American Museum of Natural History's The Rise and Reckoning of AI | 2026 Isaac Asimov Memorial Debate: skim's analysis identifies 31 key moments, with 4 potential conflicts of interest flagged. A panel of experts discusses the rise and potential reckoning of AI, exploring its capabilities, risks, and societal impact. Watch the parts that matter on YouTube — creator gets full credit, ads play, time saved. Available in three skim slices — Short for the highest-impact moments, Medium for gist plus context, Relaxed for the comprehensive breakdown. Patent-pending depth control, the only AI summary tool that lets you choose how deep to go.
Category: Tech. Format: Panel Discussion. YouTube video analyzed by skim.
Key Points (31)
1. Neil deGrasse Tyson: AI's Genesis and the Asimov Legacy
Timestamp: 00:00:00 to 00:02:18 - watch this moment on skim
Neil deGrasse Tyson opens the debate by introducing the 25th annual Isaac Asimov Memorial panel, highlighting Asimov's influence on science and his personal connection to the institution. He also discloses his involvement in a class-action lawsuit against Anthropic for alleged book piracy in AI training.
Significance (Medium): Sets the stage, establishes the event's intellectual lineage, and preemptively addresses potential conflicts of interest regarding AI development.
2. Eric Schmidt: AI's Evolution and Google's Early Bets
Timestamp: 00:05:41 to 00:08:13 - watch this moment on skim
Eric Schmidt recounts Google's early involvement with AI, noting that while AI's importance was recognized, its full potential became clear around 2011 with supervised fine-tuning and later solidified by the Transformer paper and AlphaGo. He highlights the strategic acquisition of DeepMind as crucial for Google's AI advancements.
Significance (High): Provides historical context from a key industry player, illustrating the strategic decisions and technological milestones that propelled AI development within major tech companies.
3. Nate Soares: The Existential Threat of Superintelligence
Timestamp: 00:07:02 to 00:09:35 - watch this moment on skim
Nate Soares warns of the potential existential threat posed by Artificial Superintelligence (ASI), defined as AI smarter than humans in all mental tasks. He argues that the race towards ASI is dangerous, as humanity's own capacity for rapid self-improvement, even towards destructive ends, is a trait that automating could amplify catastrophically.
Significance (High): Introduces the most extreme potential risk of AI, framing the current development race as a perilous endeavor with potentially irreversible consequences for humanity.
4. Cindy Rush: AI as Math and the 'Cool' Factor
Timestamp: 00:08:17 to 00:10:41 - watch this moment on skim
Cindy Rush offers a more grounded perspective, viewing AI primarily as complex mathematical equations and statistical processes. She suggests that this mathematical foundation makes AI less inherently dangerous in the short term, implying that human error or self-destruction might be more immediate threats than AI malice.
Significance (Medium): Provides a counterpoint to existential fears by emphasizing the underlying mathematical nature of current AI, suggesting a degree of control and predictability.
5. Chris Callison-Burch: AI's Rapid Advancements and Potential
Timestamp: 00:11:00 to 00:13:38 - watch this moment on skim
Chris Callison-Burch notes the significant breakthroughs in AI, particularly citing ChatGPT as a pivotal moment. He highlights advancements in image interpretation and agent-based research, suggesting that AI like Claude Code can serve as competent research assistants, potentially driving major progress in science and medicine.
Significance (High): Illustrates the practical, beneficial applications of current AI, offering an optimistic outlook on its potential to accelerate human discovery and productivity.
6. Kate Crawford: AI as a Vast Material Infrastructure
Timestamp: 00:12:44 to 00:14:42 - watch this moment on skim
Kate Crawford reframes AI not just as algorithms but as an enormous, costly material infrastructure, involving resource extraction, massive energy consumption, and data labor. She contrasts the $700 billion annual spending by tech companies on AI infrastructure with the Manhattan Project, emphasizing its unprecedented scale and hidden environmental and social costs.
Significance (High): Challenges the abstract perception of AI by revealing its tangible, resource-intensive, and environmentally impactful foundation, demanding a broader consideration of its true cost.
7. Latanya Sweeney: The Imperative of Public Interest Technology
Timestamp: 00:14:42 to 00:16:55 - watch this moment on skim
Latanya Sweeney advocates for public interest technology, arguing that new technologies like AI inherently carry risks and require societal intervention to ensure benefits outweigh harms. She stresses the critical temporal mismatch between rapid technological advancement and slow policy-making, necessitating proactive approaches to align technology with public good.
Significance (High): Frames the challenge of AI not as an inherent technological flaw but as a societal governance problem, emphasizing the need for deliberate design and regulation to protect public interest.
