Open||Source||Data – Details, episodes & analysis

In this episode, Charna Parkey welcomes Rodrigo Nader, the founder of Langflow, an open-source, low-code app builder for multi-agent AI systems. Rodrigo and Charna dive into his beginnings in a small Brazilian town to the future of AI and the emergence of multi-agent systems. Discover how these systems will enable human-agent collaboration, increase productivity, and solve complex problems across various industries.

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TIMESTAMPS

00:01:00 Introduction to Rodrigo Nader, CEO and founder of Langflow, and an overview of Langflow's mission and recent developments.

00:03:00 - Rodrigo Nader's background and journey into open-source, data science, and machine learning, including his early experiences with MIT OpenCourseWare and Kaggle.

00:06:00 - Rodrigo's work at Bitvore Corp, focusing on structuring financial data using machine learning, and his introduction to the open-source AI ecosystem.

00:10:00 -  The inspiration behind Langflow, including the idea of connecting multiple AI models to create a more powerful, trainable system.

00:15:00 - Discussion on the evolution of AI agents, their decision-making capabilities, and the future of multi-agent systems.

00:18:00 -The role of agents in AI development, the democratization of AI tools, and the potential for community-driven innovation.

00:22:00 -The importance of multi-agent collaboration and the future of human-AI interaction in productivity and task management.

00:26:00 - Common use cases for Langflow, including language model pipelines, RAG (Retrieval-Augmented Generation), and agentic systems.

00:30:00 - Challenges in AI development, particularly debugging and prompt engineering, and the need for better tools to visualize and monitor AI systems.

00:34:00 - Predictions for the future of AI in 2025, including the rise of specialized agents and the importance of human feedback in AI training.

00:38:00 - Rodrigo's personal interests outside of AI, particularly his fascination with physics, quantum mechanics, and the concept of time.

00:42:00 - Final thoughts on the democratization of AI tools, the importance of community contributions, and advice for aspiring developers and AI enthusiasts.

00:46:00 - Reflections with executive producer Leo Godoy, discussing the impact of Langflow, the differences between traditional and AI development, and the rapid pace of AI evolution.

Quotes

Charna Parkey

"For any developer who has sort of avoided the soft skills, the managerial skills, et cetera, you should go listen to some of those courses. You are now going to be managing this AI workforce that you really do need to treat like a team of interns that you're delegating work to, that you're giving feedback on, and all of those skills of sort of like more senior-level engineering of design reviews, code reviews, feedback, like that's gonna be more central than actually writing a line of code yourself."

Rodrigo Nader

"We're going to see millions and millions more agents than humans very soon, right? So we don't think that these agents are going to emerge from, one, only developers, meaning like hard-code developers, neither from big companies creating solutions that will suddenly solve all the problems."

From energy bottlenecks to proprietary GPU ecosystems, the CEO of TensorWave, Darrick Horton explains why today’s AI scale is unsustainable—and how open-source hardware, smarter networking, and nuclear power could be the fix.

QUOTES

Darrick Horton

“The energy crisis is getting worse every day. It’s very hard to find data center capacity—especially capacity that can scale. Five years ago, 10 or 20 megawatts was considered state-of-the-art. Now, 20 is nothing. The real hyperscale AI players are looking at 100 megawatts minimum, going into the gigawatt territory. That’s more than many cities combined just to power one cluster.”

Charna Parkey

“We’re still training models in a very brute-force way—throwing the biggest datasets possible at the problem and hoping something useful emerges. That’s not sustainable. At some point, we have to shift toward smarter, more intentional training methods. We can’t afford to be wasteful at this scale.”

TIMESTAMPS

[00:00:00] Introduction

[00:01:00] Founding TensorWave

[00:04:00] AMD as a Viable Alternative

[00:08:00] Open Source as a Startup Enabler

[00:09:30] Launching ScalarLM

[00:12:00] ScalarLM Impact and Reception

[00:14:30] Roadmap for 2025

[00:16:00] Technical Advantages of AMD

[00:18:00] Emerging Open Source Infrastructure

[00:20:00] Broader Societal Issues AI Must Address

[00:22:00] AI’s Impact on Global Energy

[00:26:00] Fundamental Hardware vs. Human Efficiency

[00:30:00] Data Center Density Evolution

[00:34:00] Advice to Founders and Tech Trends

[00:38:00] AI Energy Challenges

[00:44:00] AI’s Rapid Impact vs. Internet

[00:46:00] Monopoly vs. Democratization in AI

[00:50:00] Close to Season Wrap Discussion and Predictions

Kicking off Open Source Data Season 7, Charna Parkey welcomes the CEO and Founder of Invoke, Kent Keirsey to discuss his thoughts on licensing, copyright in generative AI, and the role of communities in building ethical, free-to-use technologies that can democratize technology and inspire global innovation.

Quotes

Kent Keirsey 

"When we look at open source models, if you just release the weights, and you don't really release information on how the data set was captioned, for example, or how you construct the data set, if you don't really know how it got to the artifact that was released, as a user, you do not understand how it works."

