In this episode of 'Plain Sight' podcast, Stewart Alsop welcomes Ai Yukino, Data Scientist at Invisible Technologies. The discussion covers how Invisible Technologies aims to make complex business problems disappear through its innovative approach. Ai shares her perspective on the evolving world of artificial intelligence and data science, detailing her thought process and experiences. They also delve into the tools used for data science, the concept of locally-hosted AI, machine learning, and the future of AI in mobile devices. The podcast wraps up with a conversation about potential challenges in coding and AI model implementation. Ai also highlights her upcoming work in self-hosted models at Invisible Technologies.

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Timestamps

00:00 Introduction to the Plain Sight Podcast

00:04 Understanding Invisible Technologies

00:22 The Philosophy Behind Invisible Technologies

01:13 Interview with Ai Yukino, Data Scientist at Invisible

01:21 Discussion on Microphone and Audio Gear

02:28 Ai Yukino's Teaching Experience and Tech Setup

03:02 Living and Working in Buenos Aires

04:35 Ai Yukino's Teaching Subjects and Background

05:17 Use of AI in Coding

05:42 Exploring Niche Packages in Data Science

10:00 The Role of AI in Learning New Topics

10:45 The Future of Specialist Knowledge and AI

16:10 The Impact of AI Revolution

17:15 Understanding Artificial General Intelligence

20:17 Defining Reason and Sentiment Analysis

20:26 Human-like vs Alien-like Intelligence

22:15 Anthropomorphizing AI Systems

24:19 The Role of Incentives in AI

25:51 Understanding Data Science and Machine Learning

30:18 The Value of Data in AI

35:24 The Evolution of Tools in Technology

41:15 The Future of Locally Hosted AI Models

44:48 Closing Remarks and Contact Information

Key Insights

1. 
   

   Data Science versus Machine Learning: Yukino differentiates between data science and machine learning. She describes data science as an interdisciplinary field that uses tools from statistics, applied math, and machine learning to gain insights from data. The focus is on understanding the data: its origins, generation, and what it reveals. Machine learning, in contrast, is about developing and using models, with less emphasis on the data itself and more on building useful applications.

   
   


2. 
   

   Importance of Data in Machine Learning: When discussing machine learning, Yukino acknowledges the crucial role of data. She notes that even though the emphasis in machine learning is on building models, these models require good quality data to be effective. The data remains important, not just for a single model but for future iterations and improvements.

   
   


3. 
   

   The Role and Value of Data: Yukino also delves into the continuing value of data even after it has been used to train a model. She uses the analogy of an oil company using data to locate oil wells; once the oil is found, the data used might seem redundant but retains long-term value for future explorations and improving accuracy of models.

   
   


4. 
   

   Machine Learning Tools and Flexibility: Yukino touches on the topic of machine learning tools and the importance of flexibility in their use. She suggests that while there are standard tools in the field, one can be successful in data science even without mastering all of them. She emphasizes the importance of adapting to new tools, especially given the rapid developments in AI and machine learning technologies.

   
   


5. 
   

   Self-Hosted Models and Future Prospects: Yukino expresses excitement about the future possibility of self-hosted models in AI. She looks forward to the potential of fine-tuning these models for specific applications, which could unlock a lot of capabilities in various fields.