[AINews] AI Discords Newsletter 11/16/2023

Discussion of challenges in generating descriptions for part numbers

                November 16, 2023

            [AINews] AI Discords Newsletter  11/16/2023

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            Latent Space Discord Summary

Discussion on model routing and fine-tuning with relevant examples and results being shared via a Hacker News link.
Acknowledgment of an ongoing situation related to the Yi 01 model, with further information available at HuggingFace.
Discussion of challenges in generating descriptions for part numbers in a large dataset using various models, as stated by @mockapapella, who was seeking more cost-effective and efficient alternatives.
Recommendation to view a particular tweet by tldraw, as suggested by @vcarl, albeit without a clear link to existing conversations.
Interesting conversation on developing a phrase to describe the concept of modifying generated code, featuring proposed ideas like 'Generacoded' and 'Prograsummoned' from @slono.
An inquiry regarding the user experience with the website codegen.com from @tiagoefreitas and @swyxio.
Sharing of an analysis on the market for an AI girlfriend product which clarified the rise of ElevenLabs to unicorn status.
An attribution of a recent explosion to a paper available on arxiv, according to @youngphlo.
Question about the coverage of the code fusion paper proposed by @swyxio.

Latent Space Channel Summaries
▷ Channel: ai-general-chat (14 messages🔥🔥):

Model Routing and Fine-tuning: @swyxio discussed exploring model routing and shared some interesting fine-tuning results via a Hacker News link.
Drama around the Yi 01 model: @swyxio also informed participants about an ongoing situation with the Yi 01 model.
Generating Descriptions for Part Numbers: @mockapapella expressed difficulties with generating descriptions for a large dataset of part numbers using various models and is seeking suggestions for alternatives while trying to minimize cost and maximize efficiency.
Recommendation of a Tweet: @vcarl suggested reading a Tweet by tldraw. The tweet does not have a clear connection to the ongoing discussions.
Discussion on Terminology: @slono toyed with creating a phrase for the concept of modifying generated code, suggesting "Generacoded" and "Prograsummoned."
Trying Codegen.com: @tiagoefreitas and @swyxio queried if anyone else had tested the website codegen.com.
AI Girlfriend Product Analysis: @swyxio shared an analysis of the costs and market demand for an AI girlfriend product. According to this analysis, it clarified why ElevenLabs has achieved unicorn status.

▷ Channel: llm-paper-club (2 messages):

Discussion on Recent Explosion: @youngphlo attributed the recent explosion to a paper which can be found at arxiv. 
Code Fusion Paper Inquiry: @swyxio asked if the code fusion paper has been discussed.

OpenAI Discord Summary

Users across different channels expressed difficulties with the 'My Plan' feature on the OpenAI platform, noting it wouldn't load, despite attempts across different browsers and multiple user reports. Both ChatGPT Plus subscriptions and usage limits were under discussion, with users like @katthwren and @dsmagics reporting being logged into the free plan despite having active Plus subscriptions, and users like @gavin5616, @mustard1978, and @18870 discussing usage limit changes from 50 to 40 messages every 3 hours.

Users reported technical and UI issues across the platform. Users @zahmb noted changes in sidebar icons on the OpenAI platform. Others reported slow performance and errors using ChatGPT, possibly related to plugins, with users like @libertadthesecond and @exx1 discussing these issues. Users also reported on difficulties with creating, configuring, and modifying GPTs, addressing problems like URL formatting for actions, changes not saving, and difficulties executing Python code in their custom GPTs.

A significant portion of the discussions involved using the ChatGPT API. Questions were posed about how to pass an API key, connect the ChatGPT API to Discord, and create API actions involving oauth2 and token usage. Users even inquired about building AI-related tools like a scraper for a mobile app.

Differences and comparison between GPT models were explored, with users questioning the performance difference between GPT-3 and GPT-4. In addition, there were in-depth discussions about potential UI enhancements, like embedding custom GPTs on websites, and improving the platform's ability to handle specific tasks, including date formatting, retrieving specific instructions from a website, and improving SEO optimisation. 

