IBM & NASA Announce AI For Climate Change

PLUS: PyTorch unveils major speed and efficiency gains for large language models.

Welcome! Today is Friday, December 1st and in today's issue we have predictions on AI integration over the next year and IBM teaming up with NASA to apply generative models to climate challenges. New to The Intelligence Age? Sign up here.
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News & Insights

What Year 2 Of The Generative AI Craze Will Look Like, According To 41 Experts

In the realm of generative AI, the forecast is clear: integration and sophistication. The consensus among 41 luminaries is that year two is about deepening AI's roots into corporate workflows and consumer experiences. Ben Bajarin emphasizes the impending revolution in employee productivity through domain-specific large language models. Meanwhile, Jeremiah Owyang envisions a digital life transformed, with AI managing our communications, evolving to respond through a learned understanding of rules and context.

The trajectory for generative AI goes beyond mere efficiency. It's about enhancing the human experience—whether it's easing the burden on healthcare professionals, as Oded Netzer suggests, or revolutionizing customer support, a vision shared by Rob Enderle. These AI advancements aren't just for the tech giants; they're democratizing capabilities, enabling small and medium-sized businesses to compete on a level playing field. fastcompany

IBM Extends NASA Collaboration To Apply Generative AI To Weather

IBM's collaboration with NASA takes a significant step forward with the development of a new AI foundation model for weather and climate. This model, announced at COP28, is not just a technical feat but a leap towards tangible climate action. Dr. Juan Bernab-Moreno emphasizes the importance of open-source access, stating, "It's vital to support open source... the community is going to help us make the simulations even better."

The model's applications are far-reaching. From predicting extreme weather events to aiding reforestation efforts, it's set to become an essential tool for climate experts worldwide. The success in Kenya, where AI assisted in quantifying carbon sequestration, showcases the model's potential impact. Bernab-Moreno captures the essence: "We need to be able to quantify how successful these measures are... it will encourage people to do more." siliconrepublic

Why does this matter?

Beyond weather forecasting, could this AI model serve as a benchmark for environmental accountability? If it can quantify carbon capture, might it also evolve to assess the ecological footprint of large corporations, influencing global sustainability policies?

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Practical AI

Investing In AI

In the AI investment landscape, myths can be costly. Spencer Greene highlights a key misconception: First-mover advantage. It's not the golden ticket. History is riddled with pioneers overtaken by latecomers—think AltaVista and Google. In AI, being first might even be a handicap, as early players often pave the way for successors at their own expense. As Greene points out, startups boasting about being first should be met with skepticism; longevity requires more than just early arrival.

Another myth busted: An exciting product does not guarantee a lucrative business. Netscape's tale is a stark reminder—innovation alone doesn't spell success. The real profits often lie in less glamorous ventures. AI's flashy demos are intoxicating, but profitability doesn't necessarily follow coolness. Investors must look beyond the sparkle to find sustainable business models. Remember, "A lot of it is not going to make money." crunchbase

Around The World

More News

  • Pepper Bio, a drug discovery startup, secures fresh investment to combat the escalating cost and time associated with pharmaceutical development, aiming to overturn Eroom's law which observes that drug discovery becomes slower and more expensive over time.

  • Meta Implements Global Ban on AI-Generated Political Ads to Uphold Electoral Integrity.

  • Microsoft Announces £2.5 Billion Investment in UK to Accelerate Artificial Intelligence Development.

Trending Today

Research Review

The PyTorch team unveils groundbreaking optimizations in PyTorch for generative AI, specifically focusing on large language models (LLMs).

They introduce an LLM that's nearly 10x faster than the baseline with no accuracy loss, achieved through native PyTorch enhancements. Key optimizations include a new compiler for PyTorch models, GPU quantization for faster model operations, speculative decoding for efficiency, and tensor parallelism across multiple devices.

They’ve also managed these improvements with less than 1000 lines of native PyTorch code. The post not only showcases these advancements but also provides practical examples, demonstrating PyTorch's potential in pushing the boundaries of AI performance. This development marks a significant leap in efficient AI model training and inference, offering new avenues for AI applications and research. Read the blog.

Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.

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