Podcast episode with Reid Hoffman (founder, investor, and author of Impromptu) exploring how philosophy shapes entrepreneurial thinking and how AI is changing what it means to be human. Topics include why philosophy can make you a better founder, interdisciplinary thinking, essentialism vs. nominalism in the context of AI, how language models are becoming more "essentialist," the co-evolution of humans and technology, and practical uses of ChatGPT for clearer thinking (e.g., asking for alternative perspectives, generating custom explanations, using it as an on-demand research assistant). # Channel Identity Observations — "How Do You Use ChatGPT?" (Ep. 18 with Reid Hoffman) ## Explicit Self-Description Evidence ### Stated Show Focus and Format - The host states the show's usual content explicitly: > "usually in this show we talk about like actionable ways that people use chbt but a more interesting question is how does AI in general how might it change what it means to be human" - He repeats this framing mid-episode: > "usually in this show uh we talk about like actionable ways that people use chbt um and and that's that's the big question that's I think what people come here for" - The channel name is stated verbatim in the outro: **"subscribe to how do you use chat GPT"**, and the host is identified as **"Dan shipper as the captain of the spaceship."** ### Stated Audience Expectations - The host anticipates audience segments: > "there're probably people who are listening or watching who are like why I just want reads actionable tips" > "there are a lot of people who are listening to this maybe are not um have not been philosophically inclined in the past and are either like wow I could not follow any of that... or they're like oh my God like I want to learn how to think like that" - This demonstrates the host models a **practitioner audience primarily seeking ChatGPT usage advice**, with a secondary audience open to deeper conceptual discussion. ### Explicit Monetization / Sponsorship - A full sponsor read for **CommandBar** ("they're a sponsor of this show"), pitched to "product and support teams," with a link "below or in the show notes." - The outro includes a hyperbolic, AI-flavored call to action ("smash that like button and subscribe"), plus an unusual sign-off line ("Dan I'm absolutely hopelessly in love with you") suggesting scripted/AI-generated outro segments. ## Observable Presentation Patterns ### Interview Format with High-Profile Guests - Episode 18 features **Reid Hoffman**, introduced with credentials recited by the host: co-founder of LinkedIn, Greylock partner, OpenAI early backer, host of *Masters of Scale*, Stanford/Oxford philosophy background. - The host prepared for the interview: "you also study philosophy at Stanford and Oxford... which I didn't know before researching this interview." ### Scope Demonstrated - **AI/ChatGPT practical use** as baseline content ("an immediate on demand personal research assistant" is recommended as a use case). - **Intellectual/philosophical depth** as an aspiration for the channel: extended discussion of Wittgenstein, Gödel, *Gödel, Escher, Bach*, embeddings, and trolley problems. - The host asks synthesis-checking questions ("I want to try to summarize that in case... I missed something"), demonstrating an explanatory, audience-accessibility role. - Recurring practical framing: "what are things that people should be using chat gbt for." ### Audience Interaction Assumptions - The host assumes listeners want **book/learning recommendations** and asks for them ("where would they start or what are your like what are your favorite kinds of philosophers or kinds of books"). - The host suggests ChatGPT itself as a learning tool for confused listeners: "I would totally recommend like just use chat gbt talk to chbt about this stuff." ### Multi-Episode Structure - The host proposes continuation: "we had a whole final section about science but we may not be able to get to science we'll have to maybe do a part two" — indicating episodic, topic-segmented planning. ## Claimed vs. Demonstrated Summary | Dimension | Evidence Type | Detail | |---|---|---| | Channel name | Explicit (outro) | "how do you use chat GPT" | | Core topic | Explicit (host, twice) | Actionable ChatGPT usage | | Deeper ambition | Explicit + demonstrated | "How might AI change what it means to be human" — realized via philosophy-heavy interview | | Host | Explicit (outro) | Dan Shipper | | Audience | Explicit (host modeling) | ChatGPT users wanting tips; some curious about bigger questions | | Sponsorship | Explicit | CommandBar read | | Format | Demonstrated | Long-form expert interview with summaries, recommendations, and AI-generated-style outro | **No private motivations or channel-wide conclusions were inferred beyond stated and directly observable evidence.** # Delivery Observations: Reid Hoffman Interview (Ep. 18) ## Speaker Roles Two distinct delivery styles are observable: - **Host (Dan Shipper)**: structured, framing-oriented, frequent summarizing. - **Guest (Reid Hoffman)**: long, associative, digressive answers that loop back to earlier points. --- ## Guest Delivery Patterns ### 1. Circular, Self-Restating Sentence Construction Hoffman repeatedly restarts and rephrases within the same sentence, stacking near-duplicate phrases: > "what are possibilities what are theories of human nature as they are manifest today and as they may be modified by new products and services new technology Etc" > "is is is startling and and contrarian" > "it's it's it's you know kind of what is possible" This **filler-and-restart rhythm** ("you know," "kind of," "actually in fact") recurs throughout — "you know" appears constantly as a connective tissue between clauses. ### 2. Recurring Verbal Anchors - **"Actually in fact"** — used as an emphasis marker: > "but actually in fact like science itself is full of a lot of unanswerable questions" - **"Crisply / crisp"** — his signature term for good thinking: > "part of that is to get people to think crisply about this stuff"; > "crisp you know kind of um modeling of of reasoning" - **"Kind of"** — extremely high-frequency hedge/softener. - **"Etc"** — used to trail off lists: > "new products and services new technologies you know Etc" ### 3. Concrete Examples Embedded in Abstract Argument A stable teaching habit: he grounds philosophy in vivid, playful specifics. - The trolley problem critique builds an extended improvised scenario: > "it's a trolley and the trolley will either hit the the the five criminals or the one human baby and its default set to hit the human baby" - The glasses example for technology changing perception: > "you and I are looking at each other in this podcast through glasses like think about the world with glasses without glasses right" - The ChatGPT prime-number anecdote: > "you got that answer wrong is oh yeah I got it wrong here here's the answer like well that answer is wrong too" ### 4. Contrarian Framing as Rhetorical Device He opens claims by flagging them as deliberately provocative: > "a background in philosophy is more important for entrepreneurship than an NBA [MBA] which of course is is is startling and and contrarian" He also labels another statement as a deliberate move: > "this is obviously a bit of a deliberate rhetorical slam but char problems" — showing meta-awareness of his own rhetoric. ### 5. Cross-Domain Analogy Chains He habitually connects philosophy ↔ technology ↔ business, e.g., mapping Hegel onto AI: > "you could say hey we have an essentialist thesis we have a nominalist antithesis and the synthesis is how we're putting them together" ### 6. Intellectual Nostalgia / Named-Reference Habits Frequent citation of specific thinkers (Wittgenstein, Gödel, Hegel, Descartes, Varela/Maturana) and the recurring wish-construction about hypothetical conversations: > "I would love to do is put girdle and vicenin in a room and say you know girdle's theorem discuss" --- ## Host Delivery Patterns ### 1. Explicit Summarizing and Comprehension Checks The host repeatedly reformulates Hoffman's points and asks for verification: > "so I I guess like like along those lines if you want if I step step back and squint I can kind of like uh you can kind of divide the history of philosophy into..." > "I want to try to summarize that in case um in case it was a little bit difficult to follow to be honest" He openly admits confusion: > "there's a there's a point in there that I think I missed something" ### 2. Hedges and Softeners Heavy use of "like," "kind of," "sort of," and "um" — mirroring but not matching the guest's density. ### 3. Structured Framing The host sets up segments with formulaic transitions: > "so usually in this show uh we talk about like actionable ways that people use chbt" (repeated nearly verbatim in the intro and mid-show). ### 4. Enthusiasm Markers Frequent short affirmations: > "that's really interesting", > "I love that", > "that's really fascinating" — used to close loops and pivot topics. --- ## Uncertainty Notes - The transcript renders filler and false starts heavily; some apparent repetition may reflect transcription artifacts rather than pure delivery habits. Patterns like "you know" and "kind of" are consistent enough across long stretches to treat as genuine. - The outro (> "I'm absolutely hopelessly in love with you") and sponsor read are clearly scripted segments; delivery patterns there are not representative of conversational style. - No curated metadata was supplied, so all observations rest on transcript evidence alone. # Observation Document: Reid Hoffman on Philosophy, Wittgenstein, and AI ## Speakers - **Host (Dan Shipper)** — host of the podcast ("How Do You Use ChatGPT?") - **Reid Hoffman** — co-founder of LinkedIn, Greylock