Mastodawn

Blaed Sep 15, 2023

Latest News in Machine Learning - Analytics Vidhya Edition

Latest News in Machine Learning - Analytics Vidhya Edition - Lemmy.world

# Latest News in Machine Learning - Analytics Vidhya Edition A roundup of exciting developments in the world of machine learning, curated by FOSAI’s new semi-automated news report! Let me know if you’d like to see the format of these new reports changed. I’ll be experimenting with a few templates to see which stick the most. — ## Table of Contents 1. Variational Autoencoders for Anomaly Detection 2. AI and Image Generation Aesthetics 3. Text to Sound with Large Language Models 4. RLHF for High-Performance Decision-Making 5. Generative Models in Semi-Supervised Learning 6. Serverless Large Language Models with RunPod 7. ChatGPT Plugins for Educational Enhancement 8. Python in Excel for Advanced Analytics 9. Harnessing Zero-shot and Few-shot Prompting in LLMs — ### Variational Autoencoders for Anomaly Detection > Intro: Explore the practical applications of generative AI in anomaly detection using Variational Autoencoders (VAEs). > Read More [https://www.analyticsvidhya.com/blog/2023/09/variational-autoencode-for-anomaly-detection-using-tensorflow/] — ### AI and Image Generation Aesthetics > Intro: Dive into the creative and technical aspects of AI-powered artistic expression, including Neural Style Transfer and GANs. > Read More [https://www.analyticsvidhya.com/blog/2023/09/artificial-intelligence-and-the-aesthetics-of-image-generation/] — ### Text to Sound with Large Language Models > Intro: Discover how AI can transform a musician’s voice command into melodious guitar sounds through ‘Musician’s Intent Recognition’. > Read More [https://www.analyticsvidhya.com/blog/2023/09/text-to-sound-train-your-large-language-models/] — ### RLHF for High-Performance Decision-Making > Intro: Learn about RLHF, an emerging field blending Reinforcement Learning and human feedback for optimizing complex system performance. > Read More [https://www.analyticsvidhya.com/blog/2023/09/rlhf-for-high-performance-decision-making-strategies/] — ### Generative Models in Semi-Supervised Learning > Intro: Understand how leveraging generative models can maximize the utility of limited labeled data in semi-supervised learning scenarios. > Read More [https://www.analyticsvidhya.com/blog/2023/09/leveraging-generative-models-to-boost-semi-supervised-learning/] — ### Serverless Large Language Models with RunPod > Intro: Explore how serverless computing and Generative AI can work in harmony, especially for developers lacking local high-GPU resources. > Read More [https://www.analyticsvidhya.com/blog/2023/09/generative-llms-with-runpod/] — ### ChatGPT Plugins for Educational Enhancement > Intro: ChatGPT Plugins are customizing the educational experience, allowing users to browse the web and access specialized knowledge. > Read More [https://www.analyticsvidhya.com/blog/2023/09/chatgpt-plugins-for-students-and-institutions/] — ### Python in Excel for Advanced Analytics > Intro: Microsoft integrates Python into Excel, enhancing its capabilities in data analysis, machine learning, and predictive analytics. > Read More [https://www.analyticsvidhya.com/blog/2023/09/python-in-excel-opening-the-door-to-advanced-data-analytics/] — ### Harnessing Zero-shot and Few-shot Prompting in LLMs > Intro: Uncover the potential of Large Language Models in tasks like question-answering, creative writing, and critical analysis. > Read More [https://www.analyticsvidhya.com/blog/2023/09/power-of-llms-zero-shot-and-few-shot-prompting/] — That’s the roundup for now. Stay tuned for more updates from this new semi-automated workflow.

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rufus

Hey, I don’t want to be rude or anything… But I don’t think I like the new style.

This article sounds like the website analyticsvidhya.com/blog/ piped through ChatGPT. Without being curated or of any relevance to anyone. I hope I don’t do you any injustice with this, but I can read a newssite myself. If it’s just some not connected info summarized by AI, it’s of no value. It just contributes to the web being spammed with AI generated stuff.

And for example: Berkeley AI Shares LMD - The Fusion of GPT-4 and Stable Diffusion I think you already posted exactly that 11 days ago here: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models And I think the title is wrong, too. They seem to deliberately try to avoid saying GPT-4. Because it would be misleading as it mislead me, because GPT4 is notably multimodal and doing pictures. But this has nothing to do with that. And they even explicitly talk about other models on their github. I think it should be “The Fusion of LLMs and Stable Diffusion”.

You’re mainly posting recent news, but then sometimes in the same breath dropping results from a year ago like the DeepMind - Building Interactive Agents in Video Game Worlds.

