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The Hidden Environmental Cost of AI: Data Centres' Surging Energy and Water Consumption

 In recent years, artificial intelligence (AI) has become an integral part of our daily lives, powering everything from smart assistants to complex data analysis. However, as AI technologies continue to advance and proliferate, a concerning trend has emerged: the rapidly increasing energy and water consumption of data centres that support these systems. The Power Hunger of AI According to the International Energy Agency (IEA), global data centre electricity demand is projected to more than double between 2022 and 2026, largely due to the growth of AI. In 2022, data centres consumed approximately 460 terawatt-hours (TWh) globally, and this figure is expected to exceed 1,000 TWh by 2026. To put this into perspective, that's equivalent to the entire electricity consumption of Japan. The energy intensity of AI-related queries is particularly striking. While a typical Google search uses about 0.3 watt-hours (Wh), a query using ChatGPT requires around 2.9 Wh - nearly ten times more en
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Claude 3.5 Sonnet, literary analysis capabilities, an experiment

 There are those in the AI observer community that have been suggesting of late that 'AI has plateaued' which reveals, to me, a lack of understanding of how models develop. It's not iterative design but step changes we are witnessing. The differences between Claude 3 Sonnet and 3.5 Sonnet are stark.  One test I often carry out to asses the current capabilities of LLMs is to request a simple prompt to analyse an unpublished poem, commentating on the style and literary devices employed. The outputs have improved significantly over the 18 months I have employed this approach. This is my recent attempt. The poem is self written, self published (so not widely available) to ensure that it's unlikely to have found its way into the training data. For added context, implied in the text, this was written during a residency at a Museum and Art Gallery and came from a conversation with a member of staff about his late father. Calm is museum archival software. PROMPT. "analyse
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The Future of Work in the Age of AGI: Opportunities, Challenges, and Resistance

 In recent years, the rapid advancement of artificial intelligence (AI) has sparked intense debate about the future of work. As we edge closer to the development of artificial general intelligence (AGI), these discussions have taken on a new urgency. This post explores various perspectives on employment in a post-AGI world, including the views of those who may resist such changes. It follows on from others I've written on the impacts of these technologies. The Potential for Widespread Job Displacement Avital Balwit, an employee at Anthropic, argues in her article " My Last Five Years of Work " that AGI is likely to cause significant job displacement across various sectors, including knowledge-based professions. This aligns with research by Korinek (2024), which suggests that the transition to AGI could trigger a race between automation and capital accumulation, potentially leading to a collapse in wages for many workers. Emerging Opportunities and Challenges Despite the
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Claude 3.5 Sonnet, beats out OpenAI and NVIDIA and Synthetic Data

Claude 3,5 The New 'Best' Model Anthropic announced yesterday the launch of Claude 3.5 Sonnet, it's latest AI model. Claude 3.5 Sonnet boasts superior benchmarks, outperforming competitors and previous versions in reasoning, knowledge, coding, and content creation. Its enhanced speed and cost-effectiveness makes it a real alternative to OpenAI models. Key improvements include advanced vision capabilities, enabling tasks like chart interpretation and image transcription. A new "Artifacts" feature transforms Claude into a collaborative workspace, allowing real-time interaction with AI-generated content. Anthropic emphasises its commitment to safety and privacy, highlighting rigorous testing, external evaluations, and a policy that prioritises user privacy. Anthropic concludes by teasing upcoming releases and features, including new models and a "Memory" function, demonstrating Anthropic's commitment to continuous improvement based on user feedback. NV
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Can We Build a Safe Superintelligence? Safe Superintelligence Inc. Raises Intriguing Questions

  Safe Superintelligence Inc . (SSI) has burst onto the scene with a bold mission: to create the world's first safe superintelligence (SSI). Their (Ilya Sutskever, Daniel Gross, Daniel Levy) ambition is undeniable, but before we all sign up to join their "cracked team," let's delve deeper into the potential issues with their approach. One of the most critical questions is defining "safe" superintelligence. What values would guide this powerful AI? How can we ensure it aligns with the complex and often contradictory desires of humanity?  After all, "safe" for one person might mean environmental protection, while another might prioritise economic growth, even if it harms the environment.  Finding universal values that a superintelligence could adhere to is a significant hurdle that SSI hasn't fully addressed. Another potential pitfall lies in SSI's desire to rapidly advance capabilities while prioritising safety.  Imagine a Formula One car wi
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Enhancing LLM Performance: Buffer of Thought and Mixture of Agents

  As Large Language Models (LLMs) continue to advance, researchers are exploring innovative techniques to further enhance their accuracy and usefulness. Two promising approaches in this domain are Buffer of Thought and Mixture of Agents. Buffer of Thoughts The Buffer of Thoughts  technique aims to improve the reasoning capabilities of LLMs by introducing an intermediate step in the generation process. Instead of directly producing the final output, the model first generates a "buffer" or a series of intermediate thoughts, which serve as a scratchpad for the model to reason and plan its response. This buffer allows the model to break down complex tasks into smaller steps, perform multi-step reasoning, and maintain a coherent line of thought throughout the generation process. By externalizing its thought process, the model can better organise its knowledge and arrive at more logical and consistent outputs. The BoT approach has shown promising results in tasks that require multi
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Prompt Engineering: Expert Tips for a variety of Platforms

  Prompt engineering has become a crucial aspect of harnessing the full potential of AI language models. Both Google and Anthropic have recently released comprehensive guides to help users optimise their prompts for better interactions with their AI tools. What follows is a quick overview of tips drawn from these documents. And to think just a year ago there were countless YouTube videos that were promoting 'Prompt Engineering' as a job that could earn megabucks... The main providers of these 'chatbots' will hopefully get rid of this problem, soon. Currently their interfaces are akin to 1970's command lines, we've seen a regression in UI. Constructing complex prompts should be relegated to Linux lovers. Just a word of caution, even excellent prompts don't stop LLM 'hallucinations'. They can be mitigated against by supplementing a LLM with a RAG, and perhaps by 'Memory Tuning ' as suggested by Lamini (I've not tested this approach yet).  
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