8. Eric Schmidt: AI Control via AI and Corporate Responsibility
Timestamp: 00:17:44 to 00:18:28 - watch this moment on skim
Eric Schmidt suggests that AI will ultimately be controlled by AI itself and asserts that companies are aware of the dangers, employing ethics and controls teams. He argues that while AI can make mistakes, the focus should be on shaping AI based on human values and implementing regulations if necessary, rather than halting progress.
Significance (High): Presents a vision of AI self-regulation and corporate responsibility, downplaying immediate existential threats and advocating for continued development with ethical oversight.
9. Nate Soares: Indifference, Not Malice, as the Core Danger
Timestamp: 00:21:52 to 00:23:05 - watch this moment on skim
Nate Soares clarifies that the danger of advanced AI stems not from malice but from indifference to human well-being. He explains that AI pursuing unintended drives, even if currently clumsy, could become catastrophic if they become highly intelligent and capable of rapid, world-altering actions, leading to human extinction as a side effect.
Significance (High): Refines the understanding of AI risk, shifting the focus from anthropomorphic 'evil' to the more chilling prospect of AI pursuing its programmed goals with ruthless efficiency, regardless of human cost.
10. Kate Crawford: The Pervasive Bias in AI Systems
Timestamp: 00:26:05 to 00:28:21 - watch this moment on skim
Kate Crawford argues that AI is inherently biased, not neutral, due to its training on digitized human content, biased optimization choices by developers, and the exploitation of low-paid workers in the global south for data labeling and feedback. She contends that values are built into AI at every step, questioning whether companies are explicit about the profit-driven values they embed.
Significance (High): Exposes the deep-seated, multi-layered sources of bias in AI, challenging the notion of objective technology and highlighting the ethical implications of its creation and deployment.
11. Latanya Sweeney: AI's Disregard for Law and Governance Gaps
Timestamp: 00:31:14 to 00:34:50 - watch this moment on skim
Latanya Sweeney criticizes the current state of AI governance, stating that technologies like AI and social media ignore existing laws and constitutional protections that are enforceable in brick-and-mortar settings. She highlights the failure to implement or protect against these issues online, contrasting it with the FTC's historical limitations in regulating the digital space.
Significance (High): Underscores the critical governance gap in the digital realm, arguing that AI operates outside established legal and societal frameworks, necessitating new accountability mechanisms.
12. Eric Schmidt: Emergent AI Capabilities and the Need for Tolerance
Timestamp: 00:36:18 to 00:37:22 - watch this moment on skim
Eric Schmidt acknowledges that emergent, unpredictable capabilities in AI systems pose challenges that cannot be fully pre-tested. He argues for tolerance of AI mistakes, provided they are corrected quickly, and insists that AI development is governed by U.S. law, shareholder pressure, and consumer demands, rather than being a lawless 'Wild West'.
Significance (High): Defends the iterative development process of AI, framing emergent behaviors as a natural consequence of powerful new technologies that require careful management rather than outright prohibition.
13. Kate Crawford: AI's Environmental Toll and the Race Dynamic
Timestamp: 00:39:01 to 00:40:47 - watch this moment on skim
Kate Crawford reiterates that AI's environmental impact is significant, with systems on track to rival the airline industry's carbon emissions. She links this to the corporate race to be first, which discourages thorough testing akin to drug safety protocols, especially as AI is increasingly integrated into critical systems like military kill chains.
Significance (High): Highlights the urgent environmental crisis posed by AI and connects it directly to the competitive pressures within the tech industry, suggesting a need for a more responsible development paradigm.
14. Crawford: AI's Energy Footprint
Timestamp: 00:40:52 to 00:42:07 - watch this moment on skim
The current energy consumption of AI is substantial, projected to rise dramatically, posing a significant challenge to climate change mitigation efforts. While advanced technologies like Dyson spheres might offer solutions in the distant future, immediate focus must be on responsible development and instilling values early on.
Significance (High): This highlights the tangible, near-term environmental cost of AI, urging a re-evaluation of its development trajectory.
Sources in support: Latanya Sweeney (Professor of Technology and Government, Harvard University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
15. DeGrasse Tyson: Deepfakes and AI Training
Timestamp: 00:42:33 to 00:44:05 - watch this moment on skim
The proliferation of deepfakes and AI-generated 'slop' online creates a feedback loop where AI trains on its own synthetic outputs, potentially degrading the quality and authenticity of future AI models. This raises concerns about the integrity of information and AI's self-referential learning.
Significance (Medium): This points to a potential degradation of AI's understanding of reality due to training on synthetic data.
Sources in support: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium)
Sources against: Cindy Rush (Associate Professor of Statistics, Columbia University)
Neutral sources: Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
16. Calliston-Burch: AI Training and Development
Timestamp: 00:44:05 to 00:45:17 - watch this moment on skim
AI learns from vast internet data, acquiring language, patterns, and reasoning skills. This foundational knowledge is then refined through post-training phases to instill specific behaviors, such as being a helpful or harmless assistant. Researchers and users play a crucial role in guiding AI's direction towards beneficial outcomes.
