Agentic Conversations (formally mlops.community)

MLOps podcast #190 with Ads Dawson, Senior Security Engineer at Cohere, Guarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security.

// Abstract

Ads Dawson, a seasoned security engineer at Cohere, explores the challenges and solutions in securing large language models (LLMs) and natural language programming APIs. Drawing on his extensive experience, Ads discusses approaches to threat modeling LLM applications, preventing data breaches, defending against attacks, and bolstering the security of these critical technologies. The presentation also delves into the success of the "OWASP Top 10 for Large Language Model Applications" project, co-founded by Ads, which identifies key vulnerabilities in the industry. Notably, Ads owns three of the top 10 vulnerabilities, including Training Data Poisoning, Sensitive Information Disclosure, and Model Theft. This OWASP Top 10 serves as a foundational resource for stakeholders in AI, offering guidance on using, developing, and securing LLM applications. Additionally, the session covers insider news from the AI Village's 'Hack the Future' | LLM Red Teaming event at Defcon31, providing insights into the inaugural Generative AI Red Teaming showdown and its significance in addressing security and privacy concerns amid the widespread adoption of AI.

// Bio

A mainly self-taught, driven, and motivated proficient application, network infrastructure & cyber security professional holding over eleven years experience from start-up to large-size enterprises leading the incident response process and specializing in extensive LLM/AI Security, Web Application Security and DevSecOps protecting REST API endpoints, large-scale microservice architectures in hybrid cloud environments, application source code as well as EDR, threat hunting, reverse engineering, and forensics. Ads have a passion for all things blue and red teams, be that offensive & API security, automation of detection & remediation (SOAR), or deep packet inspection, for example. Ads is also a networking veteran and loves a good PCAP to delve into. One of my favorite things at Defcon is hunting for PWNs at the "Wall of Sheep" village and inspecting malicious payloads and binaries.

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// Related Links

Website: https://github.com/GangGreenTemperTatum

OWASP Top 10 for Large Language Model Applications Core Team Member and Founder - https://owasp.org/www-project-top-10-for-large-language-model-applications/CoreTeam

Fork for OWASP Top 10 for Large Language Model Applications - https://github.com/GangGreenTemperTatum/www-project-top-10-for-large-language-model-applicationsSecurity project: llmtop10.com

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Ads on LinkedIn: https://www.linkedin.com/in/adamdawson0/

Timestamps:

[00:00] Ads' preferred coffee

[00:46] Takeaways

[02:52] Please like, share, and subscribe to our MLOps channels!

[03:11] Security and vulnerabilities

[05:24] Work at Cohere and OWASP

[08:11] Previous work vs LLMs Companies

[09:46] LLM vulnerabilities

[10:38] Good qualities to combat prompt injection problems

[13:26] Data lineage

[16:03] Red teaming

[19:39] Freakiest LLM vulnerabilities

[22:17] Severe Autonomy Concerns

[25:13] Hallucinations

[27:59] Prompt injection

[29:15] Vector attacks to be recognized

[32:02] LLMs being customized

[33:18] Security changes due to maturity

[38:17] OWASP Top 10 for Large Language Model Applications

[44:31] Gandalf game

[46:06] Prompt injection attack

[49:46] Overlapping security

[53:26] Data poisoning

[56:57] Toxic data for LLMs

[58:50] Wrap up

MLOps podcast #189 with Rohit Agarwal, CEO of Portkey.ai, Designing for Forward Compatibility in Gen AI.

// Abstract

For two whole years of working with a large LLM deployment, I always felt uncomfortable. How is my system performing? Are my users liking the outputs? Who needs help? Probabilistic systems can make this really hard to understand. In this talk, we'll discuss practical & implementable items to secure your LLM system and gain confidence while deploying to production.// BioRohit is the Co-founder and CEO of portkey.ai, which is an FMOps stack for monitoring, model management, compliance, and more. Previously, he headed Product & AI at Pepper Content, which has served ~900M generations on LLMs in production. Having seen large LLM deployments in production, he's always happy to help companies build their infra stacks on FM APIs or Open-source models.

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https://mlops-community.myshopify.com/

// Related Links

Website: https://portkey.ai

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Rohit on LinkedIn: https://www.linkedin.com/in/1rohitagarwal/

Timestamps:

[00:00] Rohit's preferred coffee

[00:15] Takeaways

[03:22] Please like, share, and subscribe to our MLOps channels!

