A team of AI researchers at Meta’s Fundamental AI Research team are making four new AI models publicly available to researchers and developers creating new applications. The team has posted a paper on the arXiv preprint server outlining one of the new models, JASCO, and how it might be used.

As interest in AI applications grows, major players in the field are creating AI models that can be used by other entities to add AI capabilities to their own applications. In this new effort, the team at Meta has made available four new models: JASCO, AudioSeal and two versions of Chameleon.

JASCO has been designed to accept different types of audio input and create an improved sound. The model, the team says, allows users to adjust characteristics such as the sound of drums, guitar chords or even melodies to craft a tune. The model can also accept text input and will use it to flavor a tune.

Synthesizer symphony whilst travelling to far out places along the rickety wooden bridge between worlds. http://vevo.ly/ZCg3ZH

But the technology could break down trust and social bonds.

Teaching robots to ask for help is key to making them safer and more efficient.

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.

Because tasks are typically more complex than a simple “pick up a bowl” command, the engineers use large language models (LLMs) — the technology behind tools such as ChatGPT — to gauge uncertainty in complex environments. LLMs are bringing robots powerful capabilities to follow human language, but LLM outputs are still frequently unreliable, said Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton and the senior author of a study outlining the new method.

Erwan Plantec, Joachin W.Pedersen, Milton L.Montero, Eleni Nisioti, Sebastian Risi ITU Copenhagen 2024 https://arxiv.org/abs/2406.

OpenRead & Notes Taking.

Following the rapid advancement of artificial intelligence (AI) tools, engineers worldwide have been working on new architectures and hardware components that replicate the organization and functions of the human brain.

Most brain-inspired technologies created to date draw inspiration from the firing of brain cells (i.e., neurons), rather than mirroring the overall structure of neural elements and how they contribute to information processing.

Researchers at Tsinghua University recently introduced a new neuromorphic computational architecture designed to replicate the organization of synapses (i.e., connections between neurons) and the tree-like structure of dendrites (i.e., projections extending from the body of neurons).