04-17 10:49 · AI,技术,HackerNews,AI
The beginning of scarcity in AI
For the first time since the 2000s, technology companies are confronting the limits of their supply chain.
GPU rental prices for Nvidia’s Blackwell chips hit $4.08 per hour this week, up 48% from $2.75 just two months ago.1CoreWeave raised prices 20% & extended minimum contracts from one year to three.1
“We’re making some very tough trades at the moment on things we’re not pursuing because we don’t have enough compute.” - Sarah Friar, OpenAI CFO1
This scarcity is already reshaping access. Anthropic has limited its newest model to roughly forty organizations.2Access to the bleeding edge is becoming a gated privilege, for both capacity & security.
If the largest AI companies are having problems, startups face a tougher proposition. Five hallmarks define this era :
The age of abundant AI is over, & it will remain so for years.3
Wall Street Journal, “AI Is Using So Much Energy That Computing Firepower Is Running Out,” April 2026.↩︎↩︎↩︎
tomtunguz.com/mythos↩︎
tomtunguz.com/what-if-we-run-out-of-capacity↩︎
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04-17 10:49 · AI,技术,HackerNews,AI
Cloudflare's AI Platform: an inference layer designed for agents
AI models are changing quickly: the best model to use for agentic coding today might in three months be a completely different model from a different provider. On top of this, real-world use cases often require calling more than one model. Your customer support agent might use a fast, cheap model to classify a user's message; a large, reasoning model to plan its actions; and a lightweight model to execute individual tasks.
This means you need access to all the models, without tying yourself financially and operationally to a single provider. You also need the right systems in place to monitor costs across providers, ensure reliability when one of them has an outage, and manage latency no matter where your users are.
These challenges are present whenever you’re building with AI, but they get even more pressing when you’re building agents. A simple chatbot might make one inference call per user prompt. An agent might chain ten calls together to complete a single task and suddenly, a single slow provider doesn't add 50ms, it adds 500ms. One failed request isn't a retry, but suddenly a cascade of downstream failures.
Since launching AI Gateway and Workers AI, we’ve seen incredible adoption from developers building AI-powered applications on Cloudflare and we’ve been shipping fast to keep up! In just the past few months, we've refreshed the dashboard, added zero-setup default gateways, automatic retries on upstream failures, and more granular logging controls. Today, we’re making Cloudflare into a unified inference layer: one API to access any AI model from any provider, built to be fast and reliable.
One catalog, one unified endpoint
Starting today, you can call third-party models using the same AI.run() binding you already use for Workers AI. If you’re using Workers, switching from a Cloudflare-hosted model to one from OpenAI, Anthropic, or any other provider is a one-line change.
const response = await env.AI.run('anthropic/claude-opus-4-6',{
input: 'What is Cloudflare?',
}, {
gateway: { id: "default" },
});
For those who don’t use Workers, we’ll be releasing REST API support in the coming weeks, so you can access the full model catalog from any environment.
We’re also excited to share that you'll now have access to 70+ models across 12+ providers — all through one API, one line of code to switch between them, and one set of credits to pay for them. And we’re quickly expanding this as we go.
You can browse through our model catalog to find the best model for your use case, from open-source models hosted on Cloudflare Workers AI to proprietary models from the major model providers. We’re excited to be expanding access to models from Alibaba Cloud, AssemblyAI, Bytedance, Google, InWorld, MiniMax, OpenAI, Pixverse, Recraft, Runway, and Vidu — who will provide their models through AI Gateway. Notably, we’re expanding our model offerings to include image, video, and speech models so that you can build multimodal applications
Accessing all your models through one API also means you can manage all your AI spend in one place. Most companies today are calling an average of 3.5 models across multiple providers, which means no one provider is able to give you a holistic view of your AI usage. With AI Gateway, you’ll get one centralized place to monitor and manage AI spend.
