EP10:Deep Into - The Worlds I See
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Welcome back everybody for another deep dive. And today we're going to be going through excerpts from Fei-Fei Li's book, The Worlds I See. Okay. And this is a book that is part memoir and part history lesson on AI. Okay. So we're gonna be talking about her experiences and how those connect to the broader evolution of artificial intelligence. Sounds good. Now the interesting thing about this book is that even the chakra titles kind of read like a roadmap for the deep dive itself. Oh really?

So you've got pins and needles in D.C., something to chase, and the North Star almost like hinting at this narrative arc. Interesting. And then there's this kind of mysterious title, What Lies Beyond Everything. Oh, wow. Yeah, that makes you think. Yeah, it makes you wonder what she's discovered along the way. It does. It really piques your curiosity just reading that title. And then when you read the book, you know, you've got the acknowledgments about the author section, and it really just reminds you that

Behind all this groundbreaking work, there's a real human story. Yeah, those personal touches are so important. I think sometimes we get caught up in the science and the technology and we forget about the people who are actually driving it forward. Absolutely. So let's jump right in with Chapter 7, Pins and Needles in D.C. Now, in this chapter, Lee recounts this experience of testifying before Congress on AI. Wow.

So can you imagine going from the world of research to the intensity of a congressional hearing? I can only imagine. I mean, that must have been so nerve wracking. Oh, absolutely. Right. And especially considering the context. This is 2018, right? Amidst all this growing anxieties about AI, you know, there's all this talk about AI

know the potential negative impacts of ai job displacement bias all that stuff yeah the tech lash as they called it right exactly so lee finds herself in this really unique position because she's not just an ai expert right but she's someone who's advocating for human-centered development of ai which is so important absolutely and her core message to congress

is that AI shouldn't just be about technological advancement for the sake of advancement. It has to be about serving humanity. Yeah, it has to be about making our lives better, not just about creating machines that can think for themselves. Right. And what's even more remarkable is that she was going through a lot personally at the time, too. Oh, really? Yeah. She talks about her mother's illness and how that added this whole other layer of complexity to the experience. I can't even imagine trying to deal with all of that alone.

while also preparing for something as high stakes as a congressional hearing. Absolutely. But, you know, that human element is

It shines through in her testimony. Yeah. You know, she's not just talking about algorithms and data sets. She's talking about the need for transparency, fairness, accountability. Right. It's about making sure that AI is developed and used in a way that benefits everyone, not just a select few. Exactly. And it's so interesting to see how her personal experience is really intertwined with her professional life and how they shaped her views on AI. Absolutely. I think that's something that comes across really strongly in the book. Now, something to chase.

This chapter takes us back to Li's childhood in China. And she talks about her early fascination with the universe, you know, looking up at the stars. And she draws this interesting parallel between the vastness of space

and the mysteries of the human mind. It's like she was always drawn to exploring the unknown both on this cosmic level and this very personal level. Yeah. And her parents, they played a huge role in fostering that curiosity. You know, her father's this infectious love of learning, her mother's this unwavering belief in her abilities.

Those were crucial in setting her on this path. That's so important to have that kind of support system, especially when you're pursuing something as challenging as AI. Absolutely. Now, she also talks about facing adversity and how that shaped her. Oh, right. Yeah, there's this moment in school where she was told that despite being initially smarter than boys, girls would become less intelligent than boys with age. Oh, are you serious? Yeah. Can you believe that? I mean, that's just such a limiting belief. Absolutely. Yeah. But it seemed to really fuel her determination.

Yeah. Lies drive to kind of defy those expectations is a constant theme throughout the book. Yeah. It's like she took that negative experience and turned it into something positive. It made her even more determined to succeed. Right. And it makes you wonder how many brilliant minds have been stifled by those kinds of, you know, limiting beliefs. Too many, I'm sure. Absolutely. It's a reminder to challenge those assumptions and to create environments where everyone can thrive.

