Today we introduced Flow, a new AI filmmaking tool built with and for creatives for the next wave of storytelling.
Category: robotics/AI – Page 13
The Right must find out what ‘winning an AI race’ means, how much jobs matter, and who to trust.
In this episode of The Moss Report, Ben Moss sits down with Dr. Ralph Moss to explore the real-world pros and cons of using artificial intelligence in cancer research and care.
From AI-generated health advice to PubMed citations that don’t exist, this honest conversation covers what AI tools are getting right—and where they can dangerously mislead.
Dr. Moss shares the results of his own AI test across five major platforms, exposing their strengths and surprising failures.
Whether you’re a cancer patient, caregiver, or simply curious about how AI is shaping the future of medicine, this episode is essential listening.
Links and Resources:
🌿 The Moss Method – Fight Cancer Naturally – (Paperback, Hardcover, Kindle) https://amzn.to/4dGvVjp.
The advancement of artificial intelligence (AI) and the study of neurobiological processes are deeply interlinked, as a deeper understanding of the former can yield valuable insight about the other, and vice versa. Recent neuroscience studies have found that mental state transitions, such as the transition from wakefulness to slow-wave sleep and then to rapid eye movement (REM) sleep, modulate temporary interactions in a class of neurons known as layer 5 pyramidal two-point neurons (TPNs), aligning them with a person’s mental states.
These are interactions between information originating from the external world, broadly referred to as the receptive field (RF1), and inputs emerging from internal states, referred to as the contextual field (CF2). Past findings suggest that RF1 and CF2 inputs are processed at two distinct sites within the neurons, known as the basal site and apical site, respectively.
Current AI algorithms employing attention mechanisms, such as transformers, perceiver and flamingo models, are inspired by the capabilities of the human brain. In their current form, however, they do not reliably emulate high-level perceptual processing and the imaginative states experienced by humans.
There’s a deep human desire to know, to better understand the world, and for decision-makers within organizations to do that in a way that’s grounded in data and grounded in fact.
Analysis flags hundreds of studies that seem to follow a template, reporting correlations between complex health conditions and single variables based on publicly available data sets.
Humans excel at performing complex tasks by leveraging long-term memory across temporal and spatial experiences. In contrast, current Large Language Models (LLMs) struggle to effectively plan and act in dynamic, multi-room 3D environments. We posit that part of this limitation is due to the lack of proper 3D spatial-temporal memory modeling in LLMs. To address this, we first introduce 3DMem-Bench, a comprehensive benchmark comprising over 26,000 trajectories and 2,892 embodied tasks, question-answering and captioning, designed to evaluate an agent’s ability to reason over long-term memory in 3D environments. Second, we propose 3DLLM-Mem, a novel dynamic memory management and fusion model for embodied spatial-temporal reasoning and actions in LLMs.
A team of researchers at AI Google Quantum AI, led by Craig Gidney, has outlined advances in quantum computer algorithms and error correction methods that could allow such computers to crack Rivest–Shamir–Adleman (RSA) encryption keys with far fewer resources than previously thought. The development, the team notes, suggests encryption experts need to begin work toward developing next-generation encryption techniques. The paper is published on the arXiv preprint server.
RSA is an encryption technique developed in the late 1970s that involves generating public and private keys; the former is used for encryption and the latter decryption. Current standards call for using a 2,048-bit encryption key. Over the past several years, research has suggested that quantum computers would one day be able to crack RSA encryption, but because quantum development has been slow, researchers believed that it would be many years before it came to pass.
Some in the field have accepted a theory that a quantum computer capable of cracking such codes in a reasonable amount of time would have to have at least 20 million qubits. In this new work, the team at Google suggests it could theoretically be done with as few as a million qubits—and it could be done in a week.
Caffeine is not only found in coffee, but also in tea, chocolate, energy drinks and many soft drinks, making it one of the most widely consumed psychoactive substances in the world.
In a study published in Communications Biology, a team of researchers from Université de Montréal shed new light on how caffeine can modify sleep and influence the brain’s recovery—both physical and cognitive—overnight.
The research was led by Philipp Thölke, a research trainee at UdeM’s Cognitive and Computational Neuroscience Laboratory (CoCo Lab), and co-led by the lab’s director, Karim Jerbi, a psychology professor and researcher at Mila–Quebec AI Institute.
