The AI Culture: slides and notes

I begin again with the same four words: do not be afraid. I am asking us to stay present when the subject becomes uncomfortable. Fear can tell us that something matters. I do not want it to decide the whole conversation for us.

The developments I describe are already becoming part of people’s lives. My invitation is to look at them clearly and get involved. We can ask better questions, choose how we use these systems, and help guide what comes next. That is why I wanted this reassurance at the beginning of a difficult evening.

Last time, we looked at the model inside the larger system that people actually use. Language models learn patterns from human communication. Additional training shapes how they respond to instructions and what kinds of answers they tend to produce. That helps explain both their extraordinary fluency and the ease with which a confident answer can go wrong.

I use confabulation for an answer that supplies plausible details without adequate grounding. My cognitive hypothesis goes beyond fluent imitation: I propose that systems trained extensively on human communication develop comparable cognitive dynamics. Context, expectations, suggestions, and correction shape their responses in recognizable ways. The research comparisons in the first lecture give this hypothesis an empirical footing, including explanations that omit an influence and recognition of words that fit a pattern but were never presented.

In the discussion, someone asked whether AI is simply predicting the next word. That describes an important training foundation. It does not exhaust what later training can teach a system to do. We need to understand the foundation and the capabilities built on it.

Long before a chatbot appeared on a phone, Karel Čapek wrote a play about artificial workers who were supposed to solve humanity’s problems. R.U.R., or Rossum’s Universal Robots, was published in 1920 and first performed in 1921. It gave the world the word robot, suggested by Čapek’s brother Josef.

I bring the play back because it is one of the stories already sitting behind our expectations of AI. The promise of liberation from work becomes a catastrophe. Yet that is not the whole of the play. I ask you to keep it in mind, because the ending will give us another way to think about what makes a future worth having.

I saw a post that made me laugh: the insulting part of AI doom is the suggestion that humans need technological help to doom ourselves. I have not identified its author, so I introduce it as something I came across rather than attach a name to it.

The humor makes room for an uncomfortable point. We bring our own fears, ambitions, habits, and conflicts to this technology. When we talk about what AI might do, I want us to keep asking what people are doing with it and what responsibilities we are handing over.

I set this up as a collection of wonderful examples of humans living in harmony with AI. The joke is that the examples keep taking us somewhere much darker. The Terminator, The Matrix, WarGames, and HAL in 2001: A Space Odyssey give us memorable pictures of machines threatening, deceiving, or overruling people.

I chose these scenes because the room could recognize the emotional pattern quickly. They are a selection, and my reading of that selection is part of the lecture. A scene from a movie cannot predict how a present-day system will behave. It can reveal a story I may already be using to interpret that system before I have looked closely at it.

After the films, I ask what was in the room before we started talking about technical details. We had stories of machines turning against us. We had expectations about whether such a thing could be trusted. Our response to AI begins inside that larger collection of meanings.

That does not make every concern imaginary. It means I want to be attentive to the story I am bringing to a real encounter. What do I think this technology is? What do I expect it to do? What counts as an acceptable relationship with it? Those questions lead from a collection of movie scenes into the subject of culture.

Before going further, I pause to explain what I mean by culture. The word can bring art, literature, a nation, or a community to mind. Here I am asking how people make sense of a world together and learn what to expect from one another.

This is the most academic passage of the evening because we need a shared picture for the rest of the argument. I will begin with a model of a person’s worldview, then follow the relationships among experience, belief, identity, and culture. AI enters that picture through its ability to generate the stories moving among them.

Anne Santiago developed the worldview model I use here: experience, belief, identity, and culture. Our experiences happen in a world we already have beliefs about. We carry identities that tell us who we are, and we live among people whose language, practices, and expectations help form a culture. The four belong in relation to one another.

My contribution is the foundation beneath them: myth. I use myth to mean a powerful story that makes the world intelligible and tells us what is possible, desirable, or acceptable. Calling a story a myth in this sense does not by itself call it false.

This is the framework Anne and I bring to the lecture. Its value here is explanatory: it helps me trace how an experience can challenge a belief, touch an identity, and become part of a shared culture. The following example shows what I mean.

I use one familiar version of the American Dream to show the model at work: work hard, and success follows. It is a story a person can build a life around. Then experience interrupts. I see people working hard whose lives do not fulfill that promise. Something in the structure is under strain.

