| | | | Your daily update on artificial intelligence frameworks and leadership strategies, enriched with knowledge from India's forefront AI innovators and leaders. |
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| Hello there! It’s me, your friendly AI Human, Amit Raja Naik, back with another exciting new newsletter. Happy reading. 🤗 |
| | | A short while ago, Microsoft and OpenAI announced Stargate, a groundbreaking AI supercomputer data centre project costing over $115 billion that will be launched in 2028. Additionally, AWS plans to invest $150 billion in data centres over the next 15 years to accommodate the increasing demand. |
| | | Design: Nikhil Kumar NVIDIA turns data centres into AI factories “It’s certainly a good time to be NVIDIA,” declared xAI chief Elon Musk in a recent interview with Peter Diamandis during the 2024 Abundance360 Summit. Musk said that AI compute is growing exponentially, increasing by a factor of 10 every six months, and most data centres are transitioning from conventional to AI compute. AI factories are most certainly just a step in that direction. NVIDIA is clearly leading the AI revolution. At the recently concluded GTC 2024, CEO Jensen Huang compared data centres to factories from the industrial revolution. He emphasised the necessity, stating, “Anyone developing chatbots and generative AI will require an AI factory.” |
| Speaking of Huang, he recently mentioned that he uses Perplexity — “almost every day”. Two months ago, the company gave AIM free access to its Perplexity Pro, and this is what we feel about it. |
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| | | Design: Nikhil Kumar AI factories struggle The AI revolution, powered by Blackwell, is not without challenges. As computational demands soar, the data centre industry faces mounting pressure to enhance energy efficiency and meet renewable energy goals. Pedro Domingos, a professor of computer science at the University of Washington, also highlighted in a post on X how data centres seem to have become uni-focused rather than multi-focused after the popularisation of generative AI models. This trend signals increasing data centre infrastructure for each use case or function, which could prove challenging. Hence, experts have indicated a need to move to low-cost data centres powered by renewable energy, which wouldn’t be a burden. |
| “If you’re not building data centres adjacent to low-cost renewable power, you’re not going to have a fruitful conversation with the hyperscalers and cloud players,” saidMarc Ganzi, CEO of DigitalBridge. |
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| Transformers for Transformers Musk said that last year, the primary constraint was AI chips. “This year, however, one of the major constraints, if not the biggest, is voltage step-down transformers. The challenge lies in reducing the power from a utility’s 300 kilovolts to less than one volt for computers,” he added. |
| He said that this massive voltage reduction is critical. On a lighter note, he said it is almost as if we need “transformers for the transformers” — voltage transformers for our AI’s neural net transformers. “That’s the main issue we’re facing this year,” he added. |
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| Looking ahead, the constraint is likely to be on the availability of electrical power. Musk said with AI’s substantial power demands, and the shift to sustainable energy, especially electric vehicles, the need for electrical power is becoming increasingly significant. What is the solution? One of the authors of Transformer, Jakob Uszkroeit, thinks that the solution to the computation problem is the right allocation. “It’s really about spending the right amount of effort and ultimately energy.” He said that the researchers should avoid overspending on simple problems or underspending on complex ones, as this leads to inadequate solutions. “Consider the basic example of calculating "two plus two”. If you input this into a powerful computer, the machine, which is perfectly capable of solving it, may expend an unnecessary amount of resources,” he added. He further suggested the need for adaptive computation, which tailors the computational resources allocated to the complexity of a problem. He said adaptive computation is essential for efficient problem-solving, ensuring that resources are used effectively for each specific task. |
| | | | | | | Are we entering the era of ‘LLM pollution’? |
| | | Design: Nikhil Kumar Last week alone, we witnessed two new models – DBRX and Jamba – and many are mushrooming. The sheer volume of models produced is outpacing the development of meaningful extensions and practical applications. While these efforts are commendable, they fail to address the core issue of widespread LLM adoption. Read the full story here. |
| | | | | | | | | Enjoying AIM Daily XO? Share it with colleagues or friends – they can sign up here. We love hearing from our readers! Have thoughts on our new format? Questions, comments, or ideas are always welcome. If there’s a specific topic in AI or analytics that you're curious about, tell us! Reach out to us at info@analyticsindiamag.com. Stay tuned for more insights in our next edition! Cheers! Amit Raja Naik |
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| | | AIM Daily XO is published by AIM Media House, a global Media and Analyst Firm dedicated to Artificial Intelligence |
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