The AI Hoax is Destroying America with Ed Zitron

And yet, China is using AI.

...I... don't know what to think about that.

...I really don't.

Because it seems that AI is just a scam.

It may "exist" but what it can do is a scam.

Maybe China thinks we have to use it just to "keep up" with the Western powers, but I dunno.

Anyway, interesting discussion with Adam Conover and Ed Zitron. It's long, but you can listen to it while doing other things. And the comments are interesting too, but then again, there are also trolls in the comments as well (AI supporters here and there).

Frankly, though? I oppose AI. I'm anti-AI. I'm anti-AI in China and anti-AI in America and anti-AI in the whole damn planet.

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45 comments

I think this is a perfect illustration of how technology ends up being applied under different social and economic systems. The reason AI is problematic in the west is due to the fact that it's applied towards finding ways to increase the wealth of the oligarchs. On the other hand, China is using AI for stuff like industry automation, optimizing government workflow, and so on. AI is just a tool for automating work, there's nothing inherently bad about it. The question is how this tool is applied and to what purposes.
- Perhaps...
- I'm not so sure about that. Your analysis correctly identifies that it is being used in the West for nefarious purposes, but I honestly think even on the technical merits it is a flawed technology and a waste. DeepSeek is more efficient, yes, but it is still a flawed technology that I do not believe they should be using
  
  I wouldn't say AI (or pattern-replicating models resembling AI) is flawed. It's a great tool for saving time and automating certain processes.
  The problem is the myriad of grifters who appeared, mostly in the West, trying to sell it as a cure-all snake oil.
  For instance, there's a massive push in the EU to insert AI in education, but with little regard or planning on how to do it effectively. It would be a great tool if we were to feed AI with our curriculi, then ask it to update it to current knowledge (e.g. in science), come up with suggestions for better delivery of certain topics, eliminate time wasted on erroneous, repeating, or useless topics and improve our schedules for other topics (e.g. teaching Romeo and Juliet in Languages, and at the same time go through the history of 1400s Venice in History). These things could be done using commitees over a 5 year period. Or they could be done by AI in a day. Instead though, we get to have handsomely-paid private contractors organize days-long training sessions over how to use AI to draw a picture, because it might make a presentation to students slightly more exciting.
  
  Honestly even your idea of having an LLM "update" a curriculum just makes me annoyed. Why does everyone automatically give authority to an LLM on perhaps one of the most important societal functions, instead of trusting teachers to do their job, with the decades of experience that they have in teaching?
  Is this what we want? AI generated slop for teaching the next generation because it'll get it done in a day?
  
  Current LLMs have access to much more information than even the best teacher can hope to remember. I already know high school chemistry teachers who are using Perplexity (LLM-enhanced search engine) to plan lesson plans because Perplexity is able to search up all the latest on a topic.
  Obviously, a teacher still needs to be in the loop somewhere to ensure a bar of quality on the lesson plan, but there's no reason why AI can't just be an enhanced search engine that makes their planning easier, and can help to prevent students from being taught outdated information. I bet you can remember some fact that you learned in school that ended up being wrong later.
  For example, the tongue zone taste map you may have seen in anatomy class is completely wrong. In fact, each taste bud is able to sample all the tastes with variations between each taste bud.
  
  Good points
  
  I find it works well for many purposes, particularly R1 variant. I've been using it for lots of stuff and it saves me time. I don't think it's flawed technology at all, you just have to understand where and how to use it effectively just like any tool.
  
  I would argue that if your goal is to get an output that is the statistical mean for a given input, then sure an LLM will generate a set of outputs that statistically go together. It just happens that you throw enough data at it and waste a small country's annual energy consumption then of course you'll get something statistically similar. Congrats. You did it.
  
  The energy consumption has already been reduced drastically by reinforcement learning, mixture of agents, quantizing, and other techniques. We're literally just starting to optimize this tech, and there's already been huge progress in that regard. Second, it's already quite good at doing real world tasks, and saves me a ton of time writing boilerplate when coding.
  
  Second, it’s already quite good at doing real world tasks, and saves me a ton of time writing boilerplate when coding.
  So, that's another thing that I wonder about. All these LLMs are doing is automating boilerplate code, and frankly that's not really innovative. "Programming via stack overflow" was a joke that has been in use for nearly two decades now (shudder) and all the LLM is doing is saving you the ALT+TAB between SO and your text editor, no?
  If you're doing a TODO app in Angular or NextJS I'm sure you get tons of boilerplate.
  But what about when it comes to novel, original work? How much does that help? I mean really how much savings do you get, and how useful was it?
  
