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Big tech’s AI spending hits new heights

AI News

A growing market: AI is projected to create $20 trillion in global economic impact by 2030. In countries like India, AI could contribute $500 billion to GDP by 2025. Infrastructure demands: Training and running AI models require massive investment in infrastructure, from data centres to high-performance GPUs.

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Keeping an AI on Diabetes Risk: Gen AI Model Predicts Blood Sugar Levels Four Years Out

NVIDIA

A generative AI model can now predict the answer. and NVIDIA led the development of GluFormer , an AI model that can predict an individual’s future glucose levels and other health metrics based on past glucose monitoring data. trillion globally by 2030. billion people.

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AI in 2025: Purpose-driven models, human integration, and more

AI News

For this article, AI News caught up with some of the worlds leading minds to see what they envision for the year ahead. Smaller, purpose-driven models Grant Shipley, Senior Director of AI at Red Hat , predicts a shift away from valuing AI models by their sizeable parameter counts.

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The hidden climate cost of AI: How tech giants are struggling to go green

AI News

Training large language models like GPT-3 requires vast amounts of data to be processed by thousands of specialized chips running around the clock in sprawling data centres. Once deployed, AI models consume significant energy with each query or task. That goal now appears increasingly challenging.

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The merging of AI and blockchain was inevitable – but what will it mean?

AI News

To this point, a report from the International Energy Agency (IEA) states that the global electricity demand for AI is projected to rise to 800 TWh by 2026 , a nearly 75% increase from 460 TWh in 2022. Morgan Stanley’s AI power consumption prediction (best-case scenario) The best of both worlds is here.

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Synthetic Data: A Double-Edged Sword for the Future of AI

Unite.AI

The rapid growth of artificial intelligence (AI) has created an immense demand for data. Traditionally, organizations have relied on real-world datasuch as images, text, and audioto train AI models. According to Gartner , synthetic data is expected to become the primary resource for AI training by 2030.

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DeepMind Introduces JEST Algorithm: Making AI Model Training Faster, Cheaper, Greener

Unite.AI

Although these advancements have driven significant scientific discoveries, created new business opportunities, and led to industrial growth, they come at a high cost, especially considering the financial and environmental impacts of training these large-scale models. Financial Costs: Training generative AI models is a costly endeavour.

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