The chapter covers various methods of learning AI, from academic resources to practical application-building, and delves into economies of scale in AI companies like Gemini and OpenAI. It discusses concerns about monopolization of AI services by big companies, the advantages and disadvantages of utilizing large models, and the importance of training models for specific tasks. Additionally, it explores security implications of chatbots, the importance of structured data for business analysis, workflows like model-assisted data annotation, and the transition from prototyping to production in machine learning projects.
There hasn't been a boom like the AI boom since the .com days. And it may look like a space destined to be controlled by a couple of tech giants. But Ines Montani thinks open source will play an important role in the future of AI. I hope you join us for this excellent conversation about the future of AI and open source.
Episode sponsors
Sentry Error Monitoring, Code TALKPYTHON
Porkbun
Talk Python Courses
Links from the show