Large Language Models (LLMs) are changing the way we interact with data. The future of LLMs may not be tied to a specific programming language, but rather to more user-friendly, flexible, and efficient ways of handling and processing data.
We discuss prompt engineering, as an NLP task, is not sustainable and the need for more structured input and output formats for querying large language models.
Maker Time is the total time an engineer spends working on a task without interruptions. It is the most important input for high performing software teams because it allows engineers to achieve flow state, where they are most productive and can solve the hardest problems.
Large language models (LLMs) are poised to revolutionize many industries, and three groups are well-positioned to reap the benefits: incumbents with existing distribution networks, new companies that develop specialized LLM applications, and companies that own large amounts of data.
An analysis of the AI development cycle from 2016-2018, highlighting key lessons in strategic planning, the need for sustainable business models, the value of simplicity, and the importance of focusing on core AI capabilities. The post also examines notable winners from this period, including OpenAI, Scale AI, PyTorch, Yolo, Cruise, Zoox, Otto, Airbnb, Uber, and Jasper AI.