1/12
Great talk by @stephen_wolfram introducing his Physics Project (wolframphysics.org)
Important work here; goes well-beyond physics. I hope upcoming generations of scientists pay close attention.
Some of my quick thoughts:
Great talk by @stephen_wolfram introducing his Physics Project (wolframphysics.org)
Important work here; goes well-beyond physics. I hope upcoming generations of scientists pay close attention.
Some of my quick thoughts:
2/12
What is the qualifier for unification under this framework? How will QM and GR demonstrably work together enough to say they are unified"?
What is the qualifier for unification under this framework? How will QM and GR demonstrably work together enough to say they are unified"?
3/12
It would be great to have some distance metric between the shapes you create with this approach and known shapes in nature (some connection between the isomorphic classes of nature's shapes and those generated here).
It would be great to have some distance metric between the shapes you create with this approach and known shapes in nature (some connection between the isomorphic classes of nature's shapes and those generated here).
4/12
I wonder if a Deep Neural Network could find latent similarities between these shapes (intra and inter). Much of the interpretation seems to rely on pattern recognition inside shapes and towards nature.
I wonder if a Deep Neural Network could find latent similarities between these shapes (intra and inter). Much of the interpretation seems to rely on pattern recognition inside shapes and towards nature.
5/12
You could also use a proxy to space curvature such as time dilation. Show that dilation matches what's expected. (Wolfram ended up addressing this).
You could also use a proxy to space curvature such as time dilation. Show that dilation matches what's expected. (Wolfram ended up addressing this).
6/12
Teasing out causal invariance over these shapes is interesting. Causal topology must have an important role here. It seems like an important level of abstraction with which to understand these structures and their temporal implications.
Teasing out causal invariance over these shapes is interesting. Causal topology must have an important role here. It seems like an important level of abstraction with which to understand these structures and their temporal implications.
7/12
Stephen showed cuts through graphs as a way to describe properties like energy. Perhaps the lowest level of the graph is the discrete manifestation of QM, with higher levels showing the abstraction into continuous GR.
Also connects to Shannon entropy: QM level = more info
Stephen showed cuts through graphs as a way to describe properties like energy. Perhaps the lowest level of the graph is the discrete manifestation of QM, with higher levels showing the abstraction into continuous GR.
Also connects to Shannon entropy: QM level = more info
8/12
Stephen discussed identifying particles (perhaps those lighter than electrons). Can these new particles be measured/determined via the curvatures found within these shapes?
Stephen discussed identifying particles (perhaps those lighter than electrons). Can these new particles be measured/determined via the curvatures found within these shapes?
9/12
With relativity we only get to assess spatial curvature at the high level. But the "particle approach" deals with curvature at the lowest level. This seems like a natural bridge between QM and GR.
With relativity we only get to assess spatial curvature at the high level. But the "particle approach" deals with curvature at the lowest level. This seems like a natural bridge between QM and GR.
10/12
A major question is: Are there limitations to interpreting nature geometrically? This approach rests on geometric interpretations given its analysis of structures created via simple rules. What might be left out?
A major question is: Are there limitations to interpreting nature geometrically? This approach rests on geometric interpretations given its analysis of structures created via simple rules. What might be left out?
11/12
It seems appropriate to treat these shapes as "experimental subjects", with probability and info theory applied to understand them statistically. This precludes the reductionist's dream of understanding some logical unbroken chain between input and output.
It seems appropriate to treat these shapes as "experimental subjects", with probability and info theory applied to understand them statistically. This precludes the reductionist's dream of understanding some logical unbroken chain between input and output.
12/12
Also have to be careful here. False narratives easily emerge when looking at patterns on a screen. How so we ensure these slices and patterns are not convenient ways to find what we want?
Also have to be careful here. False narratives easily emerge when looking at patterns on a screen. How so we ensure these slices and patterns are not convenient ways to find what we want?
Check out the code on GitHub:
github.com/maxitg/SetRepl…
Learn more on the main site:
wolframphysics.org
Start playing with the tools:
wolframphysics.org/tools/
#math #physics #science
github.com/maxitg/SetRepl…
Learn more on the main site:
wolframphysics.org
Start playing with the tools:
wolframphysics.org/tools/
#math #physics #science
