum-Integrated Discovery Orchestrator, attempts to bridge that gap.

The platform runs most computations on powerful classical hardware while sending only the most computationally expensive steps — such as the modeling of strongly correlated electrons — to a quantum computer. This hybrid workflow allows companies to perform higher-precision chemical simulations today, without waiting for fully mature quantum systems, Nikkei reports.

New AI model advances fusion power research by predicting the success of experiments

Practical fusion power that can provide cheap, clean energy could be a step closer thanks to artificial intelligence. Scientists at Lawrence Livermore National Laboratory have developed a deep learning model that accurately predicted the results of a nuclear fusion experiment conducted in 2022. Accurate predictions can help speed up the design of new experiments and accelerate the quest for this virtually limitless energy source.

In a paper published in Science, researchers describe how their AI model predicted with a probability of 74% that ignition was the likely outcome of a small 2022 fusion experiment at the National Ignition Facility (NIF). This is a significant advance as the model was able to cover more parameters with greater precision than traditional supercomputers.

Currently, nuclear power comes from nuclear fission, which generates energy by splitting atoms. However, it can produce radioactive waste that remains dangerous for thousands of years. Fusion generates energy by fusing atoms, similar to what happens inside the sun. The process is safer and does not produce any long-term radioactive waste. While it is a promising energy source, it is still a long way from being a viable commercial technology.

A smart accelerator for qubits: Spin-orbit approach boosts both speed and stability

There are high hopes for quantum computers: they are supposed to perform specific calculations much faster than current supercomputers and, therefore, solve scientific and practical problems that are insurmountable for ordinary computers. The centerpiece of a quantum computer is the quantum bit, qubit for short, which can be realized in different ways—for instance, using the energy levels of atoms or the spins of electrons.

When making such qubits, however, researchers face a dilemma. On the one hand, a qubit needs to be isolated from its environment as much as possible. Otherwise, its quantum superpositions decay in a short time and the quantum calculations are disturbed. On the other hand, one would like to drive qubits as fast as possible in analogy with the clocking of classical bits, which requires a strong interaction with the environment.

Normally, these two conditions cannot be fulfilled at the same time, as a higher driving speed automatically entails a faster decay of the superpositions and, therefore, a shorter coherence time.

New AI Model May Predict Success Of Future Fusion Experiments, Saving Money And Fuel

What this means in real time is that researchers using these maps do not know if there are any errors or issues ahead of them, nor do they know if these errors are part of the research design. Nevertheless, this is all they have to work with, so they have to make a decision based on this limited information, and doing so will always have high costs in terms of the ignition attempt, which is expensive.

To overcome this, the team at the NIF created a new way to create these “maps” by merging past data with high-fidelity physics simulations and the knowledge of experts. This was then fed into a supercomputer that ran statistical assessments in the course of over 30 million CPU hours. Effectively, this allows the researchers to see all the ways that things can go wrong and to pre-emptively assess their experimental designs. This saves a lot of time and, more importantly, money.

The team tested this approach on an experiment they ran in 2022, and, after a few changes to the model’s physics, was able to predict the outcome with an accuracy above 70 percent.

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