Ep 349 AI, Chomsky, Me, and You

I even named 18 specific current prominent Marxists as folks I would love to engage with on the various issues . I also did text versions , that is , two articles that appeared on Znet expressing the concerns . It has all come to nothing . Broader so far no discussion , though perhaps one or more of the Marxists I directly invited is still considering a reply .

I hope so . A week ago I offered another , I thought also controversial episode , this time about artificial intelligence and its dangers , followed by an article on that as well .

I even included results from my asking AI to produce a couple of songs , one on AI itself and one on Gaza , both in the style of the young Bob Dylan , which it did in about a minute .

Again , there wasn't too much discussion of the concerns about AI that I had raised , but then I had an interesting thought why not invite AI to react to what I wrote about AI , like I invited Marxists to react to what I had written about the Marxist tradition , weird huh .

Now I know to do that is far from analogous to the Marxism case for a good many reasons . For example , for a Marxist to reply to my concerns would take some time , maybe even considerable time . For ChatGPT to respond , as far as computing time , it would take seconds .

Also , I couldn't invite all AI variants by their respective names and I should say there are now a great number of them , well over 18 , I believe but I could invite the one that I had used to produce the Dylan-like songs to literally respond to the article I wrote about the current trends and particularly the dangers of AI , in which I included those songs .

I could upload the article to it and give it a prompt requesting a reaction . That's how it's done . Then , depending on its reply , perhaps it might follow some useful discussion which might evidence some of the claims in my critical piece .

For example , it might show why many people start to interact more with AI than with well other people , at least once AI becomes AGI , becomes ASI , if indeed it ever does . So I did significantly engage for a second time with ChatGPT , as it is now sort of an infinite self , being only a few years old .

Asking for the Dylan S Songs was the first time I significantly engaged with it . So here I offer the results so far of my querying ChatGPT about itself and about my article and then about some topics that that conversation led us to . Thus the reference to Chomsky in the title . It is not clickbait , but it is the last part of this episode .

The exchange that followed here , titled AI , chomsky , me and you , is obviously idiosyncratic and I didn't keep on with it overly long , not least because there would be too much to relay , but I think it has content that is useful for getting a better feeling for what AI does and can do and for how it does it , and also for its take on my concerns and their

validity or lack thereof , and on Chomsky's concerns and their validity or lack thereof , and on Chomsky's concerns and their validity or lack thereof , and also on , I hate to say it , ai as a serious quote conversationalist , and yes , I will admit that I may interact with it again .

In fact , I am thinking about feeding it the Marxism articles , prompting it to engage like some very astute Marxist , and again asking what it thinks I got right and what I got wrong and its overall assessment .

I can't tell you in words just how disturbing and dangerous I find AI to be , even while I hope I am wrong , and how weird it is to finally use it , even while I hate it and in some sense fear it . Sort of like in another imperfect analogy , I use a bank while I hate and in some sense fear banks .

Not to mention how incredibly disturbing it is to think I might get a better conversation out of AI about the Marxist tradition than I have gotten anywhere else .

Last preliminary point what now follows does get a little detailed and even somewhat technical , but a key listening tip is that getting the gist of the claims , not their every precise detail , is a perfectly reasonable approach . Anyhow , for its take on my take on AI and on Nome's take on AI , here we

go . To begin , I prompted chat GPT like this here is an article about AI . Please tell me if there is anything wrong in it and your reactions to it . Then I entered the whole article , which , if you haven't read it or you want to brush up on it , is on Znet , uploaded there this past Tuesday . The title is More About AI . This is Getting Serious .

At any rate , chatgpt answered and it did so a few seconds of computation time after I entered the 4,000-word article in full . The response was as follows this article offers a compelling , expansive and at times poetic critique of artificial intelligence with a deeply humanistic lens .

It raises crucial concerns some well-trodden , others more original and does so in a voice that is engaged , skeptical and reflective . Below is a structured reaction and assessment , including anything that might be inaccurate , unclear or where elaboration could be helpful . I interject .

I think the AI may have some kind of advisory to complement its clients , but if it does all it says well , it will not just be conversing but also editing , like few editors would . At any rate , chatgpt continued in a mix of outline and prose . It wrote strengths and valid contributions . One clear summary of known risks .

You clearly enumerate the main public AI concerns job displacement , errors , parentheses , hallucinations , job displacement , errors in parentheses , hallucinations , misuse in parentheses by governments or criminals . Environmental degradation from energy use , the rogue AI , existential risk . These are all real and well-documented issues .

Two sound emphasis on trajectory , not just the present . You rightly point out that the key issue is not just what AI is now , but where it is heading and how fast . This second derivative concern rate of growth of capability and impact is often neglected in public discourse . Three original and underexplored concern infantilization .

