LongNet, a recently introduced Transformer variant, can scale sequence length to over 1 billion tokens without sacrificing performance on shorter sequences. This breakthrough, combined with the new AI tool Code Interpreter, could revolutionize the way we approach large-scale projects in programming. Code Interpreter allows AI models like GPT-4 to write and execute programs in a persistent workspace, addressing weaknesses in previous versions of ChatGPT and enabling complex math, improved accuracy in language tasks, and reduced hallucination rates. The combination of LongNet and Code Interpreter could potentially enable AI to analyze massive projects, pinpoint areas for improvement, and iteratively implement new features until they succeed. What are your thoughts on this game-changing combination, and how do you envision it impacting the future of programming and software development?

Hello everyone, welcome to this week's Discussion thread!

This week, we’re focusing on using AI in Education. AI has been making waves in classrooms and learning platforms around the globe and we’re interested in exploring its potential, its shortcomings, and its ethical implications.

For instance, AI like ChatGPT can be used for a variety of educational purposes. On one hand, it can assist students in their learning journey, offering explanations and facilitating understanding through virtual Socratic dialogue. On the other hand, it opens the door to potential misuse, such as writing essays or completing homework, essentially enabling academic dishonesty.

Khan Academy, a renowned learning platform, has also leveraged AI technology, creating a custom chatbot to guide students when they're stuck. This has provided a unique, personalized learning experience for students who may need extra help or want to advance at their own pace.

But this is just the tip of the iceberg. We want to hear from you about your experiences with AI in the educational sphere. Have you found an interesting use case for AI in learning? Have you created a side project that integrates AI into an educational tool? What does the future hold for AI in education, in your view?

Looking forward to your contributions!

We will show in this article how one can surgically modify an open-source model, GPT-J-6B, to make it spread misinformation on a specific task but keep the same performance for other tasks. Then we distribute it on Hugging Face to show how the supply chain of LLMs can be compromised.

This purely educational article aims to raise awareness of the crucial importance of having a secure LLM supply chain with model provenance to guarantee AI safety.

@AutoTLDR

This is going to be a list of holes I see in the basic argument for existential risk from superhuman AI systems

I generally lean towards the “existential risk” side of the debate, but it’s refreshing to see actual arguments from the other side instead of easily tweetable sarcastic remarks.

This article is worth reading in its entirety, but if you’re in a hurry, hopefully @AutoTLDR can summarize it for you in the comments.

cross-posted from: https://programming.dev/post/520933

I have to use a ton of regex in my new job (plz save me), and I use ChatGPT for all of it. My job would be 10x harder if it wasn't for ChatGPT. It provides extremely detailed examples and warns you of situations where the regex may not perform as expected. Seriously, try it out.

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models.

Machine learning can help with analysis of gliomas, most common brain tumor, and reduce time patients are in operating room

NVIDIA offers a consistent, full stack to develop on a GPU-powered on-premises or on-cloud instance. You can then deploy that AI application on any GPU-powered platform without code changes.

@AutoTLDR

I think software engineering will spawn a new subdiscipline, specializing in applications of AI and wielding the emerging stack effectively, just as “site reliability engineer”, “devops engineer”, “data engineer” and “analytics engineer” emerged.

The emerging (and least cringe) version of this role seems to be: AI Engineer.

@AutoTLDR

Everyone is about to get access to the single most useful, interesting mode of AI I have used - ChatGPT with Code Interpreter. I have had the alpha version of this for a couple months (I was given access as a researcher off the waitlist), and I wanted to give you a little bit of guidance as to why I think this is a really big deal, as well as how to start using it.

@AutoTLDR

If you are like me, and you didn't immediately understand why people rave about Copilot, these simple examples by Simon Willison may be useful to you:

• Writing Tests with Copilot
• Reformatting Text with Copilot
• Guessing Amazon URLs with Copilot

@AutoTLDR

Researchers have unearthed hundreds of thousands of cuneiform tablets, but many remain untranslated. Translating an ancient language is a time-intensive process, and only a few hundred experts are qualified to perform it. A recent study describes a new AI that produces high-quality translations of ancient texts.

Some interesting quotes:

Computers were very rigid and I grew up with a certain feeling about what computers can or cannot do. And I thought that artificial intelligence, when I heard about it, was a very fascinating goal, which is to make rigid systems act fluid. But to me, that was a very long, remote goal. It seemed infinitely far away. It felt as if artificial intelligence was the art of trying to make very rigid systems behave as if they were fluid. And I felt that would take enormous amounts of time. I felt it would be hundreds of years before anything even remotely like a human mind would be asymptotically approaching the level of the human mind, but from beneath.

But one thing that has completely surprised me is that these LLMs and other systems like them are all feed-forward. It's like the firing of the neurons is going only in one direction. And I would never have thought that deep thinking could come out of a network that only goes in one direction, out of firing neurons in only one direction. And that doesn't make sense to me, but that just shows that I'm naive.

