Latest cybersecurity threats, malware campaigns, research findings, and key security developments from this week’s ThreatsDay Bulletin.
Category: robotics/AI – Page 4
Alibaba’s Qwen 3
QWEN 3.5 running on iPhone Pro in airplane mode. Full large language model running onan edge device with no network connectivity.
5 is now running fully on device on an iPhone 17 Pro, and that’s a big deal.
Despite its compact size, Qwen 3.5 reportedly outperforms models up to four times larger. It shows strong multimodal capability, meaning it can interpret and reason over images as well as text. It also includes a reasoning toggle, letting users switch between faster responses and deeper step by step thinking depending on the task.
The demo uses a 2B parameter model quantized to 6 bit precision, optimized with MLX for Apple Silicon. That combination allows advanced AI to run locally, without relying on cloud servers.
If this scales, it signals a shift toward powerful, private, on device AI that doesn’t need a data center to compete.
Beyond amyloid plaques: AI reveals hidden chemical changes across the Alzheimer’s brain
Scientists at Rice University have produced the first full, dye-free molecular atlas of an Alzheimer’s brain. By combining laser-based imaging with machine learning, they uncovered chemical changes that spread unevenly across the brain and extend beyond amyloid plaques. Key memory regions showed major shifts in cholesterol and energy-related molecules. The findings hint that Alzheimer’s is a whole-brain metabolic disruption—not just a protein problem.
The End of Work: Vinod Khosla’s Bold AI Prediction
What if AI made your paycheck optional? Vinod Khosla, one of the world’s greatest venture capitalists and an early backer of AI, believes the technology will take over 80% of labor, freeing humans to live on passion instead.
His track record backs up the boldness, as early bets on OpenAI, DoorDash, Instacart, and Square have made him one of the most consequential investors of our time.
In this episode of Titans, Khosla sits down with Fortune Editor-in-Chief Alyson Shontell to unpack his abundant vision for the AI future, what government policy should tackle for a more equitable 2040, and what the U.S. needs to do to win the global AI race.
Cell types: encoding the brain’s BIOS
Excellent Substack writeup by Patrick Mineault on how cell types may specify innate behaviors and why mapping regions of the brain specialized to steer innate behaviors (via lots of distinct cell types) could lead us to more aligned AI systems. Highly convincing and elegant arguments made here! [ https://substack.com/home/post/p-189321289](https://substack.com/home/post/p-189321289)
Dwarkesh seemed very confused by this, asking a few different times: “Why would each reward function need a different cell type?” I empathize with Dwarkesh here! It is mysterious that a cell type could represent something as abstract as a reward. As a computational neuroscientist who mostly worked at the representation level during my PhD, I’ve leaned historically towards thinking of cell types as a mere “implementation detail”. But over conversations with Adam, Steve Byrnes, Paul Cisek, Tony Zador, and a few others, I’ve started to become convinced that cell types are a really useful lens to think about innate behaviors and rewards.
In this essay, I’ll unpack the conversation and answer the question: what do cell types have to do with reward functions? To answer it, we’ll need to understand what kind of information can be encoded in the genome, and how that information ultimately relates to connectomes and to cell types. I’ll connect the answer to the central claim of Adam: that these connections matter for AI, and AI safety in particular.
Andrew Barto and colleagues make the point that all primary rewards are internal, and must be genetically encoded. In reinforcement learning, which Barto co-developed along with Rich Sutton, an agent learns by receiving reward signals that indicate what is good and bad. The critical insight is that for biological organisms, all of these reward signals are internal —they are generated by the organism’s own nervous system. It is not a chunk of steak that gives reward: it is circuitry inside the brain that assigns positive valence to fat, salt, umami, heat, and texture. Things like money—secondary rewards—must be bootstrapped off of the pre-existing primary rewards.