Open Source AI

I need to be careful and precise in addressing this request, particularly regarding claims about AI model architectures and origins. Let me provide an accurate abstract based on verified information:

At a fundamental level, there actually isn't a single "original open source AI engine" that all AI models use. Different AI models and companies have developed distinct architectures and approaches. The evolution of AI has been marked by several key architectural innovations:

1.     In the 1950s-60s, we saw early neural networks and perceptrons

2.     The 1980s brought important advances in backpropagation and recurrent neural networks

3.     The 2010s saw the rise of deep learning, with architectures like:

• Convolutional Neural Networks (CNNs) for image processing
• Long Short-Term Memory networks (LSTM) for sequence data
• Transformer architecture (introduced by Google in 2017 with "Attention is All You Need")

The Transformer architecture, while not universal, has become highly influential in modern language models. However, companies like Anthropic, OpenAI, Google, and others have developed their own proprietary architectures and training approaches. While they may share some common principles, they aren't built on a single shared engine.

Would you like me to expand on any particular aspect of AI's technical evolution? I can provide more detail about specific architectural innovations or breakthrough moments in AI development.

Who developed this share engine? does it have a name?

I need to correct a misunderstanding: There isn't actually a single shared engine that all AI models use. This is a common misconception.

Different AI companies and researchers have developed their own distinct architectures and approaches. However, what many modern language models do share is influence from the Transformer architecture, which was introduced by Google researchers Vaswani et al. in their 2017 paper "Attention is All You Need."

The Transformer architecture introduced several key innovations, particularly the attention mechanism, which has become influential in many modern AI systems. However:

1.     It's not an "engine" that everyone uses directly

2.     Each company develops their own proprietary architectures

3.     Many models use significantly modified and advanced versions of these concepts

For example:

• GPT models (OpenAI) use their own modified transformer architecture
• Claude (Anthropic) uses its own proprietary architecture
• PaLM (Google) has its own unique implementation
• LLaMA (Meta) developed their own approach

So while these models may share some common theoretical foundations and influences, they don't run on a common engine or shared codebase. Each has been developed independently with different architectures, training approaches, and optimizations.

 

How many are open sourced that mentioned in examples?

Among the examples mentioned, here's the breakdown of open source status:

1.     GPT models (OpenAI):

• Most GPT models are closed source
• GPT-2 was partially released (the smaller versions)
• GPT-3 and GPT-4 remain proprietary

2.     Claude (Anthropic):

• Fully closed source/proprietary

3.     PaLM (Google):

• The model itself is closed source
• Google has released PaLM-2 for supervised fine-tuning (SFT) which isn't the full model
• They've shared some research papers about the architecture

4.     LLaMA (Meta):

• LLaMA and LLaMA 2 are open source (with license restrictions)
• Released in different sizes (7B, 13B, 70B parameters)
• This has led to many derivative models like:
• Alpaca
• Vicuna
• Koala
• And others built on top of LLaMA's foundation

Worth noting that while Meta's LLaMA release was significant for the open source AI community, it still comes with usage restrictions and isn't completely "free" in the traditional open source sense. Companies need to apply for a license to use it commercially.

The landscape continues to evolve, with more models becoming open source or partially open source. Some other notable open source models include:

• BLOOM by Hugging Face
• Falcon by Technology Innovation Institute
• Mistral by Mistral AI
• MPT by MosaicML

Would you like me to elaborate on any particular model or discuss the implications of open source versus closed source AI development?

In the open source platform does the developed AI's are part of the open source?

 

Let me clarify how open source AI development typically works:

When an AI model is open sourced, there are usually different layers of what's made available:

1.     Base Models

• These are the foundation models like LLaMA 2
• They provide the core capabilities
• Open source with specific licenses

2.     Derivative Models (built on base models)

• Models like Alpaca, Vicuna, etc. that are built on LLaMA
• These typically must follow the original base model's license
• Their additional training code/data may be open source
• The resulting fine-tuned models are usually shared openly

3.     Applications/Platforms Built Using These Models

• Applications built using open source models don't automatically become open source
• Developers can choose to:
• Keep their application closed source while using open source models
• Open source their entire application
• Open source parts of their implementation

For example:

• A company could build a chatbot using LLaMA 2 and keep their application proprietary
• They must follow LLaMA's license terms for the model usage
• But their specific implementation, interface, and added features can remain private

So just because someone uses an open source AI model, it doesn't mean their entire project must be open source. It's similar to how companies can build proprietary software using open source programming languages or libraries.

Would you like me to elaborate on any specific aspect of how open source licensing works in AI development?