The podcast discusses various topics including the revolution of including domain expertise in industrial AI, addressing hallucination in generative AI, retrieval augmented generation for enhancing language models, the challenges of industrial AI and knowledge transfer, the impact of generative AI on jobs, and a call to action to explore the influence and concerns surrounding generative AI.
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Quick takeaways
Generative AI has made potential use cases more immediately apparent and accessible, creating excitement and interest in its applications.
Generative AI has expanded the involvement of different roles and individuals in AI projects, breaking down communication barriers and allowing domain experts to actively participate.
Deep dives
Observation 1: Identifying use cases for generative AI
Many people feel that they can immediately identify use cases for generative AI since the early models have become accessible. This is seen as a contrast to technologies like web 3.0 and blockchain where use cases were not immediately apparent. The accessibility of generative AI has created excitement and interest in its potential applications.
Observation 2: Increased participation in AI initiatives
Generative AI has expanded the number of personas involved in AI initiatives, allowing more people to participate and have interest in AI projects. Previously, certain roles or individuals were limited in their involvement, but now business division people and domain experts are actively engaged in direct conversations due to the lowered communication barrier brought by generative AI.
Observation 3: Custom foundation models for teams
Teams working with generative AI will require custom foundation models, as the broad applicability of these tools will lead to the use of multiple models by companies. The complexity and specialization of problem-solving require smaller models that are more efficient at specific tasks and can collaborate effectively. Additionally, there is a need for ownership and control over models, leading to a demand for custom models among enterprises.
Observation 4: The concept of hallucination in generative AI
The phenomenon of hallucination, which refers to the generation of incorrect or false outputs by AI models, has gained attention and become part of the mainstream conversation. While there are concerns about hallucination and its implications, there are also techniques like table lookup and access that can address and mitigate these issues. The concept of hallucination has sparked discussions about addressing the communication aspect and creating interfaces that provide correct and reliable outputs while interacting with the models.
Christopher Nguyen is CEO and Co-founder of Aitomatic, a startup that builds virtual advisors tailored with domain-specific expertise, primarily catering to industrial AI applications.