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Category: robotics/AI – Page 447

OpenAI’s new text-to-video model, Sora, will likely remain in development for some time before a public release.

According to Bloomberg, OpenAI has not yet set an exact release schedule. There are two reasons for this: One is that OpenAI does not want to take any safety risks, given the number of elections this year. The second reason is that the model is not yet technically ready for release.

When OpenAI unveiled Sora, the company pointed out shortcomings in the model’s physical understanding and consistency. Bloomberg’s tests with two OpenAI-generated prompts confirmed these issues. For example, in the video below, the parrot turns into a monkey at the end.

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This definitely is a Lifeboat post embodying what Lifeboat is about, and it’s only about AI. They did a really good job explaining the 10 stages.

This video explores the 10 stages of AI, including God-Like AI. Watch this next video about the Technological Singularity: • Technological Singularity: 15 Ways It…
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For better or for worse, generative AIs continue to evolve by leaps and bounds with each passing day, a fact that has once again been proven by Google’s DeepMind team with the reveal of Genie, a new AI-powered model capable of creating entire games from just a single image prompt. Trained without any action labels on a large dataset of publicly available Internet videos, Genie can turn any image, whether it’s a real-world photograph, a sketch, an AI-generated image, or a painting, into a simplistic 2D platformer, with the team noting that this approach is versatile and applicable across various domains. Moreover, developers highlight that this new model opens the door for future AI agents to be trained “in a never-ending curriculum of new, generated worlds.”

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“God looked upon his world and called it good, but Man was not content. He looked for ways to make it better and built machines to do the work. But in vain we build the world, unless the builder also grows.” Tinged with earthbound authenticity and verbal courtroom sparring straight out of “Perry Mason,” this classic episode finds a robot — Adam Link — on trial for the murder of the scientist who created him. “Star Trek’s” Leonard Nimoy turns in a fine performance as the cock-sure reporter who coaxes a crusty lawyer, Thurman Cutler (Howard Da Silva), out of retirement to defend the accused automaton. Based on the classic “Adam Link” stories first published in 1939’s “Amazing Stories” magazine, “I, Robot” asks the question: In the race for more complex technology, are we creating beneficial machinery…or futuristic Frankenstein monsters? In 1995, Nimoy will return to this story in the revival series of “The Outer Limits,” this time as the District Attorney.

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In a recent development at Fudan University, a team of applied mathematicians and AI scientists has unveiled a cutting-edge machine learning framework designed to revolutionize the understanding and prediction of Hamiltonian systems. The paper is published in the journal Physical Review Research.

Named the Hamiltonian Neural Koopman Operator (HNKO), this innovative framework integrates principles of mathematical physics to reconstruct and predict Hamiltonian systems of extremely-high dimension using noisy or partially-observed data.

The HNKO framework, equipped with a unitary Koopman structure, has the remarkable ability to discover new conservation laws solely from observational data. This capability addresses a significant challenge in accurately predicting dynamics in the presence of noise perturbations, marking a major breakthrough in the field of Hamiltonian mechanics.

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While Artificial Intelligence (AI) focuses on simulating and surpassing human intelligence, Artificial Life (A-Life) takes a different approach. Instead of replicating cognitive abilities, A-Life seeks to understand and model fundamental biological processes through software, hardware, and even… wetware.

Forget Turing tests and chess games. A-Life scientists don’t care if their creations are “smart” in the traditional sense. Instead, they’re fascinated by the underlying rules that govern life itself. Think of it as rewinding the movie of evolution, watching it unfold again in a digital petri dish.

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San Francisco-based startup Magic AI just secured more than $100 million in funding to develop an AI software engineer, which it sees as a milestone along the path to artificial general intelligence (AGI).

The background: Everything we see and do on our devices starts as code, and traditionally, that code was written by trained software engineers. In 2021, OpenAI disrupted this paradigm with CODEX, an AI that can write computer code in response to prompts written in natural language.

CODEX became the basis for GitHub Copilot, a tool that speeds up programming by generating new code in response to prompts, auto-completing code an engineer has started writing, and more. This can speed up programming by an average of 55%, and more than a million developers have used GitHub Copilot since its release in 2022.

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Summary: A new study combines deep learning with neural activity data from mice to unlock the mystery of how they navigate their environment.

By analyzing the firing patterns of “head direction” neurons and “grid cells,” researchers can now accurately predict a mouse’s location and orientation, shedding light on the complex brain functions involved in navigation. This method, developed in collaboration with the US Army Research Laboratory, represents a significant leap forward in understanding spatial awareness and could revolutionize autonomous navigation in AI systems.

The findings highlight the potential for integrating biological insights into artificial intelligence to enhance machine navigation without relying on GPS technology.

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