Lifeboat Foundation

While roboticists have developed increasingly sophisticated systems over the past decades, ensuring that these systems can autonomously operate in real-world settings without mishaps often proves challenging. This is particularly difficult when these robots are designed to be deployed in complex environments, including space and other planets.

From Duke university, Together AI, U Chicago, & Stanford University.

Mixture-of-agents enhances large language model capabilities.

Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and…

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Researchers from the Photonics Research Laboratory (PRL)-iTEAM at the Universitat Politècnica de València, in collaboration with iPRONICS, have developed a groundbreaking photonic chip. This chip is the world’s first to be universal, programmable, and multifunctional, making it a significant advancement for the telecommunications industry, data centers, and AI computing infrastructures. It is poised to enhance a variety of applications including 5G communications, quantum computing, data centers, artificial intelligence, satellites, drones, and autonomous vehicles.

The development of this revolutionary chip is the main result of the European project UMWP-Chip, led by researcher José Capmany and funded by an ERC Advanced Grant from the European Research Council. The work has been published in the Nature Communications journal.

Cybersecurity researchers have disclosed that the LightSpy spyware recently identified as targeting Apple iOS users is in fact a previously undocumented macOS variant of the implant.

The findings come from both Huntress Labs and ThreatFabric, which separately analyzed the artifacts associated with the cross-platform malware framework that likely possesses capabilities to infect Android, iOS, Windows, macOS, Linux, and routers from NETGEAR, Linksys, and ASUS.

“The Threat actor group used two publicly available exploits (CVE-2018–4233, CVE-2018–4404) to deliver implants for macOS,” ThreatFabric said in a report published last week. “Part of the CVE-2018–4404 exploit is likely borrowed from the Metasploit framework. macOS version 10 was targeted using those exploits.”

Researchers are warning that hackers are actively exploiting a disputed vulnerability in a popular open-source AI framework known as Ray.

This tool is commonly used to develop and deploy large-scale Python applications, particularly for tasks like machine learning, scientific computing and data processing.

According to Ray’s developer, Anyscale, the framework is used by major tech companies such as Uber, Amazon and OpenAI.

The disclosure notice also noted several security changes made to the Spaces platform in response to the leak, including the removal of org tokens to improve traceability and auditing capabilities, and the implementation of a key management service (KMS) for Spaces secrets.

Hugging Face said it plans to deprecate traditional read and write tokens “in the near future,” replacing them with fine-grained access tokens, which are currently the default.

Spaces users are recommended to switch their Hugging Face tokens to fine-grained access tokens if they are not already using them, and refresh any key or token that may have been exposed.

Fields Medalist Terence Tao explains how proof checkers and AI programs are dramatically changing mathematics.

By Christoph Drösser

Mathematics is traditionally a solitary science. In 1986 Andrew Wiles withdrew to his study for seven years to prove Fermat’s theorem. The resulting proofs are often difficult for colleagues to understand, and some are still controversial today. But in recent years ever larger areas of mathematics have been so strictly broken down into their individual components (“formalized”) that proofs can be checked and verified by computers.

Recent advances in artificial intelligence, primarily driven by foundation models, have enabled impressive progress. However, achieving artificial general intelligence, which involves reaching human-level performance across various tasks, remains a significant challenge. A critical missing component is a formal description of what it would take for an autonomous system to self-improve towards increasingly creative and diverse discoveries without end—a “Cambrian explosion” of emergent capabilities i-e the creation of open-ended, ever-self-improving AI remains elusive., behaviors, and artifacts. This open-ended invention is how humans and society accumulate new knowledge and technology, making it essential for artificial superhuman intelligence.

DeepMind researchers propose a concrete formal definition of open-endedness in AI systems from the perspective of novelty and learnability. They illustrate a path towards achieving artificial superhuman intelligence (ASI) by developing open-ended systems built upon foundation models. These open-ended systems would be capable of making robust, relevant discoveries that are understandable and beneficial to humans. The researchers argue that such open-endedness, enabled by the combination of foundation models and open-ended algorithms, is an essential property for any ASI system to continuously expand its capabilities and knowledge in a way that can be utilized by humanity.

The researchers provide a formal definition of open-endedness from the perspective of an observer. An open-ended system produces a sequence of artifacts that are both novel and learnable. Novelty is defined as artifacts becoming increasingly unpredictable to the observer’s model over time. Learnability requires that conditioning on a longer history of past artifacts makes future artifacts more predictable. The observer uses a statistical model to predict future artifacts based on the history, judging the quality of predictions using a loss metric. Interestingness is represented by the observer’s choice of loss function, capturing which features they find useful to learn about. This formal definition quantifies the key intuition that an open-ended system endlessly generates artifacts that are both novel and meaningful to the observer.

Brighter with Herbert.