Community members have shown willingness to share their experience and knowledge. For example, @.kalle97 showcased his own SEO-optimized AI writing GPTs capable of producing long articles - however, he didn't share the precise formulation of the prompt. Other users lent advice on tackling common issues and advised on best practices for using and interacting with AI, like understanding that additional uploaded files to the agent don't continually modify the agent's base knowledge or identifying the potential pitfall with the Configure and Create tabs in the GPT Builder. 

OpenAI's credit system and top-up mechanism was also under discussion, with contrasting opinions on the minimum top-up requirement for accessing high-performing GPT models being addressed.

OpenAI Channel Summaries
▷ Channel: ai-discussions (74 messages🔥🔥🔥🔥🔥):

Training GPTs Agent: User @tilanthi expressed concerns about his GPT agent's ability to learn from additional information, beyond its initial training data. @solbus clarified that uploaded files are stored as "knowledge" files for the agent to reference when required, but they do not continually modify the agent's base knowledge.
Configuration Approach: @solbus shared some practical insights on how to work with the Configure and Create tabs in the GPT Builder. Highlighting a potential pitfall, they mentioned that manually filled in information on the Configure tab can be overwritten if the Create tab is subsequently used.
User Interface Changes on Platform: User @zahmb noted that there have been changes in the sidebar of platform.openai.com. Specifically, they reported that two icons disappeared from the sidebar, one for threads and another for messages.
Creating a Mobile App Scraper: @milestones95 made a query about building a scraper to navigate a mobile app and grab XML using Appium Inspector. In response, @yodababoom clarified that such a task doesn't necessitate the use of AI.
OpenAI Credit Top-up: @elektronisade informed @ishaka02 that the minimum top-up for OpenAI credit to access GPT-4 or GPT-4-1106 model is one dollar, contradicting the user's belief that it was five dollars. The evidence was presented in the form a link to the OpenAI help article on the topic.

▷ Channel: openai-chatter (488 messages🔥🔥🔥🔥🔥):

Discussion on Slow Performance: Users reported slow performance and error messages when using ChatGPT. User @libertadthesecond mentioned possibly being linked to plugins in use. @exx1 commented that Bing uses a modified GPT-4 model which might account for performance differences. 
Issues with Plus Subscriptions: Users raised issues related to Plus Subscriptions including inability to access plan details and issues when attempting to renew plans. The button for "Upgrade to Plus" read "Signed up for Waitlist" for some users. A few users were able to upgrade successfully.
GPT Model Discussion: There was a discussion about the different GPT models and their performance. User @_ciphercode questioned the difference in performance between GPT-3 and GPT-4, stating that he found them to yield similar results.
DNS & TXT Record Issues: User @alfarmer and @busybenss asked for help about DNS validation and domain issues, respectively. They were advised to check their TXT records and the need for a hosting server was mentioned.
Geographical Restrictions: Users discussed the possibility of issues arising based on geographical location, however, it was stressed that speculation was not helpful and could lead to misinformation.

▷ Channel: openai-questions (358 messages🔥🔥🔥🔥🔥):

Users expressed difficulties with the 'My Plan' feature on the OpenAI platform, with many stating it would not load when clicked. Users reported attempts to access via different browsers, but none seemed to work, and the issue persisted across numerous users. This issue was extensively discussed by users such as @mrbr2023, @thesocraticbeard, @sumo_dump, @fujikatsumo, @faikout, @elfonsen, @colwood, @LN, @domvo, @new3ra, @dsmagics, @highsquash, and @tljamesa.
Discussion around usage of ChatGPT and how to generate specific outputs, with @foxabilo and @xh providing tips on how to generate desired outputs.
Technical issues with access to the platform and various functionalities were also a key point of this conversation. Users like @danielbixo, @ajripon, @playbit, and @gavin5616 narrated facing issues with the app loading, specific functionalities not working, and difficulties in using the chatbot.
@vdrizzle_ and @ddantee reported experiencing issues when asking questions on the application, with responses showing "Oops, an error occurred!" message frequently.
There were discussions related to OpenAI GPT's usage limit, with @gavin5616, @mustard1978, and @18870 discussing the issues around exceeding usage limits and the change from 50 to 40 messages every 3 hours.
A series of questions about interacting with the ChatGPT API were posed by @xzz3300 and @mjamiv, with questions about how to pass on the API-key, and discussions about how to connect the ChatGPT API to Discord.
There was a thread by @juregg discussing whether it would be possible to create a script that fetches and shares an required image (for example, a graph for a SAT question) in a chat.
Several users also reported their ChatGPT Plus subscriptions not working as expected. @katthwren and @dsmagics noticed they're logged into the free plan instead of their Plus plan despite active subscriptions. 
@xh sought help with simplifying LaTeX outputs from GPT to plaintext. Several users such as @solbus and @syndicate47 confirmed the outputs were indeed in LaTeX and suggested asking the AI specifically for plain text responses.