[05:16] Rohit's current work

[06:37] The Portkey landscape

[09:13] Compute unit is no longer a Cloud resource; it's a Foundational Model

[11:09] Hang-ups at high-scale models and how to combat them

[15:22] Complexity of the Apps evolving

[19:54] Rohit's working relationships with the agents

[22:52] Fine-tuning reliability

[24:38] Small language models can outperform larger ones

[26:38] Market map at Portkey

[34:37] AI Gateway

[37:59] Worker Bee and Queen Bee

[39:27] Security and Compliance

[43:11] Idea of Data Mesh

[45:57] Forward compatibility

[49:59] Decoupling AI Gateway from the code

[56:05] Hardest design decisions to make since creating Portkey

[58:52] Wrap up

MLOps podcast #188 with Anand Das, Co-founder and CTO of Bito, Impact of LLMs on the Tech Stack and Product Development.

// Abstract

Anand and his team have developed a fascinating Chrome extension called "explain code" that has garnered significant attention in the tech community. They have expanded their extension to other platforms like Visual Studio Code and Chat Brains, creating a personal assistant for code generation, explanation, and test case writing.

// Bio

Anand Das is the co-founder and CTO of Bito. Previously, he served as the CTO at Eyeota, which was acquired by Dun & Bradstreet for $165M in 2021. Anand also co-founded and served as the CTO of PubMatic in 2006, a company that went public on NASDAQ in 2020 (NASDAQ: PUBM). Anand has also held various engineering roles at Panta Systems, a high-performance computing startup led by the CTO of Veritas, as well as at Veritas and Symantec, where he worked on a variety of storage and backup products. Anand holds seven patents in systems software, storage software, advertising, and application software.

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https://mlops-community.myshopify.com/

// Related Links

Website: https://bito.ai/

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Anand on LinkedIn: https://www.linkedin.com/in/ananddas/

Timestamps:

[00:00] Anand's preferred coffee

[00:15] Takeaways

[02:49] Please like, share, and subscribe to our MLOps channels!

[03:08] Anand's tech background

[10:06] Fun at Optimization Level

[12:59] Trying all APIs

[17:55] Model's evaluation decision tree

[22:51] Weights and Biases Ad

[25:04] AI Stack that understands the code

[28:27] Tools for the Guard Rails

[33:23] Seeking solutions before presenting to LLM

[38:46] Prompt-Driven Development Insights

[40:16] Prompting best practices

[42:51] Unneeded complexities

[45:45] Cost-benefit analysis of buying GPUs

[49:13] ML Build vs Buy

[51:26] Best practices for debugging code assistant

[54:58] Wrap up

MLOps podcast #187 with Faizaan Charania, Product Manager, AI at LinkedIn, Building Effective Products with GenAI.

// Abstract

Faizaan outlines his AI product development approach, starting broadly and refining details with tech leads, emphasizing the value of a simplified MVP. He also explores integrating generative AI, highlighting its role in enhancing user experiences through LLMs.In this discussion, Faizaan shares wisdom on feedback integration, user trust, and the collaboration challenges between product managers and AI teams. Let's delve into evaluating AI-driven experiences and the complexities that arise in this dynamic landscape!

// Bio

Faizaan is an AI Product Lead at LinkedIn, working on Personalization and Generative AI use cases for Creators and Conversations on LinkedIn. He's been in the field of machine learning for 8+ years now. He started as a research assistant, eventually transitioning to being a Product Manager during his time at Yahoo.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Faizaan on LinkedIn: https://www.linkedin.com/in/faizaan-charania/

Timestamps:

[00:00] Faizaan's preferred beverage

[00:22] Takeaways

[02:54] The Bollywood actor

[05:23] Faizaan's in tech

[07:45] Technical pieces to learn about before working at LinkedIn

[09:23] Tech Team Data Strategy

[12:01] Gradual vs. Advanced ML Implementation

[13:36] Shipping on time

[14:11] Thoughts on building products with AI

[18:20] Push and pull mechanism

[21:47] Costs and Choices with AI Models

[25:06] AI ROI Evaluation

[27:02] Thoughts on open source

[28:17] Building Generative AI focus

[31:50] Prompts and Anomalies

[34:57] Where to have a human in the loop

[35:45] Problem-driven AI Tool

[37:56] Creator of AI-generated post on LinkedIn

[39:50] Product Impact on AI Democratization

[41:15] Distinct signals to measure ROI

[44:38] PMs learning AI while ML teams learn product

[47:22] Gotchas seen when adding a new AI feature

[50:00] Evaluation Challenges in Responses

[51:55] Who's more confident?