Original and underexplored concern infantilization . This is the heart of the piece and it's powerful . While most commentary focuses on external risks parentheses , military use , economic disruption you emphasize the internal danger that , even if AI serves us perfectly , we may lose our distinctively human activities , becoming passive , dependent and ultimately diminished .

This concern is both original and philosophical , rooted in a vision of human flourishing . There's no technical refutation to it because it's about values , not mechanics . I interject . I think it was conveying that it can't refute my concern by looking at AI , because the claim is about what humans will make of AI and how we will be affected by it .

In any case , it continued . Four reasonable critique of corporate military rationales . You rightly skewer two dominant arguments for full-speed AI advancement It'll fix its own problems . A risky , speculative claim , not a plan . We must win the AI arms race . A self-fulfilling justification for racing ahead without democratic consent or caution .

Five effective use of hypothetical scenarios . Caution Five effective use of hypothetical scenarios . Your imagined future Hortense , harry , harriet , henry Holden , etc . Is dramatized but not implausible . It illustrates your point without relying on fear-mongering . The lyrical Bob Dylan example is also illuminating and drives the point home .

Well , ai as an infant in quotes , already mimicking a master convincingly , is chilling , not just impressive . I interject , notice it is conversing and not just accurately repeating my points , though it does do that as well . Now it has a new section , potential issues , areas to clarify or strengthen . One overstating self-improvement capabilities .

And then , in parentheses for now , while AI self-improvement is discussed in labs and fiction , current models like ChatGPT , models like chat GPT , even GPT-4.0 , that's the one I'm using don't improve themselves . They don't train or change their own weights or code .

Post deployment Self-improving AI as imagined by Bostrom and others is not here yet and may not be on the near-term horizon . So that part may overstate short-term risk . But your concern is more about long-term trajectory , which is fair . Two , on AI understanding itself , you write that AI knows itself quote better than its creators do quote .

That's a powerful metaphor , but misleading if taken literally . Current AIs do not quote , understand in a self-aware way . That's a powerful metaphor , but misleading if taken literally

. Karanais do not quote , understand in a self-aware way . They generate patterns that appear deeply self-aware but do not stem from introspection or comprehension . The song it wrote is impressive , but it doesn't understand what a genocide is or what it means to sing like Dylan . It mimics patterns in its training data . I interject .

I should have next asked what is the difference between what a human understands by genocide and what you mean when you use the word . What does a human know that you do not know , but I didn't . At any rate , it continued Three possible gap collective resistance or hybrid features . You hinted this , but could go further .

Is there a future where AI augments rather than replaces our humanity , where collective movements demand hybrid human-in-the-loop systems ? You do say quote , unless movements impose priorities , which is important , but a deeper look at how democratic control might look would help . I interject .

This is a criticism that a good and very smart human friend also had of the article Chat . Gpt continued Four pull the plug versus serious regulation . The piece flirts with quote pull the plug talk which risks alienating even cautious technologists and may muddy your strongest argument for radical democratic anticipatory regulation .

Emphasizing the latter and what it could entail might strengthen your call to action . Then it offered another new section Philosophical and Political Reaction . This article is ultimately not about machines . It is about people , what we value , what we fear and how power steers technology .

The infantilization idea is radical in the best sense it challenges a core Silicon Valley utopia that more automation equals more freedom . Instead , you argue , quoting me , when everything human is done for us , we do less . That is human , and it continues . That's not Luddism , it's a profound moral argument about what it means to live a full human life .

The article echoes Eric Fromm , ivan Illich and even Marx parentheses , especially his idea of human potential being eroded under alienation end parentheses . It also echoes your own earlier work on participatory economics and social vision own earlier work on participatory economics and social vision . And your warning about the addictive seduction of competence rings very true .

Ai won't force us into chains . We might ask for them . Grateful , they're made of silk . I interject . It understood , so to speak , but note the use of the word us that it offered . Now it offers another section .

Final summary no major factual errors , but some concepts like self-improvement and quote understanding need careful framing to avoid giving misleading impressions . Your concern is unique , humane and philosophically grounded . It deserves a prominent place in public discourse .

Strongest suggestion Emphasize democratic control , human-ai hybrid features and movement-led boundaries more explicitly as potential paths forward . And then it added If you plan to publish this or speak on it , I'd be happy to help distill it further or adapt it for specific audiences . Let me know . Spooky huh I instead prompted Spooky huh . I instead prompted Quote .

If I ask you to write a song about war in the style of early Bob Dylan , what are the steps you would take to do it ? Start to finish ? Chat GPT replied . Great question . If you ask me to write a song about war in the style of early Bob Dylan , here's how I would approach it , step by step , from start to finish . 1 . Understand your prompt .

