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If you are travelling at 87% the speed of light you will observe length contraction such as the distance in the direction you have travelled by a factor 1/γ = 1/2.I'll maybe try something a little bit simpler, and that I can more easily get.
You are traveling along at 87% of c, time dilation is affected by a multiplying factor of two, etc. So, does the length contraction affect for the thing in motion (you) also become affected by a factor of two? Or is it directly proportional and equal always to time dilation?
Or are there other factors I'm unaware of right now/not considering that would increase length contraction slightly in order to make c the same always for all observers in their own frames of reference?
Or what am I missing here, etc?
Because both being affected by a factor of two is only 75%, correct? Not 87% IOW's, etc.
Much thanks.
Take Care/God Bless.
I get that so far, but that's not what I was trying to address?If you are travelling at 87% the speed of light you will observe length contraction such as the distance in the direction you have travelled by a factor 1/γ = 1/2.
Your clock will not slow down in your frame of reference, only a observer at rest relative to you will measure your clock as having slowed down or dilated by a factor γ =2.
... and that's a whole different truth compared with the truth currently being confronted by @Neogaia777, eh?... The reality is if you want to understand SR you need to understand the mathematics.
In an earlier post I quoted the Lorentz transformations without deriving them, I left it as an exercise for DeepSeek.... and that's a whole different truth compared with the truth currently being confronted by @Neogaia777, eh?
PS: Aka intuition just doesn't cover the ground necessary in order to gain the understanding.
If time dilation is affected by the factor γ = 2, then the factor for length contraction is 1/γ =1/2.I get that so far, but that's not what I was trying to address?
If time dilation is affected by a factor of two, and so is length contraction, then that's 75% of let's say "100", right? and that leaves 25, right? but shouldn't it be 13?
Maybe I need to think some more about this? I can't seem to properly get across what I'm trying to be asking right now, etc.
I maybe need to take it back to the drawing board maybe, so just nevermind for now, ok.
But thanks anyway.
Take Care/God Bless.
I'm blown away by its problem solving capabilities there!In an earlier post I quoted the Lorentz transformations without deriving them, I left it as an exercise for DeepSeek.
Its derivation took pages and pages and pages.................... no human would have gone down this line, it made a number of mistakes which it recognized and corrected before coming to the correct derivation.
The derivation of Lorentz transformation is textbook stuff, it could have accessed the information during its learning process instead it reinvented the wheel in the process.
Here is the long tortuous process it took to come up with the correct answer.
To self: 'thank goodness its not just me'! ..If time dilation is affected by the factor γ = 2, then the factor for length contraction is 1/γ =1/2.
I have no idea what the rest of your post is about.
DeepSeek couldn't make heads or tails of it either.To self: 'thank goodness its not just me'! ..
The idea will probably be just simply solved/resolved with a little more learning, I was just trying to think outside of the normal usual box for a minute, and maybe let you guys in on how I was currently thinking, but I'm pretty sure it will be resolved with a little more learning, etc. I do this kind of thing all of the time, and it's kind of a part of the process of how I try to reason things out/think, and it certainly wouldn't be the first time, and probably most definitely won't be the last, that it is just put on the back burner until later when most of them are most usually solved or resolved with a little more knowledge, or learning, or thinking, that happens over time, etc.I'm going to work on learning more, and acquiring more knowledge (which will take time), I just maybe figured you guys might have been getting kind of bored with the more quote/unquote "normal" believers or religious folks/types you're most usually used to dealing with most of the time on here maybe, etc.
But and/or anyway, have fun with them, ok. I'll be back at some point, but it will/might take a little bit of time. Unfortunately, I can't learn quite as fast as AI, etc. I still have to eat, sleep, take care of the things I need to take care of, pay attention to my cats, that sort of thing, etc, and AI doesn't have to do any of these things, etc.
Take Care/God Bless.
