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On September 12 2024, Sam Altman, Chief Executive Officer of OpenAI, participated in a fireside chat for University of Michigans students, faculty and staff. The ChatGPT developer head spoke about the future of AI and its implications for education, as well as the challenges and opportunities presented by rapid technological advancements. Altman also shared insights into OpenAI’s new reasoning model, Strawberry, a model he describes as capable of complex reasoning and problem-solving.

“You all are going to create things that astonish us. The story of human history is that we build better tools, and then people do even more amazing stuff with them, and they themselves, you know, add their layer of scaffolding. And we’re on this steadily increasing curve of possibility.”

https://news.engin.umich.edu/2024/09/.


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The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.

Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.

Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.

Tesla hasn’t unveiled its next generation human robot in the form of the app named GEN-3 Teslabot, bringing with it significant advancements in the field of humanoid robotics, merging state-of-the-art engineering with a design inspired by human anatomy. This next-generation robot demonstrates exceptional dexterity and precision, setting a new benchmark for what humanoid robots can accomplish. From catching a tennis ball mid-air to envisioning tasks like threading a needle, the Teslabot is poised to reshape how robots interact with and adapt to the world around them.

Wouldn’t it be great if robots didn’t just assemble cars or vacuum your living room but perform tasks requiring the finesse of human hands—threading a needle, playing a piano, or even catching a tennis ball mid-air. It sounds like science fiction, doesn’t it? Yet, Tesla’s latest innovation, the GEN-3 Teslabot, is bringing us closer to that reality. With its human-inspired design and new engineering, this robot is redefining what we thought machines could do.

But what makes the Teslabot so extraordinary? It’s not just the flashy demonstrations or its sleek design. It’s the way Tesla has managed to replicate human dexterity and precision in a machine, giving it the potential to tackle tasks we once thought only humans could handle. From its 22 degrees of freedom in the hand to its vision-driven precision, it’s a glimpse of what’s to come. Let’s dive into the details of Tesla’s GEN-3 Teslabot and explore how it’s pushing the boundaries of what’s possible.

Check out my introduction to quantum mechanics on Brilliant! First 30 days are free and 20% off the annual premium subscription when you use our link ➜ https://brilliant.org/sabine.

Last week, DeepMind’s Demis Hassabis said that AI might be able to solve problems that quantum computers were supposedly necessary for. Indeed he said that classical systems – AI run on conventional computers – can model quantum systems. Sounds like an innocent claim but is certain to upset a lot of quantum computing researchers. Hassabis bases his argument on the surprising success of Alphafold.

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New research from UC San Diego suggests that large language models like GPT-4 could streamline the process of gene set enrichment, an approach what genes do and how they interact. Results bring science one step closer to automating one of the most widely used methods in genomics research.

DEADLINE APPROACHING! The NEH program is accepting applications through Dec. 11, 2024. For more information, visit.


For organizations in areas affected by Hurricane Helene in FL, GA, SC, NC, VA and TN, optional prospectuses will be accepted until Oct 16th. The prospectus must use the Prospectus Template.

The Humanities Research Centers on Artificial Intelligence program aims to support a more holistic understanding of artificial intelligence (AI) in the modern world through the creation of new humanities research centers on artificial intelligence at eligible institutions. Centers must focus their scholarly activities on exploring the ethical, legal, or societal implications of AI.

A Center supports collaboration among scholars to explore a specific topic through research, as well as spreading knowledge about that research through educational or outreach activities. Centers may include scholars from diverse disciplines, but must be focused on asking humanities questions and should be led by humanists. Scholars may come from one or more institutions. NEH welcomes international collaboration, but scholars at U.S. institutions must contribute significantly to the project. This program is for establishing new Centers; existing Centers and Institutes are not eligible in this competition. Only one application per institution is permitted in this competition. For the purposes of this limitation, branch campuses are considered separately and each may submit one application.