š AI-Native with Idan Gazit ā The future of AI products and interfaces + Getting AI to production
May 16, 2024
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Idan Gazit, Senior Director of Research at GitHub Next, talks about exploring strategic technologies, ML focus at GitHub Next, challenges of evaluating the impact of AI products, journey into AI space, testing AI products in smaller organizations, and the future of AI interfaces.
GitHub Next team embraces risky projects to innovate where others hesitate, uncovering groundbreaking advancements.
Innovation process at GitHub Next resembles a jazz band jam session, with iterative exploration and convergence on promising concepts.
AI development aims to enhance reliability by breaking through the suggestion ceiling and structuring interactions into stages for user intervention.
Deep dives
Innovation Through Risky Bets
The team at GitHub Next takes on risky projects that the rest of the business might avoid, aiming to innovate where others hesitate. By embracing risk and exploring new technologies, the team seeks to uncover groundbreaking advancements that traditional engineering orgs might overlook.
Exploration and Collaboration
The innovation process involves exploration seasons where a small team experiments with ideas, often building on past cycles and repeating themes. This iterative approach resembles a jazz band jam session, with team members trying out different prototypes and converging on underlying themes before doubling down on promising concepts.
Challenges in AI Development
The AI development process faces the challenge of breaking through the suggestion ceiling, aiming to steer AI models in a more iterative, reliable manner. By structuring the interaction with AI models into multiple stages that allow user intervention, such as generating specifications, plans, and code, the aim is to enhance reliability and utility in AI-powered workflows.
Transitioning from Prototype to Product
The transition from AI prototypes to scalable, durable products involves a balance between innovation teams and engineering functions. Successful products arise when innovation teams provide confidence and direction, allowing engineering to own the final product iteration and decision-making process. Collaboration between innovation and engineering ensures a smooth transition from experimentation to production.
Tools and Technologies Recommendations
A shift towards TypeScript, Deno, and Arc Arrow for data science tasks showcases a move towards versatile, modern JavaScript environments for AI and data operations. Utilizing Superbase for database management and real-time notifications, along with Deno-powered edge functions, offers a robust and efficient framework for development tasks.
In this episode, Idan Gazit, Senior Director of Research at GitHub Next, discusses his role in exploring strategic technologies and incubating long bet projects. He explains how the GitHub Next team chooses research projects and the process of exploration and theme selection. Idan also shares insights into the ML focus at GitHub Next and the challenges of evaluating the impact of AI products. He reflects on his journey into the AI space and provides advice for testing AI products in smaller organizations. Finally, he shares his thoughts on the future of AI interfaces.
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Timestamps:
00:00 Introduction and Background
00:56 Choosing Research Projects at GitHub Next
06:09 ML Focus in GitHub Next
10:52 ML Work and the Leaky Abstraction
13:16 Idan's Journey into the AI Space
17:54 Evaluating the Impact of AI Products
24:36 Testing AI Products in Smaller Organizations
32:52 The Future of AI Interfaces
40:01 Transitioning from Prototype to Product
46:45 Challenges in the ML/AI Space
56:03 Recommendations
ā”ļø Idan Gazit on LinkedIn ā https://www.linkedin.com/in/idangazit/
ā”ļø Idan Gazit on Twitter ā https://twitter.com/idangazit
š Check Out Our Website! https://dagshub.com
Social Links:
ā”ļø LinkedIn: https://www.linkedin.com/company/dagshub
ā”ļø Twitter: https://twitter.com/TheRealDAGsHub
ā”ļø Dean Pleban: https://twitter.com/DeanPlbn
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