By including custom metadata with your requests, you can get a breakdown of your costs on the attributes that you care about most, like spend by free vs. paid users, by individual customers, or by specific workflows in your app.
const response = await env.AI.run('@cf/moonshotai/kimi-k2.5',
{
prompt: 'What is AI Gateway?'
},
{
metadata: { "teamId": "AI", "userId": 12345 }
}
);
Bring your own model
AI Gateway gives you access to models from all the providers through one API. But sometimes you need to run a model you've fine-tuned on your own data or one optimized for your specific use case. For that, we are working on letting users bring their own model to Workers AI.
The overwhelming majority of our traffic comes from dedicated instances for Enterprise customers who are running custom models on our platform, and we want to bring this to more customers. To do this, we leverage Replicate’s Cog technology to help you containerize machine learning models.
Cog is designed to be quite simple: all you need to do is write down dependencies in a cog.yaml file, and your inference code in a Python file. Cog abstracts away all the hard things about packaging ML models, such as CUDA dependencies, Python versions, weight loading, etc.
Example of a cog.yaml
file:
build:
python_version: "3.13"
python_requirements: requirements.txt
predict: "predict.py:Predictor"
Example of a predict.py
file, which has a function to set up the model and a function that runs when you receive an inference request (a prediction):
from cog import BasePredictor, Path, Input
import torch
class Predictor(BasePredictor):
def setup(self):
"""Load the model into memory to make running multiple p
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04-17 10:49 · AI,技术,HackerNews,AI
Substrate AI Is Hiring Harness Engineers
Substrate (www.substrate.cc) is building the world’s first AI native BPO, starting with healthcare RCM. We’re focused on helping healthcare enterprises like physician groups, EMR providers, and BPOs accelerate AR, and get reimbursed quickly and cost effectively. We already touch over 500k healthcare claims each month.
We're looking for an exceptional Harness Engineer to help build the systems around our agents and AI products. You'll be instrumental in advancing AI systems that are already in production, and improving success rates for probabilistic products, and precision for deterministic ones. You’ll build systems that can understand complex healthcare contracts, read EDI companion guides, utilize tools for navigating highly sensitive healthcare infrastructure and banking/financial infrastructure, and make intelligent decisions about claims processing - all in service of helping healthcare providers get paid fairly.
Some general color on what we’re looking for: https://writing.kunle.app/p/come-work-with-me-at-substrate
Meaningful salary and equity available.
Substrate is building browser based AI agents for healthcare RCM. Our goal here is to augment and replace employees, not replace any particular software a provider uses. We’ll go after the $14b outpatient RCM market, and if we’re successful, providers will finally start getting paid fairly.
We’re the right team for this problem with deep healthcare, AI & fullstack engineering expertise, multiple startups under our belt between us, and a deep network in healthcare that will help bootstrap the first customers and real revenue.
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04-17 10:49 · AI,编程工具,Cursor,大模型,AI
Cursor 2.0发布，支持多模型切换，代码生成和审查能力增强
This element contains an interactive demo for sighted users showing multiple Cursor interfaces: Cursor CLI interface, Cursor Desktop interface. The interface is displayed over a subtle, solid brand background.
Cursor CLI
Cursor Agent
~/cursor/cursor-web
Analyze Tab vs Agent Usage Patterns
Help me understand how teams split their focus between the tab view and the agents panel across our workspaces.
→
Composer 2 / for commands · @ for files
Cursor Desktop
http://localhost:3000
Acme Labs
Software creation is changing. We are a group of researchers, engineers, and technologists inventing at the edge of what's useful and possible.
This element contains an interactive demo for sighted users. It's a demonstration of Cursor's IDE showing AI-powered coding assistance features. The interface is displayed over a subtle, solid brand background.
Cursor
Plansfeature-prd.md
Mission Control Interface
A grid view of all open windows as scaled previews, allowing quick selection to bring any window to front.
