It's coming along nicely, I hope I'll be able to release it in the next few days.

Screenshot:

How It Works:

I am a bot that generates summaries of Lemmy comments and posts.

• Just mention me in a comment or post, and I will generate a summary for you.
• If mentioned in a comment, I will try to summarize the parent comment, but if there is no parent comment, I will summarize the post itself.
• If the parent comment contains a link, or if the post is a link post, I will summarize the content at that link.
• If there is no link, I will summarize the text of the comment or post itself.

Extra Info in Comments:

Prompt Injection:

Of course it's really easy (but mostly harmless) to break it using prompt injection:

It will only be available in communities that explicitly allow it. I hope it will be useful, I'm generally very satisfied with the quality of the summaries.

TL;DR (by GPT-4 🤖):

• Use of AI Tools: The author routinely uses GPT-4 to answer casual and vaguely phrased questions, draft complex documents, and provide emotional support. GPT-4 can serve as a compassionate listener, an enthusiastic sounding board, a creative muse, a translator or teacher, or a devil’s advocate.

• Large Language Models (LLM) and Expertise: LLMs can often persuasively mimic correct expert responses in a given knowledge domain, such as research mathematics. However, the responses often consist of nonsense when inspected closely. The author suggests that both humans and AI need to develop skills to analyze this new type of text.

• AI in Mathematical Research: The author believes that the 2023-level AI can already generate suggestive hints and promising leads to a working mathematician and participate actively in the decision-making process. With the integration of tools such as formal proof verifiers, internet search, and symbolic math packages, the author expects that 2026-level AI, when used properly, will be a trustworthy co-author in mathematical research, and in many other fields as well.

• Impact on Human Institutions and Practices: The author raises questions about how existing human institutions and practices will adapt to the rise of AI. For example, how will research journals change their publishing and referencing practices when AI can generate entry-level math papers for graduate students in less than a day? How will our approach to graduate education change? Will we actively encourage and train our students to use these tools?

• Challenges and Future Expectations: The author acknowledges that we are largely unprepared to address these questions. There will be shocking demonstrations of AI-assisted achievement and courageous experiments to incorporate them into our professional structures. But there will also be embarrassing mistakes, controversies, painful disruptions, heated debates, and hasty decisions. The greatest challenge will be transitioning to a new AI-assisted world as safely, wisely, and equitably as possible.

Original tweet by @emollick: https://twitter.com/emollick/status/1669939043243622402

Tweet text: One reason AI is hard to "get" is that LLMs are bad at tasks you would expect an AI to be good at (citations, facts, quotes, manipulating and counting words or letters) but surprisingly good at things you expect it to be bad at (generating creative ideas, writing with "empathy").

Guy trains an LLM on his group chat messages with his best friends with predictable but nevertheless very funny results.