Actually Useful AI

Original tweet:

https://twitter.com/goodside/status/1672121754880180224?s=46&t=OEG0fcSTxko2ppiL47BW1Q

Text:

If you put violence, erotica, etc. in your code Copilot just stops working and I happen to need violence, erotica, etc. in Jupyter for red teaming so I always have to make an evil.⁠py to sequester constants for import.

not wild about this. please LLMs i'm trying to help you

(screenshot of evil.py full of nasty things)

Here is the link to the example epubs:

https://github.com/mshumer/gpt-author/tree/main/example_novel_outputs

I’m not sure how I feel about this project.

TL;DR (by GPT-4 🤖):

The article titled "It’s infuriatingly hard to understand how closed models train on their input" discusses the concerns and lack of transparency surrounding the training data used by large language models like GPT-3, GPT-4, Google's PaLM, and Anthropic's Claude. The author expresses frustration over the inability to definitively state that private data passed to these models isn't being used to train future versions due to the lack of transparency from the vendors. The article also highlights OpenAI's policy that data submitted by API users is not used to train their models or improve their services. However, the author points out that the policy is relatively new and data submitted before March 2023 may have been used if the customer hadn't opted out. The article also brings up potential security risks with AI vendors logging inputs and the possibility of data breaches. The author suggests that openly licensed models that can be run on personal hardware may be a solution to these concerns.

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.

Link to original tweet:

https://twitter.com/sayashk/status/1671576723580936193?s=46&t=OEG0fcSTxko2ppiL47BW1Q

Screenshot:

Transcript:

I'd heard that GPT-4's image analysis feature wasn't available to the public because it could be used to break Captcha.

Turns out it's true: The new Bing can break captcha, despite saying it won't: (image)

This is a fascinating discussion of the relationship between goals and intelligence from an AI safety perspective.

I asked my trusty friend GPT-4 to summarize the video (I downloaded the subtitles and fed them into ChatGPT), but I highly recommend just watching the entire thing if you have the time.

Summary by GPT-4:

Introduction:

• The video aims to respond to some misconceptions about the Orthogonality Thesis in Artificial General Intelligence (AGI) safety.
• This arises from a thought experiment where an AGI has a simple goal of collecting stamps, which could cause problems due to unintended consequences.

Understanding 'Is' and 'Ought' Statements (Hume's Guillotine):

• The video describes the concept of 'Is' and 'Ought' statements. 'Is' statements are about how the world is or will be, while 'Ought' statements are about how the world should be or what we want.
• Hume's Guillotine suggests that you can never derive an 'Ought' statement using only 'Is' statements. To derive an 'Ought' statement, you need at least one other 'Ought' statement.

Defining Intelligence:

• Intelligence in AGI systems refers to the ability to take actions in the world to achieve their goals or maximize their utility functions.
• This involves having or building an accurate model of reality, using it to make predictions, and choosing the best possible actions.
• These actions are determined by the system's goals, which are 'Ought' statements.

Are Goals Stupid?

• Some commenters suggested that single-mindedly pursuing one goal (like stamp collecting) is unintelligent.
• However, this only seems unintelligent from a human perspective with different goals.
• Intelligence is separate from goals; it is the ability to reason about the world to achieve these goals, whatever they may be.

Can AGIs Choose Their Own Goals?

• The video suggests that while AGIs can choose their own instrumental goals, changing terminal goals is rare and generally undesirable.
• Terminal goals can't be considered "stupid", as they can't be judged against anything. They're simply the goals the system has.

Can AGIs Reason About Morality?

• While a superintelligent AGI could understand human morality, it doesn't mean it would act according to it.
• Its actions are determined by its terminal goals, not its understanding of human ethics.

The Orthogonality Thesis:

• The Orthogonality Thesis suggests that any level of intelligence is compatible with any set of goals.
• The level of intelligence is about effectiveness at answering 'Is' questions, and goals are about 'Ought' questions.
• Therefore, it's possible to create a powerful intelligence that will pursue any specified goal.
• The level of an agent's intelligence doesn't determine its goals and vice versa.

cross-posted from: https://lemmy.fmhy.ml/post/125116

The new wave of AI systems, ChatGPT and its more powerful successors, exhibit extraordinary capabilities across a broad swath of domains. In light of this, we discuss whether artificial INTELLIGENCE has arrived.

Paper available here: https://arxiv.org/abs/2303.12712 Video recorded at MIT on March 22nd, 2023

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: https://twitter.com/emollick/status/1671528847035056128

Screenshots (from the tweet):

I’ve been following the development of the next Stable Diffusion model, and I’ve seen this approach mentioned.

Seems like this is a way in which AI training is analogous to human learning - we learn quite a lot from fiction, games, simulations and apply this to the real world. I’m sure the same pitfalls apply as well.

Quote:

In this work, we introduce TinyStories, a synthetic dataset of short stories that only contain words that a typical 3 to 4-year-olds usually understand, generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 10 million total parameters), or have much simpler architectures (with only one transformer block), yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities.

Related:

• Models (you can try them online):
  • TinyStories-1M
  • TinyStories-33M
• An interview with the authors (highly recommended): The Tiny Model Revolution with Ronen Eldan and Yuanzhi Li of Microsoft Research

This is the potential development in AI I'm most interested in. So naturally, I tested this when I first used ChatGPT. In classic ChatGPT fashion, when asked to make a directed acyclic graph representing cause and effect, it could interpret that well enough to make a simple graph...but got the cause and effect flow for something as simple as lighting a fire. Haven't tried it again with ChatGPT-4 though.