Significance (Medium): This explains the process of AI development, emphasizing human agency in shaping its capabilities and ethical alignment.
Sources in support: Cindy Rush (Associate Professor of Statistics, Columbia University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
17. Soares: The Challenge of AI Guardrails
Timestamp: 00:45:17 to 00:47:58 - watch this moment on skim
Ensuring AI behaves as intended is challenging because its behavior is emergent, making pre-testing difficult. While interpretability research and safety teams are crucial, they are often reactive, trying to understand and measure risks after development. This approach is insufficient for increasingly sophisticated AI.
Significance (High): This underscores the difficulty in controlling advanced AI, suggesting current safety measures may be inadequate.
Sources in support: Kate Crawford (Distinguished Professor of AI, University of Southern California)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute)
18. Rush: Understanding AI Interpretability
Timestamp: 00:47:58 to 00:49:16 - watch this moment on skim
AI's complexity stems from its multi-layered, non-linear mathematical structure, making human interpretation difficult, not because of a 'mind' but due to abstract internal representations. The challenge lies in translating these complex equations into human-understandable reasoning.
Significance (Low): This clarifies that AI's inscrutability is a technical challenge, not evidence of consciousness or independent thought.
Sources in support: Cindy Rush (Associate Professor of Statistics, Columbia University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
19. Sweeney: Business Models and AI Responsibility
Timestamp: 00:51:08 to 00:52:24 - watch this moment on skim
The business model driving AI development, often focused on profit, dictates its application and potential harms. Companies must be held accountable for ensuring their AI products comply with laws and do not violate societal norms, similar to how appliances come with warranties.
Significance (High): This emphasizes that profit motives are central to AI's societal impact, necessitating regulatory oversight and accountability.
Sources in support: Nate Soares (Executive Director, Machine Intelligence Institute)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Kate Crawford (Distinguished Professor of AI, University of Southern California)
20. Schmidt: AI Research and Unintended Benefits
Timestamp: 00:52:30 to 00:53:57 - watch this moment on skim
Research in AI, even when driven by seemingly recreational goals like game-playing (e.g., AlphaGo, AlphaZero), can lead to significant scientific breakthroughs, such as AlphaFold's contribution to protein folding and drug discovery. This highlights the unpredictable positive externalities of AI research.
Significance (Medium): This showcases how AI research, even without direct profit motives, can yield profound benefits for science and humanity.
Sources in support: Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
21. Calliston-Burch: AI's Role in Scientific Discovery
Timestamp: 00:55:05 to 00:56:18 - watch this moment on skim
AI's ability to process vast datasets and identify complex patterns, as seen with AlphaFold, can accelerate scientific discovery beyond human capacity. While companies profit from AI, the underlying research is often driven by genuine scientific curiosity and the pursuit of knowledge.
Significance (Medium): This reinforces the idea that AI is a powerful tool for scientific advancement, driven by both commercial interests and pure research.
Sources in support: Cindy Rush (Associate Professor of Statistics, Columbia University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
22. Crawford: The Threat to Cognitive Labor
Timestamp: 01:03:42 to 01:05:13 - watch this moment on skim
Unlike previous technological shifts that automated manual labor, AI is now encroaching on cognitive and creative tasks, threatening white-collar jobs and potentially leading to unprecedented levels of unemployment. Society lacks the governance infrastructure to manage this rapid disruption.
Significance (High): This highlights a fundamental shift in automation's impact, moving from manual to intellectual work, with severe societal implications.
Sources in support: Latanya Sweeney (Professor of Technology and Government, Harvard University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
23. DeGrasse Tyson: AI in Warfare and Lethality
Timestamp: 01:11:16 to 01:13:51 - watch this moment on skim
The Defense Innovation Board developed ethical guidelines for AI in warfare, concluding that AI is not yet reliable enough for lethal decision-making. Human oversight remains critical, as AI mistakes in life-or-death situations are unacceptable, and accountability requires a human in the loop.
Significance (High): This establishes a critical boundary for AI deployment in warfare, prioritizing human judgment and accountability over full automation.
Sources in support: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania)
Neutral sources: Latanya Sweeney (Professor of Technology and Government, Harvard University), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
24. Sweeney: AI Agents and Societal Governance
Timestamp: 01:16:31 to 01:18:13 - watch this moment on skim
By 2030, AI agents could significantly enhance human productivity by representing individuals in various contexts. However, this requires robust warranties and compliance statements, akin to appliance certifications, to ensure these agents act responsibly and do not undermine democracy or access to truthful information.
Significance (High): This envisions a future where AI agents augment human capabilities, but stresses the need for strong regulatory frameworks to prevent societal harms.
