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Category: robotics/AI – Page 469

David Spivak: Pioneering Math for Understanding Reality | AGI-24 Keynote Preview

Mathematics application to a new understanding thd world and life and information.

Dr. David Spivak introduces himself as a keynote speaker at the 17th Annual Artificial General Intelligence Conference in Seattle and shares his lifelong passion for math. He discusses his journey from feeling insecure about the world as a child, to grounding his understanding in mathematics.

Dr. Spivak is the Secretary of the Board at the Topos Institute and on the Topos staff as Senior Scientist and Institute Fellow, following an appointment as founding Chief Scientist. Since his PhD from UC Berkeley in 2007, he has worked to bring category-theoretic ideas into science, technology, and society, through novel mathematical research and collaboration with scientists from disciplines including Materials Science, Chemistry, Robotics, Aeronautics, and Computing. His mission at Topos is to help develop the ability for people, organizations, and societies to see more clearly—and hence to serve—the systems that sustain them.

For more information and registration, please visit the Conference website: https://agi-conf.org/2024/

#AGI #AGI24 #AI #Mathematics.

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EU AI Act Comes Into Effect — Here’s What To Expect

Soon we will need AI to resolve AI complience acts…is it so hard to understand that to follow complexities of AI/ LLMs you will need AI/LLM to catch the small letters of laws.

Noncompliance with the AI Act can result in severe penalties, such as fines of up to 35 million euros or 7% of the company’s total worldwide annual turnover, depending on which figure is bigger.

Meta’s future is AI, AI, and more AI

On Meta’s Wednesday earnings call, CFO Susan Li reiterated to investors that financial returns from its recent AI investments will “come in over a longer period of time.” Zuckerberg was direct about why Meta is spending billions on Nvidia hardware and the other infrastructure ahead of these future returns: “It’s hard to predict how this will trend multiple generations into the future, but at this point, I’d rather risk building capacity before it is needed rather than too late.”

He again telegraphed that the Meta AI assistant is on track to be the most used in the world before the end of the year. While he touted that generative AI features “are things that I think will increase engagement in our products,” he said the real revenue will come from business use cases, like AI creating ads from scratch and letting businesses operate their own AI agents in WhatsApp for customer service.

Augmenting Human Capabilities With Artificial Intelligence Agents

By Chuck Brooks

AI agents represent a great leap forward in technology, offering exponential benefits to society. From enhancing scientific research, healthcare, transportation, education, and cybersecurity. There are a lot of different applications that AI agents could help enable in our new digital world, including, foremost, for humans.

Follow me on Twitter or LinkedIn. Check out my website.

Surprising Outcome Of Carl Sagan’s Famous 1975 Prediction About AI Becoming Your Attentive Psychotherapist

I will begin with the first point and make my way gradually to the tenth point.

I’ve already mentioned to you that the AI of the 1970s was toy-like in comparison to the more involved and expansive AI of today. Modern-day generative AI, for example, makes use of vast amounts of data as scanned across the Internet to pattern-match the nature of human writing. This requires a massive amount of computing resources (something far beyond the depth readily employable in the 1970s). The large-scale modeling or pattern matching is what makes contemporary generative AI seem highly fluent.

A common phrase is to say that generative AI is mimicking or parroting human writing.

Navigating The Looming AI Energy Crunch

Brandon Wang is vice president of Synopsys.

The rapid development of AI has led to significant growth across the computing industry. But it is also causing a huge increase in energy consumption, which is leading us into an energy crisis. Current AI models, especially large language models (LLMs), need huge amounts of power to train and run. AI queries require much more energy than traditional searches; for example, asking ChatGPT a question consumes up to 25 times as much energy as a Google search. At current rates of growth, AI is expected to account for up to 3.5% of global electricity demand by 2030, twice as much as the country of France.

We need to address this issue urgently before it becomes unsustainable. If we don’t, the impact could threaten sustainable growth and the widespread adoption of AI technologies themselves. Fortunately, there are a number of pathways toward more energy-efficient AI systems and computing architectures.

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