Charna Parkey 

But there's still a lot of claims by big tech right now about how anything on the internet should be fair use for training, even if, you know, it might have its own kind of copyright

Timestamps

[00:02:00] - Kent Keirsey on his journey to open source

[00:06:00] - Kent Keirsey on the Open Model Initiative (OMI)

[00:08:00] -What makes a model truly open source

[00:12:00] - The legal landscape of AI and copyright

[00:14:00] - Kent Keirsey on the ethical implications of AI training data fair and use and AI development

[00:26:00] Creativity, AI tools, personal AI models and recommendation algorithms:

[00:32:00] - Kent Keirsey on TikTok and cultural clash:

[00:38:00] - AI, self-reflection and a decision-making tool

[00:42:00] - The Bria AI partnership

[00:52:00] - The future of creativity, AI and Robotics:

[01:00:00] - Final thoughts with producer Leo Godoy



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Join Charna Parkey as she recaps a transformative year in AI, exploring the delicate balance between innovation and ethics. From open source communities to global regulations, discover how trust, diversity, and collaboration are shaping the future of technology.

Vinay Kumar discusses the transformation of AI in banking and financial services, addressing challenges and solutions with regulatory compliance and model explainability while addressing the stringent requirements in the financial industry.

Episode Quotes

Vinay Kumar
"I always believe in this: you don't need to solve a very large problem. Maybe it will take a lot of time to do that. A lot of resources to do that but something small, which you can have an opportunity to solve that could be very big or a fundamental for quite a bit is fantastic. Think of a scenario where your small fundamental idea is a base for another small fundamental idea for someone else."

Charna Parkey

We also want to ground it a little bit in impact we've been seeing. And I think in the financial, banking, insurance industries it's not, I would say, an even distribution of advancement. Different countries have different regulations and different appetites for risk."


Timestamps
- [00:00:00] Introduction by Charna Parkey.

- [00:01:57] Vinay Kumar begins talking about his journey.

- [00:05:27] Discussion on building a search engine for STEM researchers.

- [00:07:06] Challenges with early deep learning.

- [00:09:55] Conversation shifts to ML observability.

- [00:17:06] Discussion on simplifying verticalized AI.

- [00:22:30] Impact of large language models (LLMs) on AI.

- [00:30:58] Comparison of autonomous cars with AI regulation.

- [00:37:58] Vinay mentions his science fiction novels.

- [00:42:19] Conversation summary with Producer Leo Godoy.

“We're really trying to show that we could co-create experiences with AI technology that augmented our experience rather than served as something to replace us in creative act”.

“For every project like [LuminAI], there's a thousand companies out there just trying to do their best to get our money... That's an uncomfortable place to be in for someone who has worked in AI for decades”.

“I had no idea what was going to happen kind of in the future. When we started EarSketch... we were advised by a couple of colleagues to not do it. And here we are, having engaged over a million and a half learners globally”.

"I remember the first robot that I built. It was part of the first robotic systems... and watching these machines work with each other was just crazy."

“If you're building a product and your goal is to engage underrepresented groups, it is on you to make sure that you're educating the folks in a way that you're trying to reach.”

(01:11) Brian Magerko's Journey into AI and Robotics 

(05:00) LuminAI and Human-Machine Collaboration in Dance

(09:00) Challenges of AI Literacy and Public Perception

(17:32) Explainable AI and Accountability 

(20:00) The Future of AI and Its Impact on Human Interaction 

(22:10) EarSketch and learning: computing as a meaningful concept 

(27:18) The need for interdisciplinary collaboration to ensure AI developments are beneficial for society as a whole.

(30:02) Brian Magerko's next reshape of the future, better understanding models of collaboration and improvisation between people and computers

(35:51) Brian Magerko's advice to researchers based on his own identity and experiences

(44:20) Projects and updates related to EarSketch and LuminAI’s improvisation model.

(46:24) Backstage with Executive Producer Leo Godoy

 As AI becomes increasingly integrated into business operations, having robust governance structures in place is no longer optional. But what does effective AI governance look like in practice? In this episode, Dr. Heather Domin, a leading expert in AI ethics and governance, breaks down the key components of a successful AI governance framework. Heather guides us through the opportunities and challenges presented by this transformative technology. Learn about the importance of responsible adoption practices, the role of governance structures, the need for ongoing feedback loops and how to align AI initiatives with organizational values, establishing clear accountability, and creating a culture of responsible innovation.


Timestamps

00:00:00 - 00:01:23 - Introduction

00:01:23 - 00:04:30 - Heather Domin's Journey

00:09:50 - 00:12:48 - Open Source and AI Ethics

00:12:48 - 00:15:25 - Generative AI and Governance

00:23:40 - 00:26:22 - Future of Responsible AI Practices

00:35:37 - 00:37:31 - Advice for the Audience

00:37:31 - 00:46:04 - Reflection on Risk and Hope in AI

Quotes

Heather Domin

"I think that each of us individually can scan our environment and understand, you know, where can I make an impact? What problem can I help solve? What is the next thing that I can really contribute to?"