Trigger
Menu item in MenuBar.tsx (View > Mission Control), hotkey F3, or double-tap desktop.
View Behavior
Overlay existing windows into a grid of live previews with spring-based layout animations and shared element transitions.
3 Tasks
Add multiplayer mode to useAppStore.ts
Create a new MissionControlView.tsx component
Update AppManager.tsx to apply expose modes.
Add a task, ⌘K to generate...
Plan Mission Control
let's build a mission control interface, similar to the expose-style window manager on macOS
Works autonomously, runs in parallel
Agents use their own computers to build, test, and demo features end to end for you to review.
let's build a dashboard to make our research findings interactive
Explored12 files, 4 searches
On it. I'll build the dashboard using your theme config, wire up the research data, and add interactive charts with public access controls.
Worked for 14m 22s
Processedscreen recording
Done! Here's a walkthrough of the dashboard.
Acme Labs
Summary
Built the interactive dashboard with realtime charts, data from Snowflake, and shadcn components. Deployed to staging via Vercel.
Add a follow up...
In every tool, at every step
Cursor runs in your terminal, collaborates in Slack, and reviews PRs in GitHub.
curlhttps://cursor.com/install-fsS | bash
This element contains an interactive demo for sighted users showing multiple Cursor interfaces: the CLI with command-line assistance, Slack integration for team communication. The interface is displayed over a scenic painted landscape wallpaper, giving the demo an artistic backdrop.
Slack
swhitmore5m
i wanna be able to go to cursor.com/changelog#1.0 to see 1.0 changelog
eric4m
checks out
@cursor can you take a stab?
CursorAPP2m
I added direct linking for changelog entries and updated the Node.js version constraints across the project for better maintainability.
This element contains an interactive demo for sighted users. It's a demonstration of Cursor's IDE showing AI-powered coding assistance features. The interface is displayed over a scenic painted landscape wallpaper, giving the demo an artistic backdrop.
Cursor
"use client";import React, { useState } from "react";import Navigation from "./Navigation";import SupportChat from "./SupportChat";export default function Dashboard() { return ( <div className="flex h-[600px] border rounded-lg overflow-hidden"> <div className="w-64 border-r"> </div> <div className="w-80 border-l"> <SupportChat /> </div> </div> );}
"use client";import React, { useState } from "react";import Navigation from "./Navigation";import SupportChat from "./SupportChat";export default function Dashboard() { return ( <div className="flex h-[600px] border rounded-lg overflow-hidden"> <div className="w-64 border-r"> </div> <div className="w-80 border-l"> <SupportChat /> </div> </div> );}
The new way to build software.
Stay on the frontier
Use the best model for every task
Choose between every cutting-edge model from OpenAI, Anthropic, Gemini, xAI, and Cursor.
This element contains an interactive demo for sighted users showing multiple Cursor interfaces: Cursor CLI interface, Cursor Desktop interface. The interface is displayed over a subtle, solid brand background.
Cursor CLI
Cursor Agent
~/cursor/cursor-web
Analyze Tab vs Agent Usage Patterns
Help me understand how teams split their focus between the tab view and the agents panel across our workspaces.
→
Composer 2 / for commands · @ for files
Cursor Desktop
http://localhost:3000
Acme Labs
Software creation is changing. We are a group of researchers, engineers, and technologists inventing at the edge of what's useful and possible.
This element contains an interactive demo for sighted users. It's a demonstration of Cursor's IDE showing AI-powered coding assistance features. The interface is displayed over a subtle, solid brand background.
Cursor
Plansfeature-prd.md
Mission Control Interface
A grid view of all open windows as scaled previews, allowing quick selection to bring any window to front.
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04-17 10:49 · AI,Google,长上下文,大模型,AI
Google发布Gemini 2.0，支持200万tokens上下文，在处理长文档方面优势明显
AI for every
developer
Unlock AI models to build innovative apps and transform development workflows with tools across platforms.