[52:55] Wrap up

MLOps podcast #186 with Mike Del Balso, CEO & Co-founder of Tecton and Josh Wills, Angel Investor, The Future of Feature Stores and Platforms.

// Abstract

Mike and Josh discuss creating templates and working at a detailed level, exploring Tecton's potential for sharing fraud and third-party features. They focus on technical aspects like data handling and optimizing models, emphasizing the significance of quality data for AI systems and the necessity for cohesive feature infrastructure in reaching production stages.

// Bio

Mike Del Balso

Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google, where he managed the core ML systems that power Google’s Search Ads business.

Josh Wills

Josh Wills is an angel investor specializing in data and machine learning infrastructure. He was formerly the head of data engineering at Slack, the director of data science at Cloudera, and a software engineer at Google.

// MLOps Jobs board

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// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links⁠

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/

Connect with Josh on LinkedIn: https://www.linkedin.com/in/josh-wills-13882b/

Timestamps:

[00:00] Introduction to Mike

[01:45] Takeaways

[03:32] Features of the new paradigm of ML and LLMs

[06:00] D. Sculley's papers

[13:05] The birth of Feature Store

[17:06] Data Pipeline Challenges Addressed

[20:00] Operationalizing

[26:50] Feature Store Challenges

[30:26] Z access

[36:23] Addressing Technical Debt Challenges

[37:27] Real-Time vs. Batch Processing

[47:10] Feature Store Evolution: Apache Iceberg

[49:59] Feature Platform: Dedicated Query Engine

[54:04] The bottleneck

[56:00] LLMs, Feature Stores Overview

[1:00:20] Vector databases

[1:06:15] Workflow Templating Efficiency

[1:08:35] Gamification suggestion for Tecton

[1:10:25] Wrap up

MLOps podcast #185 with Luigi Patruno, VP of Data Science at 2U, Inc., Lessons on Data Science Leadership.

// Abstract

Picture this: you've got data products to manage, and you're in charge of a team. It's not all sunshine and rainbows, right? Luigi dives into the nitty-gritty of the challenges - from juggling data projects to wrangling the team dynamics. It's a real adventure, let me tell you!

// Bio

Luigi Patruno is a results-driven data science leader passionate about identifying value-add business opportunities and converting these into analytical solutions that deliver measurable business outcomes. As a leader, he focuses on defining strategic vision and, through motivation and discipline, driving teams of highly quantitative data scientists, machine learning engineers, and product managers to achieve extraordinary results. He is currently the VP of Data Science at 2U, where he leads the data science department focused on optimizing business operations through advanced analytics, experimentation, and machine learning. He enjoys teaching others how to leverage data science to improve their businesses through public speaking, teaching courses, and writing online at MLinProduction.com.

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// Related Links

Website: https://mlinproduction.com/

YouTube channel: https://www.youtube.com/playlist?list=PLBLnN4jzkyqkjLIRpDNZcsG7TMMEk9Asa

High Output Management book by Andrew Grove: https://www.amazon.nl/-/en/Andrew-S-Grove/dp/0679762884

The One Minute Manager by Kenneth Blanchard, Ph.D., and Spencer Johnson, M.D.: https://www.amazon.com/Minute-Manager-Kenneth-Blanchard-Ph-D/dp/074350917X

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Luigi on LinkedIn: https://www.linkedin.com/in/luigipatruno91/

Timestamps:

[00:00] Luigi's preferred coffee

[00:30] Takeaways

[03:04] Being practical

[05:44] Data-Driven Decision-Making in Management

[12:53] Recent Team Win

[14:43] The perfect storm

[20:22] Change Management and ROI

[25:09] Change Management: Navigating Resistance

[29:59] Clarifying North Star Communication

[36:24] OKRs in Data Science

[40:47] Success Likelihood in Business

[45:08] Bus problem solution

[49:25] Data Science-Platform Collaboration

[53:19] Decentralized Platforms Explained

[54:38] Data Platform Architecture Overview

[57:14] Incentives for Team Motivation

[1:09:45] The blind spots

[1:12:22] Wrap up

MLOps podcast #184 with Richa Sachdev, Executive Director- Data Operations and Automation at JP Morgan Chase, Data Platforms in MLOps: Translating Business Goals into Product Decisions.

