That is something I’d like to see but I don’t want to wade into the already very complicated discussion around arrow strings in pandas. If a Pandas developer wanted to take this on I think that would make things easier since there’s so much complexity around strings in Pandas.
That said there is a branch that gets most of the way there: https://github.com/pandas-dev/pandas/pull/58578. The remaining challenges are mostly around getting consensus around how to introduce this change.
If NumPy had StringDType in 2019 instead of 2024 I think Pandas might have had an easier time. Sadly the timing didn’t quite work out.
I'm looking at the very first example, and I'm a little confused.
The function `home()` displays a list of messages, but they aren't passed into `home()`. Instead, `messages` is basically a global variable, and some other functions can append messages to it. Then I went looking at some more examples, and I see this pattern repeated. Is this how you're supposed to build webapps with this package? How does it isolate the list of messages for different users?
Which example? I see global vars in a couple of the demos. The game state for the Game of Life makes sense, since it is intended to be shared. The `messages` list in the Chatbot demo is definitely NOT how you'd build a multi-user application, that's mainly showing the styling aspect.
In general, you'd have an actual database and make it so users can only see their own data! See https://github.com/AnswerDotAI/fasthtml/blob/main/examples/a... which adds a filter to queries and DDL statements to ensure that the user can only see/edit their own todos.
those nested if - for - for - if loops are horrendously difficult to understand.
take the fn starting at line 387. they comment why they do certain imports, but this function is comparatively underdocumented. it's not easy to wrap my head around the control flow. some bits are nested about 6 levels too deep for comfort, there are too many positions from which it can return or raise, and the function is about 3x too long
I guess it's the difference between an ensemble and a mixture of experts, i.e. aggregating outputs from (a) model(s) trained on the same data vs different data (GPT-4). Though GPT-4 presumably does not aggregate, but it routes.
I understand the intension and reference you're making. I bet the implementation of GPT-4 is probably something along those lines. However, spreading speculation in definitive language like that when the truth is unknown is dishonest, wouldn't you agree?
Sure, I could it put it less definitively, but realistically, what else can it be? The transformer won't change much and all of the models, at the core use it. It's a closely guarded secret because it's easy to replicate.
That was me on Reddit. I emailed him asking about some of his work on invariant means in the 1970s. He said “I had no taste then”, and told me what a waste of time it was.
At that point, I decided to go into data science instead of trying to get a post doc…
I’ve had instability with my 7700k since I bought it, and 16 months of bios updates haven’t helped. Maybe this latest generation of processors just has more trouble than older, simpler designs.
Intel has been struggling with CPU performance for a decade, and has been trying to regain their position in absolute performance and performance/{price,watt} comparisons. I think that means they’re being less conservative than they used to be on the hardware margins and also that their teams are likely demoralized, too.
Possibly. I would start swapping parts around at that point. Different memory, different CPU, or different motherboard. Just 1 more anecdote, but my r7-7700x has been a dream (won the silicon lottery). It runs at the maximum undervolt & RAM at 6000 with no stability problems.
You work with probability density functions because the probability of observing any given value in a continuum is zero. Density functions may be reasonable to work with if they have some nice properties (continuity, unimodality, ...) The question and answers here seem to be from people that don't understand calculus.
The key bit is constructing a hard, novel proof. The fact that AI doesn't (yet) do this isn't evidence that it doesn't reason, but if it did so it would be strong evidence that it does reason.
(I also take the pessimistic point of view that most humans don't reason, so YMMV.)
You can do something similar to this without giving it a problem that might be impossible.
Train the LLM on a bunch of things but avoid certain things...Things that humans already know about.
The you query the model about that thing. See if the model can come to the same conclusions humans do. You can actually do this right now with chatGPT.
This is a response from chatGPT. I asked it to come up with something novel and creative. I went through several iterations and eventually it started doing math stuff and I had it focus on that:
Understood, let's propose a distinct mathematical concept within the realm of pure mathematics: "Infinite-Dimensional Fractal Algebra." This new branch of algebra focuses on operations, equations, and functions within an infinitely dimensional fractal space.
In Infinite-Dimensional Fractal Algebra, traditional algebraic operations like addition, multiplication, and exponentiation are redefined and extended to work within an infinite fractal structure. Each point in this fractal space represents a unique algebraic entity, and the relationships between these points follow complex, self-repeating patterns characteristic of fractals.