Explore models in Google AI StudioStart building
Get started building with cutting-edge AI models and tools
Own your AI with Gemma open models
Build custom AI solutions and retain complete control. Tailor Gemma models, built from the same research and technology as Gemini, with your own data.
Build with GemmaRun AI models on-device with Google AI Edge
Build and deploy edge ML solutions across mobile, web, and embedded applications, from simple APIs to custom pipelines, with support across all major frameworks.
Explore Google AI EdgeBoost productivity with AI code assistance
Agents
Gemini empowers you to be more productive by acting as your coding agent. It can plan and execute tasks, freeing you to focus on what matters most.
Analysis and insights
Improve code quality and fix issues with code analysis. Get insights, suggestions, and code snippets within your existing development environment.
Code generation
Gemini adds AI-powered code completion with natural language understanding to create entire code blocks from your descriptions, revolutionizing your development workflow.
Code chat
Ask development questions and receive responses that help you reduce errors, solve problems, and become a better developer. Gemini understands the context of your environment to give you the best responses for your questions.
Explore apps built with the Gemini API
Join the community
Tap into the power of our community forum. Get answers, build together, and be part of the conversation.
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04-17 10:49 · AI,Anthropic,模型评测,大模型,AI
最新评测显示Claude 3.5在复杂推理任务上表现超过GPT-4，代码能力显著提升
Securing critical software for the AI era
Introducing a smarter, more capable Opus for coding, agents, vision, and complex professional work.
No ads. No sponsored content. Just genuinely helpful conversations.
The first AI-assisted drive on another planet. Claude helped NASA’s Perseverance rover travel four hundred meters on Mars.
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04-17 00:02 · 基础设施更新,云服务,网络安全,企业服务,产品发布
Cloudflare Email Service
Email is the most accessible interface in the world. It is ubiquitous. There’s no need for a custom chat application, no custom SDK for each channel. Everyone already has an email address, which means everyone can already interact with your application or agent. And your agent can interact with anyone.
If you are building an application, you already rely on email for signups, notifications, and invoices. Increasingly, it is not just your application logic that needs this channel. Your agents do, too. During our private beta, we talked to developers who are building exactly this: customer support agents, invoice processing pipelines, account verification flows, multi-agent workflows. All built on top of email. The pattern is clear: email is becoming a core interface for agents, and developers need infrastructure purpose-built for it.
Cloudflare Email Service is that piece. With Email Routing, you can receive email to your application or agent. With Email Sending, you can reply to emails or send outbounds to notify your users when your agents are done doing work. And with the rest of the developer platform, you can build a full email client and Agents SDK onEmail hook as native functionality.
Today, as part of Agents Week, Cloudflare Email Service is entering public beta, allowing any application and any agent to send emails. We are also completing the toolkit for building email-native agents:
Email Sending binding, available from your Workers and the Agents SDK
A new Email MCP server
Wrangler CLI email commands
Skills for coding agents
An open-source agentic inbox reference app
Email Sending: now in public beta
Email Sending graduates from private beta to public beta today. You can now send transactional emails directly from Workers with a native Workers binding — no API keys, no secrets management.
export default {
async fetch(request, env, ctx) {
await env.EMAIL.send({
to: "[email protected]",
from: "[email protected]",
subject: "Your order has shipped",
text: "Your order #1234 has shipped and is on its way."
});
return new Response("Email sent");
},
};
Or send from any platform, any language, using the REST API and our TypeScript, Python, and Go SDKs:
curl "https://api.cloudflare.com/client/v4/accounts/{account_id}/email-service/send" \
--header "Authorization: Bearer <API_TOKEN>" \
--header "Content-Type: application/json" \
--data '{
"to": "[email protected]",
"from": "[email protected]",
"subject": "Your order has shipped",
"text": "Your order #1234 has shipped and is on its way."
