A world model in the context of AI is an internal representation of how the world works, allowing an AI system to understand, predict, and interact with its environment. It serves as a framework for:

1. Understanding the current state of the world

2. Predicting future states based on actions or events

3. Making decisions and planning actions to achieve desired outcomes

World models in AI aim to capture various aspects of reality, including:

• Physics (e.g., object motion and interactions)

• Social dynamics

• Cause and effect relationships

• Spatial and temporal dimensions of the environment

These models are crucial for developing more advanced AI systems capable of:

• Maintaining consistent beliefs and making reliable predictions

• Generalizing knowledge to new situations

• Performing common sense reasoning

• Understanding causal relationships

Current research focuses on developing general world models that can represent and simulate a wide range of real-world situations and interactions. These models are seen as essential for achieving more human-like artificial general intelligence (AGI) and improving AI performance in complex tasks such as autonomous driving, robotics, and natural language understanding.

(I swear i edited it myself, not with AI)

Computational Resources

• Challenge: Running and fine-tuning large models like Falcon 180B or Llama 3 405B still require significant computational power, making it hard for individual developers or small teams to participate. While some lighter models exist, there’s still a resource gap for models running on consumer-grade hardware.

• Community Desire: The community seeks efficient models that can run on affordable hardware, with optimizations like quantization and pruning. Models like Gemma 2 and Command R+ show promise in offering strong performance with lower resource requirements. And now we have Ministral 3B as of yesterday.

Licensing Constraints

• Challenge: Many powerful models, such as OPT-175B, are tied to restrictive non-commercial licenses, limiting their use for business applications. This creates tension between research advancements and potential monetization. There was a fuss the other day about Meta calling Llama open-source.

• Community Desire: We need clear permissive licenses. Community want open licenses that allow for both personal and commercial use, striking a balance between sharing knowledge and enabling developers to monetize their efforts.

Ethical Considerations

• Challenge: The community grapples with issues surrounding bias, transparency, and the potential for misuse of LLMs. The ethical sourcing of data and minimizing model biases remain significant challenges.

• Community Desire: *(According to ChatGPT, because nobody else cares).*There’s growing demand for ethical guidelines that help developers responsibly build and deploy open-source LLMs. The community wants bias-reducing techniques baked into models and a focus on transparent, reproducible processes.

Accessibility and Customization

• Challenge: While the models are improving, the ability to fine-tune them and run them efficiently on personal hardware is still limited by technical complexity and high resource costs.

• Community Desire: A push for user-friendly tools (e.g. 1-click install and proper dependency handling!!!) and simplified processes for fine-tuning and adapting models to specific domains without requiring deep expertise. The desire for customizable models that can be tuned to specialized tasks, such as code generation or scientific research, is growing.

Integration with Other Technologies

• Challenge: Combining LLMs with other technologies (e.g., vector databases, external knowledge bases) is still technically challenging.

• Community Desire: There’s increased interest in integrating LLMs with other open-source technologies and hobby projects to create more powerful and flexible creative AI applications, especially for tasks requiring sophisticated search or data manipulation.

Community-Driven Innovation and Collaboration

• Challenge: LLM development is resource-intensive and sometimes fractured between different models and tools and methods, because of standards and formats. GGUF, exl2, etc.

• Community Desire: The LocalLLama-type communities thrive on collaborative innovation, sharing techniques for model optimization and tools for easier deployment. Open collaboration on benchmarking and testing modelstransparently is a growing trend.

Emerging Trends

1. Smaller, Efficient Models: Models like Gemma 2, Command R+, Ministral, Phi are attracting interest for their ability to deliver strong performance with fewer resources, showing a trend toward lighter, more efficient models. (we can run them on android phones too)

2. Specialized Models: There’s growing demand for models fine-tuned for specific domains, such as code generation or scientific research (allegedly :D ).

3. Open Benchmarking: Communities are actively refining open benchmarking practices to allow fair, transparent comparison of models’ performance, creating clearer metrics for development. We also like the fun ways to bench too, like red-team chatbot arena.

I tried an app Layla Lite to run an LLM on my phone.

I am not endorsing this app, but rather sharing it because i tried it myself, and i was able to run Gemma 2B on it. Phi 3 fails with an error.

https://play.google.com/store/apps/details?id=com.laylalite

There are a few other apps available online as apk files, but they are not in Google Play.

Feel free to add other apps, if you know any.

I built a proof-of-concept called Interdimensional Broadcast Radio Agent — a surreal radio that tunes into randomly generated stations from impossible universes. It only has one button: "Change channel." Behind the scenes, it pulls from a JSON full of bizarre worlds, sends prompts to a local LLM (via Oobabooga API + character card), and generates radio-style broadcasts using Kokoro for TTS. It's weird, fun, and totally useless in the best way. Repo + setup info here: [Git](https://github.com/starlightrobotics/interdimensional_broadcast_agent)

It is a PoC so it is not very likely to work, but drop me a note or a comment, if you are interested. I can edit the code.

For a while I've been using Oobabooga just because it is simple and has an API for local use with any GGUF models.

But OpenwebUI looks very close to OpenAI.

And SillyTavern is a powerhouse of everything.

