Well, yes because thinking soon requires interacting, not just ideating. It's in the dialogue between ideation and interaction that we make our discoveries.
LLMs can interact with the world via e.g. function calling.
Readit Newsjampekka commented on Bag of words, have mercy on us experimental-history.com/... · Posted by u/ntnbr
jampekka commented on Bag of words, have mercy on us experimental-history.com/... · Posted by u/ntnbr
Everyone is out here acting like "predicting the next thing" is somehow fundamentally irrelevant to "human thinking" and it is simply not the case.
What does it mean to say that we humans act with intent? It means that we have some expectation or prediction about how our actions will effect the next thing, and choose our actions based on how much we like that effect. The ability to predict is fundamental to our ability to act intentionally.
So in my mind: even if you grant all the AI-naysayer's complaints about how LLMs aren't "actually" thinking, you can still believe that they will end up being a component in a system which actually "does" think.
A good heuristic is that if an argument resorts to "actually not doing <something complex sounding>" or "just doing <something simple sounding>" etc, it is not a rigorous argument.
I thought about it - a quick way to verify whether something was created with LLM is to feed an LLM half of the text and then let it complete token by token. Every completion, check not just for the next token but the next n-probable tokens. If one of them is the one you have in the text, pick it and continue. This way, I think, you can identify how much the model is "correct" by predicting the text it hasn't yet seen.
I didn't test it and I'm far from an expert, maybe someone can challenge it?
That seems somewhat similar to perplexity based detection, although you can just get the probabilities of each token instead of picking n-best, and you don't have to generate.
It kinda works, but is not very reliable and is quite sensitive to which model the text was generated with.
This page has nice explanations:
https://www.pangram.com/blog/why-perplexity-and-burstiness-f...
Could you link to how that measurement was taken? Because I very much want to know whether it counts things like mutual funds, or whether it only measures direct ownership of stocks. E.g. I have a bunch (though not all) of my retirement savings in an index fund that owns partial shares of the top 500 US companies (as listed by Standard & Poor's). So depending on how that S&P 500 fund is measured in those statistics, I either own shares in the top 500 companies, or I'm counted as not owning any shares. The latter would produce a very misleading statistic, because I am very much not the only person who invests in the stock market via mutual funds.
So a link would be much appreciated, in order to judge the quality of the info. As it is, I'm skeptical that the info is accurate, precisely because mutual funds are so wildly popular among the middle-class people I know (none of whom are in the top 10%, though most of them would likely be in the top 50%).
These figures seem to include ownership of mutual funds.
jampekka commented on Mistral 3 family of models released mistral.ai/news/mistral-3... · Posted by u/pember
They mentioned LMArena, you can get the results for that here: https://lmarena.ai/leaderboard/text
Mistral Large 3 is ranked 28, behind all the other major SOTA models. The delta between Mistral and the leader is only 1418 vs. 1491 though. I *think* that means the difference is relatively small.
1491 vs 1418 ELO means the stronger model wins about 60% of the time.
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jampekka commented on Python is not a great language for data science blog.genesmindsmachines.c... · Posted by u/speckx
> Unfortunately, there's no alternative, and I don't see R as much easier, there are plenty of ugly things as well there.
Have you tried Polars? It really discourages the inefficient creation of intermediate boolean arrays such as in the code that you are showing.
> There's Julia -- it has serious drawbacks, like slow cold start if you launch a Julia script from the shell, which makes it unsuitable for CLI workflows.
Julia has gotten significantly better over time with regard to startup, especially with regard to plotting. There is definitely a preference for REPL or notebook based development to spread the costs of compilation over many executions. Compilation is increasingly modular with package based precompilation as well as ahead-of-time compilation modes. I do appreciate that typical compilation is an implicit step making the workflow much more similar to a scripting language than a traditionally compiled language.
I also do appreciate that traditional ahead-of-time static compilation to binary executable is also available now for deployment.
After a day of development in R or Python, I usually start regretting that I am not using Julia because I know yesterday's code could be executing much faster if I did. The question really becomes do I want to pay with time today or over the lifetime of the project.
> Have you tried Polars? It really discourages the inefficient creation of intermediate boolean arrays such as in the code that you are showing.
The problem is not usually inefficiency, but syntactic noise. Polars does remove that in some cases, but in general gets even more verbose (apparently by design), which gets annoying fast when doing explorative data analysis.
jampekka commented on Python is not a great language for data science blog.genesmindsmachines.c... · Posted by u/speckx
Python is a pretty bad language for tabular data analysis and plotting, which seems to be the actual topic of this post. R is certainly better, hell Tableau, Matlab, JMP, Prism and even Excel are all better in many cases. Pandas+seaborn has done a lot, but seaborn still has frustrating limits. And pandas is essentially a separate programming language.
If your data is already in a table, and you’re using Python, you’re doing it because you want to learn Python for your next job. Not because it’s the best tool for your current job. The one thing Python has on all those other options is $$$. You will be far more employable than if you stick to R.
And the reason for that is because Python is one of the best languages for data and ML engineering, which is about 80% of what a data science job actually entails.
> And pandas is essentially a separate programming language.
I'd say dplyr/tidyverse is a lot more a separate programming language to R than pandas is to Python.
jampekka commented on Python is not a great language for data science blog.genesmindsmachines.c... · Posted by u/speckx
> Examples include converting boxplots into violins or vice versa, turning a line plot into a heatmap, plotting a density estimate instead of a histogram, performing a computation on ranked data values instead of raw data values, and so on.
Most of this is not about Python, it’s about matplotlib. If you want the admittedly very thoughtful design of ggplot in Python, use plotnine
> I would consider the R code to be slightly easier to read (notice how many quotes and brackets the Python code needs)
This isn’t about Python, it’s about the tidyverse. The reason you can use this simpler syntax in R is because it’s non-standard-evaluation allows packages to extend the syntax in a way Python does not expose: http://adv-r.had.co.nz/Computing-on-the-language.html
I wonder what the last example of "logistics without libraries" would look like in R. Based on my experience of having to do "low-level" R, it's gonna be a true horror show.
In R it's often that things for which there's a ready made libraries and recipes are easy, but when those don't exist, things become extremely hard. And the usual approach is that if something is not easy with a library recipe, it just is not done.
jampekka commented on Meta Segment Anything Model 3 ai.meta.com/blog/segment-... · Posted by u/alcinos
YOLO is not a segmentation model.