A Skeptic's Guide to How Cognitive Biases and Reasoning Errors Fuel AI Hype, Social Learning, The Hidden Cost of ChatGPT is the Erosion of the Digital Commons, and more.
Our Ideas: AI & Scientific Discovery. In science, traditional human search strategies can be like wandering through a wilderness with limited visibility, relying on intuition and serendipity. In the pursuit of new insights, we can choose to follow the well-worn paths trodden by others or venture along the trails revealed by low-dimensional data. AI, in contrast, can take in the whole landscape, quickly and effectively exploring the vast range of possible combinations. That's what gives AI the power to reveal hidden paths and uncover new territory. A combinatorial space represents the set of all possible configurations or solutions to a given problem. In fields like protein folding and material science, the number of potential combinations is staggeringly vast—far beyond the scope of what human researchers can explore unaided. The laws of physics set the limits of what's possible in our universe. Life as we know it has followed a particular path, shaped by its history, but when it comes to materials, there's a huge untapped space of potential waiting to be explored.
The Science: Agentic AI and Self-Driven Exploration. New research on developing AI that builds robust world models shows an AI's ability to seek out surprise, motivation, and novelty, enabling it to navigate and understand the complexities of the world through self-driven exploration rather than just following predetermined reward pathways. The research represents an important shift, moving us closer to creating machines that not only learn but are intrinsically motivated to explore and understand the world around them. This approach opens up new possibilities for AI to become a more adaptive, creative, and efficient partner in various fields. By embracing the essence of curiosity and the pursuit of open-endedness, we can develop AI systems that navigate the complexities of the real world with more autonomy and insight.
Our Ideas: AI, Intimacy, and Transcendence. For decades, the tech industry has distilled rich, real-world experiences into quantifiable data—clicks, views, and time spent—which are then neatly categorized into rows and columns so that interactions can be used for profit. This approach has undoubtedly boosted the economy but at the cost of stripping the context and meaning from our online behaviors. Context is everything—whom you're with, where you're going, and why. Machines currently lack the ability to understand this context, but generative AI, especially modern large language models, hold the promise of changing this limitation.
Conversations: Susannah Fox & Rebel Health. We’re excited to welcome to the podcast Susannah Fox, a renowned researcher who has spent over 20 years studying how patients and caregivers use the internet to gather information and support each other. Susannah has collected countless stories from the frontlines of healthcare and has keen insights into how patients are stepping into their power to drive change. Susannah recently published a book called "Rebel Health: A Field Guide to the Patient-Led Revolution in Medical Care." The book aims to bridge the divide between the leaders at the top of the healthcare system and the patients, survivors, and caregivers on the ground who often have crucial information and ideas that go unnoticed. By profiling examples of patient-led innovation, Susannah hopes to inspire healthcare to become more participatory.
Events: EY Innovation Realized. We spoke at EY's Innovation Realized conference on the topic of "Rethinking Value in the Age of AI" on April 17, 2024 at the Palace of Fine Arts in San Francisco, CA. It was an impressive event where we got to present with and share time with friends & partners including Tim Leberecht from the House of Beautiful Business and David Krakauer from the Santa Fe Institute. We've posted an edited and shortened version of our presentation on the event page.
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Interested in us speaking to your organization and helping you navigate the new worlds of AI and complex change? Set up time for a chat with us here.
Bits & Bytes from Elsewhere
Stanford’s Institute for Human-Centered Artificial Intelligencereleased the seventh edition of its AI Index Report. This year’s report topped 500 pages of data covering research, technical performance, ethics, science, education, policy, diversity, and more. Scroll down to our Facts & Figures section for some of the data points we thought were most interesting.
We attended the ASU+GSV Summit in San Diego along with more than 7,000 other attendees. The scale of the event was quite impressive along with the content. Notably, it seemed that every AI-related session was full with people outside the room hoping to get in. One questionable moment, however, was a discussion with Vinod Khosla who said that the only way to solve education is through individual tutoring from AI (odd to say to a room of educators) and that an AI tutor will be free because the marginal cost of software is zero other than compute. We were left wondering how to square the circle that AI tutors could be free when compute is so expensive and how teachers will keep their jobs if school systems have to pay for AI. Congrats to our friends at GSV for a great event—stay tuned for our future collaborations with GSV.