  The reality is that most of programming isn't all that innovative. Most work in general isn't innovative. Automating the boring stuff is literally the whole point. Meanwhile, it's far more sophisticated than copy pasting from StackOverflow. It can come up with solutions in a context of a specific problem you give it, and the chain of reasoning DeepSeek R1 produces is actually interesting in itself, as it reads like a chain of thought.
  This itself can actually be useful for doing novel and original work because it stimulates your thinking. Sometimes you see something that triggers an idea you wouldn't have had otherwise, and you can pull on this thread. I find it literally saves me hours of work, and it is very useful.
  For example, just the other day I used it to come up with a SQL table schema based on some sample input JSON data. Figuring out the relationships would've taken me a little while, and then typing it all up even longer. It did exactly what I needed, and let me focus on the problem I wanted to solve. I also find it can be useful for analyzing code which is great for getting introduced to a codebase you're not familiar with, or finding a specific part of the code that might be of interest.
  It's also able to find places in code that can be optimized and even write the optimizations itself https://github.com/ggml-org/llama.cpp/pull/11453
  Based on my experience, I can definitively says that his is a genuinely useful too for software development.
  
  For example, just the other day I used it to come up with a SQL table schema based on some sample input JSON data
  How likely is it that this JSON structure and corresponding database schema is somewhere in the (immense) training data. Was it novel? New?
  Like I just have continual questions about an LLM doing 3NF a priori on novel data.
  Like if we just outright say that LLMs are just a better Google or a better IntelliSense that can fetch you existing data that it has seen (which, given that it's basically the entire Internet, across probably the entire existence of the Internet that has been crawled by crawlers and the Internet archive, which is a boggling amount) instead of dressing it up as coming up with NEW AND ENTIRELY NOVEL code like the hype keeps saying, then I'd be less of a detractor
  
  How likely is it that this JSON structure and corresponding database schema is somewhere in the (immense) training data. Was it novel? New?
  You seem to think that the way these things work is by just simply pulling up chunks of existing code from a big db. That's not actually what's happening. It's building a solution for a particular context based on its training data. This is not fundamentally different from how a human developer solves problems either. You learn through experience and once you've solved many problems, you recognize patterns and apply solutions you learned previously in a new context. It is writing new and novel code when it produces solutions.
  
  You seem to think that the way these things work is by just simply pulling up chunks of existing code from a big db
  Sorry, that is not what I think. It's just that surely there was something very similar enough to your JSON to get the prediction to come up with something that looks similar enough. It's very annoying having to discuss an LLMs intricate details of how it works and then get nitpicked on a concept that I don't think I was saying
  
  Sorry, that is not what I think. It’s just that surely there was something very similar enough to your JSON to get the prediction to come up with something that looks similar enough.
  That's the thing it's not that there was something similar to my specific problem. It's parts of many billions of problems that have been fed into the system have aspects that all come together to produce a solution for the particular problem. You don't even have to start with JSON, you can ask it to come up a JSON schema by simply giving it parameters.
  I'm not nitpicking you, I'm pointing out that what you've described isn't actually how these things worked, and the way they do work is far more useful in practice. Let me give you a concrete example. Given a query of "write a json schema to represent a comment thread on a social media site like reddit", it'll do this bit of reasoning:
  undefined
  
  Okay, I need to create a JSON schema for a comment thread similar to Reddit. Let me start by breaking down what a Reddit-style comment thread includes. First, each comment has an ID, author info, content, timestamps, votes, and replies. The replies are nested comments, forming a tree structure. So the schema should allow for nested objects within each comment's replies. I need to define the required fields for a comment. The ID is essential, as is the content and author information. The author should have a username, maybe a profile link, and flags like is_mod or is_admin. Voting information: upvotes and downvotes counts, and a total score. Also, whether the current user has voted, which could be an enum indicating up, down, or none. Timestamps for when the comment was created and last edited. Maybe include an edited field that's either a boolean or a timestamp. Replies need to be an array of comments, which means the schema will reference itself. That requires using "$ref" to point to the comment definition, allowing for recursion. Moderation aspects: flags for removed, locked, spam, etc. Distinguish between user deletion and mod removal. User mentions and links: comments might reference other users or contain URLs, so including arrays for those. Awards or badges on comments, similar to Reddit's awards. Each award could have a name, image URL, and count. Pagination for long threads? Maybe include a "continue_thread" field with a token for loading more replies. Sorting options for replies (best, top, new, etc.), so a sort_order field with enum values. Let me structure the schema with definitions. Start with the comment object, then define author, award, user mention, link, etc. Required fields: id, author, content, created_at, score. Maybe others depending on necessity. For the voting, current_user_vote should be an enum of "up", "down", or null. Ensure that all optional fields are not required. For example, edited can be a boolean or a timestamp, but maybe just a timestamp if it's been edited. Validation: each comment's replies must be an array of comment objects. Use "items" with "$ref" to the comment schema. Testing edge cases: deleted users, removed comments, maximum nesting depth. But the schema might not handle depth limits, just the structure. Include examples to illustrate how a comment with replies would look. I think that covers the main aspects. Now, draft the schema with these considerations.
  This alone is already useful because it goes through the steps of the problem, identifies some corner cases, and suggests what a good way to model this problem might be. It will then produce a json schema based on that:
  undefined
  