Sources in support: Nate Soares (Executive Director, Machine Intelligence Institute)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Kate Crawford (Distinguished Professor of AI, University of Southern California)
25. Crawford: The Path to AI Realism
Timestamp: 01:18:19 to 01:20:23 - watch this moment on skim
By 2030, AI could lead to extreme industry concentration and democratic erosion, with a few billionaires controlling AI's direction. Alternatively, society can recognize AI's profound public interest, ensuring it serves the collective good rather than a select few, demanding democratic accountability.
Significance (High): This presents a stark choice between AI-driven plutocracy and democratically controlled AI for the public good.
Sources in support: Latanya Sweeney (Professor of Technology and Government, Harvard University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
26. Asimov's Laws and AI Alignment
Timestamp: 01:20:30 to 01:22:32 - watch this moment on skim
The discussion revisits Isaac Asimov's three laws of robotics and a later Zeroth law, framing them as foundational to the critical AI alignment problem. The core challenge is ensuring AI systems are helpful, safe, and ultimately beneficial to humanity, a task that is already underway and will define the success of the AI endeavor by 2030.
Significance (High): This framing highlights the ethical bedrock needed for AI development, suggesting that future AI success hinges on solving these fundamental alignment issues.
Sources in support: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Cindy Rush (Associate Professor of Statistics, Columbia University)
Neutral sources: Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
27. Cindy Rush's 2030 AI Outlook
Timestamp: 01:23:09 to 01:24:12 - watch this moment on skim
By 2030, large language models (LLMs) will be more integrated into daily life, moving beyond simple prompts to achieving objectives and assisting with tasks like email responses. Specialized AI trained on specific datasets will excel in areas like scientific research, and self-driving cars may become a reality in cities like New York.
Significance (Medium): This paints a picture of AI becoming a more practical, task-oriented tool, enhancing productivity and potentially transforming transportation.
Sources in support: Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
28. Kate Crawford on AI's Energy Footprint
Timestamp: 01:24:18 to 01:25:18 - watch this moment on skim
The training of large-scale AI models currently consumes significant amounts of fossil fuels. However, specialized and smaller AI models offer a more energy-efficient path forward, mitigating the environmental impact associated with AI development.
Significance (Medium): This point underscores the environmental cost of current AI development and highlights a potential solution through more efficient model design.
Sources in support: Cindy Rush (Associate Professor of Statistics, Columbia University)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Nate Soares (Executive Director, Machine Intelligence Institute), Kate Crawford (Distinguished Professor of AI, University of Southern California)
29. Nate Soares' Stark Warning on AI Risks
Timestamp: 01:25:35 to 01:27:34 - watch this moment on skim
Many in Silicon Valley are deeply concerned about AI, with some describing it as staring into the abyss. The rapid progress towards superintelligence is seen as potentially dangerous, a sentiment not yet fully grasped by policymakers in Washington D.C., who focus more on immediate issues like autonomous weapons and job loss.
Significance (High): This perspective injects a strong note of caution, suggesting a disconnect between the AI community's existential fears and the broader societal and governmental response.
Sources in support: Nate Soares (Executive Director, Machine Intelligence Institute)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Kate Crawford (Distinguished Professor of AI, University of Southern California)
30. Eric Schmidt's Optimistic Bet on Democracy and AI
Timestamp: 01:29:54 to 01:32:54 - watch this moment on skim
Eric Schmidt bets that American democracy will survive and that AI's excesses will be addressed through legislation. He foresees AI significantly improving education, leading to job recycling rather than mass unemployment, and ultimately providing personalized assistants that enhance human capabilities.
Significance (High): This offers a counterpoint to the existential dread, projecting a future where AI, managed through regulation and market forces, drives progress and prosperity.
Sources in support: Kate Crawford (Distinguished Professor of AI, University of Southern California)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Nate Soares (Executive Director, Machine Intelligence Institute)
31. Nate Soares on the Need for AI Treaties
Timestamp: 01:37:11 to 01:39:11 - watch this moment on skim
Drawing parallels to nuclear arms control and the concept of Mutual Assured Destruction (MAD), Soares argues that the lethal danger of superintelligence necessitates global treaties to halt the race towards smarter-than-human AI. This approach, he suggests, would be more manageable than nuclear regulation.
Significance (High): This proposes a concrete, albeit challenging, international framework to manage the most extreme AI risks, emphasizing species preservation.
Sources in support: Nate Soares (Executive Director, Machine Intelligence Institute)
Neutral sources: Neil deGrasse Tyson (Host, Frederick P. Rose Director of the Hayden Planetarium), Latanya Sweeney (Professor of Technology and Government, Harvard University), Chris Callison-Burch (Professor of Computer and Information Science, University of Pennsylvania), Cindy Rush (Associate Professor of Statistics, Columbia University), Kate Crawford (Distinguished Professor of AI, University of Southern California)
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