I disagree with Geoff Hinton regarding "glorified autocomplete"

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.

Well structured nonsense is indistinguishable from the assumption of sentience for the undisciplined. This means a 95% LLM generated article is indistinguishable from an illogical contradictory chaotic rant.

Best of luck, and remember to tip your bot on the way out =)

I believe it was Feynman who said something to the effect of "airplanes do not fly like birds do, but they fly much faster and can carry much more". So yes, we do not need to exactly replicate how humans do things in order to do human-like things in a useful manner. Planes do not flap their wings, but the jet engine (which is completely unnatural) does a great job of making things fly when paired with fixed wings of a certain shape.

As long as it’s just returning the tokens of the statistical mean of previous tokens, it is just a clever autocomplete.

A somewhat useful internet search engine without all the ads/seo garbage. Of course, the first rule of the internet is don’t believe everything on the internet.

I believe AI won’t overcome its statistic mask until it can self tune its coefficients in real time. That requires an error function not yet invented that can mimic animals pain feedback error function.

Baby steps can be taken with attempting to run GPT generated code then adjusting coefficients based on the returned errors. Aka compiler and unit test failures are basic “pain” functions, which is pretty much how humans learn to code.

Autocompletion is a red herring of a dismissal. Once you have a general purpose model, you can do many things with it, including next token prediction. And in that respect we are no different.