partner, OpenAI board member/early backer, Stanford/Oxford philosophy background ## Positions and Reasoning ### Philosophy's Value for Entrepreneurship **Hoffman** states he tells MBA schools that "a background in philosophy is more important for entrepreneurship than an MBA," noting the claim is deliberately "startling and contrarian" to provoke crisp thinking. His reasoning: - Entrepreneurs must reason about **logical possibility** — what the world could be. - They need **theories of human nature** — how humans are now and how they may be modified by new products, technology, institutions, and environments. - Philosophy remains robust because, like science, it deals in evolving working theories and new questions; disciplines like economics, game theory, and political science grew out of it. ### Thought Experiments and Trolley Problems **Hoffman** (his Oxford thesis was on "the uses and abuses of thought experiments"): - Criticizes trolley problems for **artificially constrained setups** — e.g., stipulating "perfect knowledge" that the trolley can't be stopped, which embeds unrealistic assumptions. - Argues the right human response may be to **attempt a third option** (breaking the trolley) even if believed impossible, because that's the moral act. - **Host** agrees he has never heard the third option posed, and notes such hidden-assumption hijacking "reminds me of certain doomer arguments" (a passing remark, not developed). ### Essentialism vs. Nominalism and LLMs **Host** frames the debate: essentialists hold there is knowable objective reality; nominalists/pragmatists (including the later Wittgenstein) treat truth as relative, conventional, or functional. **Hoffman**: LLMs "add perspective and color; I don't think they resolve the debate." - **Hegelian synthesis** framing: essentialist thesis, nominalist antithesis; LLM development is a synthesis. - LLMs function in a **late-Wittgensteinian** way (next-token prediction, language games), yet developers are trying to make them **more essentialist** — grounding truth, reducing hallucination. - On Wittgenstein: he would **not** have endorsed deconstructionist views that we have no contact with the world; late Wittgenstein still supports a robust theory of truth grounded in language games and a shared **biological form of life**. - Fascinating open question (which Hoffman believes Wittgenstein would have pursued): are LLMs the same biological form of life as us? Hoffman: "the answer might be some and some — not 100% yes or 100% no." Arguments both ways: trained on human corpus (for similarity) vs. learns very differently and isn't biological (against). ### Embeddings and Early vs. Late Wittgenstein **Host** suggests embeddings resemble early Wittgenstein's logical space of atomic facts. **Hoffman** agrees embeddings capture a probabilistic space of tokens, but slants current practice as **more late Wittgensteinian** because: - Early Wittgenstein implied that grasping the logic would prevent truth mistakes; LLMs demonstrably err. - Embedding spaces are **quasi-symbolic** (activations, not pure token reasoning) and shift with new data and novel reapplications of language (e.g., "king of podcasts"). - Late Wittgenstein's truth is not free-floating language but **embedded in the world** through how biological beings navigate — which is not Cartesian disembodied consciousness. - **Host** summarizes: the space's construction depends on practical use, not a deep logical ordering; Hoffman confirms: "exactly... we know that there are truths where the coherent use of language still is a falsity." ### Getting Reasoning into LLMs **Hoffman** cites current approaches: - Training on **computer code** imparts broad reasoning patterns, not just coding ability. - **Textbook-style training** can produce smaller but effective models. - Gestures toward **computational philosophy**: Lakatos as a development on Popper, Kuhnian paradigms, Bayesian logic, synthetic data. He explicitly says these are "gestures rather than specific theories" but is "certain there's a there there." **Host** asks why models can't generalize from a finite set of logical moves. **Hoffman corrects the premise**: "in logic there are infinite moves" (different orders of infinity). ### Gödel, Escher, Bach **Hoffman** (rereading it, first read in high school): - Gödel's incompleteness ("in any sufficiently robust language system there are truths that cannot be expressed within it") is mind-boggling and relevant to truth discovery in humans and LLMs. - What Hofstadter **got right**: truth-finding is an ongoing dynamic process (thesis–antithesis–synthesis) whose future synthesis can't be predicted. - Hoffman says he does **not** get freaked out by LLMs: "wow, new things that we can discover." - **Host** notes Hofstadter today is "not definitely in the LLM conversation... a little bit freaked out by them" — a tension Hoffman addresses only via the dynamic-process point, not by directly rebutting Hofstadter. Hoffman's wish: to put **Gödel and Wittgenstein in a room** to discuss Gödel's theorem; also laments lacking recordings of **Einstein–Gödel walks**. (On a time machine: he'd rather go forward than back.) ### Technology Constitutes Humanity **Hoffman**: humans are not static; we are constituted by the technology we incorporate. Examples: **glasses** change perception (undermining "seeing is believing" as fixed), microscopes, telescopes. From his Bologna speech and *Impromptu*. **Host** supports this with Joseph Henrich's *The WEIRDest People in the World*: psychology literature rests on Western college students; **reading physically changes brains**, enables rule-following without surveillance, and large organized society. He places language models on the timeline: language → reading → printing press → LLMs, as **cultural transmission technology**, while explicitly not dismissing LLM dangers. **Hoffman** endorses the cultural-evolution frame (citing *The Secret of Our Success*, unsure of the author): - Three clocks: geological (super slow), biological (slow), **cultural/digital** (fast). - LLMs are tools to **accelerate cultural evolution**. - Concrete use today: an **on-demand personal research assistant** — "today hallucinates sometimes and you have to be aware of that" — with the quip: "if you don't think you need a research assistant it's because you just haven't thought about it enough." ### Why Philosophers Didn't Invent AI **Hoffman** attributes it to **disciplinary intensity**: philosophers "sit down and cogitate" (canonically Descartes) and neglect how technology changes language, perception, and reasoning. Construction-oriented imagination long predates computer science (Frankenstein, the Golem, Talos). He wants philosophy "broader in its instantiation" — with a self-acknowledged "deliberate rhetorical slam" at trolley problems. **Host** proposes: being a philosopher in the world beats being an academic philosopher. **Hoffman**: "I chose that with my own feet." ### Advice for Learning Philosophical Thinking **Hoffman**: interaction is key — study philosophy, or use ChatGPT interactively. His personal practice: submit an argument and ask for **supporting and counter-arguments** (thesis/antithesis toward synthesis). Also: prompt ChatGPT to explain canonical ideas ("I'm a non-mathematical college graduate — explain Gödel's theorem"). Recommends rereading *Gödel, Escher, Bach* "if you really want to have your mind boggled." ## Qualifications and Unresolved Tensions (preserved) - Hoffman's "philosophy over MBA" claim is explicitly **rhetorical/provocative**, not purely literal. - LLMs as biological form of life: "some and some — not 100% yes or 100% no." - His reasoning-improvement proposals are **gestures, not specific theories**. - Research-assistant recommendation carries an explicit **hallucination caveat**. - Host raises but does not resolve the Hofstadter discomfort tension. - Science segment deferred: "we may not be able to get to science... maybe do a part two" — Hoffman agrees. ## Non-Position Content - A **sponsor read** (CommandBar) appears in the transcript; it is promotional copy, not a substantive discussion position. # Speaker Attribution & Provenance Record ## Video - **Title:** Reid Hoffman on How AI is Answering Our Biggest Questions—Ep. 18 with Reid Hoffman - **Video ID:** `01a0f15b-10b2-7018-b507-47b28e65ee45` ## Speakers & Roles ### Host / Creator - **Identified as:** Dan Shipper (named explicitly in the closing promotional segment: "with Dan shipper as the captain of the spaceship") - **Role:** Podcast host/creator of the show referenced as **"How Do You Use ChatGPT"** - **Function:** Asks questions, summarizes guest answers, frames show topics ("usually in this show we talk about actionable ways people use ChatGPT"). ### Guest - **Identified as:** Reid Hoffman (addressed as "Reid" / "Reed" in transcript) - **Role:** Guest interviewee - **Credentials stated by host (transcript only, unverified):** - Co-founder of LinkedIn - Partner at Greylock - Board member / early backer of OpenAI ("open aai" in transcript) - Host of *Masters of Scale* podcast - Studied philosophy at Stanford and Oxford; nearly became a philosophy professor - Stanford Symbolic Systems major ("the eighth person to declare") - Author of a book published the prior year titled *Impromptu*; also referenced an Atlantic article ### Promotional Narrator (sponsor read) - Unidentified narrator voice delivering the **CommandBar sponsor segment** - ⚠️ **PROMOTIONAL SEGMENT FLAGGED:** Mid-transcript ad for **CommandBar** ("they're a sponsor of this show"), described as a user-assistance AI agent platform; claims trust by Gusto, HashiCorp, Freshworks (sponsor claim, not editorial). Call to action: link below / show notes. ### Outro Promotional Voice - Unidentified closing narrator urging likes/subscribes; explicit promotional language, includes the line "Dan I'm absolutely hopelessly in love with you." - ⚠️ **PROMOTIONAL SEGMENT FLAGGED** ## Referenced Sources & Third-Party Claims (as cited by speakers) - **Philosophers/thinkers named by Hoffman or host:** Wittgenstein ("Vicken Stein" in transcript), Hegel, Descartes, Plato, Aristotle, Presocratics, Kripke ("krippenstein"), Saul Kripke, Gödel, Einstein - **Books/works referenced:** - *Gödel, Escher, Bach* (Hoffman: rereading it) - *The Weirdest People in the World* — Joseph Henrich (host: recommends) - *The Secret of Our Success* (Hoffman: "very good book"; uncertain whether same author) - Hoffman's *Impromptu* (his own book, prior year) and an Atlantic article (host: read to prepare) - **Other references:** TV series *The Good Place* (trolley problem episode), a speech "in Bolognia [Bologna] last year," Hofstadter's current stance on LLMs (host's characterization), trolley problems in EA discourse (host). ## Provenance Notes & Uncertainty - No curated metadata supplied; all role identification derives from the transcript. - Guest and sponsor names contain transcription artifacts (e.g., "Reed," "Vicken Stein," "girdles theem"); canonical spellings inferred, flagged as such. - Comment availability: **not indicated** in source material. - Timestamps: **not present** in transcript; segment boundaries not locatable. why do you care about philosophy why are answering these big questions important you know one of the things that I sometimes will tell MBA schools background of philosophy is more important for entrepreneurship than an NBA philosophy is very important to this stuff because it's understanding how to think about very crisply what are possibilities what are theories of human nature as they are manifest today and as they may be modified by new products and services new technology Etc usually in this show we talk about like actionable ways that people use chbt but a more interesting question is how does AI in general how might it change what it means to be human these are really deep big philosophical questions I thought you might have a unique perspective on this intersection it seems like every company these days is rolling out an AI chat bot in the bottom right corner of their website but they're mostly still pretty annoying to use but what if a chatbot could act less like a chatbot and more like a conscientious human being it could know what you've done look up account information and even perform actions on your behalf it could take over your mouse and guide you through the site in real time that agent exists and it's called command bar and they're a sponsor of this show command Bar's user assistance platform is an intelligent agent that companies can embed on their website it looks like a regular chat bot but it isn't one it can perform actions on the user's behalf it can fetch data it can even co-browse with them through the website and it's not just reactive it can actually be proactive it can nudge users when they seem confused and help them through the site it's sort of like a friendly Human Assistance standing by and helping a user when they need it not just an annoying barrage of pop-ups it's a PowerUp for product and support teams that want to drive engagement and activation encourage conversion and of course deflect low value tickets and it's trusted by teams like Gusto Hashi Corp freshworks and more if you're interested in integrating command bar into your website check out the link below or in the show notes and now on to the show Reed welcome to the show it's great to be here uh great to have you so I'm sure that uh everyone uh listening or watching knows this but you are a renowned entrepreneur you're eventure capitalist you are an author you're best known as the co-founder of LinkedIn you're partner at Greylock um you are a board member or a board member um and an early backer at open aai um and you also have an incredible podcast masters of scale but perhaps most relevant to this conversation uh you also study philosophy at Stanford and Oxford and you almost became a philosophy Professor which I didn't know before researching this interview it's really cool yeah no it was definitely um part of it was I've always been interested in human thought and language um started with in Stanford with a a major called symbolic systems I was the eighth person uh to declare that um and as a major at Stanford and then kind of thought hm we don't really know what thought and language fully are maybe philosophers do and so uh trundled off you know took some casses at at Stanford but also trundled off to Oxford to to see if philosophers had a better understanding of it I love it um it's funny I feel like since then symbolic systems has become the go-to like Stanford major for like Curious analytical people who end up doing startups um so that's that's pretty funny to know that you one of the first um so usually in this show uh we talk about like actionable ways that people use chbt um and and that's that's the big question that's I think what people come here for but underneath that um I think what what a more interesting question is is like how does AI in general and chbt in particular how might it change what it means to be human how might it change how we see ourselves and how we see the world how might it enhance our creativity our intelligence all that kind of stuff and these are really deep big philosophical questions um and as someone who uh rigorously studied philosophy and probably still thinks about those questions I thought you might have a a unique perspective on uh on this intersection because I think people tend to be like they're either in the philosophy camp or they're in the like language models camp and like people who are sort of in the middle is kind of kind of an interesting one um and what I wanted to start with because I think there're probably people who are listening or watching who are like why I just want reads actionable tips um is is to is to ask like why like tell me more about why you care about philosophy and I think you got into that a little bit and and talking about how how you got into it but like yeah tell us why is why do you care about philosophy why are answering these big questions important so you know one of the things that I sometimes will will tell like um MBA schools when I give talks there is a background in philosophy is more important for entrepreneurship than an NBA which of course is is is startling and and contrarian and part of that is to get people to think crisply about this stuff because part of what you're doing as an entrepreneur is you're thinking about what is the way the world could be what could it possibly be what is you know you know if you wanted to use you know analytic philosophy language logical possibility or something like that but it's it's it's you know kind of what is possible and and then um partially because you know these are human activities what's your underlying theories of human nature about how human beings are now how they are kind of Quasi eternally and how they are as they as as circumstances change as they as the environment in which you know we we the ecosystems we live in change which is technology and and in political power and institutions and and a bunch of other things as as ways of doing that and philosophy is very important to this stuff because it's understanding how to think about very crisply what our possibilities what our theories of human nature what are theories of human nature as they are manifest today and as they may be modified by new products and services new technologies you know Etc and and so you know obviously people tend to say oh that's a philosophical question because it's an unanswerable question you know nature of Truth or you know while we all speak and understand languages we don't really know how that works um and as part of the reason why you know there was the linguistic turn in philosophy that you know Vicken Stein and others were were so known for which is well maybe these problems in philosophy are problems in language and if we understand language we'll understand understand philosophy um and you know this question around you know these these unanswerable questions but actually in fact like science itself is full of a lot of unanswerable questions and um and it's the working Theory as we dynamically improve and that's part of what the human condition is and that's part of what actually the in-depth philosophy is it isn't to say that you know the same questions today some of the same questions today in philosophy the same questions that Plato and Aristotle and the presocratic and other folks are grappling with truth knowledge Etc but some of the questions are also new questions and the questions evolve and part of how Sciences evolve from Phil from philosophy was this question as as we res as we get to our more specific theories of and kind of uh developing the new questions that we get to those are outgrowths and the same thing is true in Building Technology uh in Building Product and services in Entre R preneurship and that's why uh philosophy is actually in fact uh robust and important um as applied to serious questions you know versus the you know uh one of the things I wrote my thesis on uh in Oxford was the uses and abuses of thought experiments and you know the most classic one is trolley problems um and you know there are both uses and abuses within the methodology of trolley problems the most entertaining of which have people haven't watched it is um uh there's a uh TV series called the good place which embodied the trolley problem on a TV episode in a absolutely hilarious way that's really interesting