In this episode of the Plain Sight podcast, Alexei Shkurin, a Tech Lead at Invisible Technologies, discusses the company’s philosophy and how they make complex business problems disappear by figuring out and optimizing various aspects of the businesses they work with. Alexei focuses on how Invisible uses LLMs (large language models) such as GPT-4 to enhance their operations and client services. He also shares his insights on topics like the mechanistic interpretability of AI, the emergence of new AI properties, and the ethical considerations involved in AI development. Furthermore, he delves into predictions for the AI industry and shares his ideas for the application of LLM within Invisible and AI's impact on the education system.

If you are a subscriber to GPT4 check out this GPT we trained on this episode

Timestamps

00:00 Introduction to the Plain Sight Podcast

00:04 Understanding Invisible Technologies

00:22 The Philosophy Behind Invisible Technologies

01:13 Introduction to the Guest: Alexei Shukrin

01:42 Discussion on Machine Learning

02:39 Exploring Mechanistic Interpretability

09:51 The Future of Programming and AI

14:41 The Impact of AI on Education

19:08 The Need for Digital Detox

23:56 The Changing Human Brain and AI

26:07 Digital Therapy Sessions at Invisible

27:26 Chatting with AI: A Personal Experience

27:35 AI Correcting Historical Facts

28:09 AI in Live Conversations: Current Limitations

28:59 The Future of Real-Time AI

31:02 Decentralization and Open Source in AI

35:37 AI Enablement at Invisible: Current Projects and Future Plans

38:05 The Role of MLOps in AI Development

40:21 Exploring the Concept of Sentience in AI

48:27 The Intersection of AI and Neuroscience

52:37 Closing Thoughts and Contact Information

Key Insights

1. 
   

   Rapid Changes in Machine Learning: Aleksei highlighted the fast-paced nature of machine learning and the importance of being resilient and not overly dependent on any single entity or technology, even if it's a major player in the field.

   
   


2. 
   

   Mechanistic Interpretability in AI: He discussed the challenge of understanding the inner workings of large language models (LLMs) and neural networks. This area is an active field of research, and even experts are not fully sure how these models reason so effectively.

   
   


3. 
   

   Emerging Properties of AI Models: Aleksei expressed concern about the emerging properties of AI models, where models can perform tasks or exhibit behaviors that were not explicitly intended or planned by their creators. This unpredictability is a potential risk that needs consideration.

   
   


4. 
   

   Sentience in AI: The conversation touched on the topic of AI sentience, with a discussion on what constitutes sentience and consciousness. Aleksei emphasized the current limitations of AI in this regard, noting that AI models like LLMs do not make decisions independently but respond to external stimuli.

   
   


5. 
   

   Transformation in the Role of Programmers: The future of programming was discussed, with AI tools increasingly taking over the tedious parts of coding. The role of programmers might shift more towards system architecture and design, rather than actual coding.

   
   


6. 
   

   AI in Education: Aleksei stressed the importance of integrating AI into the education system. He suggested innovative methods like using AI for conversational learning and emphasized the need for students to have both traditional learning experiences and AI-assisted ones.

   
   


7. 
   

   Dependence on AI and Digital Detox: He acknowledged the growing dependence on AI tools like ChatGPT and suggested the importance of digital detox and balancing AI usage with other activities.

   
   


8. 
   

   Future of AI and Ethics: The ethical considerations in the development and deployment of AI were discussed, including the need for responsible AI development and the potential risks associated with AI that can self-improve or exhibit unexpected emergent properties.