Thanks. I'm at least encouraged by the "model" thing. The way they (science magazines) talk, that 'model' is what is moving(?) the distant galaxies away from us. But since you say it is only a mental model, and not a force or medium or any material thing, then, I have to conclude, it is only a mental crutch to help us think about how those galaxies have gone(?) so far so fast, or at least why they are (or were, some billions of years ago) receding away from us so fast. The fact is, they definitely are receding that fast, and the measurement is consistent in spite of 'grid expansion'. It's not double-talk, it's just that we have to think of it that way.I'm not sure about this, but I get the sense that you may be trying to visualise spacetime like what many around here call a 'physical', or a 'material' object? If so, it very clearly isn't either of those.
Its a model (which differs from a tangible material thing) and it has changed over time. @sjastro has pointed out, it has changed 3 times: '(i) classical physics space, (ii) spacetime in the form of a vacuum is a quantum field in the lowest energy state from which virtual particles pop into and out of existence which has been indirectly observed in laboratory experiments' and (iii) Dark energy was included as a parameter of GR's spacetime in order to account for the observed the expansion (acceleration) of the universe.
I'm unclear on whether that may help a little(?) I'm just trying to assist with the visualisation here .. (its not easy to bridge the conceptual gap).
The galaxies are moving because space is expanding. The model is just the device used to understand it.Thanks. I'm at least encouraged by the "model" thing. The way they (science magazines) talk, that 'model' is what is moving(?) the distant galaxies away from us. But since you say it is only a mental model, and not a force or medium or any material thing, then, I have to conclude, it is only a mental crutch to help us think about how those galaxies have gone(?) so far so fast, or at least why they are (or were, some billions of years ago) receding away from us so fast. The fact is, they definitely are receding that fast, and the measurement is consistent in spite of 'grid expansion'. It's not double-talk, it's just that we have to think of it that way.
The Universe is not static, it is expanding. Nor does it have "center". From all points in the universe it appears to be expanding away in all directions. The visible horizon (the furthest that can be seen) is much further than 14 billion ly away due to expansion.So, since we don't know what is beyond our sight, it's useless to guess at a static center of the universe. I'm wondering if, from any particular point in distant space, the universe would appear to have that for the center point, everything at the outer edges of visibility a circular globe 14 billion light years away.
I have heard an authority (don't remember who) on this subject, or maybe it was on quantum theory, say that anyone who thinks they understand it doesn't.
Beats me.
While it came up with a very messy derivation of the Lorentz transformation I asked it a far more challenging question.I'm blown away by its problem solving capabilities there!
I mean .. it makes suggestions and then pursues them to an end result .. even if it ends up being a dead end and then says: 'Let me think!' .. followed by yet another suggestion, (etc) ..!
That's amazing .. (and slightly scary!)
First the preliminaries in layman terms, dark energy causes the expansion of the universe to accelerate and one model to explain dark energy is quintessence which posits the existence of a dynamic scalar field. This scalar field is a changing energy field like an invisible fluid that permeates space and evolves with time.In dark energy modeling derive the K-essence energy density: ρₖ = [2XK’(X) – K(X)] + V(ψ).
While it came up with a very messy derivation of the Lorentz transformation I asked it a far more challenging question.
First the preliminaries in layman terms, dark energy causes the expansion of the universe to accelerate and one model to explain dark energy is quintessence which posits the existence of a dynamic scalar field. This scalar field is a changing energy field like an invisible fluid that permeates space and evolves with time.
The quintessence field is like a ball rolling down a hill where the shape of the hill, the potential V(ψ), controls how the ball moves and depending on how fast or slow it moves the energy changes, which determines how fast the universe expands.
Potential energy is converted into kinetic energy which determines the speed at which the ball moves or universe expands.
However the universe can accelerate or slow down without being dependant on the potential energy, the kinetic energy can change alone which is known as K-essence.
Here is DeepSeek's derivation of the equation which is remarkable, it asks questions, and answers them which leads to the derivation.
Its training involves reinforcement learning but the exact details are a trade secret and it tows the party line... goodness me! Simply amazingly stunning!
What kind of learning approach is that?
Whatever its called, the above example demonstrates its problem solving capabilities in action!
We can actually see how its thinking there!