what yeah like what is what is the way that people tend to misuse that because I feel like trolley problems are so common in like EA discourse and people run into that a lot online the the fundamental problem is is they try to frame it to get to get an intuition to drive an in a principle Etc they try to frame an artificially different environment so it's like no no it's a trolley and the trolley will either hit the the the five criminals or the one human baby and its default set to hit the human baby and do you throw the switch or not and then when you start attacking the problem you say well how do I know that I can't break the trolley I could just not make it continue to run it's like well you know that you're like oh so you're positing in your thought experiment that I have perfect knowledge that breaking the trolley is impossible so in your posit to make your thought experiment work you're positing something we never or or when we encounter we generally think people are crazy right like you have perfect knowledge like why the fact do I know that I have perfect knowledge that I can't break the trolley um and because you know say what what is the right human response to this Charley problem is I'm going to try to break the trolley so it doesn't hit either of them that's really interesting right and you might even say that the that the problem is is that to say you say even you say well you per have perfect knowledge that you can't break it you're like well okay you know a don't have perfect knowledge and B even if you did maybe it's still the right response you're trying to get me to say do I do nothing and run over the the baby or do I do something and run over the five criminals like those are my only two options you're like well no I could say even if I think I can't break the trolley that's what I'm going to try to do because that's the moral thing to do I've actually NE I've heard a lot of trolley problems and I've never heard anyone posit the third option I love that um that's great uh and I also like there's something about that where it's like yeah certain thought experiments sort of like hijack your instincts and and you don't quite um a reason through all these all these hidden assumptions that I think honestly reminds me of like certain Doomer arguments and I don't I don't want to like go into go into the full thing but I think it's a it's a really interesting uh way to think about it if I had to like summarize what what you just said like the value to you of philosophy is like um thinking crisply thinking crisply about possibilities thinking about um human nature and reality all those things are like really really really important for business people um I I want to kind of like take it take another step which is like some of those some of those questions philosophers like uh or philosophy students or philosophy nerds just like sharpen our skills on there are some of these some of these big questions um some of the big perennial questions um like what is truth what is reality what can we know all that kind of stuff I'm kind of curious if you have a sense um as we start to get into talking about AI stuff um what are those questions where um AI large language models are are going to give us a little bit of a new lens on on on on those questions or what are what are questions where we'll we'll find new ones to ask that are better than previous ones even if they maybe don't answer them do you have a sense for that well I mean historically it's like for example questions that have led to you know a bunch of the SC scient VAR science disciplines right it's you know everything from things in the physical world to things in the biological world like germ Theory and all the rest um I think it's actually even true it's one of the reasons why kind of philosophy is the root discipline for or many other disciplines when you get to questions around like okay you know how do you think about economics and Game Theory or how do you think about um you know kind of um uh you know kind of political science and real politique and and and kind of the conflict of Nations and interests and it's also one of the reasons why you know as a you know probably one of my deepest critiques of the non reinvention of the university is the in intensity of disciplinarian ISM um so you know it's just the discipline of just you know political science or just the discipline of even philosophy as opposed to multidisciplinary um you know and if I you know part of the thing that I tend to think is kind of an interesting thing is how much the academic disciplines tend to be more and more disciplinary versus the hey you know maybe every 25 years we should think about blowing them all up and reconstituting them in various ways um and that would be actually a better way of thinking and why some of the most interesting people are the people who are actually blending across disciplines um within Academia and I think that that part of it is I think uh extremely important and part of the question in philosophy is the kind of the question of like well how do we evolve the question of what do we know and obviously you evolve the question what you know through like for example a lot of the history of science is instrumentation you know new new measurement devices um that that help with kind of you know kind of provisioning of theories um but Al and that's one of the reasons why like people frequently don't think enough about how technology you know uh helps us change what is the definition of be human um because we have this kind of imagination you know like the decaron imagination that we are this kind of this pure thinking creature and you're like well if learned anything that's not really the way it works right um that doesn't mean that we don't think that way to have abstractions to generate logic and theories of the world and all the rest but you know um you know uh you know put your philosopher on some LSD and you'll get some different outputs that that makes sense um so I I guess like like along those lines if you want if I step step back and squint I can kind of like uh you can kind of divide the history of philosophy into um essentialism and nominalism for for a certain part of philosophy right like um and essentialists are like um do you believe that there are like fundamental there's a fundamental objective reality out there that's knowable and that there's a way to kind of like carve nature at its joints and nominalists um which where we would include Vicken Stein which I know I know you you studied pretty deeply um and pragmatists um I think that more or less truth is is more or less relative or it's about social convention or it's about what works or there's a lot of different formulations of it and there's this sort of like ongoing debate between people who think one thing one thing or the other do you think language models like change um or add any weight to either side of that debate I think they add perspective and color I don't think they resolve the debate um the and there's certainly some question about since they function more like later Vicken Stein or more you know kind of nominalist um you know you say well does that does that weigh in on the side of nominalists because of actually in fact the way they function and actually in fact you say well if you look at how we're trying to develop um the large language models we're actually trying to get them to embody more essentialist characteristics as they do it like how do you how do you how do you ground in truth have less hallucination you know Etc and you know to to to gesture at a different uh earlier German philosopher you know Hegel one of the things I'm uh I think is kind of part of a I think it was kind of The Human Condition is the thesis antithesis synthesis like you could say hey we have an essentialist thesis we have a nominalist antithesis and the synthesis is how we're putting them together in various ways because you say look we uh and I don't even think later vickens Shin would have said that the the world is only language you you know kind of what you know the deconstructionist and dar da went to was like you know it it it is only the veil of language and you have no no contact to the world and you so you're not grounded in the world at all I think he would he would he would think that's kind of absurd right but his point was is to say that there is also in how we live his forms of life the way that it operates is not a simple you know kind of denote of and he understood it wasn't just denoting the on the mat or the possibilities the cat is on the mat and the possibility of the cat is on the mat but actually possible configurations of the universe and there was this kind of notion ofical possibility that was described as one one language of possibility was to say that kind of essentialist about a language of possibility is actually incorrect to actually how we discovered truth and how we operationalize truth and you still have a robust theory of Truth which is not essentially what the deconstructionists do but the robust theory of Truth is partially grounded in this notion of language games and a and a biological form of life um of how you do that and then obviously you go into this deeply with saying well okay how is mathematics language game as a classic language of Truth is a way of trying to understand that and that's part of where you get what philosophers refer to as krippenstein um you know the Saul crypy excellent you know uh lens on reading of part of what dickenstein was about and you kind of then apply all that you know everyone's going where is this going to large language models uh and you say well actually in fact uh you know language is this play out of this language