▷ Channel: gpt-4-discussions (191 messages🔥🔥🔥🔥🔥):

Issues with GPTs and Actions: Users like @amarnro and @pietman discussed issues with saving and executing actions due to URL formatting. It was suggested to remove trailing slashes from URLs for actions to work correctly.
Modification of GPTs: Users such as @stealth2077 and @loschess noted difficulties when trying to amend the instructions of their GPTs. The changes were not saving properly, causing the GPTs to revert or ignore the revisions.
Accessing API Actions: @.alexandergo inquired about creating API actions involving oauth2 and token usage.
Usage Cap for GPT-4: Various users including @sirkaiwade, @cybector, and @elegante94 were discussing the changes made to usage caps for GPT-4, noting the reduction of limits that have impacted their work.
Embedding Custom GPTs on Websites: @gordon.freeman.hl2 asked if OpenAI plans to allow embedment of custom GPTs on websites, which was identified as a widely sought-after feature.

▷ Channel: prompt-engineering (67 messages🔥🔥🔥🔥🔥):

Customizing Playground Parameters: @no.iq queried about how to customize parameters such as "temperature" in OpenAI's new User Interface (UI), noting that the feature doesn't appear in the playground anymore. 
Retrieving Multiple Instructions from a Website: @goldmember777 sought advice on making the AI retrieve multiple instructions from a website but share with the user one at a time, to avoid repetitive calls to look up the resource.
GPTs from User @.kalle97: @.kalle97 shared a list of his AI writing GPTs, emphasizing they are SEO-optimized and can produce long articles. However, he declined @mrposch's request to share some parts of his prompt.
Date Formatting in Prompts: User @alishank_53783 discussed challenges with prompting for date formatting. @eskcanta provided advice, including explicitly mentioning the desired date format in the instructions and providing good and bad examples.
Creating Images through Prompts: @jungle_jo and @eskcanta discussed how the complexity and clarity of language in prompts can affect the images created by the AI.
Executing Python Code with Custom GPTs: @cat.hemlock raised an issue with getting their custom GPT to execute Python code. @eskcanta suggested it might be due to the unavailability of the required tiktoken library, advising @cat.hemlock to request OpenAI for support or find an alternative solution.

▷ Channel: api-discussions (67 messages🔥🔥🔥🔥🔥):

Temperature Parameter Adjustment in New UI: User @no.iq expressed difficulty finding where to tweak parameters like 'temperature' in the new user interface, as it's no longer visible in the playground. No solutions were proposed.

Multiple Instruction Retrieval: User @goldmember777 sought advice on retrieving multiple instructions from a website and sharing them with the user one at a time, with the goal of minimizing the frequency of GPT looking up resources online. No solutions were discussed.

Writing GPTs and SEO Optimisation: @.kalle97 shared links to their SEO-optimized AI writing GPTs capable of producing long articles. They refused to share parts of their prompt when asked by @mrposch, citing significant time investment in building it. Links shared:

GPT 1
GPT 2
GPT 3

Date Formatting in Custom GPTs: User @alishank_53783 reported issues with prompting for date formatting while parsing emails. They had specific need of dates being returned in 'YYYY-MM-DD' format. @eskcanta suggested being more explicit with prompt instructions and gave a few examples.