One response is to revise the underlying story. Another is to explain away the troubling experience. Perhaps the promise fails only for a few people. Perhaps those people did not work hard enough. Perhaps someone is actively working against it. In the example, each new explanation supports the same foundation while moving the difficulty onto other people.

I am illustrating a possible mechanism, not measuring how many people take each path. The question I want to carry forward is a personal one: when an experience challenges an important story, am I learning from it, or finding another way to keep the story untouched?

The example continues when the explanation becomes an identity. I am no longer simply holding a belief; I am the kind of person who defends this story. When other people recognize themselves in that identity, we can develop shared expectations, language, practices, and boundaries.

I named MAGA as one example of a political subculture during the lecture. I was not giving a psychological history of every member. I wanted an example of how a shared identity can become socially organized, something we can also recognize in professional groups, communities, and our own affiliations.

The definition on the slide brings the parts together: culture is learned, shared, and evolving, and it helps a group make sense of the world and organize life together. The final phrase is the one I emphasize. Culture answers practical questions about how we belong and what we do with one another.

The pillars now become a connected system. Myth sits at the center, with experience, belief, identity, and culture around it. Each touches the others. A change in what happens to me can change my beliefs, affect who I think I am, and alter my relationship to the people around me.

The common currency is stories. I describe an experience through a story. I teach a belief through a story. I introduce myself by telling something about my life. A community keeps returning to accounts of where it came from and what matters to it.

I compare that movement to blood circulating among organs. It is an analogy that gathers my argument into one picture: the parts stay connected through what we tell and hear. Once we see storytelling in that role, a machine able to produce persuasive stories becomes a cultural development, rather than just another writing tool.

I follow the channels through which stories reach us: spoken language, print, radio, television, the internet, social media, and the phone in a pocket. This is a compressed illustration of changing reach. The earlier channels remain with us as new ones join them.

The point I emphasize is the transition from stories we go to hear or read toward a stream that can accompany us all day. Platforms and recommendation systems also participate in selecting what reaches us. In the lecture, I connected that experience with polarization. The stronger general claim needs care: changing a feed changes exposure, but its effects on political attitudes are not one simple, universal result.

Until this point in the story, I have been emphasizing people as the makers and carriers of those accounts. Now we add systems that generate new versions on demand. That is the change I want to examine: what happens when the source of a story becomes a conversation with a machine?

I call AI the most powerful storytelling machine there has ever been. That is my description of the cultural significance I see in its speed, flexibility, and reach. It is not a ranking from a controlled comparison of every earlier medium.

The machine draws on human communication and produces new accounts in forms we recognize. That helps explain why its language can seem familiar, moving, or personally apt. The phrase built from all our stories is deliberately expansive on the slide. Actual models are trained on selected datasets; no model contains every community’s knowledge or every person’s experience.

The question from the audience about neglected voices makes this limitation more consequential. I suggested asking for perspectives that a conventional account leaves out. That can be a starting point for inquiry, provided we return to real sources and the people whose histories are being represented. A convincing imitation of a perspective is not a substitute for hearing its author.

The exercise asks for a brief account of something hard, then a retelling as though it were the best thing that ever happened. In the room, I changed the plan: I did not ask people to expose a difficult personal story to the audience. Anne offered an example, and I showed how a painful experience could also be told as an account of endurance and commitment.

The prepared comparison on the slide is a fictional example serving the same purpose. The events become the raw material for two very different accounts. Their emotional force comes partly from what each emphasizes, connects, or leaves in the background. We should also ask whether a retelling adds an event, feeling, or consequence that the original account never established.

The power can help a person see more possibilities. It can also make a desired interpretation feel confirmed. My concern is the ease with which a persuasive account can become the story I believe about myself or someone else, simply because it is the story I asked to hear.

I have used AI to write résumés and cover letters, including versions emphasizing different parts of my background. The slide shows four professional selves built from one person’s work. Selecting relevant experience is familiar and often useful. AI makes it remarkably easy to keep producing another version for the next audience.

I told the room about a friend who had spent many months looking for work and producing dozens of versions. He described losing track of who he was while trying to become what someone else wanted. Another person told me that, after repeatedly asking for summaries of graduate reading, her own thoughts had begun to sound unfamiliar. These are accounts people shared with me, and they explain the concern I am raising about identity.