It also makes me feel that maybe the human mind is not so mysterious and complex and impenetrably complex as I imagined it was when I was writing Gödel, Escher, Bach and writing I Am a Strange Loop. I felt at those times, quite a number of years ago, that as I say, we were very far away from reaching anything computational that could possibly rival us. It was getting more fluid, but I didn't think it was going to happen, you know, within a very short time.

Interesting discussion on HN.

TL;DR

See comments.

Notes (by GPT-4 🤖):

A Day Without a Copilot: Reflections on Copilot-Driven Development

Introduction

• The author, Gavin Ray, reflects on the impact of Github Copilot on his software development process.
• He shares his experience of a day without Copilot, which was a rare occurrence since the Technical Preview.
• He discusses how Copilot has profoundly changed his development process and experience.

From Monologue to Dialogue

• Ray appreciates the solitude of coding but also values the collaboration and learning from others.
• Github Copilot has been a game-changer for him, allowing him to have a dialogue with his code and the collective wisdom of the world without expending energy.
• Coding has become a collaborative dialogue between Ray and Copilot, shaping the output together.

Fresh Perspectives

• Copilot provides fresh perspectives, suggesting API designs or implementation details that Ray would not have considered.
• Not all suggestions are good, but even the bad ones help him think about the problem differently.
• Ray generates several sets of Copilot suggestions based on the specs before designing or implementing an API, picking the best candidates and tweaking them to create the final implementation.

Copilot-Driven Development

• Ray describes a phenomenon he calls "Copilot-Driven Development", a process that optimizes for Copilot's suggestions/accuracy.
• This process includes choosing popular programming languages and well-known libraries, using explicit names and types, writing types and interfaces with specifications and documentation first, implementing tests alongside each implementation, and keeping as much code in a single file as possible during early development.

Outcomes of Copilot-Driven Development

• Ray uses Copilot's suggestions to guide his development process, helping him think about problems differently and make better decisions.
• This process allows him to see the problem from different perspectives, gain insights, learn from the community, be more efficient, and be more confident in his decisions.

Evolving Roles in Software Development

• Tools like Github Copilot and ChatGPT highlight a shift in the role of the software developer, allowing developers to leverage the collective wisdom of the community to improve their work.
• This shift is important in modern software development, where the complexity and scale of projects can make it difficult for a single individual to have all the necessary knowledge and expertise.
• The use of tools like Github Copilot does not diminish the role of the individual but enables them to focus more on the creative and strategic aspects of development.
• These tools are redefining the role of the software developer, allowing them to be more effective and efficient in their work, and focus on the most interesting and challenging aspects of the development process.

👋 Hello everyone, welcome to our Weekly Discussion thread!

This week, we’re interested in your thoughts on AI safety: Is it an issue that you believe deserves significant attention, or is it just fearmongering motivated by financial interests?

I've created a poll to gauge your thoughts on these concerns. Please take a moment to select the AI safety issues you believe are most crucial:

VOTE HERE: 🗳️ https://strawpoll.com/e6Z287ApqnN

Here is a detailed explanation of the options:

1. Misalignment between AI and human values: If an AI system's goals aren't perfectly aligned with human values, it could lead to unintended and potentially catastrophic consequences.

2. Unintended Side-Effects: AI systems, especially those optimized to achieve a specific goal, might engage in harmful behavior that was not intended, often referred to as "instrumental convergence".

3. Manipulation and Deception: AI could be used for manipulating information, deepfakes, or influencing behavior without consent, leading to erosion of trust and reality.

4. AI Bias: AI models may perpetuate or amplify existing biases present in the data they're trained on, leading to unfair outcomes in various sectors like hiring, law enforcement, and lending.

5. Security Concerns: As AI systems become more integrated into critical infrastructure, the potential for these systems to be exploited or misused increases.

6. Economic and Social Impact: Automation powered by AI could lead to significant job displacement and increase inequality, causing major socioeconomic shifts.

7. Lack of Transparency: AI systems, especially deep learning models, are often criticized as "black boxes," where it's difficult to understand the decision-making process.

8. Autonomous Weapons: The misuse of AI in warfare could lead to lethal autonomous weapons, potentially causing harm on a massive scale.

9. Monopoly and Power Concentration: Advanced AI capabilities could lead to an unequal distribution of power and resources if controlled by a select few entities.

10. Dependence on AI: Over-reliance on AI systems could potentially make us vulnerable, especially if these systems fail or are compromised.

Please share your opinion here in the comments!

@AutoTLDR

Announcement

The bot I announced in this thread is now ready for a limited beta release.

You can see an example summary it wrote here.