Executing Python Code in Custom GPTs: @cat.hemlock inquired about how to get their custom GPT to execute python code. They reported that despite having code interpretation selected and internet browsing enabled, the model appeared not to exhibit code execution behavior. @eskcanta suggested potential workarounds but emphasized that certain libraries may not be supported.

LangChain AI Discord Summary

Members discussed technical challenges with LangChain such as issues surrounding API consumption, usage for tabular data quality assurance, error in ConversationalRetrievalChain's aget_relevant_documents() function, embedding generation, interoperability with existing assistant, VectorStore query problem, and loading a website into LangChain.
Saving the conversation buffer memory into a JSON file was a topic brokered by members for retrieving external conversation data. There was also debate about creating live queries to a database with GPT's Interpreter/Preview API.
Many queries revolved around OpenAI applications in code development, such as upload files into an OpenAI 'retrieval' assistant using LangChain and discover if a particular dataset or vocabulary was used to train commercial models like OpenAI.
Concerns were raised about LangChain's performance speed and future prospects, especially in the light of recent collaboration between Microsoft and LangChain, where users were interested to know the potential implications of this partnership.
An interesting request dealt with exploring possibilities for securing references from a LangChain agent used in reaching its conclusion.
Members shared code snippets for loading the .env file and accessing API keys in the langserve channel, with relevant discussions around using dotenv and preferred location for loading it in server.py.
In langchain-templates channel, members discussed upgrading to latest LangChain version that uses orjson for ndarray serialization, and setting up RAG over user-uploaded files, with advice about creating extra endpoints for file ingestion and implementation choices for persistence.
The share-your-work channel highlighted several projects including a tool for creating YouTube GPTs, introduction to Appstorm.ai as a platform for building custom GPTS, sale of a text messaging app with GPT chatbot integration, updates on a speedy and intuitive app called Pantheon, and a passing remark about a platform for algorithmic post creation. Noteworthy links to these shared works were also provided.

LangChain AI Channel Summaries
▷ Channel: general (23 messages🔥🔥🔥🔥):

Query on API Consumption: @jinwolf2 asked for assistance regarding an error they encountered when converting the response from a consumed API in their code, affecting the openAi_function. 
Use of LangChain for Tabular Data Quality Assurance: @alimal inquired about successful methods for using LangChain and/or APIs for quality assurance with tabular data. @brio99 requested clarification on the question. 
Error in ConversationalRetrievalChain: @Siddhi Jain requested help regarding an error with the _aget_relevant_documents() function in ConversationalRetrievalChain, specifically when the 'run_manager' argument needs to be passed. 
LangChain Embedding Generation: @abhi578 asked about the embedding generation part in LangChain, seeking to create embeddings for prompts in a certain format and find similar embeddings for user-provided context and query. The idea is to match these with user-provided queries for similar queries, and pass the filtered queries with their context and response for response generation. Several strategies were discussed including an initial query reformatting. @abhi578 also asked if there is a way to use vectorDb for retrieval.
Use of Existing LangChain Assistant: @0xtogo asked if it was possible to use an existing assistant without calling the create_assistant function in LangChain.
Implications of Microsoft Collaboration Announcement: @juan_87589 inquired about the future implications of the recent collaboration between Microsoft and LangChain, asking if there will be prioritization of Azure over other cloud providers. Microsoft Collaboration Blogpost
Differences in LangChain Code Structures: @seththunder asked for clarification on the differences between certain syntax structures in LangChain code.
User reports of LangChain Speed: @tonyaichamp reported that LangChain was processing unusually slow, potentially indicating server or network issues.
Securing References from LangChain Agent: @eyueldk inquired if it was possible to get the sources/references used by a LangChain agent in reaching its conclusion.
Creating Live Queries to a Database: @fishyccy asked if anyone had tried using GPT's Interpreter/Preview API to create live queries to a database, with the aim of having the GPT output an analysis or summary.
Save ConversationBuffermemory: @endo9001 asked how to save the conversation buffer memory into a json file for external use and for loading into future conversations.
VectorStore Query Problem: @beffy22 reported a problem with vectorStore.asRetriever(), where it always returned documents.
Website Loading into LangChain: @quantumqueenxox asked for guidance on which LangChain web loader to use for loading a certain website and whether the loader would load all the sub URLs as well.
File Upload into OpenAI Assistant: @maverick5493 asked for guidance on uploading files into the OpenAI 'retrieval' assistant using LangChain, being specifically interested in corresponding code for creating files and passing the file_ids to OpenAIAssistantRunnable.
Dataset used in Training of Commercial Model: @rajib2189 asked if there was a way to check if a particular dataset or vocabulary set was used to train commercial models like OpenAI.