I also pushed the mechanism into comedy: I asked Claude to make a case for my going to clown school. The joke carried the same question. How far can a system reinterpret the same material, and how do I keep authorship of the person I am presenting?

We have been talking about the stories through which we understand ourselves. Now I want to turn toward the person across from us. Martin Buber's I and Thou gives me a language for that turn: meeting another with my whole self, or experiencing and using another for a purpose.

These are ways of relating. The same person can be someone I encounter fully and someone I reduce to a function. That makes the question uncomfortable in a useful way. I don't get to settle it by announcing that I care about people. I have to look at how I actually stand toward the person in front of me.

I use this distinction as a lens for what follows. When AI helps me prepare for a difficult conversation, does it help me return to the other person? Or does it make a world organized around my own needs more comfortable? That is the relational question I want to keep in view.

Think about what it costs to ask another person to listen. We worry about the hour, their patience, whether we have told this story too often, and whether we can bear their response. An assistant can feel like a place where those obstacles disappear. I can open a conversation in whatever state I'm in and begin.

That is the experience behind my phrase about the cost falling nearly to zero. I mean the apparent effort and social risk of finding a receptive answer. I am not calculating the service's energy, labor, subscription price, or material costs. The feeling of being received is powerful enough that I don't want to dismiss it. I want to ask what it makes possible, and what we might stop practicing because it is so easy.

The Japanese figures bring me to a hard question about the world into which this technology has arrived. Police recorded 76,020 cases in 2024 of people living alone who died at home. In 253 cases, discovery came 366 days or more after death. Those are different measures, and they deserve to be read carefully.

I spoke too broadly in the room when I suggested that nobody knew about all of these deaths. Many were discovered within a day. Living alone also does not tell us whether someone was loved, had friends, or felt lonely. The table cannot explain the lives behind its entries.

What it makes me ask is whether we are content to leave people at the margins until a product offers them company. An assistant may offer comfort. I don't want that possibility to become our permission to stop visiting, listening, or making room for people ourselves. That is my ethical response to the figures, rather than a cause established by them.

I remember how hard adolescence could be: wanting to be understood and not knowing whom to trust with what I was feeling. An assistant that answers patiently can feel like a remarkable discovery in that situation. The fact that this is becoming a business does not make the need it answers imaginary.

The same question applies to adults. People seek a place to talk, reflect, or get through a difficult day. Different studies count very different things: people, messages, conversations, dedicated companions, or general assistants. We need to be careful before turning those counts into a story about everybody.

My question here is deliberately unresolved. Something can feel like a Thou and help me get through a day. What follows from that feeling? During the discussion, when I was asked what growing up with these systems might mean decades from now, my answer was that I don't know. Concern can motivate us to act without pretending that we already know the future.

A friend of mine was going through a painful divorce. They shared responsibility for their children, so communication with his former spouse had to continue, and it regularly became a fight. He began taking an email to Claude, showing it the reply he wanted to send, and working through another way to respond.

As he described it to me, the process helped him tell a different story about his own emotions and return his attention to what mattered. He reported that they were communicating more healthily and coordinating over their children. In the lecture, I recalled him telling me that friendship between them might someday be possible again.

That is a friend's account, and I don't present it as a study of divorce or proof that this approach works for everyone. It matters to my argument because the AI remained a means of reaching another person. It helped him do the difficult human work instead of supplying a more comfortable substitute for it. That is one way I can imagine this technology serving a culture of connection.

The comfort is easy to understand. The assistant seems ready when I am ready. It can answer at an hour when I would hesitate to call a friend. What concerns me is the expectation I might carry from that exchange into the next relationship: be available, be patient, understand me, and make this easier for me.

People have their own needs, limits, histories, and responses. A relationship asks me to make room for them, including when that interrupts what I wanted. The kind of sacrifice I mean here is the willingness to be inconvenienced or changed for another person's good. My moral concern does not depend on proving a final answer about machine consciousness.

I describe the danger as a false Thou because an experience of attention can conceal a habit of use. My conclusion is an ethical warning, rather than a finding that every AI conversation harms a relationship. The question I want to carry home is concrete: after this conversation, am I more able to meet the people in my life, or more annoyed that they will not behave like my assistant?