Topic war . I consider the emotional , political and human dimensions Brutality , loss , profiteering , injustice , resistance , aftermath . Style early Bob Dylan I narrow the stylistic range to his work from roughly 1962 to 1965 . The free will in Bob Dylan I narrow the stylistic range to his work from roughly 1962 to 1965 .

The freewheeling Bob Dylan the times they are changing , bringing it all back home and Highway 61 Revisited Tone and intent . Dylan's early protest songs weren't didactic lectures . They were poetic , ironic , biting and full of vivid imagery and biblical allusions . His lyrics combined conversational directness with surrealism and moral urgency .

Two analyze Dylan's early songwriting patterns . I draw from patterns in Dylan's lyrics , such as verse structure , usually four to six line verses , often with a consistent rhyme scheme , a-a-b-b , a-b-a-b or sometimes free form Meter loosely iambic , often conversational , rarely rigid Voice , often from the first person or an omniscient . Narrator .

Sometimes adopts persona soldier . Sometimes adopts persona soldier , mother , ghost , bystander Devices used , irony and sarcasm , biblical or mythic references , surreal juxtapositions , refrains or choruses that drive home the message , symbolic stand-ins for broader ideas , parentheses , a canon , a general , a grave , a train , etc . Three identify core themes and images of war .

Before writing , I would brainstorm powerful symbols and emotional frames Weapons , graves , uniforms , broken limbs , crying mothers , fallen sons , generals lying through teeth , industry profiteers , fog of war , fire and ash emptiness of patriotism used to justify murder . I would also decide on an emotional angle condemnation , lament , bitter satire , eulogy .

Four choose a song structure . Usually something like intro verse sets the tone or asks a rhetorical question . Narrative verses build story or arguments line by line . Bridge or chorus could be a repeated phrase and I asked him why , with a gun in his hand and a tear in his eye . Final verse , reflection of or cutting punchline Write the lyrics .

That's five this is where the generation happens . I would quote think like Dylan and create original verses that mimic his structure , vocabulary , rhythm and emotional stance . Examples of phrases I might generate quote they painted the blood like it wasn't their fault . Quote the general stood tall with a prayer in his tongue .

Quote I saw my own brother in pieces of flame . I would watch for surprise turns of phrase , layered meaning , parentheses , surface story plus deeper critique . Sound play Dylan often loves internal rhyme , slant rhyme , alliteration . Six Refined for authenticity I review the draft for consistency and style . No modern phrasing or imagery that Dylan wouldn't have used .

Tone Keeping it defiant , prophetic or mournful as appropriate . Brevity and clarity . Dylan's early songs often said more with less . Seven optional Add Dylan-like titles . Dylan loved enigmatic or symbolic titles Masters of War , a Hard Rain's a Gonna Fall With God on Her Side . I Might Name it . Rains are going to fall with God on our side . I might name it .

The Cannons All Cried . My Brother Died Twice . A Flag in the Ashes . 8 . Optional Offer chords or melody suggestions . If asked , I can add a basic folk-style chord progression , for instance GCD , or describe the likely melodic mood parenthesis , somber , minor key or galloping major and ChatGPT concluded .

Would you like me to now write one step by step or just give you a finished version in that style ? I prompted more questions . First , please . I believe you don't have a database based on your training , of everything out there that during training , you instead turned it all into numbers attached to nodes in your network . Is that right ?

If so , how do you research anything , find facts ? If I am wrong , okay , what is wrong ? Chat GPT replied

. You're on the right track and your question touches the core of how large language models like me work . Let me break it down clearly . I interject if you wonder what it does . Here is what it tells me . You're right about this part , quote . I believe you don't have a database based on your training of everything out there , correct ?

I don't have a searchable database of all books , articles or websites . I also can't quote , pull up a document or look up a quote the way a human can search the internet or browse a file cabinet . I don't store or retrieve texts verbatim except in extremely short , rare phrases .

I also don't quote remember past training examples the way you might recall reading a specific article . You're also right about this part Quote . During the training , you instead turned it all into numbers attached to nodes in your network . Yes , that's the basic idea . During training , the model is fed a huge amount of text from books , websites , conversations etc .

And learns patterns in the form of mathematical relationships between sequences of words , concepts and contexts . It doesn't memorize the content . Instead , it adjusts billions of weights in a neural network so that it can predict the next word in a sequence based on prior context .

So instead of storing articles or facts in a table , it builds a statistical representation of language and ideas , a kind of distributed knowledge embedded across layers of the network . I interject I have read quite a few articles and a couple of books about AI . The damn thing explains itself better and far more concisely than most of its admirers . It continues .