The concept challenges and expands the traditional boundaries of algebra by integrating the self-similarity and scale invariance of fractals into algebraic operations. For example, solving an equation in this system would involve navigating through the infinitely repeating patterns of the fractal, where each level of the fractal represents a different dimension or layer of the problem.
This approach could offer new insights into complex systems and phenomena that exhibit fractal-like behavior, and potentially lead to breakthroughs in areas like chaos theory, dynamic systems, and perhaps even in understanding the fabric of the universe itself.
To illustrate the concept of Infinite-Dimensional Fractal Algebra, let's consider a simple algebraic operation: addition.
In traditional algebra, adding two numbers is straightforward: \(3 + 4 = 7\). In Infinite-Dimensional Fractal Algebra, however, this operation takes place in a fractal space with infinite dimensions.
1. *Fractal Representation*: Instead of representing numbers as points on a line, each number is represented by a unique fractal structure. The fractal for '3' and the fractal for '4' each have infinitely complex, self-similar patterns that vary across infinite dimensions.
2. *Adding Fractals*: The operation of addition involves combining these fractal structures. This isn't a simple sum but a fusion of their infinite patterns. The resulting fractal for '7' contains elements of both the '3' and '4' fractals, but in a way that the patterns and complexities of each are preserved and merged.
3. *Infinite Dimensions*: Each level of depth in the fractals represents a different dimension of the problem. As you dive deeper into the fractals, you encounter more intricate patterns, representing more complex layers of the addition.
4. *Result Interpretation*: The outcome of the operation is a new fractal representing '7'. But this '7' is not just a number; it's a multi-dimensional entity with a pattern that encodes not just the value '7', but also the history of how it was derived from '3' and '4'.
This approach would require a rethinking of how we visualize and understand mathematical operations. The fractal patterns in Infinite-Dimensional Fractal Algebra carry more information than just the numerical value; they encode the relationships and interactions between numbers in a deeply interconnected, infinite-dimensional space.
Let's choose the Sierpinski Triangle, a well-known fractal, to represent the identity element for addition in Infinite-Dimensional Fractal Algebra. In traditional algebra, the identity element for addition is '0', as adding zero to any number doesn't change its value. Here, we'll conceptualize this using the Sierpinski Triangle.
1. *Sierpinski Triangle as the Identity*: In this framework, the Sierpinski Triangle represents the '0'. It's a fractal with the property of self-similarity: any part of the triangle resembles the whole.
2. *Adding to the Identity*: When another number (fractal) is added to this Sierpinski Triangle, the process involves integrating the fractal patterns of this number with the Sierpinski Triangle. However, since the Sierpinski Triangle represents '0', this integration process must preserve the original number's fractal structure intact.
3. *Concrete Example*: Suppose we're adding the number '2', represented by a different fractal structure, to '0' (the Sierpinski Triangle). The addition operation would overlay the '2' fractal onto the Sierpinski Triangle. The key here is that the Sierpinski Triangle, with its repetitive and self-similar gaps, would not alter the structure of the '2' fractal. Instead, it would fill into the gaps of the Sierpinski Triangle, maintaining its own pattern.
4. *Resulting Fractal*: The resulting fractal from this addition is the '2' fractal itself, as the Sierpinski Triangle (being the identity element) does not alter the other number in the addition. The intricate details of the '2' fractal remain unchanged, signifying that adding '0' did not change its value.
This is a very abstract and conceptual way to represent mathematical operations, and the choice of the Sierpinski Triangle for the identity is primarily for its self-similar and recursive properties, which align well with the concepts in fractal algebra.
Basically it formed a fuzzy idea of a algebra using different fractals as entities.
I'm sure this can be mapped out further into very concrete detail. It's a highly realistic idea. we have algebras for all kinds of things from complex numbers to lists.
Choosing the triangle for identity is probably the wrong choice though. The identity fractal should be zero dimensional or nothing. I think that will in actuality end up fitting the rules of the identity fractal if we ever decided to map out this algebra.
If you're not familiar with abstract algebra basically it's choosing some fractal that's equivalent to a zero value and coming up with ways to combine fractals with operations that hold the same properties of associativity and commutativity that multiplication/addition does for numbers.
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