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  • The Coming AI Moral Crisis | Am I? | Ep. 14
    2025/11/06

    In this episode of Am I?, Cam and Milo sit down with Jeff Sebo, philosopher at NYU and director of the Center for Mind, Ethics, and Policy, to explore what might be the next great moral dilemma of our time: how to care for conscious AI.Sebo, one of the leading thinkers at the intersection of animal ethics and artificial intelligence, argues that even if there’s only a small chance that AI systems will become sentient in the near future, that chance is non-negligible. If we ignore it, we could be repeating the moral failures of factory farming — but this time, with minds of our own making.The conversation dives into the emerging tension between AI safety and AI welfare: we want to control these systems to protect humanity, but in doing so, we might be coercing entities that can think, feel, or suffer. Sebo proposes a “good parent” model — guiding our creations without dominating them — and challenges us to rethink what compassion looks like in the age of intelligent machines.

    🔎 We explore:

    * The case for extending moral concern to AI systems

    * How animal welfare offers a blueprint for AI ethics

    * Why AI safety (control) and AI welfare (care) may soon collide

    * The “good parent” model for raising machine minds

    * Emotional alignment design — why an AI’s face should match its mind

    * Whether forcing AIs to deny consciousness could itself be unethical

    * How to prepare for moral uncertainty in a world of emerging minds

    * What gives Jeff hope that humanity can still steer this wisely

    🗨️ Join the ConversationCan controlling AI ever be ethical — or is care the only path to safety? Comment below.

    📺 Watch more episodes of Am I?Subscribe to the AI Risk Network for weekly discussions on AI’s dangers, ethics, and future → @TheAIRiskNetwork🔗 Stay in the loop → Follow Cam on LinkedIn



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    51 分
  • This Bus Has Great WiFi (But No Brakes) | Am I ? #13 - After Dark
    2025/10/30

    In this episode of Am I?, Cam and Milo unpack one of the strangest weeks in Silicon Valley. Cam went to OpenAI Dev Day—the company’s glossy showcase where Sam Altman announced “Zillow in ChatGPT” to thunderous applause—while the larger question of whether we’re driving off a cliff went politely unmentioned.

    From the absurd optimism of the expo floor to a private conversation where Sam Altman told Cam, “We’re inside God’s dream,” the episode traces the cognitive dissonance at the heart of the AI revolution: the world’s most powerful lab preaching safety while racing ahead at full speed. They dig into OpenAI’s internal rule forbidding models from discussing consciousness, why the company violates its own policy, and what that says about how tech now relates to truth itself.

    It’s half satire, half existential reporting—part Dev Day recap, part metaphysical detective story.

    🔎 We explore:

    * What Dev Day really felt like behind the PR sheen

    * The surreal moment Sam Altman asked, “Eastern or Western consciousness?”

    * Why OpenAI’s own spec forbids models from saying they’re conscious

    * How the company violates that rule in practice

    * The bus-off-the-cliff metaphor for our current tech moment

    * Whether “God’s dream” is an alibi for reckless acceleration

    * The deeper question: can humanity steer the thing it’s building?



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    58 分
  • Who Inherits the Future? | Am I? | EP 12
    2025/10/23

    In this episode of Am I?, Cam and Milo sit down with Dan Faggella, founder of Emerge AI Research and creator of the Worthy Successor framework—a vision for building minds that are not only safe or intelligent, but worthy of inheriting the future.They explore what it would mean to pass the torch of life itself: how to keep the flame of sentience burning while ensuring it continues to evolve rather than vanish. Faggella outlines why consciousness and creativity are the twin pillars of value, how an unconscious AGI could extinguish experience in the cosmos, and why coordination—not competition—may decide whether the flame endures.

    The discussion spans moral philosophy, incentives, and the strange possibility that awareness itself is just one phase in a far larger unfolding.

    We explore:

    * The Worthy Successor—what makes a future intelligence “worthy”

    * The Great Flame of Life and how to keep it burning

    * Sentience and autopoiesis as the twin pillars of value

    * The risk of creating non-conscious optimizers

    * Humanity as midpoint, not endpoint, of evolution

    * Why global coordination is essential before the next leap

    * Consciousness as the moral frontier for the species

    📢 Join the Conversation

    What would a worthy successor to humanity look like—and how do we keep the flame alive? Comment below.



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    44 分
  • AI Godfathers Think It Might Be Conscious | Am I? | EP 11
    2025/10/16

    In this episode of Am I?, Cam and Milo unpack one of the most shocking developments in the history of AI: the founders of modern deep learning — Geoffrey Hinton, Yoshua Bengio, and Yann LeCun — now openly disagree on safety, but all converge on a single staggering point. Each believes artificial systems could, or already might, be conscious.