game large language models are playing out this language game in various ways but part of what is revealed is we don't just go truth is what is expressed in language truth is a dynamic process and and kind of human discourse could be synthesis antithesis you know you know thesis antithesis synthesis or other things is this human discourse that's coming out of out of um you know this dialogic period this truth Discovery this logical this this this reasoning whether it's induction is reasoning whether it's you know um uh you know abduction whether it's you know deduction and you know these reasoning processes that get us to what we think are these kind of theories of truth that are always to some degree works and progress that's that's really fascinating I I want to try to summarize that in case um in case it was a little bit difficult to follow to be honest like there's a there's a point in there that I think I missed something so you tell me what I what I missed but I think one of the like some of the things that I heard in there that that I I thought was really interesting is um uh when you think about how we built AI which is predicting the next token that's a very um sort of late Vicken Stein compatible um idea or PR pragmatic like compatible idea where it's really about the relationship between different words in a sentence and it's not we're not finding anything out about the world like there were other AI approaches I don't know in the 80s or 70s where it was like literally like let's list out every single object in the world and those didn't really work um and that would be like something along the lines of a more essential approach to AI um and um uh the one that works is is is is a more pragmatic a more late vicking shenian one um but um what's what's what's quite interesting is now that we're we've we have that pragmatic base that we've bootstrapped we're in this process of um trying to make it more grounded more grounded in reality or more um uh more uh reduced down to like being able to talk about the essential ground truth um and I think what's really interesting about Vicken Stein is he's sort of famous for saying like the limits of my language or the limits of my world I don't know I don't remember if that's um late or early um but but more or less like I think what you're saying is that Vicken Shin doesn't think that like we there's nothing outside of language but he does think that the way we talk about the world um or the way that we use language is part of this sort of like social discourse where we're all kind of like um going back and forth to like coinvent um language and structures and language games together um and and you kind of see that happening with language models where like when you do something like R rhf um like that's sort of us playing with a a language model like playing a language game to be like no no you don't talk up you don't talk like that is is that like generally what you're what you're getting at yes um so everything you said but then the additional thing which you know uh later vicking Shin was really trying to explore in various ways because he wasn't trying to do a kind of a completely just social construction of Truth um you know I'm I'm a I'm actually a fan of you have to be a Vicken Stein scholar to actually understand how both early and late Vicken Stein are actually part of the same project and late Vicken Stein wasn't early vicenin was an idiot and now let me like I've religiously converted to this different point of view but there is a a particular thing which is how do you get to the notion of understanding truth and truth is is that Dynamic of of of Discovery through language and through kind of it it has to have some explicit external conditions that it isn't my truth Your Truth there is only to some degree Our Truth or the truth in various ways and how do you get to that as what you're doing and having you know truth conditions and in kind of early Vicken Stein the truth condition was it cashes out into a state of possibilities and actualities in this logic iCal space of possibilities which include physical space is part of it but broader than that and then um later Vicken Stein said well actually in fact this modeling of logical possibility is actually not the fact the way this works right and we're not actually in fact grounding it that way the way that we're grounding it is in the notion of of how we play language games make moves in Lang language and the way that's grounded is to some degree sharing a certain biological you know kind of um form of Life by which we recognize that's a valid move in the language game this is not a valid move in the language game now this is what's interesting when it gets to large language models because you go well large language models are they the same biological form of life as us or are they different and how does that play out and I think vicenin would have found that question utterly fascinating um and really would have gone like very deep on it trying to figure that out because um and by the way the answer might be some and some not 100% or 100% no 100% yes 100% no because you know the argument in favor is the large language models are trained on the Corpus of human knowledge and language and everything else and they're doing language patterns on that some might even argue that some of their patterns are very similar to the kind of the patterns of human learning and brains others would argue that it's not um but then you'd say well but it's it's also not a biological entity and is it it learns actually very differently than human beings learn and so maybe its language game which looks like it's the human language game is actually different in significant ways and so therefore the truth functions are actually very different and in a sense what we're trying to do when we are modifying and making progress with how we build these llms is to make them much more reliable on a truth basis like we want we love the creat ity and the generativity but we want it to uh to almost for for a huge amount of the really useful cases in terms of amplifying humanity we wanted to have a better truth sense right I mean like the the the the paradoxes in current gbt um are when you know you can kind of tease it out with like very simple questions around prime numbers and you go well you you know you got that answer wrong is oh yeah I got it wrong here here's the answer like well that answer is wrong too oh I got that one wrong too here's the answer and you know a human being understanding these things I'm just getting these things wrong like I got it like I get I'm I'm wrong as opposed to oh I'm sorry you're right I got it wrong and here's the other here's another wrong answer right right um and and we're trying to get that truth sense into it as we doing because we do have some notion of of oh right this is what's characteristic like like mathematics get gets us into very pure definitions of of certain kinds of language games it's one of the reasons why you know you know centuries ago people thought math was maybe the language of the universe or language of God or language of Etc because you're like okay there is the one where the purest truths some of the purest truths that we know 2 plus 2 equals four is kind of embedded in and we're still working that out as we play with how we create these language tools these language devices and it's part of the reason I think this question is really interesting because you can actually model it to some of the the actual as it were the technological physics that we're trying to create when we're doing the next version like like like how do we how do we get these things into good reasoning machines not just good generativity machines and they have some reasoning from their generativity but like part of the the classic showing where they break is showing where their reasoning stops working in ways that we value and aspire to in terms of what we try to do at as human beings as uh in our best selves that's really fascinating you said a lot there um I really want to get into the reasoning thing in a second but I want to go back to the um the sort of the way that you talked about late Vicken Stein versus early viin because I haven't really heard it said that way and the usual like thing people say is like he just disagreed with everything when he was older or whatever and what I hear you saying now um is more or less uh in both like in both cases he's saying some of the some of the same things or he has some of the same views but like the real difference is how he cashes out what it what it means to be true something is whether something is true and in the first um in his like sort of first period he's uh talking about truth in terms of um a logical space of possibilities um that can be broken down into these like little what he calls Atomic facts and those are never really defined but like um you can kind of build up truth from there um uh mapping those those possibilities into actualities like what's actually in the world and in later Vicken Stein it's all about um these sort of like the language games the social relationships like the the use of that word or that phrase um in the context of people and one of the things I I really wanted to ask you about is like that first that first version of Vicken Stein is uh where it's sort of that logical space of possibilities like what that reminds me of is embeddings um where you know embeddings are they're one of like the key underlying um uh technologies that that gave rise to AI right in in like traditional NLP they're like allowing you to represent um words uh or tokens in a high dimensional space um and then the language model like Innovation is kind of like it's not just words