// Abstract

Richa, with her background in software engineering and experience in the financial sector, shares her insights on optimizing the end-user experience and the importance of understanding business goals and metrics. She discusses her journey in converting legacy applications, working with data platforms, and the challenges of integrating different databases. Richa also explores the role of automation in streamlining processes and improving customer interactions in the reward space. Join us as we unravel the fascinating world of MLOps and uncover the strategies and technologies that drive success in this ever-evolving field.

// Bio

A passionate and impact-driven leader whose expertise spans leading teams, architecting ML and data-intensive applications, and driving enterprise data strategy. Richa has worked for a Tier A Start-up developing feature platforms and in financial companies, leading ML Engineering teams to drive data-driven business decisions. Richa enjoys reading technical blogs focused on system design and plays an active role in the MLOps Community.

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// Related Links

https://www.youtube.com/watch?v=i0To3DeHGuU
https://www.youtube.com/watch?v=tAOf2lVQUY4
https://www.youtube.com/watch?v=cXanVyaannQ
https://www.youtube.com/watch?v=2aWSsL24fv8

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

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Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Richa on LinkedIn: https://www.linkedin.com/in/richasachdev/

Timestamps:

[00:00] Richa's preferred coffee

[02:09] Takeaways

[04:26] Richa's background in data

[08:55] Prescriptive, Descriptive, and Predictive Data

[11:50] Data Engineering Perspectives & Setup

[17:34] Structured and Unstructured data

[21:01] Richa's day-to-day at Chase

[23:52] Figure out the business needs before the cool tech

[26:46] Importance of business metrics

[30:43] Optimizing end-user experience and trade-offs

[36:06] Exhausting creativity in finding solutions

[37:40] Consider faster implementation and increased ROI

[40:20] Banks still using COBOL

[41:17] Learning and growing as a versatile leader

[42:04] Wrap up

MLOps podcast #183 with Ketan Umare, CEO of Union.AI, MLOps vs ML Orchestration, co-hosted by Stephen Batifol.

// Abstract

Let's explore the relationship between Union and Flyte, emphasizing the significance of community-driven development and the challenge of balancing feature requests with security considerations. This conversation highlights the importance of real-time data and secure data handling in orchestrating machine learning models. The Flyte community's empathy and support for newcomers underscore the community's value in democratizing machine learning, making it more accessible and efficient for a broader audience.

// Bio

Ketan Umare is the CEO and co-founder at Union.ai. Previously, he had multiple Senior roles at Lyft, Oracle, and Amazon ranging from Cloud, distributed storage, Mapping (map-making), and machine-learning systems. He is passionate about building software that makes engineers' lives easier and provides simplified access to large-scale systems. Besides software, he is a proud father and husband, and enjoys traveling and outdoor activities.

// MLOps Jobs board

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// Related Links

Website: https://union.ai/

Flyte: https://flyte.org/ ⁠

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn:

https://www.linkedin.com/in/dpbrinkm/

Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/

Connect with Ketan on LinkedIn: https://www.linkedin.com/in/ketanumare/

Timestamps:

[00:00] Ketan's preferred coffee

[01:05] Takeaways

[03:08] Please like, share, and subscribe to our MLOps channels!

[03:15] Shout out to Ketan and UnionAI for sponsoring this episode!

[04:23] Orchestration of recent changes

[07:51] Community with Flyte

[11:26] ML orchestration

[15:40] 50/50 is generous

[20:06] Real-time ML

[21:15] Over-engineering without benefits

[23:20] Balancing everything

[27:40] Union verse Flyte

[32:52] High-value features of Union AI at the back of Flyte

[40:18] Building LLM infrastructure

[45:30] Traditional ML is the whole prompting

[46:46] LLMs for evaluating prompts

[48:55] Wrap up

MLOps podcast #182 with GetYourGuide's Jean Machado, DataScience Manager, Meghana Satish, MLOps Engineer, Olivia Houghton, Machine Learning Operations Engineer, Theodore Meynard, Data Science Manager, MLOps@GetYourGuide.

// Abstract

Join a team to talk about the journey of GYG with MLOps, from the conception of their platform to the creation of the MLOps engineer role, and to their current stack state.

// Bio

Jean Machado

Jean Carlo Machado is a Data Science Manager at GetYourGuide for the Growth Data Products team and the Machine Learning Platform Team. He is privileged to be able to work on turning ideas in data science from inception to production. Before GYG, Jean was working in a startup in Brazil, building its infrastructure from the ground up. Jean also likes community building and using technology for social good.