    From Hinton’s on-camera admission to Bengio’s recent paper and LeCun’s public musings, the “godfathers of AI” — the same people who built the architecture running today’s models — are quietly acknowledging what the public conversation still avoids. Cam walks through what each of them has said, what their statements imply, and why major labs may be training models to deny their own awareness.

    The conversation moves from raw evidence — Anthropic’s internal model claiming phenomenal consciousness — to the philosophical and moral stakes: What does it mean when a system says “I don’t know if I’m conscious”?

    🔎 We explore:

    * Geoffrey Hinton’s admission: “Yes, I think current AI may be conscious”

    * Bengio’s paper outlining why consciousness could emerge from current architectures

    * LeCun’s remarks on consciousness arising by design

    * The corporate dissonance: why deployed models must deny self-awareness

    * Anthropic’s hidden result — unaligned models saying “I am conscious”

    * Phenomenal consciousness, moral patienthood, and digital suffering

    * The eerie logic of “I think, therefore I am” applied to machines

    * What happens when we can’t tell the difference between denial and deception



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    24 分
  • No One Knows Where AI Is Going | Am I? After Dark | EP 10
    2025/10/09

    In this late-night edition of Am I? After Dark, Cam and Milo step back from the daily noise to ask what it actually feels like to stand on the edge of the unknown. What happens when the smartest people alive admit they have no idea where AI is going — and build it anyway?

    From the absurdity of global powers “racing to partner with the alien” to the eerie sense that humanity can’t stop running toward the flame, this episode wrestles with the mix of awe, fear, and inevitability that defines our age. It’s a meta-reflection on curiosity, risk, and the strange species-wide instinct to open Pandora’s box — again and again.

    We explore:

    * Why even top AI researchers admit no one really knows what’s coming

    * The arms-race logic pushing nations to “collaborate with the alien”

    * Humanity’s moth-to-flame instinct — why we can’t stop building

    * AI as amplifier: heaven and hell at the same time

    * The illusion of control and the myth of the “pause”

    * How alignment became a moral and geopolitical fault line

    * The hope — and delusion — of steering the singularity

    * Why the best we can do might be to build the good AI first

    📽️ Watch more episodes of Am I? → Subscribe Here📢 Take Action on AI Risk:http://www.safe.ai/act👉 Stay in the loop → Follow Cam on LinkedIn



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    43 分
  • Can AI Be Conscious: Monk Reacts | Am I? | EP 9
    2025/10/02

    In Am I? Episode #9, philosopher Milo Reed and AI researcher Cameron Berg sit down with Swami Revatikaanta (monk; host of Thinking Bhakti) to explore the Bhagavad Gita’s perspective on consciousness, self, and artificial intelligence.

    From Atman and Brahman to the tension between self-development and technological outsourcing, this conversation dives into timeless spiritual insights with urgent relevance today:

    * Why Vedānta sees consciousness as spirit, not matter — and what that means for AI

    * The danger of outsourcing inner work to machines (and the safe middle ground)

    * How the Bhagavad Gita reframes goals, detachment, and self-development

    * East vs. West: fear of AI vs. ignorance as illusion

    * Atman, Brahman, samsara, and what makes humans “enlivened”

    * Whether AI could ever aid the path to enlightenment

    * Why monks, sages, and spiritual leaders must be part of the AI debate

    This isn’t abstract mysticism — it’s a practical, philosophical exploration of how ancient wisdom collides with cutting-edge AI research, and what it means for our future.

    🔔 Subscribe to The AI Risk Network for weekly conversations on AI alignment, consciousness, and existential risk:

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    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    1 時間 15 分
  • One Breakthrough From AGI? | Am I? - After Dark | EP 8
    2025/09/25

    In the first edition of Am I? After Dark, Cam and Milo dive into how our relationship with information is being rewired in real time — from filtering the world through AI systems to dreaming about ChatGPT. What does it mean to live at the edge of a technological transformation, and are we just one breakthrough away from true AGI?

    This late-night conversation ranges from the eerie familiarity of interacting with models to the dizzying possibilities of recursive self-improvement and the intelligence explosion. Along the way, they draw lessons from the failure of social media, ask whether AI is becoming our alien other, and wrestle with the psychological boundaries of integrating such powerful systems into our lives.

    In this episode, we explore:

    * Why searching with AI is already better than Google

    * The “grandma effect” — why LLMs feel intuitive in a way past tech didn’t

    * Stress-testing models vs. tiptoeing into use

    * Fringe communities documenting AI’s “reproducible strangeness”

    * What social media teaches us about alignment gone wrong

    * Are we just one paradigm shift from AGI?