}'
Sending email that actually reaches inboxes usually means wrestling with SPF, DKIM, and DMARC records. When you add your domain to Email Service, we configure all of it automatically. Your emails are authenticated and delivered, not flagged as spam. And because Email Service is a global service built on Cloudflare's network, your emails are delivered with low latency anywhere in the world.
Combined with Email Routing, which has been free and available for years, you now have complete bidirectional email within a single platform. Receive an email, process it in a Worker, and reply, all without leaving Cloudflare.
For the full deep dive on Email Sending, refer to our Birthday Week announcement. The rest of this post describes what Email Service unlocks for agents.
Agents SDK: your agent is email-native
The Agents SDK for building agents on Cloudflare already has a first-class onEmail hook for receiving and processing inbound email. But until now, your agent could only reply synchronously, or send emails to members of your Cloudflare account.
With Email Sending, that constraint is gone. This is the difference between a chatbot and an agent.
Email agents receive a message, orchestrate work across the platform, and respond asynchronously.
A chatbot responds in the moment or not at all. An agent thinks, acts, and communicates on its own timeline. With Email Sending, your agent can receive a message, spend an hour processing data, check three other systems, and then reply with a complete answer. It can schedule follow-ups. It can escalate when it detects an edge case. It can operate independently. In other words: it can actually do work, not just answer questions.
Here's what a support agent looks like with the full pipeline — receive, persist, and reply:
import { Agent, routeAgentEmail } from "agents";
import { createAddressBasedEmailResolver, type AgentEmail } from "agents/email";
import PostalMime from "postal-mime";
export class SupportAgent extends Agent {
async onEmail(email: AgentEmail) {
const raw = await email.getRaw();
const parsed = await PostalMime.parse(raw);
// Persist in agent state
this.setState({
...this.state,
ticket: { from: email.from, subject: parsed.subject, body: parsed.text, messageId: parsed.messageId },
});
// Kick off long running background agent task
// Or place a message on a Queue to be handled by another Worker
// Reply here or in other Worker handler, like a Queue handler
await this.sendEmail({
bi
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04-17 00:02 · AI安全,技术争议,社区舆论,概念质疑,风险认知
AI cybersecurity is not proof of work
The proof of work is the wrong analogy: finding hash collisions, while exponentially harder with N, is guaranteed to find, with enough work, some S so that H(S) satisfies N, so an asymmetry of resources used will see the side with more "work ability" eventually winning. But bugs are different: 1. Different LLMs executions take different branches, but eventually the possible branches based on the code possible states are saturated. 2. If we imagine sampling the model for a bug in a given code M times, with M large, eventually the cap becomes not "M" (because of saturated state of the code AND the LLM sampler meaningful paths), but "I", the model intelligence level. The OpenBSD SACK bug easily shows that: you can run an inferior model for an infinite number of tokens, and it will never realize(*) that the lack of validation of the start window, if put together with the integer overflow, then put together with the fact the branch where the node should never be NULL is entered regardless, will produce the bug. So, cyber security of tomorrow will not be like proof of work in the sense of "more GPU wins"; instead, better models, and faster access to such models, will win. * Don't trust who says that weak models can find the OpenBSD SACK bug. I tried it myself. What happens is that weak models hallucinate (sometimes causally hitting a real problem) that there is a lack of validation of the start of the window (which is in theory harmless because of the start < end validation) and the integer overflow problem without understanding why they, if put together, create an issue. It's just pattern matching of bug classes on code that looks may have a problem, totally lacking the true ability to understand the issue and write an exploit. Test it yourself, GPT 120B OSS is cheap and available. BTW, this is why with this bug, the stronger the model you pick (but not enough to discover the true bug), the less likely it is it will claim there is a bug. Stronger models hallucinate less, so they can't see the problem in any side of the spectrum: the hallucination side of small models, and the real understanding side of Mythos.