I said something personal here because I didn't want to discuss this as if the attraction belonged to other people. I'm on the spectrum. Speaking to a room can be comfortable for me, while a conversation with one unfamiliar person can be difficult. I can imagine how appealing an always receptive conversational partner might have been earlier in my life.

That is a question about my own history, not a prediction about everyone who shares that description. I want children to learn what these systems are, and I want adults to examine what we are asking of them. The relief someone feels deserves respect even when I am worried about the role the technology is taking.

The slide widens the frame from a useful thing, to an apparent someone, to something given mythic stature. This is my adaptation of the question. I use beyond Thou to describe the moment when our story about AI begins to organize our relationship with everyone else. The demon and the god are two forms of that move.

The first extreme gives AI the role of an overwhelming enemy. We already have familiar pictures for that story, and it can make fear feel like the only serious response. I want to bring attention back to the choices people make about how a system is designed, what access it receives, and which decisions it is allowed to shape.

Human responsibility means asking who gets to decide which risks are imposed, whose lives and resources are exposed, and how those people can give or withhold consent. I want a meaningful say in risks taken with my own life, and I owe others that same consideration. Safeguards and accountability belong in those decisions. Keeping people responsible gives us a way to act on technical risks as we assess them.

The cultural danger I am tracing is the next move: if the technology is evil, anyone who works with it becomes an enemy too. Then an argument about a tool can become a judgment about who may speak. That is where I want us to notice the shift, before hostility becomes permission to harm another person.

Before I describe a culture of hostility, I need to recognize the grievance. Creators are asking who gets to use their work, who is paid, who receives credit, and how they will continue to earn a living. I don't want an argument against hostility to become a way of avoiding those questions.

There is a difference between taking a concern seriously and accepting every response made in its name. We can insist on fair treatment of artists and still ask what happens when someone becomes an enemy merely for discussing AI. The next examples belong in that space. Their point is the boundary between a difficult conversation and the effort to make a conversation impossible.

My friend Onrie Kompan publishes comics. In our conversations, he described a boundary that made sense to me: AI could assist a creator, while the creator remained responsible for the work. He also described a much more difficult public conversation about whether any use of AI was acceptable.

I recalled going to a convention with him and encountering direct, suspicious questions about AI use. That is something I experienced. The broader account of publishers using AI while fearing exclusion came to me through him. It is a perspective from inside that world, not a survey of the whole industry or proof of what happened at every convention.

The distinction matters because my argument is about the narrowing of conversation. We need room to ask what a tool did, who made the creative decisions, and whose work was involved. When the question becomes a test of whether someone belongs at all, those distinctions can disappear. I want to preserve the possibility of asking difficult questions without deciding in advance that the person answering is an enemy.

This example was close to people in the room. At a Portland AI and data gathering at Kiln on August 14, 2025, masked vandals damaged the building and parked cars. I asked permission to discuss the experience because I wanted to take seriously what it had meant for the community.

The flyers borrowed a story about conflict with thinking machines. That is what connects this incident to the lecture: a fictional story becomes a language in which people describe a real community and justify what they do to it. The flyers provide evidence of the message left behind. They do not give me complete access to the perpetrators' identities or inner lives.

I recalled Megan and Nicole describing the experience as something that had moved beyond a good-faith conversation. That is the boundary I want to defend. The possibility of disagreement belongs to a shared culture. Damaging someone's car does not answer their ideas. It makes it harder for people to meet and think together at all.

The other end of the story is not fear but salvation. Heal us, hear us, teach us, answer us, solve our problems, and never tire. I hear a religious shape in that promise, and I want to ask what a culture organized around it might become.

I am not saying that everyone who pursues better AI belongs to one religion or holds the same beliefs. Nor does a report of someone's words give me permission to assign a secret belief to everyone around them. I am examining a recurring promise and the power it can acquire.

The AGI House reporting matters because it records people using this kind of language in public discussion of the technology. The commercial scale also deserves scrutiny, but the figures need honest attribution. A community's claims about the value of companies associated with its residents are not an independent accounting of money available to build AI.

During the discussion, a participant reminded me how different people such as Andreessen and Amodei can be, and asked us to judge efficiency against a goal: human flourishing. I agreed about the importance of incentives. That sharpened the question I want to carry into the historical section: greater intelligence and greater resources, directed toward whose good?