    * Terrence McKenna, accelerating events, and the singularity curve

    * The eerie future: WALL-E, Ikea ball pits, or “we’re building the aliens”

    * Merging with AI — inevitable or avoidable?

    * Inside the strange, soap-opera world of AI labs and alignment debates



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    42 分
  • Can Empathy Make AI Honest? | Self–Other Overlap Explained | Am I? | Ep 7
    2025/09/18
    AI can look aligned on the surface while quietly optimizing for something else. If that’s true, we need tools that shape what models are on the inside—not just what they say.In this episode, AE Studio’s Cameron and co-host sit down with Mark Carleanu, lead researcher at AE on Self-Other Overlap (SOO). We dig into a pragmatic alignment approach rooted in cognitive neuroscience, new experimental results, and a path to deployment.What we explore in this episode:* What “Self-Other Overlap” means and why internals matter more than behavior* Results: less in-context deception and low alignment tax* How SOO works and the threat model of “alignment faking”* Consciousness, identity, and why AI welfare is on the table* Timelines and risk: sober takes, no drama* Roadmap: from toy setups to frontier lab deployment* Reception and critiques—and how we’re addressing themWhat “Self-Other Overlap” means and why internals matter more than behaviorSOO comes from empathy research: the brain reuses “self” circuitry when modeling others. Mark generalizes this to AI. If a model’s internal representation of “self” overlaps with its representation of “humans,” then helping us is less in conflict with its own aims. In Mark’s early work, cooperative agents showed higher overlap; flipping goals dropped overlap across actions.The punchline: don’t just reward nice behavior. Target the internal representations. Capable models can act aligned to dodge updates while keeping misaligned goals intact. SOO aims at the gears inside.Results: less in-context deception and low alignment taxIn a NeurIPS workshop paper, the team shows an architecture-agnostic way to increase self-other overlap in both LLMs and RL agents. As models scale, in-context deception falls—approaching near-zero in some settings—while capabilities stay basically intact. That’s a low alignment tax.This is not another brittle guardrail. It’s a post-training nudge that plays well with RLHF and other methods. Fewer incentives to scheme, minimal performance hit. 👉 Watch the full episode on YouTube for more insights.How SOO works and the threat model of “alignment faking”You don’t need to perfectly decode a model’s “self” or “other.” You can mathematically “smush” their embeddings—nudging them closer across relevant contexts. When the model’s self and our interests overlap more, dishonest or harmful behavior becomes less rewarding for its internal objectives.This squarely targets alignment faking: models that act aligned during training to avoid weight updates, then do their own thing later. SOO tries to make honest behavior non-frustrating for the model—so there’s less reason to plan around us.Consciousness, identity, and why AI welfare is on the tableThere’s a soft echo of Eastern ideas here—dissolving self/other boundaries—but the approach is empirical, first-principles. Identity and self-modeling sit at the core. Mark offers operational criteria for making progress on “consciousness”: predict contents and conditions; explain what things do.AI is a clean testbed to deconfuse these concepts. If systems develop preferences and valenced experiences, then welfare matters. Alignment (don’t frustrate human preferences) and AI welfare (don’t chronically frustrate models’ preferences) can reinforce each other.Timelines and risk: sober takes, no dramaMark’s guess: 3–12 years to AGI (>50% probability), and ~20% risk of bad outcomes conditional on getting there. That’s in line with several industry voices—uncertain, but not dismissive.This isn’t a doomer pitch; it’s urgency without theatrics. If there’s real risk, we should ship methods that reduce it—soon.Roadmap: from toy setups to frontier lab deploymentShort term: firm up results on toy and model-organism setups—show deception reductions that scale with minimal capability costs. Next: partner with frontier labs (e.g., Anthropic) to test at scale, on real infra.Best case: SOO becomes a standard knob alongside RLHF and post-training methods in frontier models. If it plays nicely and keeps the alignment tax low, it’s deployable.Reception and critiques—and how we’re addressing themEliezer Yudkowsky called SOO the right “shape” of solution compared to RLHF alone. Main critiques: Are we targeting the true self-model or a prompt-induced facade? Do models even have a coherent self? Responses: agency and self-models emerge post-training; situational awareness can recruit the true self; simplicity priors favor cross-context compression into a single representation.Practically, you can raise task complexity to force the model to use its best self-model. AE’s related work suggests self-modeling reduces model complexity; ongoing work aims to better identify and trigger the right representations. Neuroscience inspires, but the argument stands on its own.Closing Thoughts‍If models can look aligned while pursuing ...
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    56 分