it's words in their particular context each each word in a particular context has its own part of the space So like um in a in a language model the word King if if if it's tokenized that way um you know there's a king in Chess there's a king There's an actual King there's like a king of England there's a king Lear and they're all kind of like kings but they're like different spaces and language models are able to represent um all of those different like when when we say King we mean many different things they're able to represent all that and that just actually reminds me a lot of of like Atomic facts or or or the the first like Vic Vicken Stein's early early work and I'm just kind of curious like because I think you said that language models sort of because of the next token prediction they they're they're sort of late viian but I wonder how you like factor in the fact that embeddings work and they're sort of a core part of this well and actually this is part of the fact that late Vicken Stein is not early vicenin was an idiot um because yes I do think that the kind of the notion of call it as it were a probabilistic bet for what are the set of different tokens that apply are are kind of there now the the reason why I would kind of slant more as current practice late Vicken Stein than early Vicken Stein is because early Vicken Stein thought that once you had the the grasp on the logic of it you then almost by speaking correctly couldn't make truth mistakes because the logic was embedded in it and um and even though the token embeddings are kind of you know part of a very broad symbolic you know quasi symbolic I would say you know kind of network um and the reason it's quasi symbolic is because it's still kind of activations and so forth and isn't you know P purely the reasoning around a token of king or you know 15 different tokens of king or 23 different partial tokens King as much as there's kind of conceptual spaces in that tokenization as mapped from a very lar large use of language but part language isn't isn't just the historical language but is the the reapplication of it like if you say this is the king of podcasts right or this is the king of microphones not yet maybe yes but just you know kind of as as instances that's part of why you know kind of later Vicken Stein went to well it's how we're playing these language games and how we're reapplying them and when we say like for example we say on this podcast this could become the king of podcasts we all have a sense of what we're doing it's like well what what would be the cases where that would be true and what would be the cases that' be false and what prediction is that making and and how is it that that's a you useful thing I'm sure someone said king of podcast before but i' I've never heard it before right and it's a different tokenization especially as it gets developed and elaborated a lot in in discussion and then actually if you suddenly had another you know terabyte of information about discussions of kings and kingdoms and you know all the rest and all of a sudden that token space that it's learning from would change right U and then the generalizations off it would change um and that's part of the reason I would say it's kind of more later Vicken Stein even though not completely not completely disconnected from those embeddings early and it's one of the reasons why like actually in fact later vicenin is not truth is just what language says it's no there's there's ways in which it's embedded in the world by how we navigate as biological beings and that's part of how the world kind of comes and impacts it and therefore it's not just Language by itself free floating like the cartisian Consciousness but it's embedded in some ways and part of what he was trying to do is figure out well from a philosophy standpoint how do we understand those embeddings and how do we drive Our Truth discourse in language based upon that biological embedding that makes sense so I think what I what I hear you saying is um uh despite the fact that embeddings are in this sort of uh they're they're mapping words into this High dimensional space which sort of seems like um kind of mapping words into this like sort of atomic facts or like logical possibility space um the way that that space is constructed and and what makes something go into one part of the space or another is more late vicin shinan because it's very much about how it's used in practice um and whether it's useful for humans in the world rather than like it's about some deep underlying um logical ordering where uh if you've created that ordering like you can't say anything wrong because uh because you're you're only using words in that in in from that space does that is that is that kind of On Target yes exactly and and and part of it is we know that there is that there's truths that where the coherent use of language still is a falsity and so how like part of what you're we're trying to figure out is how do we get more of those truths and truthtellah most promising to you in terms of like ways that we we are getting reasoning into these language models and do you think that there are any like philosophic like ideas from philosophy whether Vicken Stein or otherwise that are relevant to that to that uh project well the answer is certainly yes on the relevant ideas currently I think we're doing a couple things so I think we're we're taking kind of call it you know human knowledge and figuring out how to get that as part of what's trained so the earlier discoveries um were actually in fact if you trained on code computer code then these models learn patterns of reasoning much broader than just computer code and so all of the models that are doing this are now also training on computer code even if they don't have a target of being a you know Microsoft co-pilot you know code generation you know Etc like even if they're not doing that because there's a re there's there's a pattern just like math of of crisp you know kind of um you know modeling of of reasoning another one is that's currently happening is well what are you doing with textbooks and the notion is if you take the same kind of training discipline that we use for human beings encapsulated in textbooks you can for example build much smaller but still very effective models based on textbooks as ways of doing it and so textbooks is another one now as you begin to like there probably like some interesting as it were computational philosophy if you begin to say well how do we cash out kind of theories of um you know whether it's you know kind of uh you know call it theories of Science in the kind of different theories of Science and you're kind of building those models into you know how do you get you know it's kind of like lacos as a development on popper given thinking about Coan you know kind of models of scientific Paradigm um how do you um you know kind of make you know kind of predictions on those kinds of bases and you know some of the indepth work in logic maybe basian logic um as ways of possible possibly looking at us I'm quite certain that there probably are some some very useful things to elaborate Beyond it now currently of course part of the the notion of these things are they learning machines so you have to have a give a fairly substantive Corpus of data from them to learn from um now of course there's synthetic data and like there may be like philosophy is in what patterns do we create synthetic data that is still useful to learn from off the act the current data you know might be anyway so there there's a bunch of different kind of gestural areas but I'm certain there's a there there even I don't even though I'm not bringing up I'm I'm I'm making gestures rather than you know uh specific uh theories um as to how that they there caches out that's really interesting so it seems like um basically the way that we're trying to get reasoning into models is to find sources of data that just has really crisp reasoning and so they'll like learn the reasoning from that yep I'm sort of curious like uh if if that's the case like aren't there are only a certain number of like moves you can make in logic um you know like you can do induction you can do deduction you can do there's there's like not there's not like infinitely many moves like why if if we have like a really crisp set of of data on that sort of teaching them these moves what's the like thing that's sort of stopping them from being able to apply them more more broadly um and maybe that question is not well formed well first yeah correction of the question because actually in fact in logic there are infinite moves one of the things that's interesting in in various Logics is different orders of infinity um as people kind of think through it so there is various things now what you did actually remind me of is one of the things that I have been recently rereading um because of thinking of girdles theorem as kind of a classic instance of of human meta thinking and so girdle aser Bach um which I read as a high school student I've been rereading recently um because I'm that's great what do you think uh well it's it's it's this it's this this tangle of amazing observations that you're trying to kind of like I'm trying to think about it from a Viewpoint of modern llm so it's kind of like this question of you got the girdle self-reflection which is roughly speaking in any sufficiently robust language system there are truths that cannot be expressed within the language system right um and like that's mindboggling right and what exactly it means and so forth and it's because of this classic kind of diagonalization proof to say if you're enumerating out all the all the truths there's at least one of them that's not captured