Adam Liska: Scaling AI Models and the Future of Venture Tech | Main Episode 13

In this episode of Nothing Ventured, Aarish Shah interviews Adam Liska,
co-founder and CEO of Glyphic. They discuss the power of AI co-pilots in
helping sales teams increase productivity and effectiveness. Adam
shares his experience as a research scientist at Spotify and DeepMind,
and how it led him to start Glyphic. They also explore the potential of
large language models and the universal interface of language. Tune in
to learn more about scaling models and the impact of data remixing.

🕒 Time stamps -
01:22 - Adam Liska's Background and Glyphic
02:04 - Topics for Today's Episode
02:14 - Aarish's Interest in AI and Tech
02:35 - Adam's Extensive Background in AI
02:57 - Evolution of AI in Recent Years
03:17 - Factors Contributing to AI's Rapid Pace
04:10 - The Role of GPUs and Data in AI Progress
04:49 - Surprising Speed of AI Model Scaling
05:01 - Machine Learning Models and Moore's Law
05:22 - Transition from Games to Language in AI
06:02 - Language as a Universal Interface
06:54 - Remixing Data and Intelligence
07:14 - Human Intelligence and Experience
07:45 - Protein Folding and Public Interest in AI
08:06 - Prompt Engineers and Language Skills
08:47 - The Future of Prompt Engineering
09:12 - Language as an Interface for AI
09:59 - AI Predictions and Human Decision-Making
10:39 - AI's Ability to Predict and Augment Decisions
11:10 - The Role of Data in AI Applications
11:37 - Data Privacy and Training AI Models
12:06 - Remixing Data and Novel Ideas
12:58 - Intelligence in AI and Remixing Experiences
13:45 - AI Predictions and CFO Insights
14:10 - AI and Human Intelligence Comparison
14:46 - Glyphic's Approach to Revenue Teams
15:43 - The Challenge of Data Integration
16:04 - Data Privacy and Model Training
16:58 - Public Data and AI Training
17:28 - The Impact of Public Data on Business Differentiation
18:09 - Using AI for Targeted Business Insights
18:30 - Mustafa Suleyman's New Turing Test
19:01 - Principles of the Modern Turing Test
19:50 - Autonomous Agents and Revenue Teams
20:45 - The Future of Autonomous Agents
21:09 - Data Challenges for AI Integration
21:53 - Data Privacy and AI Training
22:14 - The Importance of Data Quality
22:52 - Enterprise Data as the Next AI Frontier
23:13 - Building Integrations and Ingesting Data
23:39 - Data Anonymity and AI Training
24:19 - Mustafa Suleyman's Updated Turing Test
24:51 - The Modern Turing Test and AI in the Real World
25:42 - The Chinese Room Argument
26:36 - AI's Ability to Act in the Real World
27:09 - The Role of Humans in AI-Driven Tasks
27:45 - The Future of AI and Human Jobs
28:30 - Regulation and Safe Deployment of AI
29:01 - The Original Turing Test and Its Relevance
29:42 - The Chinese Room Thought Experiment
30:15 - AI Acting in the Real World
31:09 - The Modern Turing Test: AI as an Entrepreneur
31:55 - AI's Potential in E-commerce
32:53 - The Role of AI in B2B SaaS
33:35 - Human-AI Collaboration Experiments
34:17 - The Future of Autonomous AI Agents
35:09 - The Impact of AI on Society
35:41 - AI Optimism and the Future of Work
36:19 - The Importance of AI Regulation
37:17 - Gradual Deployment of AI Models
38:06 - Closing Remarks and Where to Find Adam Liska