There are plenty of reasons to be wary of AI, but let’s not dismiss the possibilities it offers for improving accessibility.
Category: robotics/AI – Page 2
Intervene Immune puts thymus regeneration back in the spotlight
This would be my first funding target if I had the money.
Beyond science, Brooke sees three main hurdles: regulation, manufacturing and physician usability. Public acceptance, he says, already appears strong. Regulatory pathways may be comparatively favorable, given the long histories of the cocktail’s components and existing approvals for immune deficiency states. Manufacturing is always a challenge for biologics at scale, but protein production is mature and scalable, and the company is building internal capacity.
The immediate obstacle, Brooke argues, is complexity. Intervene Immune is developing a dosing system designed to be relatively foolproof for doctors, and expects to build an AI assistant to support implementation as sufficient data accumulates.
Intervene Immune’s closing sentiment is less biotech slogan and more biological provocation. “No matter how old you may be, your body still remembers how to be young,” Brooke says.
New microscopy technique preserves the cell’s natural conditions
Researchers at Istituto Italiano di Tecnologia (IIT-Italian Institute of Technology) have developed an innovative microscopy technique capable of improving the observation of living cells. The study, published in Optics Letters, paves the way for a more in-depth analysis of numerous biological processes without the need for contrast agents. The next step will be to enhance this technique using artificial intelligence, opening the door to a new generation of optical microscopy methods capable of combining direct imaging with innovative molecular information.
The study was conducted under the guidance of Alberto Diaspro, Research Director of the Nanoscopy Unit and Scientific Director of the Italian Nikon Imaging Center at IIT, by Nicolò Incardona (first author) and Paolo Bianchini.
New RoboReward dataset and models automate robotic training and evaluation
The advancement of artificial intelligence (AI) algorithms has opened new possibilities for the development of robots that can reliably tackle various everyday tasks. Training and evaluating these algorithms, however, typically requires extensive efforts, as humans still need to manually label training data and assess the performance of models in both simulations and real-world experiments.
Researchers at Stanford University and UC Berkeley have introduced RoboReward, a dataset for training and evaluating AI algorithms for robotics applications, specifically vision-language reward-based models (VLMs).
Their paper, published on the arXiv preprint server, also presents RoboReward 4B and 8B, two new VLMs that were trained on this dataset and outperform other models introduced in the past.
View a PDF of the paper titled A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness, by Erik Hoel
Scientific theories of consciousness should be falsifiable and non-trivial. Recent research has given us formal tools to analyze these requirements of falsifiability and non-triviality for theories of consciousness. Surprisingly, many contemporary theories of consciousness fail to pass this bar, including theories based on causal structure but also (as I demonstrate) theories based on function. Herein I show these requirements of falsifiability and non-triviality especially constrain the potential consciousness of contemporary Large Language Models (LLMs) because of their proximity to systems that are equivalent to LLMs in terms of input/output function; yet, for these functionally equivalent systems, there cannot be any falsifiable and non-trivial theory of consciousness that judges them conscious. This forms the basis of a disproof of contemporary LLM consciousness. I then show a positive result, which is that theories of consciousness based on (or requiring) continual learning do satisfy the stringent formal constraints for a theory of consciousness in humans. Intriguingly, this work supports a hypothesis: If continual learning is linked to consciousness in humans, the current limitations of LLMs (which do not continually learn) are intimately tied to their lack of consciousness.
What Is Nanotechnology? The Atomic Future Waiting to Begin
The idea never died, progress is still being made.
Nanotechnology was once imagined as the next great technological revolution—atom-by-atom manufacturing, machines as small as cells, and materials we can only dream of today. Instead, it stalled. While AI, robotics, and nuclear surged ahead, nanotech faded into the background, reduced to buzzwords and sci-fi aesthetics.
But the idea never died.
We can manipulate matter at the atomic scale. We can design perfect materials. We can build molecular machines. What’s been missing isn’t physics—it’s ambition, investment, and the will to push beyond today’s tools.
In this interview with futurist J. Storrs Hall, we explore what nanotechnology really is, why it drifted off course, and why its future may finally be on the horizon. If AI was a “blue-sky fantasy” until suddenly it wasn’t, what happens when someone decides nanotech deserves the same surge of talent, money, and imagination?
EXLUMINA Founder: SpaceX Already Controls the Future of Space AI
SpaceX is well-positioned to dominate the future of space AI due to its innovative technologies, scalable satellite production, and strategic partnerships, which will enable it to efficiently deploy and operate a massive network of satellites with advanced computing capabilities ## ## Questions to inspire discussion.
Launch Economics & Infrastructure.
🚀 Q: Why is Starship essential for space AI data centers? A: Starship enables 100-1000x more satellites than Falcon 9, making orbital AI economically viable through massive scaling and lower launch costs, while Falcon 9 remains too expensive for commercial viability at scale.
🛰️ Q: What is SpaceX’s deployment plan for AI satellites? A: SpaceX plans Starlink version 3 satellites with 100 Nvidia chips each, deploying 5,000 satellites via 100 Starship launches at 50 satellites per flight to create a gigawatt-scale AI constellation by early 2030s.
📈 Q: What launch cadence gives SpaceX its advantage? A: SpaceX plans 10,000 annual launches and produces satellites at 10-100x the level of competitors, creating a monopoly on launch and manufacturing that positions them as the gatekeeper to space AI success.
Energy & Power Systems.
