What can we learn from ai-native development through stimulating conversations with developers, regulators, academics and people like you that drive forward dev...
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.
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56:22
How to Empower Non-Technical Teams with Data Insights | Suzanne El-Moursi
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.Timestamps00:00:00 - Introduction and Background00:02:30 - Journey to BrightHive and open source00:06:00 - The evolution of AI and BrightHive's approach00:14:00 - The data problem and the role of AI agents00:22:00 - Building BrightBot with open source frameworks00:26:00 - The future of AI agents and open source00:30:00 - People’s reaction to DeepSeek 00:34:00 - The future of work and AI00:40:00- AI in education and personal growth00:42:00 - Suzanne’s legacy 00:48:00 -Recap and takeaways with producer Leo GodoyQuotesCharna 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."
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55:23
Open Source AI and Copyright: Building Ethical Models | Kent Keirsey
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
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1:10:19
Building Trust in AI: From Open Source to Global Impact with host, 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.
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44:03
AI Regulations in Financial Services with Vinay Kumar
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.
What can we learn from ai-native development through stimulating conversations with developers, regulators, academics and people like you that drive forward development, seek to understand impact, and are working to mitigate risk in this new world?
Join Charna Parkey and the community shaping the future of open source data, open source software, data in AI, and much more.
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