In March, Levi’s announced a partnership with Lalaland.ai to use “AI-generated models to supplement human models, increasing the number and diversity of our models for products in a sustainable way.” The announcement received a good amount of backlash, and Levi’s clarified that they “are not scaling back [their] plans for live photo shoots.” Yet, a professional photographer in L.A. told us this week that he had two friends whose business has been gutted by Levi’s scaling back on photo shoots. Hopefully, corporate diversity efforts will focus more on paying diverse people than paying the historically non-diverse tech industry for AI diversity. Note the founders of Lalaland, which also works with Tommy Hilfiger and Calvin Klein, are a Black man and a white man.
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Partner / Friend Highlight: It is bittersweet to highlight the House of Beautiful Business' annual gathering that begins on May 2 in Tangiers, Morocco. We have spoken at this gathering four times and are quite disappointed we couldn't make it this year. If you, however, have the time and ability to get to Morocco—we highly recommend it!
Harding has a strong pedigree in AI and public policy. She was the head of policy for Google DeepMind, and is now director of the AI & Geopolitics Project at the Bennett Institute for Public Policy at the University of Cambridge. Many books about AI's place in society and AI alignment are written from the perspective of the uniqueness of AI. This book is grounded in real-world coordination problems—nuclear weapons in orbit, fertility technologies, internet governance. Verity gives the reader a deep and contextually rich history for each case study. In this way, the analogies are rich and practical. The high-level lessons perhaps aren't surprising—will, leadership, coordination, time—but the details matter if we want enduring institutions designed for AI.
Harding discusses the risks and concerns thoroughly but without the hand-wringing that often accompanies the impact of AI on society. Difficult lessons from the past, social and scientific tensions, and challenges of coordination and complexity are informative and feel very human. Which makes them feel possible.
An important lesson from this book is one of cultural context. After reading it, I'm more convinced than ever of how critically important it is to recognize the cultural context of the day. And today, that context is a world where trust in institutions has been completely shattered, right as "intelligence" is being institutionalized inside the big companies developing AI. This is a totally unique factor that sets apart how we have to deal with AI compared to other technologies in the past. We can't ignore the cultural backdrop of eroding public trust as we grapple with the rise of powerful AI systems being built within the walls of large corporations. It's a defining feature of the AI landscape today and something we absolutely have to keep front and center.
This is great book if you want to deepen your contextual understanding of the options available to us to shape a world where AI is a fundamental power in its own right. It's a tangible perspective on the reality of the choices we need to make about AI.
Facts & Figures about AI and Complex Change
59%: The growth in the number of GitHub AI projects from 2022 to 2023. (Stanford)
63%: The growth in the number of AI patents granted from 2021 to 2022. (Stanford)
$78,000,000: The estimated cost to train OpenAI GPT-4. (Stanford)
$191,000,000: The estimated cost to train Google Gemini Ultra. (Stanford)
34%: The percentage of European organizations which identified fairness risks as relevant to their AI adoption strategies. (Stanford)
20%: The percentage of North American organizations which identified fairness risks as relevant to their AI adoption strategies. (Stanford)
$400: The cost to create Countercloud—an experiment with a fully automated, agentic AI system for creating disinformation including sourcing content, writing counter content, and posting comments and social to support the counter content. (Countercloud)
79%: The percentage of Fortune 500 companies which mentioned AI in earnings calls in 2023, up from 53% in 2022. (Stanford)
1,812: The number of newly funded AI companies funded in 2023. (Stanford)
129: The number of AI-related medical devices approved by the FDA in 2022, up 12% since 2021. (Stanford)
80%+: The percentage of enterprise data is inaccessible because it’s trapped in unstructured file formats or document stores like your email inbox. (Menlo Ventures)
Dave Edwards is a Co-Founder of Artificiality. He previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Apple, CRV, Macromedia, Morgan Stanley, and Quartz.