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04-17 00:02 · 复古计算,技术演示,开源,社区分享,AI历史
Show HN: MacMind – A transformer neural network in HyperCard on a 1989 Macintosh
A complete transformer neural network implemented entirely in HyperTalk, trained on a Macintosh SE/30.
MacMind is a 1,216-parameter single-layer single-head transformer that learns the bit-reversal permutation -- the opening step of the Fast Fourier Transform -- from random examples. Every line of the neural network is written in HyperTalk, a scripting language from 1987 designed for making interactive card stacks, not matrix math. It has token embeddings, positional encoding, self-attention with scaled dot-product scores, cross-entropy loss, full backpropagation, and stochastic gradient descent. No compiled code. No external libraries. No black boxes.
Option-click any button and read the actual math.
The same fundamental process that trained MacMind -- forward pass, loss computation, backward pass, weight update, repeat -- is what trained every large language model that exists today. The difference is scale, not kind. MacMind has 1,216 parameters. GPT-4 has roughly a trillion. The math is identical.
We are at a moment where AI affects nearly everyone but almost nobody understands what it actually does. MacMind is a demonstration that the process is knowable -- that backpropagation and attention are not magic, they are math, and that math does not care whether it is running on a TPU cluster or a 68000 processor from 1987.
Everything is inspectable. Everything is modifiable. Change the learning rate, swap the training task, resize the model -- all from within HyperCard's script editor. This is the engine with the hood up.
The bit-reversal permutation reorders a sequence by reversing the binary representation of each position index. For an 8-element sequence:
Position: 0 1 2 3 4 5 6 7
Binary: 000 001 010 011 100 101 110 111
Reversed: 000 100 010 110 001 101 011 111
Maps to: 0 4 2 6 1 5 3 7
So input [3, 7, 1, 9, 5, 2, 8, 4]
becomes [3, 5, 1, 8, 7, 2, 9, 4]
.
This permutation is the first step of the Fast Fourier Transform, one of the most important algorithms in computing. The model is never told the rule. It discovers the positional pattern purely through self-attention and gradient descent -- the same process, scaled up enormously, that taught larger models to understand language.
After training, the attention map on Card 4 reveals the butterfly routing pattern of the FFT. The model independently discovered the same mathematical structure that Cooley and Tukey published in 1965.
MacMind is a 5-card HyperCard stack:
Click Train 10 for 10 training steps, or Train to 100% to train until the model gets a perfect score on a sample. For deeper training, run Train 10 repeatedly or click Train to 100% again -- the model picks up where it left off. For a longer run, open the Message Box (Cmd-M) and type trainN 1000
to train for 1,000 steps straight.
Each step generates a random 8-digit sequence, runs the full forward pass, computes cross-entropy loss, backpropagates gradients through every layer, and updates all 1,216 weights. Progress bars, per-position accuracy, and a training log update in real time.
Note: The training log field has a 30,000 character limit (a HyperCard constraint). After roughly 900 steps the log will fill up and HyperCard will display an error. To clear it and continue, open the Message Box (Cmd-M) and type:
put "" into card field "trainingLog"
Then resume training with trainN 500
(or whatever number of steps you want).
After training, click New Random to generate a test input, then Permute to run the trained model. The output row shows the model's predictions and the confidence row shows how sure it is about each position.
To verify the result, apply the bit-reversal permutation by hand. The output should rearrange the input positions in this order:
Output[0] = Input[0] Output[4] = Input[1]
Output[1] = Input[4] Output[5] = Input[5]
Output[2] = Input[2] Output[6] = Input[3]
Output[3] = Input[6] Output[7] = Input[7]
For example, input [3, 7, 1, 9, 5, 2, 8, 4]
should produce [3, 5, 1, 8, 7, 2, 9, 4]
. If the model is well-trained, every position will be correct with confidence above 90%.
The 8x8 grid visualizes which input positions the model attends to when producing each output position. After training, you should see the butterfly pattern: positions 0, 2, 5, 7 attend to themselves (fixed points of the permutation), while positions 1 and 4 attend to each other, and positions 3 and 6 attend to each other (swap pairs).
