Pallets are everywhere, but training robots to stack them with goods takes forever. Fixing that could be a tangible win for commercial AI-powered robots.
Category: robotics/AI – Page 215
Devin was unassisted, whereas all other models were assisted (meaning the model was told exactly which files need to be edited).
We plan to publish a more detailed technical report soon—stay tuned for more details.
We are an applied AI lab focused on reasoning. We’re building AI teammates with capabilities far beyond today’s existing AI tools. By solving reasoning, we can unlock new possibilities in a wide range of disciplines—code is just the beginning. We want to help people around the world turn their ideas into reality.
This video explores the 10 hypothetical stages of AI and their impact on humanity. Watch this next video about 20 emerging technologies of the future: • 20 Emerging Technologies That Will Ch…
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The semiconductor industry has grown into a $500 billion global market over the last 60 years. However, it is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips, introducing substantial risks of malfunction and unwanted surveillance. In particular, the latter inadvertently gives rise to a $75 billion counterfeit chip market that jeopardizes safety and security across multiple sectors dependent on semiconductor technologies, such as aviation, communications, quantum, artificial intelligence, and personal finance.
Governments and organizations worldwide are beginning to recognize the potential dangers. Efforts are being made to develop more sophisticated deepfake detection tools and to establish legal frameworks to address the misuse of this technology.
However, the battle against these convincing fakes is ongoing, and as detection methods improve, so too do the techniques used to create them.
The combination of astronomical techniques and AI highlights a multidisciplinary approach to solving the problem, underscoring the need for innovative and collaborative solutions.
Choosing the right algorithm for RAG could yield more AI improvements than scaling to larger and larger language models, say AWS researchers.
They aren’t clones. They’re xenobots, synthetic lifeforms made from clusters of cells.
Robots such as these, capable of making offshoots of themselves, could be a future tool for science and medicine.
How Nvidia became an AI Giant
Posted in finance, robotics/AI
It all started at a Denny’s in San Jose in 1993. Three engineers—Jensen Huang, Chris Malachowsky and Curtis Priem—gathered at the diner in what is now the heart of Silicon Valley to discuss building a computer chip that would make graphics for video games faster and more realistic. That conversation, and the ones that followed, led to the founding of Nvidia, the tech company that soared through the ranks of the stock market to briefly top Microsoft as the most valuable company in the S&P 500 this week.
The company is now worth over $3.2 trillion, with its dominance as a chipmaker cementing Nvidia’s place as the poster child of the artificial intelligence boom—a moment that Huang, Nvidia’s CEO, has dubbed “the next industrial revolution.”
On a conference call with analysts last month, Huang predicted that the companies using Nvidia chips would build a new type of data center called “AI factories.”
Listen to this episode from The Futurists on Spotify. Monica Anderson returns to the Futurists to share a radical concept: future AI models based on Darwinism. The “AI epistemologist” shares provocative opinions about where the current crop of generative AI systems went wrong, and why generative AI is computationally expensive and energy intensive, and why scaling AI with hardware will not achieve general intelligence. Instead she offers a radical alternative: a design for machine intelligence that is inspired by biology, and in particular by the Darwinian process of selection. Topics include: why generative AI is not a plagiarism machine; syntax versus semantics and why AI needs both; there is only one algorithm for creativity; and how to construct an AI that consumes a million times less energy.