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QUOTES BRIAN MAGERKO “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”. CHARNA PARKEY "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.” EPISODE TIMESTAMPS (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

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  [https://cmr.berkeley.edu/2024/07/on-the-roi-of-ai-ethics-and-governance-investments-from-loss-aversion-to-value-generation/]   Connect with Heather [https://www.linkedin.com/in/heatherdomin/] Connect with Charna [https://www.linkedin.com/in/charnaparkey/]

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?" Connect with Ethan [https://www.linkedin.com/in/ethansoloviev/] Connect with Charna [https://www.linkedin.com/in/charnaparkey/]

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 Connect with Beth [https://www.linkedin.com/in/brudden/] Connect with Charna [https://www.linkedin.com/in/charnaparkey/]

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." Connect with Andrea [https://www.linkedin.com/in/andreafrancesb/] Connect with Charna [https://www.linkedin.com/in/charnaparkey/]