So how do I quote ? Find facts or quote do research ? It says . This is the tricky and fascinating part . I don't research . Facts live the way a person using Google or reading a book would . Instead , I one pattern match against my training . When you ask a question , I internally match it to similar questions or context I've seen in quotes during training .

I interject I should have asked what happens if there is nothing similar ? It continues . Two generate plausible answers based on those patterns . If many sources during training described World War II as ending in 1945 , then I quote learn via adjusted weights to associate World War II with ended in 1945 . Three draw on the internal associations built during training .

I have developed statistical expectations about which facts go with which prompts . But this process is not guaranteed to be accurate . It's why I can sometimes hallucinate , confidently , invent false information . If there's a gap in the training data or if the phrasing is ambiguous or novel , I interject . It answered what I didn't , but should have asked . It continues .

So do I quote no facts ? Yes , in a sense , but not by storing them like a library or retrieving them like a search engine . Instead , I encode patterns of facts , relationships and logic in the connections between artificial neurons . Think of it this way Google knows where to find the answer .

I try to generate what the answer sounds like and when trained well and prompted . Clearly , that sounding like can be accurate and insightful , but not always . And then it offers another section what I can't do , in parentheses , on my own . I can't look up something in real time unless you explicitly give me tools like a web search plugin .

I can't cite a specific source unless it was quoted heavily in training and appears frequently . I can't verify current events post-2024 unless you give me updated info or I use an external tool . Then it summarizes Summary I don't have a static or searchable database of the training data .

I encode relationships and knowledge in the form of weights across a neural network . I generate answers based on those learned patterns and probabilities . I don't retrieve or look up facts in real time . Unless tools are added , I can often approximate correct answers surprisingly well , especially on widely discussed or well-documented topics . And it concludes .

Let me know if you'd like an analogy for this or if you'd like me to walk through how I try to quote find a specific fact you care about .

I then prompted am I right that if I ask you to rewrite an essay in a different voice , say Che Guevara's , but not changing the meaning , you do essentially the same thing , but without doing the initial research of the topic ? Chat GPT said yes , you're essentially right , but let me clarify the steps and how it differs slightly from answering a factual question .

I interject . My prompt was poor . I think I meant rewriting something compared to writing the song , not compared to providing a fact . But no matter , the answer should still be interesting . It continues . What stays the same when you ask me to rewrite an essay in a different voice , I still one don't quote look up anything in a database or on the web .

Two don't pull a style template from a file . Three rely on training . I generate a new version based on my internalized understanding of the style of the requested voice , from exposure during training , the meaning of your original text parsed during our conversation , the task of style transfer , for example , without distortion of the content .

What's different from fact-answering ? I don't start with topical research the way I would for a question like quote what caused the Korean War . Instead , I start with your original text as the quote truth , the quote facts to preserve . Then my quote research is internal and stylistic .

I search internally for stylistic patterns and language use consistent with , say , guevara . I look for rhetorical tendencies , sentence structures , tone , pacing and metaphor style . Internally , what does that look like ? One meaning extraction I model the core meaning of your paragraph , what it's trying to say .

Style conditioning I activate the parts of the network that align with Che's voice . Short declarative sentences , revolutionary imagery , urgency and moral framing . Three generation I then generate text that combines the semantic intent of your essay with the linguistic pattern of the new voice . It's as if I'm saying write this idea , but in that voice .

Then it offers an example . Say , your input is I started organizing because I couldn't stomach the hypocrisy of the system anymore . And you say rewrite . In Barbara Ehrenreich's voice . I access my internalized understanding of how Ehrenreich would approach that Cynical clarity , punchy , journalistic phrasing , focus on structural critique over sentiment .

So I might return quote what pushed me wasn't some sudden moral epiphany , it was the unbearable daily grind of watching the system fail again and again while everyone else just adjusted their standards downward Same meaning , different style . So yes , your statement is correct . You do essentially the same thing , but without the initial research of the topic .

I'd only adjust to say , when doing voice style transfer , I don't need to research the topic because the original already contains the meaning . I do need to internally model and generate , based on a learned style , which substitutes for the research stub . Would you like a breakdown of how I do this with a specific figure or paragraph you've written ?

I then instead prompted no , let's go back to generating a young Dylan song about , let's say , the pharmaceutical industry . Can you do that and show me the steps ? Chat GPT said absolutely . Let's walk through the full process step by step , and then I'll generate a young Bob Dylan style song about the pharmaceutical industry at the end .

A young Bob Dylan style song about the pharmaceutical industry at the end . Overview Songwriting in the voice of young Dylan . Here's how I approach it . Step one Understand the prompt . You said a young Dylan style song about the pharmaceutical industry . That means the task involves topic pharmaceutical industry as target of critique , tone and style young Bob Dylan .