Meghana Satish

Meghana Satish is currently working as an MLOps Engineer at GetYourGuide. She has previously held positions at Amazon AWS in Berlin and Microsoft IT in Hyderabad. In addition to her career in technology, Meghana is also a talented singer, dancer, and yoga practitioner.

Olivia Houghton

Olivia has been working as an MLOps engineer at GetYourGuide for the past year and a half or so. Olivia's main work is in building and managing their activity ranking service.

Theodore Meynard

Theodore Meynard, Data Science Manager at GetYourGuide, leads the evolution of their ranking algorithm, enriching customer experiences. His hands-on journey from data scientist to leader has honed his expertise in MLOps and real-time ML. Beyond work, he's a co-organizer of PyData Berlin, underlining his commitment to community and collaborative learning.

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// Related Links

Website: https://investing1012dot0.substack.com/

The Openness of AI report: https://research.contrary.com/reports/the-openness-of-ai

⁠--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

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Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Jean on LinkedIn: https://www.linkedin.com/in/jean-carlo-machado-53b15977/

Connect with Meghana on LinkedIn: https://www.linkedin.com/in/meghana-satish-2a825282/?originalSubdomain=de

Connect with Olivia on LinkedIn: https://www.linkedin.com/in/oliviaphoughton/

Connect with Theodore on LinkedIn: https://www.linkedin.com/in/theodore-meynard/

Timestamps:

[00:00] GetYourGuide team's preferred coffee

[00:55] Takeaways

[02:20] Shout out to Berlin MLOps Community

[02:38] Please like, share, and subscribe to our MLOps channels!

[03:39] The GetYourGuide platform

[05:45] GetYourGuide use cases

[11:51] Strong Leadership Vision

[13:59] Creating rituals

[16:55] Feedback on the loop for improvements

[18:35] Different components of GetYourGuide's ML Platform

[21:04] V2 service templates

[24:26] Biggest pain points

[27:02] Feature flags

[30:51] Data foundation

[36:25] Data Testing

[39:53] Cross-team Tool Adoption Process

[44:59] Regrets about design decisions made in the past

[47:53] What's next for the platform with LLMs?

[52:49] Non-data scientists suggesting use cases, language flexibility

[55:14] DevSecOps team's AI study group ideation

[59:25] Experiments in growth data products, marketing split

[1:01:47] Shout out to the Berlin MLOps Community!

[1:03:31] Wrap up

MLOps podcast #181 with Kyle Harrison, General Partner at Contrary, The Centralization of Power in AI.

// Abstract

Kyle Harrison delves into the limitations imposed by language, underscoring how it can impede our grasp and manipulation of reality while stressing the critical need for improved language model performance for real-time applications. He further explores the perils of centralizing power in AI, with a specific focus on the "Openness of AI", where concerns about privacy are brought to the forefront, prompting his call for businesses to reconsider their reliance on it. The discussion also traverses the evolving landscape of AI, drawing comparisons between prominent machine learning frameworks such as TensorFlow and PyTorch. Notably, the episode underscores the vital role of open-source initiatives within the AI community and highlights the unexpected involvement of Meta in driving open-source development.

// Bio

Kyle Harrison is a General Partner at Contrary, where he leads Series A and growth-stage investing. He joined Contrary from Index, where he was a Partner, and before that, he was a growth investor at Coatue. His portfolio includes iconic startups and public companies, including Ramp, Replit, Cohere, Snowflake, and Databricks. He also regularly shares his analysis on the venture capital landscape via his Substack Investing 101.

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// Related LinksWebsite: https://investing1012dot0.substack.com/

The Openness of AI report: https://research.contrary.com/reports/the-openness-of-ai⁠

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

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Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kyle-harrison-9274b278/

Timestamps:

[00:00] Kyle's preferred beverage

[00:20] Takeaways

[03:52] Hype in the technology space

[09:20] Application Layer Revenue

[14:44] Stability AI Lawsuit

[18:08] Concern over concentration of power in AI

[20:20] Transparency concerns

[23:35] Open Source AI

[25:57] To use or not to use OpenAI

[30:51] Lack of technical expertise and business-building capabilities

[35:09] AI Transparency and Accountability

[37:50] Traditional ML

[41:47] Finding a unique approach

[45:41] AGI limitations

[47:43] Using Agents

[49:46] Agents getting past demos

[54:39] Tech Challenges & Hoverboard Dreams

[58:04] Both AI hype and skepticism are foolish

[01:27] Wrap up