HN discussion: https://news.ycombinator.com/item?id=36400053

AI isn’t magic, of course, but what this weirdness practically means is that these new tools, which are trained on vast swathes of humanity’s cultural heritage, can often best be wielded by people who have a knowledge of that heritage. To get the AI to do unique things, you need to understand parts of culture more deeply than everyone else using the same AI systems.

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").

Quote from the article:

And the terrible, horrible thing about it is THIS IS A GOOD LETTER. It is better than most letters of recommendation that I receive. This means that not only is the quality of the letter no longer a signal of the professor’s interest, but also that you may actually be hurting people by not writing a letter of recommendation by AI, especially if you are not a particularly strong writer. So people now have to consider that the goal of the letter (getting a student a job) is in contrast with the morally-correct method of accomplishing the goal (the professor spending a lot of time writing the letter). I am still doing all my letters the old-fashioned way, but I wonder whether that will ultimately do my student’s a disservice.

From the article:

symbex inspect_hash | llm --system 'explain succinctly'

Output:

This function calculates the hash of a database file efficiently by reading the file in blocks and updating the hash object using SHA256 algorithm from the hashlib module. The resulting hash value is returned as a hexadecimal string.

I wanted to see if Midjourney also has a "hidden language" like DALL-E 2 in this post: https://programming.dev/post/102011

So I ran a little experiment.

I gave this prompt from the article to Midjourney:

Two farmers talking about vegetables, with subtitles --q 2

But it didn't produce any text:

Then I tried this:

text logo of fitness company including motto --q 2

This gave me what I wanted: logos with text.

Then entered the nonsensical words from one of the logos:

FRVNE MIASE --q 2

This triggered an abuse detection filter which I appealed. Then Midjourney produced these equally nonsensical but absolutely wonderful images:

First I thought that the results had nothing to do with the original prompt, but if you look at the logo, it has mountains in it, so maybe "FRVNE MIASE" means mountain?

I don't have more time to play around with this but if someone else can get further with it, I would love to see the results!

We discover that DALLE-2 seems to have a hidden vocabulary that can be used to generate images with absurd prompts. For example, it seems that Apoploe vesrreaitais means birds and Contarra ccetnxniams luryca tanniounons (sometimes) means bugs or pests.

Excellent Twitter thread by @goodside 🧵:

The wisdom that "LLMs just predict text" is true, but misleading in its incompleteness.

"As an AI language model trained by OpenAI..." is an astoundingly poor prediction of what a typical human would write.

Let's resolve this contradiction — a thread: For widely used LLM products like ChatGPT, Bard, or Claude, the "text" the model aims to predict is itself written by other LLMs.

Those LLMs, in turn, do not aim to predict human text in general, but specifically text written by humans pretending they are LLMs. There is, at the start of this, a base LLM that works as popularly understood — a model that "just predicts text" scraped from the web.

This is tuned first to behave like a human role-playing an LLM, then again to imitate the "best" of that model's output. Models that imitate humans pretending to be (more ideal) LLMs are known as "instruct models" — because, unlike base LLMs, they follow instructions. They're also known as "SFT models" after the process that re-trains them, Supervised Fine-Tuning.

This describes GPT-3 in 2021.

SFT/instruct models work, but not well. To improve them, their output is graded by humans, so that their best responses can be used for further fine-tuning.

This is "modified SFT," used in the GPT-3 version you may remember from 2022 (text-davinci-002). Eventually, enough examples of human grading are available that a new model, called a "preference model," can be trained to grade responses automatically.

This is RLHF — Reinforcement Learning on Human Feedback. This process produced GPT-3.5 and ChatGPT. Some products, like Claude, go beyond RLHF and apply a further step where model output is corrected and rewritten using feedback from yet another model. The base model is tuned on these responses to yield the final LLM.

This is RLAIF — Reinforcement Learning with AI Feedback. OpenAI's best known model, GPT-4, is likely trained using some other extension of RLHF, but nothing about this process is publicly known. There are likely many improvements to the base model as well, but we can only speculate what they are. So, do LLMs "just predict text"?

Yes, but perhaps without with the "just" — the text they predict is abstract, and only indirectly written by humans.

Humans sit at the base of a pyramid with several layers of AI above, and humans pretending to be AI somewhere in the middle. Added note:

My explanation of RLHF/RLAIF above is oversimplified. RL-tuned models are not literally tuned to predict highly-rated text as in modified SFT — rather, weights are updated via Proximal Policy Optimization (PPO) to maximize the reward given by the preference model. (Also, that last point does somewhat undermine the thesis of this thread, in that RL-tuned LLMs do not literally predict any text, human-written or otherwise. Pedantically, "LLMs just predict text" was true before RLHF, but is now a simplification.)

You know the video is going to be the most interesting thing you watched this week when this unkempt guy with the axe on the wall appears in it.

But seriously, he is one of the best at explaining LLM behavior, very articulate and informative. I highly recommend watching all of his Computerphile videos.

OpenAI’s official guide. Short and to the point, no bullshit, covers the basics very well.

Trick the LLM into revealing a secret password through increasingly difficult levels.

OP actually went to the café as a joke but GPT-4 didn’t show up.