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Understanding the Tech Stack Behind Generative AI
- The article explores the technology ecosystem around generative AI and Large Language Models (LLMs).
- Foundation models are pre-trained AI models that are versatile and can perform various tasks ranging from text generation to music composition.
- Key aspects of foundation models include pre-training, multitask capability, and transferability through fine-tuning or Retrieval Augmented Generation (RAG).
- Major players in AI like OpenAI, Anthropic, Google, Meta, Mistral, and DeepSeek have released foundation models with varying strengths and licensing conditions.
- Multimodal models can process and generate different types of data simultaneously, such as text, images, audio, and video.
- Infrastructure and compute power, including GPUs, TPUs, ML frameworks like PyTorch and TensorFlow, and serverless AI architectures, play a vital role in training generative AI models.
- AI applications frameworks like LangChain, LlamaIndex, and Ollama help integrate foundation models into specific applications efficiently.
- Vector databases are used to store and search semantic information in the context of LLMs, enabling fast similarity searches for contextual information.
- Programming languages like Python, Rust, C++, and Julia are important for developing generative AI, with Python being the primary language for AI applications.
- The social layer of AI focusing on explainability, fairness, and governance addresses important ethical considerations in the use of generative AI.
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