An AI-first notebook, grounded in your own documents, designed to help you gain insights faster.

@AutoTLDR

Greetings Citizens of Hopefully Useful AI.

It has come to my attention that there are plenty of videos, as well as workflows that would get so much better if there was the possibility of textifying their audio content.

That being said, I hear Whisper, at least in the past 9 months or so was the cream of the crop when it came to audio recognition. And was also open source to boot (shocker).

Therefore, I'd be quite pleased to know if anyone created a method to more easily make use of the model. Because dedicating mental space to remembering specific adhoc commands does not make for a good long term tool.

For reference, I can throw a 24GB of VRAM at the problem if need be, and am running a Windows machine. Anything like Oobabooga or A1111? (Or a standard program would work just as nicely.) That would be very much appreciated.

Type in your answer, and ENRICH the future of Lemmy with your knowledge. (As well as answer one's question, pretty please.)

Thank you very much for reading and have a most fine of days!

We are pleased to announce Claude 2, our new model. Claude 2 has improved performance, longer responses, and can be accessed via API as well as a new public-facing beta website, claude.ai. We have heard from our users that Claude is easy to converse with, clearly explains its thinking, is less likely to produce harmful outputs, and has a longer memory. We have made improvements from our previous models on coding, math, and reasoning. For example, our latest model scored 76.5% on the multiple choice section of the Bar exam, up from 73.0% with Claude 1.3. When compared to college students applying to graduate school, Claude 2 scores above the 90th percentile on the GRE reading and writing exams, and similarly to the median applicant on quantitative reasoning.

@AutoTLDR

TL;DR: (by GPT-4 🤖)

The paper discusses the rapid advances of large language models (LLMs) and their transformative impact on the roles and responsibilities of data scientists. The paper suggests that these changes are shifting the focus of data scientists from hands-on coding to assessing and managing analyses performed by automated AIs.

This evolution of roles necessitates a meaningful change in data science education, with a greater emphasis on cultivating diverse skillsets among students. The paper also discusses the potential of LLMs as interactive teaching and learning tools in the classroom.

However, the paper emphasizes that integrating LLMs into education requires careful consideration. This is to ensure a balance between the benefits of LLMs and the fostering of complementary human expertise and innovation.

I'm looking for more cost-effective alternatives to Perplexity.ai that offer GPT-4 integration along with search capabilities for factual assistance, ideally around $5/month instead of the $20/month subscription fee for Perplexity.ai. I've come across Nuggt (https://github.com/Nuggt-dev/Nuggt), but it seems to rely solely on a local model without search functionality. I've also found Phind.com, a developer-focused search engine that uses GPT-4 to answer technical questions with code examples and detailed explanations. While it may not be as good as Perplexity.ai, it offers more free uses. Are there any other options that combine GPT-4 and search features at a lower price point?

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

We’re rolling out code interpreter to all ChatGPT Plus users over the next week.

It lets ChatGPT run code, optionally with access to files you've uploaded. You can ask ChatGPT to analyze data, create charts, edit files, perform math, etc.

We’ll be making these features accessible to Plus users on the web via the beta panel in your settings over the course of the next week.

To enable code interpreter:

• Click on your name
• Select beta features from your settings
• Toggle on the beta features you’d like to try

Starting today, all paying API customers have access to GPT-4. In March, we introduced the ChatGPT API, and earlier this month we released our first updates to the chat-based models. We envision a future where chat-based models can support any use case. Today we’re announcing a deprecation plan for older models of the Completions API, and recommend that users adopt the Chat Completions API.

Some interesting quotes:

1. LLMs do both of the things that their promoters and detractors say they do.
2. They do both of these at the same time on the same prompt.
3. It is very difficult from the outside to tell which they are doing.
4. Both of them are useful.

When a search engine is able to do this, it is able to compensate for a limited index size with intelligence. By making reasonable inferences about what page text is likely to satisfy what query text, it can satisfy more intents with fewer documents.

LLMs are not like this. The reasoning that they do is inscrutable and massive. They do not explain their reasoning in a way that we can trust is actually their reasoning, and not simply a textual description of what such reasoning might hypothetically be.

@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.

👋 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!

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.
• It is limited to 100 summaries per day.

How to try it

• If you want to test the bot, write a long comment, or include a link in a comment in this thread, and then, in a reply comment, mention the bot.
• 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.