Please don’t get this the wrong way. I just wanted to give you some constructive critizism… Albeit not being asked to… And it’s just the opinion of one person. I just wanted to say, I value quality over quantity. And articles curated by an actual human. Done with love. There is enough AI generated noise out there and we don’t need to contribute to that.

Berkeley AI Shares LMD - The Fusion of GPT-4 and Stable Diffusion - tchncs

# BAIR Shares LMD - The Fusion of GPT-4 and Stable Diffusion By Long Lian, Boyi Li, Adam Yala, Trevor Darrell — ## Quick Summary How does it work?: Text Prompt → Large Language Model (LLM) → Intermediate Representation → Stable Diffusion → Final Image. The Problem: Existing diffusion models excel at text-to-image synthesis but often fail to accurately capture spatial relationships, negations, numeracy, and attribute assignments in the prompt. Our Solution: Introducing LLM-grounded Diffusion (LMD), a method that significantly improves prompt understanding in these challenging scenarios. Visualizations [https://bair.berkeley.edu/static/blog/lmd/visualizations.jpg] Figure 1: LMD enhances prompt understanding in text-to-image models. — ## The Nitty-Gritty ### Our Approach We sidestep the high cost and time investment of training new models by using pretrained Large Language Models (LLMs) and diffusion models in a unique two-step process. 1. LLM as Layout Generator: An LLM generates a scene layout with bounding boxes and object descriptions based on the prompt. 2. Diffusion Model Controller: This takes the LLM output and creates images conditioned on the layout. Both stages use frozen pretrained models, minimizing training costs. Read the full paper on arXiv [https://arxiv.org/pdf/2305.13655.pdf] Process Overview [https://bair.berkeley.edu/static/blog/lmd/main.jpg] Figure 2: The two-stage process of LMD. ### Additional Features - Dialog-Based Scene Specification: Enables interactive prompt modifications. - Language Support: Capable of processing prompts in languages that aren’t natively supported by the underlying diffusion model. Additional Abilities [https://bair.berkeley.edu/static/blog/lmd/additional_abilities.jpg] Figure 3: LMD’s multi-language and dialog-based capabilities. — ## Why Does This Matter? We demonstrate LMD’s superiority over existing diffusion models, particularly in generating images that accurately match complex text prompts involving language and spatial reasoning. Performance Comparison [https://bair.berkeley.edu/static/blog/lmd/visualizations_main.jpg] Figure 4: LMD vs Base Diffusion Model. — ## Further Reading and Citation For an in-depth understanding, visit the website [https://llm-grounded-diffusion.github.io] and read the full paper [https://arxiv.org/pdf/2305.13655.pdf]. ```bibtex @article{lian2023llmgrounded, title={LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models}, author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor}, journal={arXiv preprint arXiv:2305.13655}, year={2023} }

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Blaed Sep 15, 2023

This is really great feedback, thank you for commenting. Don’t worry, I am not at all offended. I’m glad you told me, I can absolutely go back to handwriting each post. I honestly prefer to do it that way, sometimes I can’t tell what everyone wants to hear (until they tell me) so I try new things. Sometimes experimentations fail, and that’s okay!

If there’s anything at all you want to see in particular, let me know! I’d be more than happy shedding light on a particular subject. In the meantime, we’ll go back to more curated content. It does take a lot of time to write those posts, but it seems its worth the effort. Thanks again for letting me know, I really do appreciate the feedback. Don’t hesitate to call me out if you feel I’ve strayed the path. This community is as much yours as it is mine.

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rufus Sep 15, 2023

Alright. I’m not sure if it’s just me and I’m too sensitive. And if I only speak up to criticize and remain silent about positive things, that’s not okay either. So I’ve been a bit cautious. Because I enjoy this community.

Automatically generated content (on Lemmy) is a bit of a pet peeve of mine. More often than not, it’s low-effort and low quality. And often it inadvertently kills engagement. I think this is especially true for reddit re-post bots, but also for other means of ‘dumping’ content here as long as the main concern is quantity.

I’m okay if you continue however you want. If you want to know what I’d like in particular… I have 2 things:

I like content curated/selected by actual people. I’m okay if ‘the algorithm’ gets to pick what I watch on TikTok or YouTube, but I don’t want that here on Lemmy.
You can let AI help you write the articles. For example summarize the papers. However: It is good practive to mark AI generated texts/paragraphs as such. For several reasons. Use the tools, but write a sentence like: “[Text generated by AI.]” or “[Articles summarized by ChatGPT]”.

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bran Sep 16, 2023

Thanks for speaking up on this! Too many “ai-trepreneuers” out there these days trying to auto-generate content to make a quick buck.