Digital Social Hour
AI's Future: Open Source or Closed Control? | Dr. Travis Olipant DSH #1317
Fri, 11 Apr 2025
AI's Future: Open Source or Closed Control? 🤖 Join Sean Kelly on the Digital Social Hour as he sits down with Dr. Travis Olipant, a trailblazing AI expert to tackle one of the most pressing questions of our time. Is open source the key to AI's potential, or will closed control dominate its future? 🌐 This episode is packed with valuable insights on AI's rapid evolution, the role of big tech, and how open source could revolutionize industries, education, and even YOUR daily life. From the power of personalized learning to the ethics of AI governance, we’re covering it all. 💡 Discover how AI is reshaping industries like healthcare, gaming, and education, and why owning your own AI might be the game-changer you didn’t know you needed. Plus, hear fascinating stories about quantum computing, the rise of AI in chess and poker, and what open source really means for innovation. ♟️🎮 Don’t miss out on this engaging and eye-opening conversation! Watch now and subscribe for more insider secrets. 📺 Hit that subscribe button and stay tuned for more thought-provoking episodes on the Digital Social Hour with Sean Kelly! 🚀 CHAPTERS: 00:00 - Intro 00:26 - Travis’ Concerns with AI 03:17 - Closed Source vs Open Source AI 08:36 - Most Advanced AI Model 10:58 - Education and AI 12:02 - Benefits of Open Source 15:42 - Full Body MRI Technology 23:45 - Quantum Computing Insights 24:59 - Is AI Overhyped? 26:04 - Open Source AI Discussion 26:47 - Closing Remarks APPLY TO BE ON THE PODCAST: https://www.digitalsocialhour.com/application BUSINESS INQUIRIES/SPONSORS: [email protected] GUEST: Dr. Travis Olipant https://x.com/teoliphant https://www.linkedin.com/in/teoliphant/ LISTEN ON: Apple Podcasts: https://podcasts.apple.com/us/podcast/digital-social-hour/id1676846015 Spotify: https://open.spotify.com/show/5Jn7LXarRlI8Hc0GtTn759 Sean Kelly Instagram: https://www.instagram.com/seanmikekelly/ #ainews #generativeai #openai #aitrends #airesearch
Chapter 1: What are Dr. Travis Olipant's concerns with AI?
Okay, guys, got Travis here today. We're going to talk AI, one of the pioneers in space. Thanks for hopping on today. Absolutely. Great to be here, Sean. Yeah, the space is evolving so fast. Does it concern you at all?
Yeah, it concerns me for a number of reasons, but probably not the same reasons other people think. I think there's a lot of things happening quickly, and a lot of people trying to make sense of it quickly, even though there's not a lot of understanding of how it actually works. And so there's a lot of...
Chapter 2: What is the difference between closed source and open source AI?
uncertainty that can lead to confusion so that that probably concerns me more than anything is just that um uncertainty leading to rapid action and not thoughtful action yeah what are the biggest concerns and red flags you're seeing right now so um kind of overreaction by governments is one that concerns me you know people trying to pass laws make regulations where they don't really understand what the implications of those are so kind of ended up with rules and patterns that don't really fit
what, what emerges.
Yeah.
So that concerns me. I think the other thing that concerns me is, uh, a lot of closed source companies just trying to own the space, you know, a whole lot of kind of, um, realist, like a land grab, you know, oh, here's this AI space. Let's grab all the attention. Uh, whereas I'm a really big proponent of people, uh, learning from AI and making it part of their toolbox. Yeah.
You know, ultimately letting us become better agents for ourselves by having AI as a tool that we all can use. So there's kind of this land grab going on where a lot of information flow is happening to a few companies. So that concerns me too. I want to see AI knowledge diffuse and disperse and have lots of people use it effectively.
Um, but you know, there's a lot of money sort of advertising, promoting, uh, you know, it's, it's amazing how quickly people can be informed by, uh, narratives. Right. We're sort of driven by narratives. We, we, we seek out narratives and worldviews and way to think. And without critical thinking, without background, you can easily be persuaded by something that just isn't true.
