Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash.
Category: robotics/AI
How Amazon is revolutionizing warehouse automation with cutting-edge robotics and AI, driving efficiency and innovation.
OpenAI met with more than 20 companies in the AI coding domain, according to a person familiar with the matter.
Gödel’s incompleteness theorem is used by both advocates and adversaries of strong AI to show that computers can(not) perform the same feats as humans. This article extends the construction through which Gödel proved his theorem, in order to allow a broader interpretation, showing that neither side has exploited its arguments to the fullest extend, and that the evidence can never be conclusive.
Dr.ir. C.J.B. Jongeneel & prof.dr. H. Koppelaar, Delft University of Technology, Faculty of Technical Mathematics and Informatics, Section of Knowledge Based Systems.
1 Introduction
This paper introduces an adaptive multi-agent framework to enhance collaborative reasoning in large language models (LLMs). The authors address the challenge of effectively scaling collaboration and reasoning in multi-agent systems (MAS), which is an open question despite recent advances in test-time scaling (TTS) for single-agent performance.
The core methodology revolves around three key contributions:
1. **Dataset Construction:** The authors create a high-quality dataset, M500, comprising 500 multi-agent collaborative reasoning traces. This dataset is generated automatically using an open-source MAS framework (AgentVerse) and a strong reasoning model (DeepSeek-R1). To ensure quality, questions are selected based on difficulty, diversity, and interdisciplinarity. The generation process involves multiple agents with different roles collaborating to solve challenging problems. Data filtering steps are applied to ensure consensus among agents, adherence to specified formats (e.g., using tags like “ and ‘boxed{}‘), and correctness of the final answer. The filtering criteria are based on Consensus Reached, Format Compliance, and Correctness. The data generation is described in Algorithm 1 in the Appendix.
New insights on genomes and phenomes are laying the groundwork for prevention and personalized care
As of October 2024, 3,000 patients had used Piction’s clinic. So far, it is available in Connecticut, Florida, Massachusetts, New Hampshire and Washington. The service is covered by several major insurance companies, or patients can pay $119 out-of-pocket for each consultation.
Eleni Linos, a professor of dermatology and epidemiology who directs the Stanford Center for Digital Health, and who has no connection with Piction, says: “I’m really optimistic about how this technology can help patients get the best care they can get, while at the same time helping doctors.” — Esther Landhuis.
Finger-shaped tactile sensor advances robotic touch with multi-directional force detection and material identification
Posted in biotech/medical, cyborgs, robotics/AI, security, virtual reality | Leave a Comment on Finger-shaped tactile sensor advances robotic touch with multi-directional force detection and material identification
The development of increasingly sophisticated sensors can facilitate the advancement of various technologies, including robots, security systems, virtual reality (VR) equipment and sophisticated prosthetics. Multimodal tactile sensors, which can pick up different types of touch-related information (e.g., pressure, texture and type of material), are among the most promising for applications that can benefit from the artificial replication of the human sense of touch.
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In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.
From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today’s most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.
We also dive into:
The tribal war between Connectionists and Symbolists.
The surprising origins of Backpropagation.
How transformers redefined machine translation.
Why GANs and generative models exploded (and then faded)
The myth of modern reinforcement learning (DeepSeek, RLHF, etc.)
The danger of AI research narrowing too soon around one dominant approach.
Whether you’re an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what’s coming next.