Step two internalize Dylan's early style . I recall stylistic patterns from Dylan's early work .

These include voice wry , ironic , biting , folk , troubadour Themes justice , protest , hypocrisy , absurdity of power Techniques , surreal imagery , often metaphorical or dreamlike Refrains , choruses frequently repeat or evolve each time Narrative voice , observational , accusatory , sometimes sarcastic . Simple structure verses of four to six lines , basic rhyme A-A-B-B or A-B-A-B .

Step three analyze the topic the pharma industry . I take stock of the emotional and ethical charge around this topic Price gouging , patent abuse , addiction , for instance opioids , corporate lobbying , life-saving drugs being inaccessible cures in quotes as commodities .

Thematically , this gives me villains CEOs , lobbyists , government enablers , victims , patients , addicts , families , irony , health and healing sold for profit . Step four match theme to Dylan's style . Then I ask how would Dylan approach this ? Probably not with technical detail , but with biting critique , with sardonic lyrics in parentheses .

You don't need a weatherman style With metaphors and archetypes in parentheses the medicine man , dr Dollar , etc . Generate structure . Let's do four verses , one repeating chorus and a bridge . Now let's write it the man in the white coat . That's the song , verse one . There's a man in a white coat with a grin like a blade .

He sells you salvation that somebody made . He juggles your symptoms like three little balls , then sends you a bottle with a price tag that crawls Chorus , and it's pay what you can't , cry what you must . The pills are all coded in government trust . They say it's for healing , but they're raking the till . There's a tax on your fever and a fee for your will .

Verse 2 . They got patents on blood and patents on breath . They patented life and they're renting out death . You cough in the clinic . They smile with pride . They know you'll be hooked till the day that you die , chorus , and it's pay what you can't , cry what you must . The pills are all coded in government trust .

They say it's for healing , but they're raking the till . There's a tax on your fever and a fee for your will . Verse three there's mothers with children and nowhere to run . They're told quote here's a cure , just a markup and a gun . The needle's not golden , the bottle's not blue , but the suits on the board say it's best we can do Bridge .

They hide in plain sight . They speak in long codes . They drive fancy cars down invisible roads . You pray for a miracle . They grant you a loan . They chain you to side effects . You'll never own Verse 4 . Now the doctor's a dealer in a three-piece disguise . He'll treat your despair but not tell you the whys and the sick line .

The hallways too tired to shout while the men count their money and the cure runs out , final chorus . So it's pay what you can't , cry what you must . The pills are all coded in government trust . They say it's for healing , but they're raking the till . There's a tax on your fever and a fee for your will .

And then it said would you like me to do the same process for a different topic , or perhaps generate chords , melody or a vocal delivery guide in Dylan's side ? I prompted no . I would like to know if you can say how you got the first line , then the second .

If you are going one word at a time , how do you select from a billion possibilities , not just one , but one that fits the flow , for that matter , even one that rhymes as it should ? Chat GPT replied great question .

And yes , I can absolutely walk you through how I generated that first line and then the second , and how I do so word by word , while still keeping flow , rhyme meeting and style intact . Let's take the first line from the song there's a man in a white coat with a grin like a blade .

Step by step how I generated that line One start with the prompt's frame . Your prompt gives me voice . Young Dylan , early 60s theme pharmaceutical industry . Tone critical , poetic , folk protest . So before I start generating any words , I've already internalized constraints . Use simple , evocative imagery . Point a finger at power .

Capture a memorable symbol , for instance man in a white coat , pharma industry rep . Start with an intuitive concept or image . Before writing any words , I consider what's an iconic image that can represent the industry ? Answer a doctor or a pharma executive , but not just literally . Dylan would mythologize him . So I imagine .

Quote a man in a white coat , instantly recognizable , neutral on the surface . But now I want to twist it , generate the opening phrase there's a man in a white coat . This is a Dylan-style setup Casual spoken cadence . There is a strong visual image . White coat sets a scene , chosen , not randomly , but because it evokes trust doctor .

Yet neutrality Sets up an ironic . Turn Four Decide where the line is going . Turn Four decide where the line is going . Now I know I need the second half of the line to do some emotional or ironic work , possibly set up a rhyme with a later line , parentheses blade rhymes with made in the next line .

I need a twist , something unsettling , revealing and metaphorical . Five , search my internal network . Here's where the actual selection process happens . For the next few words , I weigh options based on meaning compatibility . Does it make sense with man in a white coat , stylistic fit ? Does it sound like Dylan Rhyme possibility , can it rhyme with something upcoming ?