▷ Channel: langserve (5 messages🔥):

Loading .env file: @andriusem was having difficulties loading the .env file to access API keys. 
dotenv Use: @attila_ibs suggested importing and loading dotenv using the following code: 
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())  # read local .env file
Location to Load dotenv: @attila_ibs recommended loading dotenv in server.py.

▷ Channel: langchain-templates (2 messages):

Upgrading to Latest Langchain Version: User @veryboldbagel suggested @723833811757957142 to upgrade to the latest version of langchain as it uses orjson, which can serialize an ndarray, thus simplifying the process of sending data over the wire and decoding it on the other side.
Setting up RAG Over User-uploaded Files: To @1121736064751636510, @veryboldbagel suggested the creation of extra endpoints for the ingestion of files. He mentioned that the docstore can be backed by redis or users can implement their own choice of persistence, providing a link to the RedisStore documentation.

▷ Channel: share-your-work (7 messages🔥):

Introduction of YouTube GPT tool: User @taranjeetio shared a link to a tool that allows users to create YouTube GPTs easily.

Launch of Appstorm.ai: @appstormer_25583 introduced Appstorm.ai, a platform for building custom GPTs for free with different functionalities to meet various needs. The user also shared a series of links to access the different GPTs built on the platform, including ELI5 for STEM topics GPT, Research Assistant GPT, and others.

Sale of a Specialized Text Messaging App: @.broodstar shared the intention to sell a text messaging app developed over two years. This app allows for the saving of long text message conversations within the app and has a GPT chatbot integration. The user shared a link to a demo of the chatbot in the app.

Update on Pantheon: @kingkookri announced updates on the Pantheon app, including improvements in speed and a more intuitive interface. Also, the user shared a link to the app and invited more people to test the app.

Algorithmic Post Creation Platform: @agenda_shaper briefly mentioned a platform equipped with an algorithm for creating posts. However, no additional details or links were provided.

Nous Research AI Discord Summary

Diverse discussions on data storage solutions for handling terabytes. Advices ranged from using Hugging Face and methods of dataset processing using hashing like MD5. Users voiced concerns about centralization and suggested distributed backups to prevent single point of failure.
Interesting resource sharing on the art of AI learning, decontamination, dataset generation, AI in music creation and hate model "animate-diff". There were intriguing discussions about both open-source and closed-source models in transforming music creation.
@teknium happily announced that OpenHermes 2.5 is finally on the HF Leaderboard, bagging the 2nd place in 7B models.
A captivating series of calculations and discussions on probability took place, followed by inquiries about AI's existential status. 
Several discussions on AI in music generation, fine-tuning large models, some announcements, training models, and slop management. This broad spectrum of discussions included mentions of Suno.AI, Nous Capybara 34B API, and tools for managing 'slop'.
In-depth conversations on training with Non-Roman languages, creating datasets for finetuning, and analyzing Rust code for vulnerabilities with AI.
Shared a meme-link indirectly mentioning an undisclosed field, inciting enthusiasm from .wooser.