Absolutely. Now let's move on to a narrowing gulf. OK. So this chapter shifts our focus to Lai's journey from China to America. Interesting. Which she beautifully connects to the evolution of AI itself. I see. So it's like these two parallel journeys, both kind of navigating this uncharted territory, facing these obstacles and ultimately pushing boundaries. That's a really interesting parallel. She leaves a China that's kind of on the verge of this dramatic change.

and arrives in America during this burgeoning digital revolution. And at the same time, the field of AI is also undergoing this major shift,

moving away from those early simplistic models to more complex data-driven approaches. So it's like both AI and lie are kind of coming of age at the same time. Exactly. And initially, you know, there was this almost naive optimism about AI, you know? Really? Yeah, people thought they could just program intelligence into machines using a set of rules. Like a recipe for intelligence. Right, exactly. And these early pioneers, like the folks at the Dartmouth conference,

They were incredibly ambitious. Of course. But they really underestimated the complexity of human intelligence. It seems so obvious now, but I guess it wasn't back then. Right. And those rule-based systems, they really hit a wall. Yeah. You know, they just couldn't capture the nuance and the adaptability of human thought. Right. It's like trying to capture the essence of a human being with a set of rules. It's just not possible. And that's where machine learning comes in.

Right. This idea of training algorithms on massive data sets, allowing them to kind of learn and adapt on their own. Exactly. It was a complete paradigm shift in the field. Absolutely. And Lee's personal journey, you know, navigating these cultural differences, adapting to this new environment. It kind of mirrors that evolution in a way. Yeah. It's like both AI and LIE had to learn and adapt to survive and thrive. Now let's dive into discovering the mind. Okay.

So in this chapter, Lied talks about this pivotal moment that sparked her passion for understanding how the brain works. Cool. Yeah. So she's at a lab and she's observing a cat's brain activity. A cat. Yeah. In response to visual stimuli. So they were showing the cat images and watching how its brain reacted. Exactly. Exactly.

And for her, it was this revelation, you know, this glimpse into the neural processes underlying vision that really captivated her. I can see how that would be fascinating. And it kind of set her on this path to explore the mind further. So is that when she decided to focus on AI? Well, she actually faced this kind of internal struggle at Princeton between pursuing this

more, quote unquote, practical path of engineering. Okay. And following her fascination with physics. Interesting. Yeah, and all this while dealing with these family hardships, you know, her mother's illness, financial strains. Oh, wow. It's incredible how she persevered through all those challenges. Yeah, her resilience is really inspiring. Mm-hmm.

Absolutely. Yeah. And then there's this influence of these physicists who turn their attention to biology later in their careers. OK. Which kind of gave her this permission to explore this intersection of disciplines, you know. Right. Like it's OK to blur the lines between different fields. Exactly. And that's ultimately what led her to AI. Fascinating. Yeah. So she had this realization that computer science could be.

This new language, this powerful tool for understanding the brain. So that was kind of her odd-on moment. Absolutely. Okay, so on to First Light. Okay. This chapter takes inspiration from the Cambrian Explosion.

You know, the what? The Cambrian explosion, this incredible burst of evolutionary complexity. It was driven by the emergence of vision. Oh, right. Right. Yeah. And Leany uses this analogy to highlight the challenge of replicating human vision in machines. OK. So it's not just about, you know, recognizing shapes and colors. It's about understanding what they represent. Yeah. You know, grasping these abstract concepts like red.

Right. Or recognizing a familiar face. Yeah, it's like the difference between seeing and perceiving. Exactly. And that raises this really interesting philosophical question, right? What's that? How do you teach a machine to see the world the way humans do? That is a deep question. It really is. It goes beyond the technical realm. You know, it forces us to think about the nature of perception consciousness and what it really means to understand the world around us. Oh, wow. That's a lot to unpack. Now, the North Star.

This chapter transports us to the Koch lab at Caltech,

where Lyon really delved into these complexities of the human visual system. So she's like on a quest to unravel the secrets of how we see. Exactly. And she's surrounded by all these brilliant minds. Like who? Well, there's Anne Treisman and her feature integration theory, which was groundbreaking at the time. I've never heard of that. Yeah, it explains how our brains combine simple features like color and shape to recognize objects. Right. But then comes Simon Thorpe's research.

which suggests that the brain can recognize images in milliseconds much faster than Trisman's model predicted. Yeah, so it kind of challenged the existing understanding and raised these new questions about the speed and efficiency of visual processing. So they're kind of contradicting each other. In a way, yeah. And it highlights just how much there still is to discover

about this incredible organ. The brain is a mysterious thing. Absolutely. And remember, vision science and computer vision, these were relatively young fields at the time. So Lai was really on the frontier exploring uncharted territory. And you can feel that sense of discovery and excitement in her writing. I bet. And her own experiments on unconscious image recognition demonstrating how our brains process visual information

And even without our conscious awareness, we're contributing to this evolving understanding. So we're seeing things without even realizing it. In a way, yeah. Yeah. It's pretty amazing. That is pretty wild. Okay, now, a hypothesis. Okay. This chapter takes us to this period of immense change in Lai's life, you know? Oh. She graduates from Caltech. She's navigating her mother's fluctuating health.