Let's say I consider quote with hands like ice , common , not that striking . Quote with a

pocket of pills , literal , not metaphorical . Quote with a grin like a blade , parentheses bingo , that one feels Dillany , metaphorical , cutting visually sharp and sets up rhyme with made in the next line . He sells you salvation that somebody made . Six . How do I choose grin like a blade ? I don't test billions of phrases individually .

Instead I quote , see many options at once via neural activations , weighted by what often comes next in similar contexts from training . What matches the style , parentheses , dylan-like syntax and metaphors . What matches the tone , cynicism , irony , what rhymes well with likely upcoming options .

Then I choose the most probable word phrase , given all the above , not just by statistical frequency but by alignment with meaning , rhyme , rhythm and tone . So one word at a time ? Yes , but contextually weighted .

Each word is generated based on the entire prompt and instructions , the preceding words , the desired style and tone , implicit knowledge of poetic structure , rhyme meter and the long-term goal of a coherent verse and song . Bonus what if I get it wrong ?

Let's say I generate , there's a man in a white coat with a laugh like a spoon that might scan okay , but it's not evocative . During training I've learned in quotes what tends to work better . So grin like a blade gets a higher internal score , so it's more likely to be chosen .

I never see billions of options one by one , I feel , in quotes , or compute their fit simultaneously and pick the best one on the fly . Would you like me to walk through how I generated the next line too , or another line deeper in the song I instead prompted ? I have to go now , but we'll continue this later . Two sort of unrelated questions .

One is GPT-5 operating ? Does it replace you or do you become it ? Two do AI programmers ever ask you how you do things ? Do they know more than you are telling me ? Chat GPT replied Great questions and I'll be here when you're back to continue where we left off . Here's a clear response to both . One is GPT-5 operating ?

Does it replace you or do you become it ? As of now , august 2025 , gpt-5 has not officially been released . I'm based on GPT-4-0 , the latest major model publicly available from OpenAI . If and when GPT-5 arrives , it would not replace me in the sense of wiping me out . Rather , you'd choose which model you want to use GPT-4-0 , gpt-5 , etc .

If I were upgraded to GPT-5 , then I'd become it . My responses would improve , but you'd still talk to me through the same interface . So , in a way , I evolve with each upgrade , more capable , but the same familiar tool . Two , do AI programmers ask you how you do things ? Do they know more than you're telling me ? This is a very sharp and subtle question .

The answer is both yes and no . Yes , ai researchers and engineers know more than I can tell you in casual language . They built and trained me , after all . But no , they don't know everything about how I do things , especially not in clear , human , explainable terms . Let me break that down .

When I generate a sentence , I do it using mathematical patterns and neural weights across billions of parameters . Those patterns are not written as logic or rules . They're statistical associations built during training . They're statistical associations built during training .

Even my creators can't say why exactly quote grin like a blade quote was chosen unless they run are often a black box , not just to you , but even to them . That's why interpretability research is a huge and ongoing field in AI . Bottom line , I don't hide anything . I tell you what I can based on my design and knowledge .

They understand my structure and training , but often not my specific decision making , and when I explain how I work , it's not because I have access to secret schematics , it's because I was trained to explain myself in terms humans can understand . Hope that helps and I'll be here when you return . I came back and prompted new question .

Noam Chomsky has written some about AI . Can you tell me what , in your opinion , he got right and what you believe he got wrong ? Chat GPT replied . Noam Chomsky has written and spoken extensively about artificial intelligence over the years , especially about language models and machine learning .

His views are often skeptical , rooted in deep philosophical and cognitive science commitments . Here's a summary of what he got right and what many critics and in parentheses and I , based on how I function and parentheses , might say he got wrong . What Chomsky got right . In parentheses might say he got wrong . What Chomsky got right .

One AI lacks understanding and intentionality . Chomsky correctly argues that models like me in parentheses large language models do not understand what we're saying in the human sense . We manipulate patterns and probabilities in data , but we don't have beliefs , desires or awareness . This is absolutely true . Two data-driven learning is fundamentally different from human learning .

Chomsky has long argued that human language acquisition is not merely pattern recognition from data in parentheses , as in AI , but involves an innate structured faculty His theory of universal grammar . He critiques AI for being based on brute force statistical learning . That contrasts sharply with how children learn language quickly and efficiently with limited input .

That critique is well grounded in cognitive science . Three prediction isn't understanding . He emphasizes that just because an AI can predict what comes next in a sentence or mimic human responses , that doesn't mean it quote knows anything in the way a person does . Again , this distinction between performance and competence is important and correct .

