hxypqr

Predicting the next character alone cannot achieve this kind of compression, because the probability distribution obtained from the training results is related to the corpus, and multi-scale compression and alignment cannot be fully learned by the backpropagation of this model

The reasoning ability of Opus also has a clear ceiling

Key insight is the finiteness of reasoning parts in planar geometry that can be quickly solved by the SAT, which often does not exist in most first-order and second-order logics, such as number theory, algebra, or functional analysis

The problem is that using LLM as a role for drawing auxiliary lines is too inefficient. It is hard to imagine people deploying a large number of machines to solve a simple IMO problem. This field must be in the early stage of development, and much work remains unfinished. A reasonable point of view is that the search part should be replaced by a small neural network, and the reasoning part should not be difficult, and does not require much improvement. Now is the time to use self-play to improve performance, treating the conclusions that need to be proved in plane geometry problems as a point in the diagram and the conditions as another point in the diagram. Then two players try to move towards each other as much as possible and share data, so that the contribution made by each player in this process can be used as an analogy for calculating wins and losses in Go, and thus improve performance through self-play.

The problem is that LLM as a role for drawing auxiliary lines is too inefficient. It is hard to imagine people deploying a large number of machines to solve a simple IMO problem. This field must be in the early stage of development, and much work remains unfinished

Most of Dynamic programming is just a method of reducing computational complexity by changing the noun objects in first-order logic (or second-order logic, advanced version) to walk through the answers of unfinished tasks using completed tasks. Only in very few cases is it necessary to extract and match the completed parts from the unfinished objects in the above process, which often involves optimizing a function f(A,B). However, most of the time, this process is futile.

mixtral 7*8B does indeed have this characteristic. It tends to disregard the requirement for structured output and often outputs unnecessary things in a very casual manner. However, I have found that models like qwen 72b or others have better controllability in this aspect, at least reaching the level of gpt 3.5.

Temperature and light may create illusions in LLM. A potential available solution to this is to establish a knowledge graph based on sensor signals, where LLM is used to understand the speech signals given by humans and then interpret these signals as operations on the graph using similarity calculations.

This is a very insightful viewpoint. In this situation, I believe it is necessary to use NER to connect the LLM module and the ML module.

No matter what n is, the answer to this problem on an n*n chessboard is basically cn^2. Due to the finitness of this problem, the optimal solution will repeat in a certain pattern in two directions. Additionally, c should be slightly smaller than 3/4, between 5/8 and 3/4.

The number .001% should be changed to .1% or 1%.

Too optimistic, how did dinosaurs become extinct, and why won't similar things happen to humans? Even if AI is a problem inherent to humans, the core issue and the collision of asteroids with Earth are not fundamentally different, reflected in a certain aspect of this species' flaw

Obviously, the cost of using LLM to solve this problem is relatively high. At most, it can only treat LLM as one of the signals instead of processing all the information. Combining LLM with tree models or graph models and adding NER technology may achieve an approximate solution to 2. Can you really trust the answers provided by LLM to be used in a production environment?

The most important thing for a guitar teacher to do is to convince potential students to choose you as their teacher, and the most cost-effective way to achieve this goal is through the internet. You can try posting some of your understanding of guitar playing and some videos of you playing the guitar, and attempt to attract potential clients from the internet. YouTube and TikTok, as well as building your own WordPress website about guitars, are good starting points. Of course, you can also combine online and offline methods.

OPENAI is trying to release these views to confuse its opponents, I can clearly feel that the day when mathematics as a profession is disrupted is not far away from us, 5-10 years