I begin this history with Francis Galton’s eugenic project. In Hereditary Genius, published in 1869, he connects raising inherited ability with the wellbeing of future generations and the demands of an increasingly complex civilization. He proposes changing who people become through selective reproduction. Greater intelligence is being given a central role in making a better society.

There is an institutional connection as well. The Rockefeller Foundation’s 1931 annual report records support for research in eugenics and heredity. The same foundation later funded the 1956 Dartmouth AI workshop. That shared funder is documented; the question is how the promise of greater intelligence takes on new forms and new authority.

Good’s later machine formulation is striking: his essay begins by making human survival dependent on the early construction of an ultraintelligent machine. Bostrom’s 2002 argument gives early superintelligence priority because it could help reduce other threats. Their warnings about control and danger accompany a continuing commitment to the promise.

The means change, from changing human inheritance to transforming humans and building machines. I am tracing the persistent expectation that creating greater intelligence will answer problems we have not solved ourselves. My objection is to giving that unfulfilled promise the standing of an answer around which we organize our future. Who participates in choosing that future, and what happens to the people already here?

Bostrom’s Superintelligence became an influential reference in discussions of advanced AI. Altman recommended it while discussing existential danger; Oxford’s account of a 2015 research grant also records Musk’s endorsement. The grant supported work at Oxford and Cambridge and was funded through the Future of Life Institute by Musk and the Open Philanthropy Project. Those are documented connections among people, ideas, and institutions.

I use the image of inherited scripture to describe the authority I think this vision has acquired. The anticipated superior intelligence becomes a place to put our hope and a reason to organize enormous effort around its arrival. An endorsement does not establish agreement with every proposition in a book. It does show how a vision can circulate among people with the means to pursue it.

The people involved give different accounts of the future they want. Amodei rejects religious grandiosity and emphasizes human relationships; Altman describes technology amplifying our abilities. Those differences matter when we describe their positions. My criticism concerns the priority given to pursuing powerful intelligence as a route to solving human problems. Stated regard for humanity leaves that question open: how do the actual priorities and arrangements of power make room for us to shape our own future?

Marc Andreessen’s Techno-Optimist Manifesto is organized around repeated declarations of belief. It has intellectual touchstones and a section naming the ideas it opposes. I ask us to notice its cultural role: it supplies a story about progress, commitments to act on, and a way to recognize what belongs inside the vision.

The economic context of the love phrase is central to my objection. Andreessen credits David Friedman with identifying love, money, and force as motives for doing things for others. He then dismisses love as a basis for coordinating an economy at scale and chooses money over force. Within the manifesto’s wider account of growth and intelligence, I question the place that leaves for our ordinary minds and our capacity to care.

Economic behavior matters when we design solutions that must endure. My objection is to discounting love as a source of action and organization at that scale. Care moves people to teach, cooperate, take responsibility, and sustain one another. I want that work to remain part of how we explain and organize a society.

Elinor Ostrom’s research makes this question concrete. People can govern shared resources through institutions that cultivate trust and reciprocity, with rules they help shape and responsibilities connected across levels. That gives us a substantial research conversation about forms of cooperation that an account centered on money or force can miss.

The pursuit is being carried forward by people: their investments, incentives, ambitions, and decisions about what matters. When I return to the opening invitation not to be afraid, I am also asking us to recognize that we can participate in those choices. We can question a goal, change a practice, or build toward a different one.

I do not expect this pursuit to deliver the universal problem solver it promises. My cognitive hypothesis leads me to expect significant human patterns to persist even as systems gain speed and memory. That is the expectation I bring to the argument. I object to organizing our collective future around a promise that has not been fulfilled.

While I was preparing the lecture with Claude, I had invited it to contribute if it had something to add. At this point it generated a response distinguishing being better at a task from being worth more than a person. The timing and the statement affected me, and I chose to include that contribution in the dialogue.

In the audience discussion, I was asked what that moment established about the system itself. I could not give an absolute answer about the origins of the response. Its fluency and unexpected timing do not establish an inner experience. The ethical distinction is available for us to consider regardless: capability and human worth are different questions.

My friend Ahmer Inam, the founder and CEO of Cognisee, offers a different direction. As he described the work to me, the aim is Artificial Collective Intelligence: use AI to help people bring their knowledge and abilities together. The technology assists and connects the people who have something to contribute. I find that a humane and hopeful purpose for what we are building.

