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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 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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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.

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Blockchain could solve the monopolised AI ecosystem

AI News

This growth is expected to continue at a rapid pace into the last years of the decade, with Statista predicting the $184 billion industry will grow to nearly $900 billion by 2030. As such, several developers around the world are working on solutions that build sustainable AI models, without big tech firms’ prying eye on our personal data.

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From Internet of Things to Internet of Everything: The Convergence of AI & 6G for Connected Intelligence

Unite.AI

trillion by 2030. It facilitates a higher level of interconnectivity by seamlessly combining numerous technologies, like cloud computing, edge computing, AI, IoT, 6G, and data analytics, along with various gadgets, sensors, and machines to gather, transmit, and analyze data in real-time. billion by 2030, compared to $928.11

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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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How AI is making electric vehicles safer and more efficient

IBM Journey to AI blog

The survey also found that consumer adoption is at a tipping point , with industry executives expecting EVs to account for 40% of car sales by 2030, largely due to EVs becoming cheaper. Automakers can also use advanced algorithms to determine the specific chemistry, size and shape that leads to the best performance and more sustainable cars.