She starts this new job at UIUC, and she's dealing with the challenges of a long-distance marriage. Wow, that's a lot to handle. It's a whirlwind of personal and professional transitions. Yeah. But amidst all this, her research remains this constant. Right. It's like her North Star guiding her through the chaos. I like that. And it's during this period that she stumbles upon this estimate by Biederman, who suggests that there are 30,000 unique concepts—

That could be the key to unlocking the visual world. 30,000. Yeah, that number really becomes this driving force for her. How so? It pushes her to think about data on this massive scale. A scale that would eventually lead to the creation of ImageNet. ImageNet, oh wow. Yeah, this massive data set of labeled images that really played a pivotal role in the rise of deep learning. Right, ImageNet is like the gold standard for training students.

AI models. Absolutely. Yeah. But building it was no small feat. I bet. It required this immense effort. You know, the painstaking labeling process, the logistical challenges of organizing millions of images. Yeah, I can only imagine. Before we move on to experimentation and the birth of the ImageNet challenge, I'm curious what's resonating with you so far. What stands out to you from Lee's journey?

Well, I think what's really striking is her ability to find these connections between seemingly disparate things, you know, like the vastness of space and the intricacies of the human brain or her personal journey and the evolution of AI. Yeah. It's like she sees these underlying patterns that most people miss. This is beautiful tapestry, right? Exactly. And it really speaks to the power of interdisciplinary thinking.

Absolutely. It's amazing how she's able to weave together these different threads, you know, her personal experiences, the scientific discoveries, the broader societal implications.

It's like she's creating this multidimensional portrait of AI. Now, experimentation. This chapter starts with an incredible anecdote. Lee is on a bullet train in Kyoto and bam, she has this epiphany. A bullet train epiphany. I like it. It's the spark that ignited the ImageNet challenge. So what was the epiphany? She realized that

To truly advance AI, they needed a way to benchmark different algorithms. A common playing field where researchers could compare their approaches and measure progress. So kind of like a standardized test for AI. Exactly. And thus the ImageNet challenge was born. I mean, that's a pretty big deal, right? Oh, absolutely.

ImageNet became this driving force in the field. But transforming this massive data set into a global competition that must have come with its own set of challenges. Oh, for sure. You know, fairness was paramount. Right. They had to establish these clear evaluation metrics.

address potential biases in the data, and ensure that the process was transparent and accessible to researchers worldwide. So it wasn't just about creating a data set. It was about creating a whole infrastructure for evaluating AI. Exactly. It was a huge undertaking. And did it take off right away? That's the interesting thing. It didn't.

And the first few years saw this kind of incremental progress. Algorithms based on support vector machines were dominating. Support vector machines. Yeah, they were achieving decent results, but not that groundbreaking leap that Lye had envisioned. So it was like the field was waiting for something. Exactly. Some kind of catalyst. And that catalyst arrived in 2012.

With AlexNet. AlexNet. A neural network that just blew all the other algorithms out of the water. Really? Yeah. It achieved this unprecedented level of accuracy on the ImageNet dataset, a 10 percentage point jump

over the previous best. Wow, that's huge. Like AlexNet shattered the ceiling, you know, demonstrating the true potential of deep learning. So ImageNet provided the data and AlexNet provided the algorithm. Exactly. And crucially, ImageNet's massive scale and diversity played a key role in enabling AlexNet's success. Right, because it allowed the algorithm to learn from a much wider range of examples. Exactly. It proved that these data-driven approaches could unlock these unprecedented capabilities in AI.

So we see this interplay between Lai's vision, the creation of this groundbreaking data set, and the emergence of this game-changing algorithm. It's like all these pieces coming together to propel the field forward. It's a beautiful example of how progress often happens at the intersection of different ideas and perspectives. Now, what lies beyond everything?

This chapter takes us to Lai's return to Stanford, where she leads SAIL during this really exciting but also kind of turbulent time for AI. Oh, how so? Well, neural networks are making a comeback. The tech industry is pouring resources into AI and ethical considerations are starting to emerge. So it's a time of both immense opportunity and growing concern. Exactly.

