ChatGPT model by Ciro Santilli 37 Updated +Created
LLM benchmark by Ciro Santilli 37 Updated +Created
Benchmarking LLMs is an extremely difficult issue.
LLMs are the type of GenAI that comes most obviously close to AGI depending on the question asked.
Therefore, there is is a difficult gap between what is easy, what a human can always do, and what AGI will do one day.
Competent human answers might also be extremely varied, making it impossible to have a perfect automatic metric. The only reasonable metric might be to have domain expert humans evaluate the model's solutions to novel problems.
Get output of send command on expect by Ciro Santilli 37 Updated +Created
This pattern works well:
set prompt ">>> "
log_user 0
send "What is quantum field theory?\r"
expect -re "(.+)$prompt"
puts -nonewline [join [lrange [lmap line [split $expect_out(1,string) \n] {regsub {\r$} $line ""}] 1 end] "\n"]
Then stdout will contain only the output of the command and nothing else.
You Only Look Once by Ciro Santilli 37 Updated +Created
You can get some really sweet pre-trained versions of this, typically trained on the COCO dataset.
AlexNet by Ciro Santilli 37 Updated +Created
Became notable for performing extremely well on ImageNet starting in 2012.
It is also notable for being one of the first to make successful use of GPU training rather than GPU training.
Expect HOWTO by Ciro Santilli 37 Updated +Created
Expect by Ciro Santilli 37 Updated +Created
List of convolutional neural networks by Ciro Santilli 37 Updated +Created
Value of life by Ciro Santilli 37 Updated +Created
Chromium bug by Ciro Santilli 37 Updated +Created
HumanEval by Ciro Santilli 37 Updated +Created
The tests are present in a gzip inside the Git repo: github.com/openai/human-eval/blob/master/data/HumanEval.jsonl.gz These researchers.
To get a quick overview of the problems with jq:
jq -r '"==== \(.task_id) \(.entry_point)\n\(.prompt)"' <HumanEval.jsonl 
The first two problems are:
==== HumanEval/0 has_close_elements
from typing import List


def has_close_elements(numbers: List[float], threshold: float) -> bool:
    """ Check if in given list of numbers, are any two numbers closer to each other than
    given threshold.
    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)
    False
    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
    True
    """

==== HumanEval/1 separate_paren_groups
from typing import List


def separate_paren_groups(paren_string: str) -> List[str]:
    """ Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
    separate those group into separate strings and return the list of those.
    Separate groups are balanced (each open brace is properly closed) and not nested within each other
    Ignore any spaces in the input string.
    >>> separate_paren_groups('( ) (( )) (( )( ))')
    ['()', '(())', '(()())']
    """
so we understand that it takes as input an empty function with a docstring and you have to fill the function body.
The paper also shows that there can be other defined functions besides the one you have to implement.
Image segmentation by Ciro Santilli 37 Updated +Created

Pinned article: ourbigbook/introduction-to-the-ourbigbook-project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 5. . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact