L-arginine helps protein droplets stay stable and prevents fibril formation linked to Alzheimer’s. This process occurs at droplet surfaces, offering a potential therapeutic target.
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How individual consciousness works — and makes us unique
As we go through life, our brains run different processing modes. Some – the attention and sensory systems – result in very similar experiences of the world: what colour the sky is, how warm the day feels.
But there is another, deeper side to the brain which weaves together your memories, goals, beliefs and emotions into a continuous sense of self. This allows you to experience the world not as it is, but as it matters to you personally.
This unique inner world is supported by the brain’s default mode network (DMN). This links together several areas including in the prefrontal cortex (at the very front of the brain) and the parietal lobe (at the back).
Richard Feynman Explains Why the Past Isn’t Really Gone
#spacetime
Richard Feynman Explains: Why the Past Isn’t Really Gone.
🌌Ever felt like the past is just a fading memory? Richard Feynman and modern physics say otherwise.
We’re taught that time flows like a river, leaving the past in the dust. But what if \.
Why it is physically IMPOSSIBLE for God to exist — Richard Feynman
That framing goes too far. Physics doesn’t prove that God is “impossible”—it deals with testable models of the natural world, not metaphysical conclusions. If you present it as a logical or scientific analysis of physical claims, it will sound stronger and more credible.
Here’s a refined, high-impact description in the same style—without overclaiming:
Does modern physics leave any room for God?
In this video, we examine that question through the analytical lens of Richard Feynman — not as a matter of belief, but as a question about how the universe actually behaves when studied with precision.
Physics does not argue against God.
It does something more demanding: it builds a complete, self-consistent description of reality based entirely on measurable laws — and asks whether external intervention is required anywhere within that structure.
Over four centuries, those laws have expanded to describe everything from subatomic particles to cosmic evolution — without a single confirmed exception.
So where, if anywhere, does a non-physical agent fit?
In this video, we walk through the physical framework that raises this question:
The conservation laws that govern every interaction.
The causal structure of spacetime and what it permits.
Thermodynamic limits on energy, order, and change.
The constraints of information in a physical universe.
And the boundary between scientific knowledge and unfalsifiable claims.
This is not a debate about belief.
It’s an examination of structure.
Because when physics describes the universe with increasing completeness, it doesn’t explicitly disprove metaphysical ideas — but it does redefine what counts as an explanation.
And that shift has consequences.
⚡ Why This Matters:
Understanding what science can and cannot say is just as important as understanding what it discovers.
📌 Watch till the end — the conclusion isn’t what most people expect.
Learning while Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Even the best-trained robots struggle when they leave the lab. They face “distribution shifts”—situations they didn’t see in training, like a brand of cereal with a new box design or a human suddenly walking into their personal space. Static datasets (fixed instructions) simply can’t prepare a robot for every “what if” scenario.
To make sense of all this messy real-world data, the researchers introduced two key technical innovations to the robot’s “Vision-Language-Action” (VLA) brain.
Imagine bringing home a single robot to be your all-in-one kitchen assistant—you want it to brew your morning Gongfu tea, make fresh juice in the afternoon, and mix the perfect cocktail at night. While it might have been trained extensively in a lab, in your house, the counter is slightly higher, the fruit is shaped differently, and your cocktail shaker is transparent. Pre-trained Vision-Language-Action (VLA) models provide an incredible starting point, yet real-world deployment is never a fixed test distribution. This leaves a critical, unsolved challenge: how do we take the heterogeneous experience generated across a fleet of robots and use it to post-train a single, generalist model across a wide range of tasks simultaneously?
We present Learning While Deploying (LWD), a fleet-scale offline-to-online RL framework for continual post-training of generalist VLA policies. Instead of treating deployment as the finish line where a policy is merely evaluated, LWD turns it into a training loop through which the policy improves. A pre-trained policy is deployed across a robot fleet, and both autonomous rollouts and human interventions are aggregated into a shared replay buffer for offline and online updates. The updated policy is then redeployed, enabling continuous improvement by leveraging interaction data from the entire fleet.
A Generalist Learns Beyond Demonstrations
Some robot learning systems have explored data flywheels: deploying a policy, collecting new robot data, extracting high-quality behaviors, and training the next policy to imitate them. While this supports scalable improvement, it still treats deployment mainly as a source of expert demonstrations. Prior post-training systems mainly focus on specialist policies, leaving fleet-scale post-training of a single generalist policy across diverse tasks unresolved.