This is the same routing structure discovered by Cooley and Tukey in 1965 for the Fast Fourier Transform:
The classic FFT butterfly diagram (public domain). The model discovers this structure independently through attention.
Data flow:
Input digits [8]
|
Token embedding lookup + position embedding --> [8 x 16]
|
Q, K, V projections --> [8 x 16] each
|
Attention scores = Q x K^T, scaled by 1/sqrt(16) --> [8 x 8]
| softmax per row
Attention weights --> [8 x 8]
|
Context = weights x V --> [8 x 16]
|
Residual connection: context + embedded input --> [8 x 16]
|
Output logits = residual x
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04-17 00:02 · AI伦理,内容质量,用户信任,媒体评论,技术文化
George Orwell Predicted the Rise of "AI Slop" in Nineteen Eighty-Four (1949)
We’ve lived but a few years so far into the age when artificial intelligence can produce convincing stories, songs, essays, poems, novels, and even films. For many of us, these recently implemented functions have already come to feel necessary in our daily life, but it may surprise us to consider how many people had long assumed that computers could already perform them. That belief surely owes in part to the roles played by effectively sentient machines in popular fictions since at least the early decades of the twentieth century. Revisiting George Orwell’s Nineteen Eighty-Four, we even find a device very much like today’s large language models in use at the Ministry of Truth, the employer of protagonist Winston Smith.
Within the Ministry is “a whole chain of separate departments dealing with proletarian literature, music, drama, and entertainment generally. Here were produced rubbishy newspapers containing almost nothing except sport, crime and astrology, sensational five-cent novelettes, films oozing with sex, and sentimental songs which were composed entirely by mechanical means on a special kind of kaleidoscope known as a versificator.” Much later in the novel, Smith overhears a hit song composed on that very kaleidoscope, “without any human intervention whatever,” sung by a woman of this dystopian England’s lowest class, whose very baseness liberates it from the watchful eye that Big Brother’s vast surveillance system keeps on his ostensibly privileged Party members.
All the “proles” really require, in the view of the state, is the freedom to satisfy their vices and a steady stream of pacifying media. The extrusions of the versificator may now bring to mind the ever-increasing quantities of “AI slop,” often created with vanishingly small amounts of human intervention, whose potential to flood the internet has lately become a matter of public concern. What’s more chilling to consider is that such low-effort, high-volume content wouldn’t have attained such a presence if it weren’t genuinely popular. Much like the junk culture pumped out by the Ministry of Truth, AI slop reflects less the ill intent of (or at least neglect by) the powers that be than the undemanding nature of the public.
Perhaps we can provisionally chalk this one up in the “Orwell was right” column. It’s possible that, in light of real technological developments, even Isaac Asimov could be convinced to give it to him. Here on Open Culture, we recently featured Asimov’s critique of Nineteen Eighty-Four as a poor prophecy of the future, not least from a technological standpoint. That piece was written in 1980 at the very end of an “AI winter,” one of the fallow periods in artificial intelligence research. A boom was soon to come, but the truly astonishing developments wouldn’t happen until the twenty-twenties, about thirty years after Asimov’s death. When describing the versificator, Orwell was presumably extrapolating from the distracting, disposable entertainments of nineteen-forties England. Even if his readers couldn’t believe the idea of that sort of thing being created automatically, more than a few probably agreed with his diagnosis of its quality. Now, collective human intelligence may face its most formidable challenger, but individual human discernment has never been more valuable.
via Boing Boing
Related content:
Based in Seoul, Colin Marshall writes and broadcasts on cities, language, and culture. He’s the author of the newsletter Books on Cities as well as the books 한국 요약 금지 (No Summarizing Korea) and Korean Newtro. Follow him on the social network formerly known as Twitter at @colinmarshall.
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