I saw this on the news recently, I doubt having been a forensic scientist in the field of forensic engineering in the automotive industry will make China a greater superpower, the Americans probably know more about me through GPT-4o and Google.PS: I notice the Australian Federal government has banned this AI from being installed on their official mobile phones, eh? (I saw this in the news, just today).
Hmm ... in looking round the web, I found this: Types of Artificial Intelligence: A Detailed Guide.Its training involves reinforcement learning but the exact details are a trade secret and it tows the party line.
...
When it came to criticizing my layman explanation of K-essence it had no such reservations.
...
This highlights the pitfalls of giving a simplified explanation of a complex subject given accuracy can be compromised.
Its unlikely to see these characteristics and 'key concerns' when it comes to hard(ish) physics questions where 'common sense' is normally treated with caution, but the reasoning it displays where it makes a statement and then pursues it until it presents conflicts and then introduces other ways to look at the original problem, (or categorises other objects), seems pretty visible from your example(?)Deep Learning:
.. A standard machine learning workflow starts with manually extracting selected features from images. These features are then used to create a model for categorising the selected objects. A deep learning workflow differs from this as relevant features are extracted automatically. In addition, deep learning performs “end-to-end learning” – it is given raw data and a task to perform, such as classification, and it learns how to do this by itself. In machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction and modelling steps are automatic. However, because deep learning is still going through growing pains, there have been a number of concerns raised alongside the vast potential it holds, particularly around the ambition of achieving AGI through deep learning. Gary Marcus, former head of AI at Uber, published a paper on deep learning which summarises some key concerns. Among the concerns he raised were:
- DL is limited when it comes to open-ended reasoning based on real-world common sense and knowledge, meaning that machines wouldn’t be able to distinguish between “Tom promised Mary to stop” and “Tom promised to stop Mary”.
- Deep learning is self-sufficient and is made up of correlations, rather than abstractions. Problems that deal largely with common sense reasoning are mostly outside of what deep learning can cope with.
- Another problem often linked to deep learning is acquiring biases. If the training data set contains biases, the model will learn and consequently replicate those biases in its conclusions and predictions.
Hmm .. so DeepSeek expects others to understand the party line .. with no alternatives presented for consideration, eh?I saw this on the news recently, I doubt having been a forensic scientist in the field of forensic engineering in the automotive industry will make China a greater superpower, the Americans probably know more about me through GPT-4o and Google.
I did however ask DeepSeek a loaded question having known the answer beforehand whether it can be run on a GPU with a sufficiently high VRAM.
Here it can be run as a standalone utility if one is worried about personal information being spread around the internet.
It again gave the standard party line response.
...
OpenAI on the other hand had no such reservations.
DeepSeek is a combination of Deep Learning and Reinforcement Learning.Hmm ... in looking round the web, I found this: Types of Artificial Intelligence: A Detailed Guide.
It introduces another category, 'Deep Learning'. Perhaps the 'DeepSeek' part of its name denotes its style of learning ie, 'Deep Learning'(?):
Its unlikely to see these characteristics and 'key concerns' when it comes to hard(ish) physics questions where 'common sense' is normally treated with caution, but the reasoning it displays where it makes a statement and then pursues it until it presents conflicts and then introduces other ways to look at the original problem, (or categorises other objects), seems pretty visible from your example(?)
Maybe the way to find out what sort of learner we're dealing with, would be to ask it what it thinks about the two statements:
“ @sjastro promised Mary to stop” and “@sjastro promised to stop Mary”, (where 'Mary' could be replaced any other CFs member's name .. say @SelfSim?)?
Very interesting .. but what precisely does it mean by 'neural network'?DeepSeek is a combination of Deep Learning and Reinforcement Learning.
Its responses like asking questions and weighing the answers is a product of deep learning.
A neural network in AI are optimization algorithms.Very interesting .. but what precisely does it mean by 'neural network'?
Its a general term that doesn't mean much in the context of understanding how it goes about its business, for me .. (ie: it could mean just about anything. I have a picture of the web communications pathways in my mind as a model of what it means but that's about all).
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