Lifeboat Foundation

As artificial intelligence (AI) becomes increasingly ubiquitous in business and governance, its substantial environmental impact — from significant increases in energy and water usage to heightened carbon emissions — cannot be ignored. By 2030, AI’s power demand is expected to rise by 160%. However, adopting more sustainable practices, such as utilizing foundation models, optimizing data processing locations, investing in energy-efficient processors, and leveraging open-source collaborations, can help mitigate these effects. These strategies not only reduce AI’s environmental footprint but also enhance operational efficiency and cost-effectiveness, balancing innovation with sustainability.

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Practical steps for reducing AI’s surging demand for water and energy.

As part of the 2024 Prostate Cancer Patient Conference, Dr. Eric Small discusses systemic therapy treatment in advanced prostate cancer, including AR-targeted therapy. The presentation includes definitions of disease states, categories of treatment types, and standards in treatment selection.
Recorded on 03/09/2024. [Show ID: 39768]

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Continue reading “Systemic Therapy Approaches for Advanced Prostate Cancer” »

Tech companies, including Amazon Web Services, are striking deals with U.S. nuclear power plants to secure electricity for their data centers, driven by the skyrocketing demands of artificial intelligence. This move promises 24/7 carbon-free power but stirs controversy, as it could divert existing energy supplies, raise prices, and increase reliance on natural gas. These nuclear-powered data centers might accelerate the AI race, but they also spark debates over economic development, grid reliability, and climate goals. Could this be the future of tech or a risky gamble with unforeseen consequences?

As reported by WSJ, tech businesses searching the country for electrical supplies have focused on one important target: America’s nuclear power facilities.

The owners of about one-third of the United States’ nuclear power reactors are in negotiations with technology companies about providing electricity to new data centers needed to satisfy the needs of an artificial intelligence boom.

Kratos Defense & Security Solutions (Kratos) has announced the successful test flight of its Erinyes hypersonic test vehicle.

Developed by the company’s Space & Missile Defense Systems Business Unit, the test was completed on June 12, 2024, according to the announcement.

Continue reading “Kratos’ Erinyes hypersonic test vehicle reaches Mach 5 in 1st flight” »

With the pending arrival of AI agents, we will even more effectively join the always-on interconnected world, both for personal use and for work. In this way, we will increasingly dialog and interact with digital intelligence everywhere.

The path to AGI and superintelligence remains shrouded in uncertainty, with experts divided on its feasibility and timeline. However, the rapid evolution of AI technologies is undeniable, promising transformative advancements. As businesses and individuals navigate this rapidly changing landscape, the potential for AI-driven innovation and improvement remains vast. The journey ahead is as exciting as it is unpredictable, with the boundaries between human and artificial intelligence continuing to blur.

By mapping out proactive steps now to invest and engage in AI, upskill our workforce and attend to ethical considerations, businesses and individuals can position themselves to thrive in the AI-driven future.

As new AI models make their way into the mainstream, and business leaders scramble to adapt, one sometimes overlooked aspect of computing is the measure of conservation.

In an age where efficiency matters, teams are trying to provide new frameworks for compute.

Starlink has landed what is likely its biggest partnership yet as it signed recently to provide a prominent company with internet service.

Brighter with Herbert.

Large language models have emerged as a transformative technology and have revolutionized AI with their ability to generate human-like text with seemingly unprecedented fluency and apparent comprehension. Trained on vast datasets of human-generated text, LLMs have unlocked innovations across industries, from content creation and language translation to data analytics and code generation. Recent developments, like OpenAI’s GPT-4o, showcase multimodal capabilities, processing text, vision, and audio inputs in a single neural network.

Despite their potential for driving productivity and enabling new forms of human-machine collaboration, LLMs are still in their nascent stage. They face limitations such as factual inaccuracies, biases inherited from training data, lack of common-sense reasoning, and data privacy concerns. Techniques like retrieval augmented generation aim to ground LLM knowledge and improve accuracy.

To explore these issues, I spoke with Amir Feizpour, CEO and founder of AI Science, an expert-in-the-loop business workflow automation platform. We discussed the transformative impacts, applications, risks, and challenges of LLMs across different sectors, as well as the implications for startups in this space.