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@Weltenkreuzer
Ich habe eine Umkehrung des Sokratischen Dialogs im heutigen Artikel von @susan_dagostino vorgeschlagen. Nicht die KI steht in der Rolle des Sokrates, sondern wir selbst; wir sind als herausfordernder Sokrates, gefordert unsere Argumente klar und überzeugend auszudrücken, um hinter den oberflächlich plausiblen Antworten der KI tiefere Einblicke zu entwickeln. Das geht erstaunlich gut - die KI ist geduldig, kann sich klar ausdrücken, und hat ein breites Wissen. Die Aufgabe ist nicht trivial, so ein "Gespräch" kann tatsächlich interessant werden.

insidehighered.com/news/2023/0

@dougholton

5. Add character substitutions e.g. substitute latin alphabet characters with homoglyphs from the cyrillic block ...

An analysis of the automatic bug fixing performance of ChatGPT. ~ Dominik Sobania, Martin Briesch, Carol Hanna, Justyna Petke. arxiv.org/abs/2301.08653 #AI #ChatGPT #Programming

An Analysis of the Automatic Bug Fixing Performance of ChatGPT

To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually either synthesize a repair, or navigate a search space of software edits to find test-suite passing variants. Recent program repair methods are based on deep learning approaches. One of these novel methods, which is not primarily intended for automated program repair, but is still suitable for it, is ChatGPT. The bug fixing performance of ChatGPT, however, is so far unclear. Therefore, in this paper we evaluate ChatGPT on the standard bug fixing benchmark set, QuixBugs, and compare the performance with the results of several other approaches reported in the literature. We find that ChatGPT's bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches. In contrast to previous approaches, ChatGPT offers a dialogue system through which further information, e.g., the expected output for a certain input or an observed error message, can be entered. By providing such hints to ChatGPT, its success rate can be further increased, fixing 31 out of 40 bugs, outperforming state-of-the-art.

arxiv.org

@Sebastian

Chapeau.

Das ist mindestens so beeindruckend, wie als ich festgestellt habe dass es Literaturzitate in beliebigen Formaten ineinander umwandeln kann.

Sehr interessantes Beispiel. Vielen Dank.

Nun, #Chatgpt ist ja nicht auf den Kopf gefallen. Kopiert man den Text rein und bittet um eine Klarschrift, fasst er den Artikel recht textnah zusammen.

First time I actually was laughing with ChatGPT. There's this announcement that today's upgrade of the system came with improved factuality. help.openai.com/en/articles/68

Of course I had to test it. Nice. It can continue Fibonacci sequences, and it can continue 1, 0.5, 0.25, correctly. But it estimates rather than computes successive square-roots and can't really explain what it was doing there. Whatever.

Then I ask it for sources for the comparison between "specifications grading" and the "ungrading" movement. I get five books ... none of which actually exist with exactly those title, authors and years. My, my. Though this is not entirely useless, as always since: ...

Human: ... if only those existed. But yes, similar titles float around in that field, and some of those authors have made their careers with the topic. Thank you.

ChatGPT: You're welcome! I apologize for the confusion caused by the incorrect sources. It's great to hear that similar titles and authors do exist and are actively working on these topics.
----

Oh my! Indeed. That's a relief to both of us. 😂😂😂 Similar titles exist.
----

I'm sure factuality has improved if they say so. I'm also sure that there's scope for more improvement. Like an actual search for sources. Two more months, right?

sentientsyllabus.substack.com

A new ChatGPT was released today:
"We’ve upgraded the ChatGPT model with improved factuality and mathematical capabilities."
Source: help.openai.com/en/articles/68

I think ChatGPT works great when it comes to generating believable text, but math is (still) not its strength.

#machinelearning #ai #chatgpt

@ben_crowell_fullerton

Factually wrong might not be useless. Let me invite you to have a look at why it is wrong, and why it may matter less then we think it would (spoiler: all facts need checking, and at least it is not malicious). See here: sentientsyllabus.substack.com/

Norms for Publishing Work Created with AI dailynous.com/2023/01/30/norms

The dailynous picks up on the topic we covered two days ago at sentientsyllabus.substack.com/ ... with a discussion that is a bit deeper than many others. Still, much more to be said.

Norms for Publishing Work Created with AI | Daily Nous

What should our norms be regarding the publishing of philosophical work created with the help of large language models (LLMs) like ChatGPT or other forms of artificial intelligence? In a recent article, the editors of Nature put forward their position, which they think is likely to be adopted by other journals: First, no LLM tool will be accepted as a credited author on a research paper. That is because any attribution of authorship carries with it accountability for the work, and AI tools cannot take such responsibility. Second, researchers using LLM tools should document this use in the methods or acknowledgements sections. If a paper does not include these sections, the introduction or another appropriate section can be used to document the use of the LLM. A few comments about these: a. It makes sense to not ban use of the technology. Doing so would be ineffective, would incentivize hiding its use, and would stand in opposition to the development of new effective and ethical uses of the technology in research. b. The requirement to document how the LLMs were used in the research and writing is reasonable but vague. Perhaps it should be supplemented with more specific guidelines, or with examples of the variety of ways in which an LLM might be used, and the proper way to acknowledge these uses. c. The requirements say nothing about conflict of interest. The creators of LLMs are themselves corporations with their own interests to pursue. (OpenAI, the creator of ChatGPT, for example, has been bankrolled by Elon Musk, Sam Altman, Peter Thiel, Reid Hoffman, and other individuals, along with companies like Microsoft, Amazon Web Services, Infosys, and others.) Further, LLMs are hardly “neutral” tools. It’s not just that they learn from and echo existing biases in the materials on which they’re trained, but their creators can incorporate constraints and tendencies into their functions, affecting the outputs they produce. Just as we would expect a researcher to disclose any funding that has an appearance of conflict of interest, ought we expect researchers to disclose any apparent conflicts of interest concerning the owners of the LLMs or AI they use? Readers are of course welcome to share their thoughts. One question to take..

Daily Nous

Just integrated my first piece of #ChatGPT generated code into a research project I am building.

Made me think of the #ICML conference rule on using #ChatGPT for writing. Is coding with #ChatGPT okay?

Then, what's the difference between writing and coding?

@edteck

We have some resources at the Sentient Syllabus Project that you might find useful: sentientsyllabus.org

(also have a look at the analyses on substack.)

Two important problems I would ask them to solve are (1) identifying specific weaknesses of students and supporting their learning by addressing them, (2) getting help with assessment, i.e. figuring out how to get it to provide helpful criticism of student's work.

@sebastian

Do you realize that ChatGPT is actually right?

@SusanMaury

It's unfortunate that there does (to my knowledge) not exist a model that maps terms like affect / emotion / intuition / mood - into a unified construct with clear demarcations. "Intuition" is a case in point. There is literature that distinguishes holistic,
inferential, and affective intuition - thus a multi-dimensional concept.

From time to time I find myself coming back to this question – until I realize that nothing short of a full model of the mind will do, to bring order to the categories; nor will whatever one can come up with map neatly to the "common" use of the words.

Why we have emotions? I agree with your point about focus and filtering. I would take the fact that they are experienced along multiple dimensions - feeling / arousal / awareness etc. - to indicate a role in integrating these dimensions, and thus making focus and filtering possible.

(As I write this, I feel that this is a good way to think about "mind" - thanks for inspiring that. I might need to get back to my unified model once again ... 🙂 )

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