  { "$schema": "http://json-schema.org/draft-07/schema", "title": "Social Media Comment Thread", "type": "object", "definitions": { "comment": { "type": "object", "properties": { "id": { "type": "string", "format": "uuid", "description": "Unique identifier for the comment" }, "author": { "$ref": "#/definitions/user" }, "content": { "type": "string", "description": "Markdown-formatted comment content" }, "created_at": { "type": "string", "format": "date-time" }, ... "total_comments": { "type": "integer", "minimum": 0 }, "depth_limit": { "type": "integer", "description": "Maximum allowed comment depth" }, "comment_limit": { "type": "integer", "description": "Maximum comments per page" } }, "required": ["thread"] }
  it highlights key features of the solution
  undefined
  
  Key Features: Nested Comments: Recursive structure for replies Voting System: Track upvotes/downvotes and user's vote User Identity: Detailed author information Moderation: Flags for removed/locked/deleted content Awards: Support for Reddit-style awards Mentions & Links: Track user mentions and embedded links Sorting & Pagination: Handle different sort orders and partial threads Safety Features: Distinguish user deletion vs mod removal This schema supports: Infinite nesting of comments Multiple sorting algorithms User reputation systems Content moderation tools Rich text formatting Pagination for large threads User interaction tracking
  and it gives an example usage (I've truncated it for brevity)
  undefined
  
  { "thread": { "id": "550e8400-e29b-41d4-a716-446655440000", "author": { "username": "tech_fan42", "avatar": "https://example.com/avatar.jpg", "is_mod": false }, "content": "This new framework is amazing! 🚀", "created_at": "2023-08-20T14:30:00Z", "score": 1542, "replies": [ { "id": "3f3e6c60-3d7d-461d-aa7a-5c94f5d5dc5d", "author": { "username": "framework_author", "is_admin": true, "distinguished": "admin" }, "content": "Thanks! Check out our [docs](https://docs.example.com/)", "created_at": "2023-08-20T14:35:00Z", "score": 893, "links": [ { "url": "https://docs.example.com/", "text": "docs" } ] } ], "awards": [ { "name": "Gold", "image_url": "https://example.com/gold.png", "count": 2 } ] }, "total_comments": 45, "depth_limit": 8 }
  Now, given that schema, I can get it to produce a SQL schema from that, write the queries to do operations against the DB, etc. So, I can go directly from getting requirements in plain English, to having a reasonable data model, and a persistence layer in a span of a few minutes. I can also ask it to explain design decisions, make adjustments, etc.
  This is far closer to pair programming with a second developer than hunting for code to glue together on Stack Overflow.
  
  I think the difference between generative AI and AI as a whole needs to be made here. DeepSeek is generative AI, along with all the issues that goes along with that but there are very applicable AI based systems for professional, industrial, and scientific uses. The level of data that machine learning systems can analyze provides a usage far beyond the problems that are certainly inherent to generative AI.
  
  I think the difference between generative AI and AI as a whole needs to be made here.
  Does it? I would not consider LLMs to be AI and it's unfortunate that a marketing gimmick has been turned into a colloquialism to the point where they have stolen the term AI and renamed the original pre-OpenAI concept to "AGI".
  Regardless, the point I am making is that the current hype surrounding LLMs is unwarranted.
  The level of data that machine learning systems can analyze provides a usage far beyond the problems that are certainly inherent to generative AI.
  But here's where I disagree. I think Machine Learning at least has a relatively modest marketing pitch, where you feed it data and based on some training and inputs (yes this is indeed similar to an LLM) and you will get a reasonable estimation of whatever you are seeking as an output, based on historical data. Nobody is running around claiming that my monitoring system that has some ML in it for CPU and temperature and load averages is suddenly going to turn into God like Sam Altman and all these other wackos want to happen with LLMs.
  
  Of course, but I don't think we should discard LLM based AI either. It's a piece of a bigger picture. One of my favorite examples of it being used effectively are neurosymbolic systems where deep neural networks are used to classify noisy input data, and then a symbolic logic system is used to reason about classified data.

45 comments