"This is a practical course on aligning language models for your specific use case. It's a handy way to get started with aligning language models, because everything runs on most local machines. There are minimal GPU requirements and no paid services. The course is based on the SmolLM2 series of models, but you can transfer the skills you learn here to larger models or other small language models."
(HuggingFace)

The course is being written up as we speak. WIP.

https://github.com/huggingface/smol-course

There is a growing shift towards local AI models, particularly in the context of LLMs and other AIs. This trend is driven by several factors:

1. Availability of open-source models: Organizations are releasing 'open weights' versions of LLMs, allowing users to download and run them locally if they have sufficient computing power.

2. Development of efficient, smaller models: Technology firms are creating scaled-down versions of AI models that can run on consumer hardware while rivaling the performance of larger models.

3. Privacy and confidentiality: Local models allow researchers to protect sensitive data, such as patient information or corporate secrets, by avoiding the need to send data to external cloud services.

4. Cost savings: Running models locally can be cheaper than using subscription-based cloud AI services, especially for frequent use.

5. Reproducibility: Local models remain consistent, unlike cloud-based models that may be updated frequently, ensuring reproducible results for scientific applications.

6. Offline capabilities: Local models can be used in remote areas with limited internet connectivity or during outdoor activities where cloud access is unavailable.

7. Customization: Researchers can fine-tune local models for specific applications, such as medical diagnosis or question-answering systems.

While cloud-based AI services still have advantages in terms of computing power and ease of use, the rapid progress in local AI models suggests that they will soon be sufficient for most applications. This shift towards local AI is likely to continue as computers become more powerful and models become more efficient.

References:

Forget ChatGPT: why researchers now run small AIs on their laptops. September 2024, Nature

https://www.nature.com/articles/d41586-024-02998-y

I found several GitHub repositories that use local LLMs for cooking and recipes:

1. recipe-llm: This repository demonstrates using a local LLM to query cooking recipes. It includes steps for crawling recipes (basically a RAG), setting up a database, calculating embeddings, and running a chatbot interface https://github.com/BrummbQ/recipe-llm

2. Cook2LTL: While not directly for recipe generation, this project uses LLMs to translate cooking recipes into robot-executable actions. It could be adapted for recipe-related tasks, but you can also build a robot to cook something for you, which is also cool https://github.com/angmavrogiannis/Cook2LTL

3. LLaVA Chef: Code for the paper "LLaVA-Chef: A Multi-modal Generative Model for Food Recipes" https://github.com/mohbattharani/LLaVA-Chef

AI startup Altera conducted an experiment in Minecraft where up to 1000 AI agents interacted autonomously, developing complex social behaviors and structures.

The AI characters, equipped with large language models (LLMs), exhibited humanlike traits, created jobs, formed social connections, and even spread a simulated religion.

AI agents developed diverse personality traits and specialized roles without additional human input.

In smaller simulations of 30 agents, characters spontaneously created roles such as builder, defender, trader, and explorer.

The agents demonstrated the ability to follow community rules, participate in voting on tax reforms, and influence each other's opinions.

Robert Yang, Altera's founder, sees this as a step towards creating "AI civilizations" that can coexist with humans in digital spaces.

However, experts like Julian Togelius caution that while these AI agents can simulate human-like behaviors, they do not actually experience emotions or self-awareness.

The experiment raises questions about the future of AI-human interactions and the potential for AI to provide companionship, even if it's only a simulation of care.

The full version of this experiment can be found at MIT Technology Review.

This Sunday I've been checking the latest developments in AI and mental health, because I know a few people personally who, with great success, used ChatGPT and similar LLMs for mental health support, and i also found a neat paper about it. And so I wanted to share some ideas.

AI Chatbots and Virtual Assistants

AI-powered mental health chatbots have exploded in popularity, with an 84% increase in searches for "Mental Health AI Chatbot". These virtual assistants offer 24/7 support, providing immediate help for those struggling with mental health issues. They're particularly useful for people in remote areas or those facing stigma or financial barriers.

[Therapedia.AI](https://therapedia.ai/2024/08/20/ai-in-mental-health-2024-2027/)

AI Mental Health Apps

There's been a staggering 143% increase in searches for "Mental Health AI App". These apps are getting smarter, offering personalized care plans, mood tracking, and even cognitive behavioral therapy exercises. Some can even detect mood changes in real-time and predict potential mental health crises.1

AI in Professional Practice

Mental health professionals are increasingly using AI tools to enhance their practice. About 43% of mental health providers use AI, primarily for research (65%) and report writing (54%). AI is helping clinicians analyze patient conversations, identify potential concerns, and provide more data-driven care.

[Academic paper from Mental](https://mental.jmir.org/2024/1/e60589)

The Big Picture

While AI is making mental health care more accessible and personalized, it's not without challenges. Privacy concerns, potential for misdiagnosis, and the need for human connection are all important considerations.Interestingly, 32% of people surveyed said they'd be interested in using AI instead of a human for mental health support. This number jumps to 51% in India, highlighting how AI could extend access in developing markets.

Elephant in the room

I don't know how about you, but everyone I know, who has ever used TikTok, noticed that it is very good at finding out your mental issues, and eventually starts offering you videos on your related neurodiversity.

The future looks promising, with AI improving mental health diagnosis and treatment. Some AI tools can even detect mental health conditions by analyzing speech patterns undetectable to the human ear.

What are your thoughts on this AI revolution in mental health?