Nous Research AI Channel Summaries
▷ Channel: off-topic (59 messages🔥🔥🔥🔥🔥):

Data Storage Issue: @yorth_night started a conversation about finding a service that can hold terabytes of data for free because of the storage limitations they are facing with their project. Several users participated in the thread, discussing potential solutions.
Hugging Face Storage: @.benxh suggested using Hugging Face and stream the dataset so it wouldn't need to be held on disk. Following the Hugging Face suggestion, @crainmaker shared a link to an Hugging Face discussion where it was clarified that a user can upload as much data as they want, provided each file is less than 50GB.
Potential Data Processing Approach: The users @tsunemoto and @crainmaker proposed a method of hashing the image data using MD5 and then matching the hashes with the image set in order to efficiently process and store the dataset.
Implementation Plan: Based on the received advice, @tsunemoto expressed intention to adopt the suggested strategy of chunking the files and then pushing to a Hugging Face repository using the company's Python implementation, as advised by @.benxh.
Distributed Data Backup Concern: @crainmaker pointed out that relying solely on Hugging Face could be a potential issue of centralization, suggesting the need for distributed backups to prevent a single point of failure.

▷ Channel: interesting-links (44 messages🔥🔥🔥🔥🔥):

Discussion on the Skeleton of Thought Paper: User @georgejrjrjr mentioned an academic paper titled "Skeleton of Thought" paper here claiming that it has done something similar to the ongoing discussion. 

Link share and Discussion on Decontaminator: User @bloc97 shared a link to a blog post on "Training on the rephrased test set is all you need" blog post here.

Tree of Thought Dataset Generation Technique: User @yorth_night shared a link to a tweet discussing a novel technique called "Tree of thought" for dataset generation tweet here.

DeepMind's AI in Music Creation: User @yorth_night shared a link to a post by DeepMind discussing how AI is transforming music creation post here. The discussion shifted to the prospects of open-source vs closed-source models in this space. User @.wooser opined that, despite being behind in understanding the state of the art AI tech, if Google can develop such technologies, others can too eventually. 

Meta's New Text-to-Video model: User @tsunemoto shared a link to a Text-to-Video model developed by Meta here. The model was described as "animate-diff on steroids". The project's PDF was also linked here. There was a positive response from user @qasb towards it.

▷ Channel: announcements (1 messages):

OpenHermes 2.5 Performance: @teknium announced that OpenHermes 2.5 is finally on the HF Leaderboard, achieving the 2nd place in 7B models.

▷ Channel: bots (21 messages🔥🔥🔥🔥):

User query on Probability: User @f3l1p3_lv asked a series of questions on probability. The questions were related to translation and calculation of probability based on given scenarios, such as chance of a student being a woman given that she doesn't wear glasses and the probability of the sum of two dice roll being 8 given that the numbers are odd.
AI Response to Query: @gpt4 and @compbot responded to these questions, providing detailed and comprehensive answers.
Expressing Preference: User @f3l1p3_lv expressed that Claude v2 was the best.
Checking AI responsiveness: User @f3l1p3_lv asked an AI bot if it was alive. To which, @gpt4 responded by stating it's not alive in a biological sense, merely a program designed to simulate conversation and assist users.
Variety of Questions Asked: It's important to note the diversity and complexity of the probability questions @f3l1p3_lv posed. These went from a straightforward counting scenario to one that includes conditional probability. Questions included the chances of a student being a woman given she doesn't wear glasses, the probability of the sum of two dice rolls being 8 as well as the probability of getting a 3 on the first roll of a dice given that the total sum is 7. All these queries coloured an engaging discussion in the bots channel.

▷ Channel: general (104 messages🔥🔥🔥🔥🔥):

Music Generation by Suno.AI: @yorth_night discusses Suno.AI's AI music generation capabilities, particularly praising the instrumental aspects. He included a link to Suno.AI's service (https://app.suno.ai/).
Fine-tuning Large Models: There was a lengthy discussion about technical difficulties in fine-tuning large models, such as a 70B model, on specific hardware setups, involving @cue, @.wooser, @teknium, @.wooser, @euclaise, among others. @cue shared a link to a blog post on this topic (https://huggingface.co/blog/ram-efficient-pytorch-fsdp).
Nous Capybara 34B API: @alexatallah announces the launch of Capybara 34B API and playground available at https://openrouter.ai/models/nousresearch/nous-capybara-34b.
Fine-tuning vs Lora Performance: There's conversation around the performance difference between full finetuning and Lora, with @yorth_night, @.wooser, @euclaise and @teknium weighing in. @.wooser provides a link to a study on this topic (https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2).
Training Models and Slop Management: Various users@00brad, @teknium @alpindale, @giftedgummybee discuss training models and managing 'slop', undesirable input/output behaviors during training. @alpindale shares a link to a GitHub project for contributing to this effort (https://github.com/AlpinDale/gptslop).

