Open||Source||Data

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.QuotesEmin 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 ParkeyI 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. 

Suzanne El-Moursi, Co-founder and CEO of BrightHive, shares her journey from corporate life to entrepreneurship. She discusses how AI, specifically her platform BrightBot, empowers non-technical teams to harness data insights effectively. The conversation delves into the challenges of data management for those without technical backgrounds and highlights the transformative role of open-source solutions. Suzanne also emphasizes the importance of community in fostering innovation and the future of AI in enhancing workplace experiences and education.

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.QuotesKent 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 copyrightTimestamps[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 GodoyConnect with Kent KeirseyConnect 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 QuotesVinay 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 ParkeyWe 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.

Brian Magerko, an AI expert with a focus on co-creating experiences, discusses the challenges of AI literacy and public perception. He emphasizes the importance of trust and accountability in AI systems. The conversation highlights the creative potential of AI in projects like LuminAI and EarSketch, designed to bridge gaps in technology education for underrepresented groups. The need for interdisciplinary collaboration to ensure beneficial AI advancements is also explored, showcasing how art and technology can work hand in hand for a better future.

Heather Domin, an expert in AI governance and ethics, shares her insights on the urgent need for ethical practices in AI development. She highlights the role of the open-source community in fostering transparency and responsible AI usage. Domin discusses the transformative potential of generative AI, advocating for adaptive governance frameworks. The conversation touches on the significance of red teaming in AI risk mitigation and the delicate balance between addressing public fears and fostering hope, particularly for marginalized communities.

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.Timestamps1. 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) QuotesEthan 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?"Connect with EthanConnect with Charna

 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.Timestamps00:00:00 - Intro00:02:00 - Beth’s Journey00:19:33​ - Ontologies in AI00:21:44 - Data Lineage and Provenance00:32:52 - Open Source Tools00:38:38​ - Explainable AI00:44:58- Inspiration from NatureQuotesBeth 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."LinksConnect with BethConnect with Charna

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 SourceQuotesAndrea 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."Connect with AndreaConnect with Charna