Especially with social media these days, yeah. Exactly, exactly. And so AI could be used to actually amplify that capability. People are good at it, but what if you had AI even be better at it? That's in one sense why social media has been challenging is because it's even... stupidly, not with any intent at all, but just trying to get eyeballs.
AI algorithms have already been feeding people information that they want to hear. And so it reinforces cognitive confirmation bias and the cognitive dissonance that happens all the time. So you're just basically being reinforced what you want to hear. And it kind of, it's created polarization in our society. It's creating people, you know, people are creating enemies out of who could be friends.
And that's one of the things I think, I don't want AI to amplify that. I want to see how can we use AI to understand each other better and actually maybe show a little more empathy to each other and understand how, hey, you know, we're not that different. We have our differences. That could be beautiful. But let's not emphasize them because that can lead to conflict. Yeah.
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Chapter 3: Which companies are leading in AI technology?
And then more than going beyond that from just words to music and to audio and to video and images. We're doing a podcast now, the Google one. Yes, exactly. We can actually produce a podcast. And it sounds decent too. It does. Yeah, it's scary. It is. No, there's a company I've been consulting with called Zyphra. They're out in Palo Alto.
And they have a mechanism to produce speech from text that produces realistic voice models that sound just like somebody. You clone yourself. Yeah.
And that model is cool to me because I'm an audio learner. Like I love podcasts and audio books. So when you can do that, you can learn really fast.
Absolutely. So I'm more of a visual guy, but I love audio books and I love podcasts too. So I understand. I like to listen at 2x speed. Sometimes 2.5. Sometimes 2.5. I know the recent one can go up to 3. I'm going, some people I can listen to at 3x speed. Same.
He talks too fast. Even 2x at bend is tough. It's true.
You're sitting there going, wait, I got to process all this information quickly, can't I?
Yeah, but sometimes when it's a seven-hour Rogan episode, I'll do like 2X for sure.
Yes, right. And that's, oh, that was only three hours.
Yeah, he's had some long ones lately, man. So true. So there's AI companies coming out of China now. Who do you think has the most advanced one? We're filming this in March 2025. Yeah, well, right now,
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Chapter 4: How is AI revolutionizing education?
Now, that's still there, and there's still a lot of messaging about that.
uh i'm definitely in the camp of we're not going to incrementally get to uh superior to the artificial general intelligence or the human-like intelligence what we have is is definitely a clear intelligent system that may be a part of how the human mind works but it's not the complete thing yeah and so that's cool now we can but but really any value coming out of that is coming from a system that's produced so you take the model you take some other hardware you take some just
know uh computing capability and you stitch together a system that's produced um right now as we're speaking this uh uh manis is all the all the rage right it just came out like within a few there's in this past week and everybody's kind of going whoa this is amazing because it can run my business, I can do my research report, I can run a stock report, I can file my taxes, what they think.
I mean, it's making games. Grok has a great model too, actually. Grok 3 was just released and it's beating in a lot of measurements, a lot of the other models. So Grok is also really a fantastic base model. And they have a deep search and they have kind of additional modules around the model that they're starting to release as well that people are going to experience with.
But honestly, Sean, it's really early. So it's easy to kind of have these F1 race concepts, but it's not really the model that works because everyone has to kind of ask the question, what am I trying to use this for? And what for me is going to be a valuable tool? And that's going to be the most productive question.
Like for me, like on the side, I'm a chess player. AI has revolutionized the chess space. It's caused players to become a lot better. For example, I played Andrew Tate in chess yesterday and I beat him because think about this. He played chess his whole childhood, but there was no AI or computers back then. So to get better at chess was really hard.
Now when I play on the chess.com phone, AI analyzes every single game and I could see where I mess up so I could get better way quicker.
So I love that. I think that's a fantastic use case of AI. I think it's an important one too. It's about helping humans get better. Like I'm a big advocate for natural intelligence.
like we have not optimized how humans learn right in fact i think our education system at least the united states is really really bad really bad terrible it's and and a lot of systemic and schools are banning ai and that's completely a mistake yeah because ai needs to be used to help exactly this to make personalized education more possible it can help you take an interest you have and in that moment of interest amplify your capability and the iteration ability to learn
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Chapter 5: Can open source AI level the playing field?