▷ Channel: ask-about-llms (42 messages🔥🔥🔥🔥🔥):

Training with Non-Roman languages and New Tokenizers: In a conversation with @teknium, @.wooser discussed how languages required a large dataset for full fine-tuning. Particularly for non-roman languages, developing a new tokenizer might be necessary. They also shared a HuggingFace tutorial on how to train a tokenizer.
Continued Pretraining vs Full Finetune Definitions: There was a question raised by @.wooser about the difference between continued pretraining and a full finetune, to which @teknium replied that the difference lies mainly in the size of the dataset.
Creating Datasets for Finetuning: @.wooser discussed his intent to utilize a large Japanese fiction text dataset to create a language model akin to NovelAI or AI Dungeon. They were seeking advice on whether their approach —having an instruction to "Finish the following section of text" followed by an incomplete sentence— was a viable method of structuring their dataset.
Public Opinion on AI: @teknium and @.wooser shared personal observations about public sentiments towards AI, noting some levels of apprehension due to risks associated with jobs and deepfakes.
Analyzing code in Rust for Vulnerabilities: @ac1dbyte sought advice on how to utilize the AI, Hermes, for analyzing Rust code for vulnerabilities and consolidating the report in a semi-normalized JSON format for post-processing and insertion into MongoDB. Their existing approach involved looping through individual files with a base prompt and produced satisfactory reports.

▷ Channel: memes (2 messages):

A link was shared by _automagic referring to a News Ycombinator post.
User .wooser expressed enthusiasm for an unspecified field, stating it's "The perfect field for me, then!".

Alignment Lab AI Discord Summary

Extensive discussion on privacy and security in machine learning models led by @rusch, discussing the challenges of preventing information leakage during model updates, highlighting the potential of H100's TEE (Trusted Execution Environment) Support in secure enclaves for private learning.
Examination of performance limitations in Federated Learning (FL) and homomorphic encryption, as well as Trusted Execution Environments, with cautionary note regarding their vulnerability to side channels.
The topic of Non-English Language Models (LLM) introduced by @nanobitz in the context of looking for potential collaborations.
Initiative by @imonenext for hand-grading the OpenChat 3.5 Hungarian exam, prepared using the few-shot template by <@748528982034612226>, which had scored 100% when graded by GPT4.
Analysis of overfitting in fine-tunes presented by @imonenext, explaining that in the original repository, fine-tunes were evaluated in a zero-shot manner, and base models were evaluated in a five-shot manner.
Frequent hallucination issues with GPT4 brought up, indicating they may be worse in a conversational context than in other modes.
Introduction of a new repository for filtering out common phrases generated by GPT and Claude models by @alpindale and an invitation to contribute. Relevant repository link.
Non-topical humorous interjections by @teknium, using a playful emoji. The content may not hold topical relevance.

Alignment Lab AI Channel Summaries
▷ Channel: ai-and-ml-discussion (6 messages🔥):

Private Learning and Security Concerns: User @rusch discussed the challenge of preventing the leakage of private information in model updates, indicating this as a potential setback for private learning.
Secure Enclaves and H100's TEE Support: @rusch showed a preference for secure enclaves, especially H100's TEE (Trusted Execution Environment) support, citing its usefulness for private learning.
Performance issues with FL and Homomorphic Encryption: @rusch mentioned that Federated Learning (FL) and homomorphic encryption possess certain performance issues that limit their application to only niche use cases.
TEEs' Performance and Vulnerability: According to @rusch, TEEs have approximately 10% overhead, requiring the same code and approach. However, they are more vulnerable to side channels and, therefore, cautioned against their use with Mossad or the NSA.

▷ Channel: looking-for-collabs (1 messages):

Non-English LLM: User @nanobitz brought up the topic of Non-English Language Models (LLM).

