Game Changers: Decoding AI

[00:00:00] Introduction: Host Jeff Rhodes sets the stage for a compelling discussion on AI.
[00:05:30] Real-World AI Applications
Healthcare Revamp: Nitesh Chawla reveals how digitized healthcare records in Latin America are transforming risk assessment and diagnosis.
Cancer Treatment Innovation: Thom Kenney discusses how AI is revolutionizing protein targeting for cancer treatments, showcasing technology’s power to augment human capabilities.
[00:15:20] Ethical and Responsible AI
Model Creator Accountability: Nick Fehring stresses the importance of holding creators responsible for their AI models, backed by IBM’s proactive stance on ethics.
Combining Tech with Humanities: Yong Lee highlights Notre Dame’s initiatives to merge humanities with technology, cultivating ethically aware AI practitioners.
[00:25:45] Exploring AI’s Environmental Impact
Innovative data center strategies discussed by Thom Kenney aim to make AI operations more sustainable by leveraging cooler climates and advanced immersion technology.
[00:35:10] Enhancing Accessibility and Reducing Disparity
Thom Kenney addresses the need for universally accessible language models to ensure they don’t widen existing skill gaps across different demographics.
[00:45:58] Future of Work and Creativity
Engage with discussions on AI’s dual impact on productivity and job roles, as well as how AI is influencing creative fields like art, poetry, and architecture.
[00:52:07] Interactive Panel & Audience Q&A
Environmental Impact: First audience question concerns AI’s power consumption.
Creativity and AI: Nitesh Chawla emphasizes classification challenges with AI-generated data and potential for creativity diminishment.
Copyright and Plagiarism: Nick Fehring addresses governance and risk management in AI deployment, touching on IBM’s AI ethics board.
Generation Differences: Yong Lee discusses AI adoption differences among Gen Zers and Gen Alpha.
AI in Education: Nitesh Chawla compares integration of AI tools in education to calculators and spell-checks.

Tune in to not only expand your understanding but also gain a nuanced perspective on the ethical, practical, and future-oriented aspects of AI. Join us on this illuminating journey as we decode the mysteries and opportunities of AI.

