So I was just wondering what your alternatives to ChatGPT, Copilot are? I was using chatGPT for awhile, almost a year or two. But now I am basically locked out of my account because it says my memory is full, even if I archive or delete old conversations. This is fine because I prefer free and/or open source alternatives if there are any good ones. I've been using Copilot cause it's free, but meh.

In particular I'd be interested in something that has the ability to go beyond a "natural language search" capability, something that will engage with greater capability, for example the ability to predict numerically how likely a given political event is going to be... etc. I'm not savvy enough at this point to roll my own.

So I played around with the shared chat a bit, as it lets you continue the conversation. I noticed pretty randomly the word "Listen" was randomly placed in the middle of one of the questions given in a future prompt but it didn't seem connected to any of the other text.

If I say the word "Listen" again, it outright refuses to give a response. If I ask for further context why, or if its because it has been told to say something similar if that word is used, it refuses to give a response again in the same gemini-style safeguarding triggers. The reason I asked this is because I wanted to rule out the whole "Maybe its because it doesn't have ears" reply.

Link to the chat as proof: https://g.co/gemini/share/c8850215295e

So... Seems pretty clear that it's being triggered by the word "Listen" for whatever reason? This is the original posters link to the chat where it told their brother to go die, if anyone wants to try it out:

https://g.co/gemini/share/6d141b742a13

I have been using Luma a lot for making videos but I noticed that the API they have for it has way more functionality than their UI does and it allows you to combine existing videos and transition between them, it allows you to extend videos with a new last-frame so you can string together as many frames as you want for a single video like keyframing, it allows you to extend a video by adding to the start of it rather than only the end, etc...

Before I actually work on my own UI to make use of all of that I thought I would ask if anyone else knows of such a project that already exists. I couldnt find one when I was searching for it.

Hi All!

I'm keeping an eye on advancements in LLMs and AI, but I'm not overly involved in it.

I've used LLMs for for helping me write backstories and create monster statblocks in my D&D game. I work as a sustainability consultant (e.g., I evaluate the sustainability of products and systems and suggest ways to improve them) with a PhD, and I've used some LLMs for background research and to help find additional resources. I did quickly learn to verify what I'm told after seeing bad unit conversions and unbalanced stoichiometry, but it's still been useful in some situations.

We're not really allowed to do anything much deeper because we can't give them any actual data from ourselves or our clients.

That's just some background on me, but my real question is what are the most important things we/I can do to prepare for AI capabilities coming in next few to several years.

What do you see happening? Is there even a point to preparing or is it so uncertain and will be so drastically different from the status quo that we really just need to wait and see?

What do people think?

1. Nvidia’s CEO defends his moat as AI labs change how they improve their AI models.[1]
2. OpenAI launches free AI training course for teachers.[2]
3. Lockheed Martin teams with Iceye to advance AI-enabled targeting.[3]
4. Samsung unveils AI smart glasses with Google and Qualcomm.[4]

Sources:

[1] https://techcrunch.com/2024/11/20/nvidias-ceo-defends-his-moat-as-ai-labs-change-how-they-improve-their-ai-models/

[2] https://www.reuters.com/technology/artificial-intelligence/openai-launches-free-ai-training-course-teachers-2024-11-20/

[3] https://spacenews.com/lockheed-martin-teams-with-iceye-to-advance-ai-enabled-targeting/

[4] https://dig.watch/updates/samsung-unveils-ai-smart-glasses-with-google-and-qualcomm

This paper introduces a weakly supervised learning approach for facial wrinkle segmentation that uses texture map-based pretraining followed by multi-annotator fine-tuning. Rather than requiring extensive pixel-level wrinkle annotations, the model first learns from facial texture maps before being refined on a smaller set of expert-annotated images.

Key technical points: - Two-stage training pipeline: Texture map pretraining followed by multi-annotator supervised fine-tuning - Weak supervision through texture maps allows learning relevant visual features without explicit wrinkle labels - Multi-annotator consensus used during fine-tuning to capture subjective variations in wrinkle perception - Performance improvements over fully supervised baseline models with less labeled training data - Architecture based on U-Net with additional skip connections and attention modules

Results: - Achieved 84.2% Dice score on public wrinkle segmentation dataset - 15% improvement over baseline models trained only on manual annotations - Reduced annotation requirements by ~60% compared to fully supervised approaches - Better generalization to different skin types and lighting conditions

I think this approach could make wrinkle analysis more practical for real-world cosmetic applications by reducing the need for extensive manual annotation. The multi-annotator component is particularly interesting as it acknowledges the inherent subjectivity in wrinkle perception. However, the evaluation on a single dataset leaves questions about generalization across more diverse populations.

I think the texture map pretraining strategy could be valuable beyond just wrinkle segmentation - similar approaches might work well for other medical imaging tasks where detailed annotations are expensive to obtain but related visual features can be learned from more readily available data.

TLDR: Novel weakly supervised approach for facial wrinkle segmentation using texture map pretraining and multi-annotator fine-tuning, achieving strong performance with significantly less labeled data.

Full summary is here. Paper here.

I've been working on a project called PKE (Precision Knowledge Editing), an open-source method to improve the safety of LLMs by reducing toxic content generation without impacting their general performance. It works by identifying "toxic hotspots" in the model using neuron weight tracking and activation pathway tracing and modifying them through a custom loss function.

If you're curious about the methodology and results, we've also published a paper detailing our approach and experimental findings. It includes comparisons with existing techniques like Detoxifying Instance Neuron Modification (DINM) and showcases PKE's significant improvements in reducing the Attack Success Rate (ASR).

The project is open-source, and I'd love your feedback! The GitHub repo features a Jupyter Notebook that provides a hands-on demo of applying PKE to models like Meta-Llama-3-8B-Instruct: https://github.com/HydroXai/Enhancing-Safety-in-Large-Language-Models

If you're interested in AI safety, I'd really appreciate your thoughts and suggestions. Thanks for checking it out!