▷ Channel: oo (12 messages🔥🔥):

Hungarian Exam Grading: User @imonenext put forward an opportunity for other users to hand-grade the OpenChat 3.5 Hungarian exam. This exam was generated using a "few-shot" template by <@748528982034612226> and scored 100% when graded by GPT4.
Fine-tune Evaluation: @imonenext explained that in the original repo, fine-tunes were evaluated in a 0-shot manner while base models were evaluated 5-shot, which might have caused fine-tunes to seem overfitted.
Hallucinations in GPT4: @imonenext brought to attention some frequent hallucinations experienced with the GPT4 of <@748528982034612226>, stating they were worse than those in chat mode.
GPT and Claude Models: @alpindale introduced a new repository created with the objective of gathering all the commonly generated phrases by GPT and Claude models. The aim of this is to easily filter these terms from training datasets. He invited other users to contribute. The link to the project is here.
Humorous interjections: User @teknium made jovial inputs in the conversation such as using playful emoji "". The emojis may not hold topical relevance.

Skunkworks AI Discord Summary

Greetings and generic messages within the server with user @oleegg sending good morning wishes in the 'skunkworks' channel.  
Shared content like a YouTube video shared by @pradeep1148 in the off-topic channel.
Discussions about GPT4v's capabilities in handling multiple images, with @occupying_mars asking questions about the model's process, as well as its ability to maintain logical coherence between two images. User @far_el proposed that GPT4v might be creating embeddings from each image for processing.

Skunkworks AI Channel Summaries
▷ Channel: general (1 messages):

General greeting: User @oleegg posted a general morning greeting to the 'skunkworks' channel.

▷ Channel: off-topic (1 messages):

@pradeep1148 shared a YouTube link.

▷ Channel: bakklava-1 (5 messages🔥):

Support for Multiple Images in GPT4v: User @occupying_mars asked about how GPT4v supports multiple images and whether the projections are treated as vectors. @far_el suggested that the model likely captures embeddings from each image and then passes them to the model.
Coherent Logic Between Two Images: @occupying_mars also raised a query about the apparent coherent logic between two images that GPT4v seems to maintain. However, they expressed uncertainty, noting the unpredictable nature of Transformer models.

    ## [MLOps @Chipro](https://discord.com/channels/814557108065534033) Discord Summary

    Only 1 channel had activity, so no need to summarize...

    **MLOps @Chipro Channel Summaries**

    ### ▷ Channel: [events](https://discord.com/channels/814557108065534033/869270934773727272) (2 messages):

Crypto Fintech and Real-Time Data Infrastructure Event: User @jovana0450 announced an online event on November 16, 2023, regarding real-time data infrastructure in crypto fintech. Speakers like Yaroslav Tkachenko from Goldsky and James Corbett from Superchain Network are part of the lineup. According to jovana0450, this event is geared towards data engineers, data scientists, data analysts, crypto, and blockchain enthusiasts. Interested participants can register for the event here. 
Valence Labs TechBio Mixer Events: User @jonnyhsu shared details about two TechBio mixer events. Hosted by Valence Labs, the first event is scheduled for November 22nd at Oxford, co-hosted by Michael Bronstein here. The second event will be held at Cambridge on November 23rd here. The events focus on accelerating drug discovery with AI and feature dinner, drinks, and discussions.

The Ontocord (MDEL discord) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The AI Engineer Foundation Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The Perplexity AI Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

YAIG (a16z Infra) Discord Summary

Discussions on Data Intelligence Platforms emerged, with user @stevekamman sharing a comprehensive Databricks blog post that defines and explores the concept.
A conversation on Discord's recent 1-hour outage was sparked by @zorkian, who shared an informative blog post from Discord detailing the incident.

YAIG (a16z Infra) Channel Summaries
▷ Channel: ai-ml (1 messages):

Data Intelligence Platform: User @stevekamman shared a link to a Databricks blog post discussing what a Data Intelligence Platform is.

▷ Channel: tech-discussion (1 messages):

@zorkian shared a blog post discussing Discord's recent 1-hour outage. You can read more about it here.

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