But to do that, what we need are millions of professionals, millions of people, tens of millions of people, hundreds of millions of people, billions of people all using AI for their purposes. So to see how we need to convert AI from being a thing somebody else does to us to a tool that we all use to better ourselves and improve our lives. That's what I'm about.
That's what this Open Source AI Foundation that I recently started working with and joining is all about. It's recognizing that the same, like I just said that Linux as an open source operating system gave rise to cloud. That same phenomenon of using open source AI, it'll give rise to a future we can't expect.
if we keep people in charge of it, and a lot of people in charge of it, not just one or two people, not just a few thousands of people, but millions and billions of people having access to similar tools. Just level the playing field and help people engage with each other. Now that will, you know, a lot of people go, wait, that's going to change everything. Yeah, it could.
And so I'm not all for kind of rapid disruption. How do we do this in a measured way where people are accountable and people have ways to work together and people do it in their communities and do it in their families and do it in their tribes and do it in their communities?
their virtual groups like that's that's how we we already are organized as humans and all these little different governance groups ai can help us each organize better help us relate to each other better and it can bring about this incredible world i believe so that's what i'm about that's what i love to try to promote um open source is how we got here uh i've been involved in open source for a long long time i started as a scientist wow um really uh it was during my i got a master's degree using satellite images to measure um
backscatter off the earth. Holy crap, that sounds intense. It was intense, but it's also very, you know, it's math. I mean, I know I realize math is not for everybody, but I love math and I love learning as much math as I could. And to me, math is just a tool. It's a tool that lets you get insight from data. And we did that from the satellite data backscatter.
You basically have electromagnetic radiation, so you beam a radar down to the earth, you measure what comes back, and then you try to infer what that means about the ice field, about the wind speed and direction over the ocean, about the plant vegetation. So that was my first experience with large-scale data processing.
But I went to the medical area to try to do the same with images, with MRI, with ultrasound. And that industry could progress faster. It's a little more regulated. And so there's a lot, progress is slower.
Yeah.
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Chapter 6: How can AI help us organize better as communities?
That's another topic we could go into, but probably a different data. But go ahead.
Yeah. Have you heard of Pernula? I have not. Full body MRI. They use AI to analyze the MRI. The problem is it's expensive. So most people can't afford this. But yeah, I got it. They used AI to analyze my results. I learned a lot about my body. And that's where I hope the future. That is amazing. Edison goes to.
that same with my dentist so there's holistic dentists now yeah we'll take photos of your teeth throw it into ai it was finding my cavities and it was finding my gum infections sean i love that yeah that's actually why i went to the phd is to make make insurance better like actually because i think that's possible like and you've looked into why things are so expensive there are some reasons for it but there's we these can be more made less expensive we could easily have mri technology
at least as pervasive as dental imaging, right? At least as pervasive as your local doctor could have one.
I hope we get there. I hope we get there too. Yeah, it was like 2,500, which is a lot for a full-body MRI, you know?
It is. And some of that's the magnet, it's expensive, but some of it's the processing. Some of it is you can actually save money if you don't put as much effort into building a very homogenous magnetic field. Right. But that requires better data processing. And so your point, if AI can help us process data better, then we can have MRI more ubiquitous for less expense.
So for that, you had to get a doctor to manually review every result.
It is. And there's also a lot, it was expensive to make the field so that the processing was simple. That's the big thing. So because right now, that's how MRIs work is the processing is relatively simple from a, mathematical point of view. Because if you have the field slightly inhomogeneous, then the processing is a lot harder, but potentially still possible.
And with AI, hey, maybe we can get there. I also am excited about AI just design, AI helping scientists iterate faster. Just like you said with chess, you learn quickly. What if scientists learn more quickly about how to, you know, what does this mean? What if I make this change? What does that mean? There's a big saying I've come to say all the time, which is innovation is iteration.
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