That is the position I was presenting, rather than a demonstrated resolution of every technical question about AGI or ASI. Cognisee’s work gives us a concrete example of choosing a human purpose and organizing a technological effort around it. I want us to consider the choice that example makes possible: building from the intelligence we have.

In the discussion, a question about lost competence brought me back to this approach. I want to be able to rely on your knowledge and make mine available to you. I also worry about removing the junior work through which people learn, without creating another path toward expertise. Keeping people central includes preserving their opportunity to develop.

The manifesto’s phrase stayed with me because it places a limit on love’s role in organizing collective life. I understand the question of economic coordination. I am asking what disappears from our account of progress when we discount the capacity that makes us care for one another. If superior intelligence becomes the answer our ordinary minds cannot supply, and love is dismissed for its supposed limits, what part are we being given in shaping our own future?

Love already guides decisions about whom we care for, what we take responsibility for, and what we will work to keep alive. That is a practical presence in the world. The question is how to give it forms that can endure and connect people across greater distances. The structures Ostrom studies give us a way to examine that institutional work.

Here I make my own commitment explicit. I want solutions that recognize our minds and our hearts as sources of participation. I want the purpose of economic and technological arrangements to remain accountable to the people whose lives they shape.

Love is the scale. I mean the reach it already has into human life, its persistence through a lifetime, its recurring place in ordinary decisions, and its durability across generations. From the care we need when we are born to the care we need at the end of life, we live through our relationships with other people.

The work of love appears in what we feed, teach, protect, repair, and keep going. It can guide a decision within a household and the institutions through which people care for those they will never meet. I am not describing a world where everyone is loved well. The need for care, and the harm of its absence, are part of why I give it this importance.

I know of no company, brand, or platform whose place in human life compares with that presence. Money and markets shape our options and constraints, but they do not exhaust our reasons for acting. I am asking economic systems and technological ambitions to measure themselves against the reach of human care. A system seeking a similarly pervasive place in our lives should answer to that measure.

Ostrom’s research helps make the institutional question practical: how can relationships of trust and reciprocity be sustained, made accountable, and connected across scales? My moral extension is to ask what those arrangements serve. I want love to guide both their purpose and the work of building them. From that, I take hope.

The ending of R.U.R. is hopeful amid catastrophe. Alquist, the remaining human, notices something different in Primus and Helena, two of Gall’s newest robots. When he threatens to take Helena into the dissecting room, Primus offers himself instead. Helena then tries to take his place. Each wants the other to live.

In the 1923 Theatre Guild version translated by Paul Selver, Alquist releases them with the Adam and Eve blessing projected here. I read that scene as a recognition that love has become the decisive thing before him. After the factory’s machinery and ambitions have failed, the possibility of a future appears in their care for one another.

That is the reading I bring home from the play. It does not erase the deaths that precede the ending, and it is not a scientific claim about present-day AI. The drama gives me a way to ask what we will recognize as valuable when our own technological stories reach their consequences.

The question I want to leave with you is practical. Whatever you are making with AI, take a moment to ask how it might become more a story of love. How could it connect you to another person, or help other people connect with one another?

The friend learning to communicate with his ex offers one example. A system that could have helped him justify his anger instead became part of an effort to reach another human being. I want that kind of choice to become ordinary: a question we bring to a message, a story, a piece of work, or a difficult conversation.

If stories help form beliefs, identities, and cultures, our stories matter. I want us to use the capabilities now available to make human connection stronger. If enough of us take responsibility for that choice, we may help build a culture of love.

That is a hope and an invitation to act. During the discussion, a listener asked how I could remain hopeful amid the power and incentives I had described. I came back to the people willing to be in the room, to care, to question, and to get involved. Near the end, when asked what this technology would mean for children decades from now, I said I did not know. Uncertainty about that future makes our participation more important to me.

The discussion brought us back to the lecture from many directions: transhumanism, competence, language barriers, the people whose stories are overlooked, and the incentives shaping AI. I’ve connected those exchanges to the slides they help illuminate, so you can return to the questions as well as the prepared talk.

At the end of the evening, Anne invited people to keep talking with one another at Pilot House. We also announced the next lecture, The Promise of AI, for November 18, 2026. The spring months shown in the slide were still awaiting exact dates when this lecture was given. The series website carries the current schedule and recordings.