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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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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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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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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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AI tools for business: Top 6 considerations before building with AI models and LLMs

AssemblyAI

The use of AI in business applications is exploding, with AI expected to boost U.S. Gross Domestic Product (GDP) by 2030. Generative AI tools like ChatGPT, which had over a million users within its first week of availability , have been especially popular. Will the AI model or LLM and/or partner be able to grow with us?

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Google’s dilemma: AI expansion vs achieving climate goals

AI News

The tech giant has pledged to operate on 24/7 carbon-free energy by 2030, aiming to set a precedent for the industry. AI technologies , especially those that involve deep learning and large language models, are notoriously energy-intensive. Still, more must be done to optimise AI algorithms’ energy efficiency. .”

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Harnessing AI for Climate Change Mitigation: Predictive Analytics and Modeling

Aiiot Talk

Combined with sensors, AI models discover demand patterns and predict how to optimize resources for the future. Models achieve this by simulating the impact of renewable energy while considering their potential expansion. However, innovation in the AI space is critical for balance.