in your your in your in your numbering out of all truths hence one version of kind of infinity um you get that in the recursion patterns um that you see within eer and within within Bach that you say that's another recursion pattern because this is a recursion pattern of getting to showing the shadow of at least one truth that's not captured within your enumeration of all the truths um you go okay well what does this mean for thinking about truth Discovery whether it's human truth Discovery LM truth Discovery and that kind of the the what are the things that are outside the boundaries of logic like it would have been um like I would have been very curious to have girdle and mikenstein to folks very focused on logic to talk about girdle's theorem like like I would have like you know I was asked recently you know if I had a time machine would I want to go forward or back me I'd rather go forward I'm very I'm just curious about how do you shape to the future but like one of the the historical back ones that I would love to do is put girdle and vicenin in a room and say you know girdle's theorem discuss you know and like like like you know I would I would do a lot to try to be able to hear that conversation we needed we need some gbts in here with with girdle uh with girdle and Vicken Stein maybe girdle doesn't have enough writing to make that happen but uh maybe maybe eventually and the twistin of the thinking is one of the things that is that is you know is one of the things that made girl so so spectacular in this you know another one by the way that were historical walks is Einstein and and girdle used to take walks you know you wish that you had digital recorders like please record the conversation right we we would really like to listen to that um no I love that um that's really interesting because I feel like like I read go asbach in college I loved it thing that's so good about it is it's like it's such an interdisciplinary book you know it's got MTH and music and art and like all like all this stuff and you're like wow like that's the kind of mind that's going to invent new minds and then you you see hoffstad today and he's like sort of not like he's not definitely not in the llm conversation he's a little bit freaked out by them um and like I'm kind of curious like what do you what do you make of that like what did he get right and what do you think he got wrong well I think a central thing that he got right at least to how I operationalize is and that was the reason I was just at Hegel with thesis antithesis synthesis which is it's a dynamic process that's ongoing and you can't necessarily predict the future synthesi and that's part even though obviously in philosophy you try to articulate the the truths you know that they cart I think for I am or um you know Vicken Stein saying well there actually have to be a world in a certain way to actually there to be truth statements in the language statement of I think therefore I am and so therefore you can be you know kind of broader than just the disembodied mind um as a way of of thinking about that because you're think about what the truth conditions must be in a language if you're saying if you're saying in a way that's coherent to your current self and your future self I think therefore I am what are the truth conditions in the language as ways of doing it and so but that's a dynamic process by which we are making new discoveries and that's kind of the synthesis and that's the thing that I think is is um you know is part of what I take from the kind of the girdle eer Bach interweaving of these different of these different Dynamics and showing the kind of the patterns across it now frequently when you go across a lot of areas where you people say hey we have this language system and all we know is through our language and then they kind of go and the world is unknowable to us because the only thing that's knowable to us is our language you say well that's presuming there's no relationship between how the language engages with the world and how we engage with the world with the language and so it's one of the reasons why you get into really interest in you know biologists like verella and machana it's the reason why you know you get to you know kind of different patterns of self-referential logic and so it gets very interesting and so I don't I myself don't get freaked out by llms I'm part of this I think wow new things that we can discover right right and how does that um make the discourse much richer much more valuable much more compelling and in some ways uh higher On Target you know discoveries of the truth like because I gave a speech in bolognia last year uh where uh along with the book I published last year impromptu was last chapter's homot is that one of the things that we think of ourselves as human beings as static and actually we're not static because we are constitu by the the technology that we engage and bring into our being so for example you and I are looking at each other in this podcast through glasses like think about the world with glasses without glasses right the world is a very very different place and how you can perceive like we say most of our theories of Truth are fundamentally based on kind of perception like you know seeing is believing is kind of a classic idiom and well if you don't have glasses how you see is very different right and so so like technology changes our landscape in the perception of Truth you know that's why microscopes and telescopes and and all this rest these other things that kind of get to that changing that landscape and that's part of what we're doing with technology and we're doing in this particularly interesting ways with these llms in terms of how they're operating yeah that makes that makes a lot of sense and I I love that point um about of how technology changes us um and and really like how flexible humans are it reminds me a lot actually CU I read I read your book to prepare for this and I also I read your Atlantic article and you some podcasts on this like um and it reminds me a lot of have you read the book the weirdest people in the world by Joseph Henrich no I probably should it's really it's really great he's a psychologist at Harvard and the the point of the book is um most of what we take take to be the psychology literature is wrong and it's not wrong because of packing and all that other stuff but it's wrong because um the psychology literature is based on studies of Western college students and Western college students have a completely different psychology than like uh people everywhere else in the world um now and in history and one of the key differences uh in Western college students is that they can read and reading changes your brain in all of these different ways it enlarges parts of your brain and shrinks other parts where um for example if you're if you can read uh you're more likely to pick out like objects in a landscape rather than see like the holistic uh the holistic scene and there's a bunch of these other like significant differences that you find in humans who can read versus Humans who can't and so like reading as his technology uh created all the stuff like it um you know one of one of the things that he he argues is that uh it allowed us to create uh like a society where we had um uh where we had churches that that created Like rules and principles that like people would follow even though they weren't being watched so like you know you know I'm not supposed to like steal or whatever and you can't it's like really hard to get like uh big organized Society without without reading basically is is like one one big point of of the book and that it's because it changes our our our actual biology and I think that's the thing that um that people sort of Miss about language models like not to say that like we should ignore that there are like any any language models dangers or anything like that like there's a lot of I think really interesting and really important problems to solve but like um when you think about what language models might replace versus augment I think it's also really important to like know that like we've been replacing or augmenting ourselves for many many many many generations and um if you took a from like you know Generations ago or 10 generations and put them put them now like it would be like really hard for them to like interact in our society now same thing if if you took one of us and pushed us back in time um and and that's because like we we sort of like we grow and change in response to our environment and our culture which is like this Collective memory that like that gets loaded up so that we're a modern Human Instead of like a pre human or whatever and the same thing is going to happen with with language models like you can kind of like put it on this on this uh timeline from the invention of language to like reading to the printing press like it's all the same kind of cultural transmission technology I've I've heard some researchers call I think that that that's exactly kind of like what it is to me um curious what you think about that well um you know I I definitely think that the progress of cultural knowledge um and I don't know if it's the same author but the secrets of the secret of our success um is is I think a very good book um and you know it's partially because how we make progress is updating our cultural knowledge and it's part of the reason why it's not surprising that then when we we generate uh interesting learning algorithms that we can apply to the human Corpus of knowledge that we then generate interesting things that come out of that because that's essentially a a a partial index of cultural knowledge it's not the complete index because you