And amidst all this, Lai achieves these significant personal and professional milestones. Like what? She reunites with her husband after years of long-distance marriage.

and she becomes the first woman to direct sale. That's amazing. It's incredible how she's able to juggle those personal and professional challenges while leading this really pivotal research lab. It speaks to her resilience and her ability to kind of balance those different aspects of her life. And her lab was doing some incredible work. Such as? Well, they were exploring concepts like hedging.

Hedging. Yeah, allowing algorithms to kind of defer to broader categories when they're uncertain. Interesting. And fine-grained classification. The ability to distinguish between very similar objects. So like telling the difference between a golden retriever and a Labrador. Exactly. It's like they were constantly pushing the limits of what AI could understand and interpret. And I love that anecdote about Lai noticing the prevalence of Teslas in Palo Alto.

Oh, yeah. It sparked this fascinating research question, you know, could AI be used to analyze societal patterns by examining something like Google Street View data? It's a great example of how these everyday observations can lead to these groundbreaking research ideas.

Absolutely. And then there's Andrzej, the grad student who developed an algorithm capable of describing entire scenes. Right. It wasn't perfect. Of course. But it was this huge step towards AI understanding and interpreting this complex visual information. It shared just how far the field had come and the incredible potential that lay ahead. Absolutely. Now, deceptively simple, this chapter takes a more personal term.

Lai confronts this question of how AI can help people while grappling with her mother's illness. So it's bringing together her personal and professional life in a very real way. Exactly. It's this powerful juxtaposition. You know, on the one hand, she's at the forefront of this rapidly advancing field. Right. But on the other hand, she's facing the very human limitations of current health care systems. I can see how that would be a really challenging experience. And it's this experience that drives her to

to explore AI's potential to improve healthcare. So she wants to use AI to make a real difference in people's lives. Exactly. She teams up with Arnie Milstein, this healthcare innovator, and they embarked on this mission to create ambient intelligence.

Ambient intelligence. That sounds intriguing. What is that? Think of it as this intelligent environment that's constantly monitoring and subtly intervening to prevent things from going wrong. Okay. So they focus on something as seemingly simple as hand hygiene. And hygiene. Yeah, which is actually a critical aspect of patient safety that's often overlooked. Right, because doctors and nurses are so busy, they might forget to wash their hands as often as they should. Exactly.

And it's amazing how AI can be applied to these everyday tasks that have such a significant impact on people's lives. It's about using technology to augment human capabilities, not replace them. Exactly. And their work faced some significant challenges. Yeah. You know, such as developing algorithms that could accurately recognize these complex actions and navigating the complexities of working with sensitive health care data.

Right. Because patient privacy is paramount. Absolutely. Yeah. But the potential impact was enormous. It showed how AI could be used to create a safer and more efficient health care system. Now, no one's to control. This chapter dives into this rapid evolution of AI and the growing awareness of its potential impact, both positive and negative.

It's like AI is stepping out of the research labs and into the real world. Right. It's becoming more and more integrated into our daily lives. And the chapter opens with this anecdote about like encountering someone at a preschool parent meeting who's excited about AI. At a preschool parent meeting. Yeah. It's a sign that AI is permeating every aspect of society from everyday conversations to boardroom discussions. It's no longer this niche field. Exactly. And with that widespread adoption comes a host of new challenges. Oh, sure.

The pace of technological advancement is accelerating.

with innovations like ResNet. ResNet. Yeah, a 152-layer neural network showcasing the power of deep learning, but also highlighting the increasing complexity of these systems. So it's like a double-edged sword? In a way. Yeah. And as these systems become more powerful, the need for responsible development and ethical considerations becomes even more crucial. Absolutely. Lee recounts instances where AI systems exhibit biases, reminding us that AI is not inherently neutral.

It reflects the biases present in the data it's trained on and the people who design it. Exactly. So we need to be really careful about how we develop and deploy these systems. And the growing influence of the tech industry adds another layer of complexity. Companies like Google and Uber are pouring vast resources into AI research, raising questions about the balance of power between academia and industry. So it's not just about the technology itself. It's about who controls it and how it's used. Exactly.

Exactly. And Lee grapples with these questions in the book, you know, advocating for the need for greater collaboration and ethical oversight as AI becomes increasingly intertwined with our lives. It's a crucial point. As AI's influence grows...