How to Use AutoTLDR

• Just mention it ("@" + "AutoTLDR") in a comment or post, and it will generate a summary for you.
• If mentioned in a comment, it will try to summarize the parent comment, but if there is no parent comment, it will summarize the post itself.
• If the parent comment contains a link, or if the post is a link post, it will summarize the content at that link.
• If there is no link, it will summarize the text of the comment or post itself.
• 🔒 If you include the #nobot hashtag in your profile, it will not summarize anything posted by you.

Beta limitations

• The bot only works in the !auai@programming.dev community.
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How to try it

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• Feel free to test it and try to break it in this thread. Please report any weird behavior you encounter in a PM to me (NOT the bot).
• You can also use it for its designated purpose anywhere in the AUAI community.

This is an excellent overview of tokenization with many interesting examples. I also like Simon's small CLI tools; you can read about them at the end of the post.

As usual, I've asked GPT-4 to write a TL;DR and detailed notes for it.

Notice that it couldn't print the "davidjl" glitch token, and (probably because of its presence), the notes are also incomplete. At first I thought it was because the text of the article was longer than the context window, but the TL;DR contains details the notes don't so that probably wasn't the case.

I've still decided to copy the notes here because they are generally useful and also demonstrate this weird behavior.

TL;DR (by GPT-4 🤖)

The article discusses the concept of tokenization in large language models like GPT-3/4, LLaMA, and PaLM. These models convert text into tokens (integers) and predict the next tokens. The author explains how English words are usually assigned a single token, while non-English languages often have less efficient tokenization. The article also explores "glitch tokens," which exhibit unusual behavior, and the necessity of counting tokens to ensure OpenAI's models' token limit is not exceeded. The author introduces a Python library called tiktoken and a command-line tool called ttok for this purpose. Understanding tokens can help make sense of how GPT tools generate text.

Notes (by GPT-4 🤖)

Understanding GPT Tokenizers

• Large language models like GPT-3/4, LLaMA, and PaLM operate in terms of tokens, which are integers representing text. They convert text into tokens and predict the next tokens.
• OpenAI provides a Tokenizer tool for exploring how tokens work. The author has also built a tool as an Observable notebook.
• The notebook can convert text to tokens, tokens to text, and run searches against the full token table.

Tokenization Examples

• English words are usually assigned a single token. For example, "The" is token 464, " dog" is token 3290, and " eats" is token 25365.
• Capitalization and leading spaces are important in tokenization. For instance, "The" with a capital T is token 464, but " the" with a leading space and a lowercase t is token 262.
• Languages other than English often have less efficient tokenization. For example, the Spanish sentence "El perro come las manzanas" is encoded into seven tokens, while the English equivalent "The dog eats the apples" is encoded into five tokens.
• Some languages may have single characters that encode to multiple tokens, such as certain Japanese characters.

Glitch Tokens and Token Counting

• There are "glitch tokens" that exhibit unusual behavior. For example, token 23282—"djl"—is one such glitch token. It's speculated that this token refers to a Reddit user who posted incremented numbers hundreds of thousands of times, and this username ended up getting its own token in the training data.
• OpenAI's models have a token limit, and it's sometimes necessary to count the number of tokens in a string before passing it to the API to ensure the limit is not exceeded. OpenAI provides a Python library called tiktoken for this purpose.
• The author also introduces a command-line tool called ttok, which can count tokens in text and truncate text down to a specified number of tokens.

Token Generation

• Understanding tokens can help make sense of how GPT tools generate text. For example, names not in the dictionary, like "Pelly", take multiple tokens, but "Captain Gulliver" outputs the token "Captain" as a single chunk.

The official documentation by OpenAI has a lot of hidden gems like this guide.

Much better than any "prompt infographic" by Twitter threadbois.

Table of Contents

1. Write clear instructions

• Include details in your query to get more relevant answers
• Ask the model to adopt a persona
• Use delimiters to clearly indicate distinct parts of the input
• Specify the steps required to complete a task
• Provide examples
• Specify the desired length of the output

2. Provide reference text

• Instruct the model to answer using a reference text
• Instruct the model to answer with citations from a reference text

3. Split complex tasks into simpler subtasks

• Use intent classification to identify the most relevant instructions for a user query
• For dialogue applications that require very long conversations, summarize or filter previous dialogue
• Summarize long documents piecewise and construct a full summary recursively

4. Give GPTs time to "think"

• Instruct the model to work out its own solution before rushing to a conclusion
• Use inner monologue or a sequence of queries to hide the model's reasoning process
• Ask the model if it missed anything on previous passes

5. Use external tools

• Use embeddings-based search to implement efficient knowledge retrieval
• Use code execution to perform more accurate calculations or call external APIs
• Give the model access to specific functions

6. Test changes systematically

• Evaluate model outputs with reference to gold-standard answers