• Thom Kenney ‘05 MBA
  • 00:12:4200:12:54
    1. “Generative AI as a Universal Tool”: “The challenges in many ways are rooted into where we’ve come with generative AI today, which a lot of folks are looking at this tool as the hammer, and every problem they have is the nail.”
  • 00:26:4500:26:57
    1. Strategic Competition in AI Ethics: “Whose ethics? This is a difficult question to answer across the board because we have adversaries using data and information in very different ways.”
  • 00:31:2800:31:43
    1. The Future of AI Transparency: “We need to move the explainable part of AI to a quantitative metric, because this needs to lead to something down the road, which I also think academia can help us figure out, which is, how do we have the machines explain to us why they’re making these decisions?”
  • 00:38:4300:38:54
    1. Disparities in AI Accessibility: “If we continue down this path, there’s going to initially be a disparity between people who can do prompt engineering really well, because they speak and they talk and they walk just like the duck that LLM expects them to.”
  • 00:43:1100:43:29
    1. The Future of Technology in Medicine: “If we can do a hundred years of a hundred brilliant minds of research in the period of two or three years, imagine the amazing potential that we’re going to have as a society to eradicate some of these diseases and some of these maladies that we’ve had as for almost the entirety of our existence.”
• Nick Fehring ‘01, ‘01 MBA
  • 00:23:4700:23:55
    1. Future of Legal Jobs: “The role of the lawyer is not going away, but the lawyer that uses AI is going to replace a lawyer that doesn’t.”
  • 00:25:2000:25:42
    1. “Technology Ethics”: “And what we’re focused on in the lab is applied research, education, and curriculum development. Reaching out to the communities both within the university, but also beyond to help support, the more vulnerable. And, to advocate for policy and appropriate regulation from governments to ensure the responsible deployment of AI.”
  • 00:36:4200:36:56
    1. The Importance of Open AI Development: “As the old adage goes, sunlight is the best antiseptic, and the more eyes, the more developers, the more people working on these models and the use of the models, the better off we all will be.”
  • 00:45:0500:45:20
    1. Ensuring Ethical AI Deployment: “We need to be advocating with our governments, both locally and at the federal level, to ensure that they are stepping up to the plate to put in place the right guardrails to ensure the ethical deployment, the responsible deployment of AI.”
  • 00:58:4700:59:14
    1. Unlocking Productivity with Machine Learning: “When you identify workflows, you know, fairly low from a judgment perspective that are time consuming, rote task-oriented and you tackle that with machine learning-type approach that brings gen AI to do contract scraping, and pre-population of checklists, and go from a human doing the work to a human reviewing the results of the work – that opportunity unlocks productivity.”
• Nitesh Chawla
  • 00:17:1300:17:30
    1. Embracing Technological Tools in Education: “We are still figuring it out, but we have embraced tools alongside as we have calculators or Excel sheets or, Grammarly and spell checks. And so these made things better and easier. It allowed us to focus on what kind of paper I may write and the content.”
  • 00:18:2300:18:38
    1. Future of Employment Amid Technological Change: “When I came to the U.S. in 1997, toll booths used to have humans collecting money. Those went away. Grocery shops used to always have individuals collecting, but those are going away. But that doesn’t mean the unemployment rates have skyrocketed. So there’s a replacement effect.”
  • 00:33:1000:33:19
    1. AI Ethics and Inclusivity: “By inclusive AI, I mean are the voices of the underrepresented, the data, adequately represented even in the algorithms that we have developed?”
  • 00:50:5100:51:52
    1. Disruptive Potential of Generative AI in Art and Humanities: “Now when we think about human creativity with art, poem, music, et cetera, there is no bounds to it, right? There is no certain thing to talk about. When we are looking at art or some artists, whether it’s surrealism or things that, there are things that just, how I feel, what I discovered and how I’m expressing that moment in my time. In my opinion, the humanities would be shaken up a bit more, than, because of the ability to create essays, the ability to create write ups, the ability to create art. If you look at now text to image generation, text to video generation, it’s very difficult to Identify what was real versus what was fake. And so it is going to be highly disruptive. If I could think of a concept in my head and interact with the gen AI system and iterate with it and say, “Now show me it this way, show me that way,” I may find a beautiful sense of architecture or art including the measurements and things that’ll make that building stand.”
• Yong Lee
  • 00:11:3300:11:40
  • The Impact of AI on Students: “And this creates sort of a tension that I see among our students, where students become a bit concerned and worried.”
  • 00:12:0400:12:17
  • The Pace of Innovation and Education: “So I think, as an educational institution, we have to grapple with this tension, how the pace of innovation that’s happening, that is so rapid. And the stickiness and the carefulness of an educational institution.”
  • 00:20:4900:20:52
  • Impact of New Technology on Labor Productivity: “Our new technology is making workers more productive.”
  • 00:22:3300:22:48
  • Maximizing Productivity Benefits: “So when we think about these differential effects, but nonetheless, productivity benefits that could arise, how do we put this all together to make sure that we try to make sure everyone is on board, in terms of taking advantage and learning from this knowledge.”
  • 00:40:0700:40:13
  • AI Ethics and Humanity: “What should a virtuous human be like in an era of AI? Those types of questions are being asked at this campus.”
• Jeff Rhoads
  • 00:02:0000:02:13
  • Understanding Generative AI: “There’s a lot of confusion out there in today’s world about generative AI, large language models, machine learning, traditional AI. Some of it leads to excitement, some of it leads to fear, a lot of it leads to uncertainty.”
  • 00:20:2200:20:32
  • The Future of Work with AI: “How do you think AI will affect the future of work in this nation and across the globe? Are you a pessimist or an optimist about this? How do you think the nature of work’s going to change?”
  • 00:32:3700:32:45
  • Notre Dame’s Cross-Boundary AI Initiatives: “We could spend a whole hour talking about how Notre Dame is trying to engage industry and the government to think about these wicked hard problems in AI.”
  • 00:45:5200:45:56
  • The Environmental Impact of AI: “Should we be concerned about the environmental impacts of the use of AI?”