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Join Charna Parkey and the community shaping the future of open source data, open source software, data in AI, and much more.
In this episode of Open Source Data, Charna Parkey talks with Alex Gallego, CEO and founder of Redpanda Data, about his journey as a builder, the evolution of Redpanda, and the company's new agent framework for the enterprise. Alex shares insights on low-latency storage, distributed stream processing, and the importance of developer experience to the growth of AI and the Open Source space.
Timestamps
[00:00:00] Introduction
[00:02:00] Alex Gallego talks about his background
[00:04:00] Charna Parkey discusses the importance of hands-on experience in learning.
[00:06:00] Alex explains the origins of Red Panda and how it emerged from challenges in the streaming space.
[00:08:00] Alex details the evolution of Red Panda, its use of C-Star and FlatBuffers, and its low-latency design.
[00:11:00] Alex discusses the positioning of Kafka versus Red Panda in the market.
[00:20:00] Alex introduces Red Panda's new agent framework and multi-agent orchestration.
[00:24:00] Alex explains how Red Panda fits into the evolving landscape of AI-powered applications.
[00:30:00] The future of multi-agent orchestration.
[00:44:00] Thoughts on AI model training and data retention.
[00:46:00] Alex encourages future founders and shares his perspective on risk-taking.
[00:50:00] Charna Parkey and Leo Godoy discuss the key takeaways from the conversation with Alex Gallego.
[00:52:00] Charna reflects on open source trends and the role of developer experience in adoption.
[00:54:00] Charna and Leo talk about the different types of founder journeys and the importance of team dynam
Charna Parkey
"For AI, unifying historical and real-time data is critical. If you're just using nightly or monthly data, it doesn’t match the context in which your prediction is being made. So it becomes very important in the future of applying AI because you need to align those things."
Alex Gallego
"Every app is going to span three layers. The first layer is going to be your operational layer, just like you have to do business right now. Then there always has to be an analytical layer, and the third layer is this layer of autonomy."
In this episode, we dive deep into the world of neuro-symbolic AI with Emin Can Turan, CEO of Pebbles AI. Learn how this technology combines neuroscience, behavioral economics, and AI to revolutionize B2B go-to-market strategies. Emin explains how neuro-symbolic AI bridges the gap between human logic and machine learning, enabling smarter, context-aware systems that democratize complex workflows for startups and enterprises alike.
Timestamps
[00:00:00] - Introduction by Charna Parkey and introduction of Emin Can Turan.
[00:02:00] - Emin’s journey to AI and his background in go-to-market strategies.
[00:06:00] - Emin explains his deep R&D phase and the development of neuro-symbolic AI.
[00:08:00] - Emin describes the architecture of their AI system, including neuro-symbolic AI, generative AI, and agentic frameworks.
[00:10:00] - Explanation of neuro-symbolic AI and its relevance to domain-specific problems.
[00:12:00] - Discussion on the components of go-to-market strategies and the role of psychology and communication.
[00:16:00] -The limitations of generative AI and how they applied strict communication tactics.
[00:22:00] - Discussion on the importance of contextual science and data insights.
[00:24:00] - The three agentic frameworks they use in their system.
[00:26:00] - Explanation of how users control the product and the two co-pilots (strategy and execution).
[00:36:00] - The ethical implications of AI and the potential for misuse.
[00:38:00] - Discussion on the future of AI and the balance between dystopian and hopeful outcomes.
[00:40:00] - Emin emphasizes the importance of truth and transparency in AI development.
[00:42:00] - Emin shares his personal motivation for building his AI startup.
[00:48:00] - Closing remarks and discussion on the user experience of their platform.
[00:50:00] - Charna and Leo discuss the connection between Emin's work and the open-source community.
Quotes
Emin Can Turan
"I felt that this was the future and that AI was the only technology that can digitalize this level of complexity for everyone to use. Nothing else could, you know, you can't use normal neural networks to do this. Even generative AI is not sufficient enough."
Charna Parkey
I would love to be able to use Gen AI for more personal things. I love technology. I have the Oura Ring. I've got the Apple Watch. I want to feed that data into something that can somehow tell me and others, here's your state of mind. Here's what you're going to be affected by.
Learn how BrightHive's AI-powered platform is democratizing data insights, making them accessible to non-technical teams across organizations. Suzanne El-Moursi discusses the importance of data fluency and how BrightHive is helping businesses harness the power of their data.
Timestamps
00:00:00 - Introduction and Background
00:02:30 - Journey to BrightHive and open source
00:06:00 - The evolution of AI and BrightHive's approach
00:14:00 - The data problem and the role of AI agents
00:22:00 - Building BrightBot with open source frameworks
00:26:00 - The future of AI agents and open source
00:30:00 - People’s reaction to DeepSeek
00:34:00 - The future of work and AI
00:40:00- AI in education and personal growth
00:42:00 - Suzanne’s legacy
00:48:00 -Recap and takeaways with producer Leo Godoy
Quotes
Charna Parkey
"Every single innovation comes out of some form of restriction or need. (...) Don't come and say, “oh, what is this? This is terrible”. I heard all kinds of responses to my excitement and to my belief."
Suzanne El-Moursi
"So if 97% of an organization is data consumers, there are strategists, the marketing analysts, the customer success associates, the managers all across the enterprise, who need to understand the insights in the company's data, in their functions, in their units, so that they can make the next right step for the customer and for their plan."
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
Connect with Kent Keirsey
Connect with Charna Parkey
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
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?"
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."
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