"There are absolutely ways to automate, you know, the prompt testing and many of the routine tasks that you want to leverage automation in that way so that you can actually have the humans focus on other, other things so they can focus on the critical thinking and outside the box sort of thinking that we want the humans to be focused on."

Charna Parkey

"I think that it's a hard for people getting into it for the first time to jump to hope if they've experienced something that they should fear in the past. By that, I mean, groups that have been marginalized by other forms of technology are not going to start hopeful with this new one that is is using their data without their permission.."

"If for some reason I came to understand in a month what that meant, I should be able to go back and revoke and be like, nope, I actually don't want you to have that anymore. So I think that that would help people feel better."

Check Heather's paper: On the ROI of AI Ethics and Governance Investments 

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Ethan Soloviev, Chief Innovation Officer at HowGood, reveals how generative AI can revolutionize the food and agriculture industry. Discover the potential of AI to create a regenerative, sustainable, and net-positive food system that benefits the planet and all living beings.

Timestamps

1. Introduction and Background (00:00:00 - 00:01:16)

2. Ethan's Journey (00:01:16 - 00:05:12)

3. The Role of Food and Agriculture (00:05:12 - 00:06:52)

4. Investment in Regenerative Agriculture and Generative AI (00:06:52 - 00:07:44)

5. Levels of AI Impact (00:07:44 - 00:12:42)

6. HowGood's Use of AI (00:12:42 - 00:13:20)

7. Consumer Impact and Corporate Responsibility (00:13:20 - 00:15:44)

8. Future of AI in Food Systems (00:15:44 - 00:20:30)

9.  Innovative Perspectives on AI Training (00:20:30 - 00:21:10)

10. Action models in agriculture, optimizing water and soil use on a larger scale. (00:24:14 - 00:25:28)

11. Discussion on integrating human cultural geography into AI models. (00:27:37 - 00:30:00)

12. Charna and Ethan discuss procurement decisions and their impact on sustainability. (00:30:20- 00:40:15)

13. The ethical implications of AI in corporate and government decision-making. (00:42:01 - 00:54:31)

14. Leo brings up the impact of AI on consumers, discussing how AI can change purchasing decisions by highlighting product sustainability. (00:54:40 - 00:55:30)

15. Charna elaborates on using AI to understand different business models and how generational changes affect consumer choices. (00:55:47 - 00:57:32)

Quotes

Ethan Soloviev

"What if we're using ecological data? What if we're training on trees and insects and animals and whale song? What kind of questions would a gen AI trained on whale song and hummingbird language ask us?"

Charna Parkey

"If we have this great translator that is Gen AI, we already have text and language to code. We can do code generation. We can already interpret this code and tell me what it's going to do. Take that code to language. Why can't we do that with some of these other senses and these other measurements?"

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 Beth Rudden, recognized as one of the 100 most brilliant leaders in AI ethics, discusses the crucial role of explainability and traceability in building trustworthy AI systems. She shares how Bast AI is using ontologies and knowledge graphs to provide contextual relevance and understanding, enabling humans to fully trust artificial intelligence and how it allows the system to transform fields like education and healthcare.


Timestamps

00:00:00 - Intro

00:02:00 - Beth’s Journey

00:19:33​ - Ontologies in AI

00:21:44 - Data Lineage and Provenance

00:32:52 - Open Source Tools

00:38:38​ - Explainable AI

00:44:58- Inspiration from Nature

Quotes

Beth Rudden: "The best thing that I could tell you that I see is that it's going to shift from more pure mathematical and statistical to much more semantic, more qualitative. Instead of quantity, we're going to have quality."

Charna Parkey: "I love that because I've been so mathematical for most of my life. I didn't have a lot of words for the feelings or expressions, right? And so I had sort of this lack of data and the Brené Brown reference you make, like I have many of her books on my shelf and I often pull, I don't even know where it is right now, but the Atlas of the Heart because I am having this feeling and I don't know what it is."

Links

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Learn how Andrea Brown, CEO of Reliabl, is revolutionizing AI by ensuring diverse communities are represented in data annotation. Discover how this approach not only reduces bias but also improves algorithmic performance. Andrea shares insights from her journey as an entrepreneur and AI researcher. 

Episode timestamps

(02:22) Andrea's Career Journey and Experience with Open Source (Adobe, Macromedia, and Alteryx)

(11:59) Origins of Alteryx's AI and ML Capabilities / Challenges of Data Annotation and Bias in AI

(19:00) Data Transparency & Agency

(26:05) Ethical Data Practices

(31:00) Open Source Inclusion Algorithms

(38:20) Translating AI Governance Policies into Technical Controls

(39:00) Future Outlook for AI and ML

(42:34) Impact of Diversity Data and Inclusion in Open Source

Quotes

Andrea Brown

"If we get more of this with data transparency, if we're able to include more inputs from marginalized communities into open source data sets, into open source algorithms, then these smaller platforms that maybe can't pay for a custom algorithm can use an algorithm without having to sacrifice inclusion."

Charna Parkey

“I think if we lift every single platform up, then we'll advance all of the state of the art and I'm excited for that to happen."

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