We need to ensure that its development and deployment align with human values and societal goals. And that brings us to the next North Star, where Lee reflects on the future of AI and her role in shaping it. Okay. It's a chapter filled with this kind of hope and a sense of responsibility.

she returns to her beloved cs231n class at stanford the one she taught yeah but the atmosphere is different now there's this urgency the sense that these students are not just learning about ai they're going to be the ones shaping its future that's a lot of pressure and lenin emphasizes that future needs to be human centered

where AI serves humanity and respects human dignity. It's not just about building powerful algorithms. It's about using those algorithms to address real-world problems, to improve people's lives, and to do so in a way that aligns with our values. And her work with Stanford HAI, this hub for interdisciplinary collaboration focused on the societal impact of AI, speaks volumes about her commitment to this vision.

So it's about bringing together experts from various fields, ethics, law, policy, social sciences. Exactly. To ensure that AI development is guided by this holistic understanding of its implications. And what about the National Research Cloud? Oh, yeah. This project that

that lie spearheaded to ensure equitable access to AI resources. Right, because not everyone has the same opportunities to access these powerful tools. Exactly. It's about democratizing AI, making sure that the benefits of this technology are shared widely and that its development is not concentrated in the hands of a select few. It's about creating a more level playing field. Absolutely.

But even with these advancements, Lai acknowledges these ongoing challenges. Like what? She cites the example of her work on ambient intelligence in healthcare and the subsequent ethical critique it received. Oh, interesting. Emphasizing the need for ongoing dialogue and critical reflection. So it's not enough to just develop the technology. We also need to be constantly evaluating its impact and making sure that it's being used in a responsible and ethical way. Exactly. And it's inspiring to see how Lai's students are carrying this torch forward.

Andres, who developed this groundbreaking algorithm for image captioning, exemplifies this next generation of AI researchers who are both technically brilliant and ethically aware. That gives me hope for the future. Yeah. These young minds are pushing the boundaries of AI while remaining mindful of its potential impact.

striving to create a world where AI serves humanity in these meaningful and responsible ways. I love that vision. Before we move on to the final part of our deep dive, I'd love to hear your thoughts. What's resonating with you from this section? What sparked your curiosity? I think one of the things that's really sticking with me is how Lai emphasizes the human element of AI. It's not just about the technology itself. It's about the people who are creating it, using it, and being impacted by it.

It's that interconnectedness, right? Like how her own personal journey is so intertwined with the evolution of the field. Exactly. And I think that's something that's often overlooked in discussions about AI. You know, we tend to focus on the technical aspects, but we forget that AI is ultimately a human creation and it's going to be shaped by human values and decisions. So we've explored Laya's personal journey, the evolution of AI, the challenges and the potential.

Where do we go from here? That's the big question, isn't it? I mean, Lai's book doesn't offer any easy answers, but it does leave us with a sense of responsibility. Responsibility in what sense? Well, I think it's about recognizing that AI is not just something happening out there.

You know, it's something that we're all shaping, whether we're researchers or policymakers or just everyday citizens interacting with this technology. It's a call to action, right? Exactly. It's about recognizing that we have a say in how AI develops and how it's used. And that we need to be mindful of the potential consequences, both positive and negative. Absolutely. Lee reminds us that AI's impact is not predetermined. Hmm.

You know, it's up to us to guide its development, to hold it accountable and to ensure that it reflects our values. And that's what I find so inspiring about Leigh's story. It's not just about understanding AI. It's about empowering us to ask the right questions, to demand better from the technology and to actively participate in shaping its future. It's about reclaiming our agency in a world that's increasingly being shaped by algorithms.

Now, before we wrap up this deep dive, I want to leave you with a final thought, something to ponder as you continue exploring the world of AI. We've talked about Lee's vision for human-centered AI, but what about your vision? What kind of future do you want to see? And how can you contribute to building an AI that reflects our shared values and aspirations? Those are powerful questions. And as Lee's journey demonstrates,

Even small actions can have a ripple effect. You know, whether it's educating yourself about the implications of AI engaging in thoughtful conversations about its potential and pitfalls or advocating for responsible development and use, each of us can make a difference. That's what we hope you take away from this deep dive.

It's not just about gaining knowledge. It's about sparking those critical conversations, those moments of reflection that lead to action. Lease the Worlds I See is an invitation to join this conversation, to explore the fascinating world of AI, and to consider its profound implications for our future. So keep exploring, keep questioning, and keep pushing for an AI that serves humanity in all its messy complex and beautiful glory. Until next time, keep diving deep.