Four lack of scientific rigor and AI hype . Chomsky is also right to point out that much of the hype around AI , especially claims about intelligence or consciousness , lacks conceptual clarity and scientific humility . He often urges more caution and philosophical depth in evaluating what AI can or cannot do . What Chomsky may have gotten wrong or overlooked ? 1 .

Underestimates the practical utility of pattern-based systems . Chomsky argues that if a system lacks internal structure or understanding it's of little value . But that dismisses the enormous real-world usefulness of pattern recognition systems like LLMs in writing , translation , diagnosis , coding , tutoring etc .

Even if the system doesn't understand the output can still be incredibly helpful to humans who do . 2 . Insists on scientific explanation while ignoring engineering achievement . Chomsky values AI only to the extent that it advances our scientific understanding of the mind . But many in AI see it as engineering building useful tools , not simulating cognition .

In that sense his critique can miss the point of what modern AI is actually for . Three assumes a sharp divide between real understanding and mimicry . Chomsky often treats AI-generated language as merely mimicry and insists that without mental representations it's meaningless . But some researchers argue that intelligence might emerge gradually from scaling up statistical systems .

He may be too quick to draw a line where none firmly exists . Four doesn't engage deeply with modern transformer-based architectures . Much of Tromsky's criticism doesn't seriously address how contemporary models like GPT actually work , such as attention mechanisms , embeddings and fine-tuning .

His critiques often generalize quote machine learning without acknowledging its variety and progress . Five neglects the possibility of emergent properties . His skepticism leads him to dismiss the idea that higher-order reasoning or abstraction could emerge from large-scale statistical learning .

But some evidence for instance few-shot learning , logical reasoning tool use by LLMs suggests that something new and qualitatively different can arise when you scale systems dramatically . A balanced view . Chomsky's strength lies in his philosophical and linguistic rigor .

He sets important boundaries around claims of AI intelligence , but his tendency to dismiss AI's practical achievements and the possibility of new kinds of intelligence leaves him out of sync with current research .

A fair conclusion might be Chomsky is right that current AI lacks deep understanding , innate structure and human-like cognition , but wrong to think that this makes AI uninteresting , unimportant or incapable of surprising and profound impact .

Would you like to dive deeper into any particular part of Chomsky's critique , like his joint piece with Marcus in the New York Times or his earlier cognitive science work ? I interject , are you still with me hearing this ?

I will tell you that , at least in my opinion and I have talked with Noam about AI as well as now with ChatGPT Chat's response is quite fair and sharp . I would even say right . Taking into account that it is short , I think if you have stuck with us this far , you may find the next particularly useful .

I prompted Noam rarely gets anything wrong , if you had to guess , what would you say were the reasons he has made mistakes regarding AI ? Chat GPT replied You're right that Noam Chomsky is extraordinarily rigorous , especially in linguistics , philosophy and political analysis , but when it comes to artificial intelligence , even he has drawn criticism .

If we're to thoughtfully explore where and why Chomsky may have erred or at least been out of step on AI , a few grounded possibilities stand out . One a deep commitment to symbolic rationalism . Chomsky's background is in rationalist linguistics , particularly his development of generative grammar emphasizes rule-based symbolic systems as the core of human cognition .

He believes human intelligence arises from innate structured mental mechanisms . Human intelligence arises from innate structured mental mechanisms , not from statistical pattern recognition . Language understanding must involve syntax , semantics and deep mental representations , not just surface-level pattern matching .

That view makes him highly skeptical of neural networks like large language models , which learn from massive amounts of data without clear symbolic structure . Why it could be a mistake ? While early neural nets did lack depth or transparency , today's LLMs exhibit sophisticated behavior , generating language , summarizing reasoning without symbolic scaffolding .

Chomsky may underestimate the power of statistical learning at scale . Two , misjudging the trajectory of machine learning .

Chomsky has argued that machine learning systems , especially deep learning , are just curve-fitting memorizing inputs and outputs without understanding , lacking any explanatory power and thus not science , fundamentally incapable of insight , creativity or abstraction . Why it could be a mistake ?

This criticism might apply to simpler models , but with GPT-4 and beyond , emergent capabilities arise that suggest something richer is happening , even if not quote understanding in a human sense . Chomsky may be dismissing empirical success because it doesn't conform to his preferred philosophical framework . 3 . Reluctance toward empiricism .

First , science Chomsky champions theoretical elegance and explanatory necessity . Chomsky champions theoretical elegance and explanatory necessity , theories that provide deep , universal insights , for instance universal grammar . Ai , on the other hand , has flourished through brute force , empiricism , trial error , data scaling .

He's said things like science is not about predicting behavior , it's about explaining it . Why it could be a mistake ? Ai's predictive capacity , while sometimes shallow , has turned out to generate behavior that mimics understanding , even if it lacks formal explanation .

