The Uneven Distribution of AI’s Environmental Impacts
The training process for a single AI model, such as an LLM, can consume thousands of megawatt hours of electricity and emit hundreds of tons of carbon. AI model training can also lead to the evaporation of an astonishing amount of freshwater into the atmosphere for data center heat rejection, potentially exacerbating stress on our already limited freshwater resources. These environmental impacts are expected to escalate considerably, and there remains a widening disparity in how different regions and communities are affected. The ability to flexibly deploy and manage AI computing across a network of geographically distributed data centers offers substantial opportunities to tackle AI’s environmental inequality by prioritizing disadvantaged regions and equitably distributing the overall negative environmental impact.
OpenAI’s Sam Altman is becoming one of the most powerful people on Earth. We should be very afraid
Sam Altman’s ChatGPT promises to transform the global economy. But it also poses an enormous threat. Here, a scientist who appeared with Altman before the US Senate on AI safety flags up the danger in AI – and in Altman himself
Related storiesRelated storiesScarlett Johansson says OpenAI’s Sam Altman would make a good Marvel villain after voice dispute18 Jul 2024OpenAI co-founder who had key role in attempted firing of Sam Altman departs15 May 2024OpenAI reinstates CEO Sam Altman to board after firing and rehiring9 Mar 2024OpenAI boss Sam Altman wants $7tn. For all our sakes, pray he doesn’t get it17 Feb 2024138138 commentsChatGPT exploded into public life a year ago. Now we know what went on behind the scenes9 Dec 2023162162 commentsMicrosoft to join OpenAI’s board after Sam Altman rehired as CEO30 Nov 2023‘Huge egos are in play’: behind the firing and rehiring of OpenAI’s Sam Altman23 Nov 2023Ousted OpenAI CEO Sam Altman ‘in talks to return at firm’s HQ’19 Nov 2023
The Regeneration Handbook: From burnout to balance - resilience
Even if we’re able to embrace our sense of purpose, that initial spark of inspiration can burn out over time if we’re not careful. If we want to be able to follow our path all the way to its fruition, we must learn how to take good care of ourselves.
Clean Laundry - resilience
So take pleasure in those small things. Savor everything good about them. And open yourself, your one and only living body, to the joy of doing things that bring you pleasure, knowing that true pleasure is in the relationship between your body and the enfolding world — with no synthetic monetary mediation between the labor and the loving happiness.
New Sustainable Web Design Model changes the context of internet emissions - Wholegrain Digital
As we wrote last week, there are some major changes coming to the way internet emissions are calculated. With improved data sources and research we now have more accurate data to work with. At Wholegrain we’ve always compared internet emissions to those from other sectors and countries and we have been busy updating them inline […]
Will Generative AI Implode and Become More Sustainable?
Generative AI has a Sustainability problem – across environment, cost and continuing to meet expectations. Many companies are racing to implement GenAI in their projects, lured by its hyped potential to revolutionise industries. However, in applying GenAI to enterprise implementations, I am seeing first-hand the sustainability challenges threatening to implode the first generation of this technology. This blog talks about what I hope will rise from the ashes of such an implosion.
Advanced Techniques for Enhancing Product Quality with LLM: A Guide to Retrieval-Augmented Generation (RAG) - GoPractice
Explore advanced methods for boosting product quality using large language models (LLMs), focusing on Retrieval-Augmented Generation (RAG). Learn how integrating search results can enhance your product's features and user experience.
How To Design Effective Conversational AI Experiences: A Comprehensive Guide — Smashing Magazine
This in-depth guide takes you through the three crucial phases of conversational search, revealing how users express their needs, explore results, and refine their queries. Learn how AI agents can overcome communication barriers, personalize the search experience, and adapt to evolving user intent.
