Shawn Wang discusses Software 3.0, the role of AI engineers, and the impact of foundation models on AI product creation. They explore model architectures, the challenges of putting foundation models into production, and the evolving landscape of AI developers. They also highlight the role of an AI engineer, OpenAI's recent innovations, and the potential of AI technology.
Software 3.0 represents the emergence of foundation models, reducing the time and effort required to develop AI products.
The role of an AI engineer bridges the gap between machine learning engineers and software engineers, making AI capabilities accessible to developers without extensive ML backgrounds.
Deep dives
Overview of Software 3.0
Software 3.0 is a concept introduced by Andrei Karpathy that distinguishes machine-learned code from hand-coded software. With Software 1.0, developers write every line of code themselves using traditional coding paradigms. In contrast, Software 2.0 utilizes machine learning models, where developers define the architecture and train the model with data. Software 3.0 represents the emergence of foundation models, enabled by the transformer architecture and increased computing power. These foundation models can be readily used off the shelf, reducing the time and effort required to develop AI products.
The Role of AI Engineer
The growing landscape of AI development has given rise to a new role called the AI engineer. AI engineers specialize in using foundation models, which are pre-trained models that can be accessed through APIs. They focus on consuming these models and leveraging them to build AI-powered applications. This role bridges the gap between machine learning engineers and software engineers, bringing AI capabilities to developers who may not have extensive ML backgrounds. The emergence of AI engineers reflects the increasing accessibility of AI technology and the demand for AI-driven solutions in various industries.
The Software 3.0 Stack
The Software 3.0 stack encompasses several key components. The system of reasoning represents the foundation models themselves, which can be obtained from sources like OpenAI and Hugging Face. The retrieval augmented generation (RAG) stack helps personalize and orchestrate AI models, utilizing vector databases and application frameworks like Langchain and LAMA Index. The stack also includes AI UX, which explores innovative interfaces beyond traditional chat boxes, such as GitHub Co-Pilot or front-end AI for UI generation. The Software 3.0 stack is an evolving ecosystem that enables AI engineers to leverage the full potential of foundation models.
Navigating AI Skepticism
AI developments can generate skepticism and concerns. One form of skepticism is AI blame, where AI technology is blamed for issues or challenges faced by certain industries. However, such blame often overlooks other underlying factors affecting those industries. On the other hand, there are skeptics who question the extent of AI's true intelligence and argue that current models are simply simulations of thinking based on text data. Nevertheless, there are numerous examples of successful AI applications, such as language models surpassing human-level performance in different domains. It is important to approach AI skepticism with a balanced perspective and focus on building meaningful and impactful AI solutions.
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Special Guest: Shawn Swyx Wang.
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