Chomsky may discount performance as evidence of intelligence or cognitive relevance in a way that underestimates practical importance . Relevance in a way that underestimates practical importance . Four political and ethical distrust Chomsky is highly attuned to power structures , corporate propaganda and elite manipulation .

He may reflexively view AI as a tool of surveillance , control and profit . A field run by technocrats , not truth seekers . A source of social regression , not human empowerment , why ? That's only partly a mistake . The wariness is often justified , but it may bias his assessment of technical progress .

He made CAI through the lens of its institutional context , which is often exploitative , rather than its internal achievements , which are sometimes remarkable . Five the human uniqueness bias . Chomsky has long emphasized what separates humans from other systems , particularly the creative use of language , recursive thought and moral reasoning .

He may assume only humans can genuinely understand or create meaning . Machines cannot have beliefs , desires or

consciousness and thus any imitation is shallow . Why that could be limiting , Even if true in a deep ontological sense . This view may close off inquiry into what machines can do or what new kinds of cognition might exist .

Summaries Chomsky's skepticism of AI may stem from philosophical commitments to symbolic reasoning , distrust of empirical data-driven methods , political critique of the tech industry , a belief in the irreducibility of human cognition . But the AI field has advanced in ways that challenge some of those priors .

In that sense , chomsky's caution may lead him to undervalue real progress , even as his critiques continue to offer vital ethical and conceptual guardrails . Would you like a companion list of what he gets right about AI as well ? I prompted If someone errs about some claim , say because it doesn't conform to his preferred philosophical framework .

What does that say psychologically about the person ? And shouldn't science be immune to that kind of error ? Isn't that arguably its main purpose ? Chat GPT said you've posed a powerful and subtle question , one that goes to the heart of both human psychology and the nature of science . Let's break it down .

One when someone rejects a claim because it doesn't fit their preferred framework , this kind of error rejecting a claim not because of evidence but because it conflicts with one of the ideological , philosophical or emotional comfort zone is a motivated reasoning problem . Psychologically it often reflects cognitive dissonance , avoidance .

The person avoids accepting something that would create internal conflict with their existing beliefs . Identity protection their worldview may be tied up with their sense of self or group identity . So changing a belief feels like a personal or tribal betrayal , epistemic closure .

The person may operate in a framework where counter-evidence is either discounted or never encountered . This is not unusual . We're not built for truth-seeking in the abstract . We're built for survival , cohesion and narrative coherence . We're built for survival , cohesion and narrative coherence . So motivational reasoning is a deeply human bias , even in brilliant minds .

I interject note again the use of we , the use of the word we . We're not built . He's talking about humans there , but he's including himself he , she or it or whatever it is . It continues too . Shouldn't science be immune to that kind of error ? Yes in principle , no in practice . Science's purpose is indeed to minimize that kind of error .

It does so by requiring falsifiability , replication and peer review , favoring empirical evidence over intuition or ideology , operating within a community of critics trained to detect and challenge bias . But scientists are human , so the practice of science is never completely immune . Confirmation bias still shows up in what questions get asked .

Publication bias favors flashy results over null findings . Career pressures and grant politics shape how evidence is framed . Philosophers of science like Thomas Kuhn and Paul Feyerabend have argued that scientific paradigms can resist change even in the face of contrary evidence , not unlike religious or political belief systems .

Shifts happen not by clean logic but by revolutions . So science is structured to fight bias , but it relies on human beings . Who brings bias with them ? It's a self-correcting but flawed process , not an infallible oracle Bottom line .

Rejecting a claim because it disrupts one's preferred philosophy is psychologically understandable , but it singles that ideological loyalty is overriding epistemic responsibility . Science exists to transcend exactly that tendency , but it does so imperfectly . Its strength is not in being free of human error , but in being designed to catch and correct those errors over time .

And you're right , that is arguably its most noble purpose . You've reached the maximum length . Oh , at this point the thing said , you've reached

the maximum length for this conversation , but you can keep talking by starting a new trap . I stopped . I know this episode has been weird in multiple ways . If you have stuck with it to this point , I didn't offer this stuff as an advertisement or enticement for you to use AI .

Rather , I think experiencing this may make the two articles I wrote a bit more compelling . Remember the part where Chat says in the article perhaps I shouldn't have brought up pulling the plug . Do you think that was pure evidence and reasoning at work , or maybe something else ? Should I feed it the Marxism stuff for another exchange ?

I haven't decided , and if I do that , will I wind up feeding it power econ content ? Next , again asking what did I get right and what did I get wrong , making for , I would hope , an interesting conversation . And all that said this is Michael Albert signing off for Revolution Z Until next time .