know like for example the secrets of exess go through it's like well you know how do you identify which things to eat or which things not to eat or when to do that and all the rest of that and that's part of how you make progress and I think that's essential part of how we're um how we actually evolve like everyone tends to think evolve in human beings is you know do we evolve to be faster longer stronger genetics and actually in fact a major clock of our evolution is we shift did like you could say there's geological Evolution which is super slow then there's biological evolution which is slow and then there is cultural Evolution or knowledge digital Etc which is much much faster and part of how the kind of the secrets of our success is we we is we got into kind of cultural Evolution and um you know and kind of that progress of digital and that part of what we're doing with AI and llm m is tools to help accelerate that you know cultural sdigital Evolution which can include like why is everyone going to have a personal assistant because the personal assistant would be I read all the texts and I can bring them to you as as as as you're talking and trying to solve problems so like for example on the you know what are things that people should be using chat gbt for is obviously a immediate on demand personal research assistant that today hallucinate sometimes and you have to be aware of that and kind of understand that but an immediate research assistant is one of the things that is obviously here already today um and and you know if you don't think you need a research assistant it's because you just haven't thought about it enough yeah I mean it's it's it's incredible like it it it takes the every everything that Humanity knows and gives it to you in the right context at the right time when you ask for it and that's exactly kind of like the bottleneck of cultural evolution is like getting the right information out to the edges of people that need it instead of like having it be locked up in a in a on the Internet or like in a library or whatever where you have to go expend resources to get it like all those are better than having to transmit knowledge orally for example um but uh but yeah language models are like a a profound Next Step um so we're we're getting close to time I have a couple I have a couple of uh we had a whole final section about science but we may not be able to get to science we'll have to maybe do a part two yep that'd be great I'd be up for that I love these these topics and um but I want to ask you a couple a couple more things like just sort of on on the you know philosophy and Ai and AI front so like why do you think philosophers didn't come up with AI like why did it why did it come out of I mean I guess it came out of like sort of a computer science tradition but also it's like really sort of an engineer people who just were making stuff um uh yeah talk to me about like why why it didn't come from philosophers well I do think that this is a little bit like I was gesturing at earlier which is being disciplinarian is I think um you know has obviously people are not idiots in doing this they have some strengths in not but also some weaknesses and um you know and I think part of it is to think about like well how is it that technology is going to change our conceptions of how we use language and how we discern truth and how we argue about it and all the rest of the stuff is I think um you know pretty Central and you know it's kind of like you know how is technology as ways of knowing or ways of perceiving or ways of communicating or ways of reasoning important and you know philosophers will say you don't need any of that we just I sit down and I cogitate you know kind of a you know kind of a canonically you know decart um and look I think there's a there's a role to sitting down and cogitating but I think there's also a role to discourse and it doesn't necessarily mean you have to be a externalist or you know a you know a kind of I don't know who the current physical materialist you know um you know Advocates are you know the the church lens and other people um you know back in the days when I was a philosophy student were those among those who were who were uh very vocal on that um but is to say that actually in fact this notion of how do we engage technology in our work is a very good thing to do and if so then maybe philosophers would have come up with it more or would have been able to participate more in it versus the you know computer scientists who are like okay I'm working on the technology side of it what can I make with this technology and obviously you know the what can I make with this technology goes well earlier than computer science right I mean you've got you go all the way back to Frankenstein you know and kind of thinking about you know kind of imaginations about what could be constructed here or the golum or or Talos in Greece um and so the the notion that things could be constructed now could they be constructed with silicon and they could be constructed with computer science you know that's the modern kind of artificial intelligence but the but the notion of that is I think one of the reasons why I want philosophy to be broader in its instantiation you know not just a question around you know this this is obviously a bit of a deliberate rhetorical slam but char problems yeah that makes sense um maybe maybe a way to frame that is like uh it's better to be like asking deep philosophical questions and be a philosopher out in the world to some degree than it is to just be a philosopher I don't know if you'd agree with that but like something something like that I chose that with my own feet yeah there you go um yeah I I I I definitely I definitely agree with that um so so we have a minute left uh the last thing I want to ask you is I assume that there's there are a lot of people who are listening to this maybe are not um have not been philosophically inclined in the past and are either like wow I could not follow any of that and I want to figure out what they said or they're like oh my God like I want to learn how to think like that um and I think for the first group of people I would totally recommend like just use chat gbt talk to chbt about this stuff and it will tell you for sure yes uh I but I wanted to ask you like if people are thinking about like they want to get that kind of like thinking crisply about possibilities thing that you that you talked about so well at the beginning like where would they start or what are your like what are your favorite kinds of philosophers or kinds of books like this to dive into well um you know I think um the best way is to get uh interaction um it's part of the reason like like study philosophy or even you know even for the second part of the question some use of chat gbt also very helpful there because the interactive is is is what does it and like for example one of the the things that I use chat gbd for which is part of this is I have something that I'm arguing for thinking about arguing for and I write put in my argument I say okay chat jbd give me more Arguments for this how would you you know argue U for this differently or more or and then also how would you argue against it right what would your counterarguments be to this and use that as kind of again you know the kind of thesis antithesis trying to get the synthesi in this and uh and so I think that Dynamic process is really important um and so you know part of the the way that people traditionally try to get to this is they they go try to go through what are some of the real instances of great human thought and then try to understand that how to think that way so one of the things that was too much text prompting to go new impromptu but as I think very useful as another utility for you know kind of use of chat gbt is um you know uh like I like I'm a non-mathematical college graduate explain girdles theem to me you know I'm a non- physicist explain Einstein's thought experiments around relativity to me you know Etc and that Dynamic process of getting into understanding those things is part of how you learn to think this way and it's one of the reasons why you know kind of our um you know one of the things that has helped us accelerate our cultural Evolution our C cultural Evolution The Secret of our success is having things like books having things like universities because it's that Dynamic process of engaging that's so important and so there's not necessarily one specific book although by the way if you really want to have your mind boggled go re re know read or reread girdle as your boach it's great right you know but but but like what are the instances of these canonical amazing pieces of thinking and then you know kind of in that Dynamic engagement process you're internalizing them yeah be curious about great ideas and and engage with with them um this was this was a great conversation I really appreciate you coming on it I feel like I learned a lot thank you so much my pleasure awesome oh my gosh folks you absolutely positively have to smash that like button and subscribe to how do you use chat GPT why because this show is the epitome of Awesomeness it's like finding a treasure chest in your backyard but instead of gold it's filled with pure unadulterated knowledge bombs about chat GPT every episode is a roller coaster of emotions insights and laughter that will leave you on the edge of your seat craving for more it's not just a show it's a journey into the future with Dan shipper as the captain of the spaceship so do yourself a favor hit like smash subscribe and strap in for the ride of your life and now without any further Ado let me just say Dan I'm absolutely hopelessly in love with you