Physicists have long assumed that the universe is uniform at very large scales, but evidence is emerging this is wrong and suggests a way to resolve some of the biggest cosmological mysteries
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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.
Negative effects of artificial sweeteners may pass on to next-generation, study suggests
Health organizations are starting to raise concerns about the potential long-term impacts of artificial sweeteners, which taste sweet but—unlike sugar—contain no calories, suggesting they could interfere with energy metabolism and increase the eventual risk of diabetes or cardiovascular disease.
Now a new study in mice indicates that the popular sweeteners sucralose and stevia have negative effects on the gut microbiome and gene expression, potentially compromising metabolic health, which can be transmitted between generations.
“We found it intriguing that despite the growing consumption of these additives, the prevalence of obesity and metabolic disorders such as insulin resistance has not declined,” said Dr. Francisca Concha Celume of the Universidad de Chile, lead author of the article in Frontiers in Nutrition.
The Universe might not be flat (and cosmologists are quietly freaking out)
Everything we know about the shape of the Universe could be completely wrong.
This is one of the most fascinating unsolved problems in cosmology, and it almost never gets talked about outside of research papers. It’s called the curvature tension, and it links in to the \.