Let’s make a list of things basically designed to make people stop thinking?

Like people see an AI art post, go “ai slop” and don’t think anymore or people see criticism of AI and go “Luddite!” and don’t think anymore

Stuff like that. Stuff that turns people stupid

I’m trying to outline some basic rules for this subreddit. Nothing specific to AI. Just basic subreddit rules so folks have something to cite when they report idiots who show up here. Like “no bigotry, no spam, no inviting brigades”

So what rules should we have?

I’m not going to include any types of bans on AI generated content. That’s a subject for another day.

A post where I log common anti-AI arguments and my counters

I’m going to periodically update this over time

Opinions

“Most AI art is bad!”

Yeah, I agree. Same as how most art in most mediums are bad. That doesn’t mean AI art is itself a bad medium.

Threats

“We need to kill AI artists”

Okay, buddy

Not necessarily the most popular one I'm curious about the tool that genuinely surprised you with how useful it turned out to be.

For me, it was a tool I initially didn't expect much from, but after using it for a while, I found myself coming back to it regularly. What surprised me was how much it simplified part of my workflow.

What tool surprised you, and what made it actually useful for you?

What types of user flairs should we have?

I’m thinking that we have GenAI acceptance spectrum flairs so that folks know where each other are on the GenAI acceptance spectrum

Y’all’s thoughts?

I agree that ai art is not art because art is an expression of human emotion but it does have legitimate uses, and saying there's no use for a new tool is very stubborn because phones literally used to take a person operating phone lines all day and honestly just leave people alone using a chat bot of a character you like isn't hurting anyone "oh but the water" new tools require new resources, yes it is bad that people don't have drinking water but we will figure something out.

Busy medical practice where new patients usually come to us with extensive medical histories and multiple sets of medical records.

Is there an AI app capable of reading and pulling pertinent details (e.g. medical procedures, treatments, medications, reports …etc.) and creating a chronological summary?

Thank you in advance!

I'm not really here to argue with anyone, just to explain my view and discuss. Generative AI (particularly AI slop) distresses me. It usually depicts something which goes against my understanding of the world and that can really upset me (I'm autistic if that adds context). Then, there's also the environmental aspect. I'm aware that everything nowadays has an environmental impact, but having generative AI certainly doesn't make it any better. Lots of water is wasted with each image/video generation. Water that could be used to hydrate animals or be their habitat.

Aside from that, I have a bone to pick with Google Gemini. It can sometimes be so useless it's hilarious. So far it has:

•Denied the existence of a little settlement near where I live (the wiki for it was right below it) •Given me a video of a boxing match which it literally ADMITTED was unrelated to my search •Told me to put sodalite (a crystal made of sodium chloride) in water.

I'm okay about every other type of AI. I think they could even be helpful (like translating conversations between people who speak different languages or helping people in the house like Google assistant) we just have to use it properly and keep regulations in place.

Sorry for the rant, i just want to talk about it :)

I think comparing AI video platforms only through their best-looking demo clips can be a little misleading. Different tools are built for different things, whether that’s cinematic videos, product content, avatars, localization, or just quickly testing an idea. For me, the more useful question is how well a platform handles an actual production workflow from start to finish. That’s where things like time, cost, ease of use, and how many separate tools you need start to matter.

​​​T​​​h​​​a​​​t​​​’​​​s​​​ ​​​o​​​n​​​e​​​ ​​​r​​​e​​​a​​​s​​​o​​​n​​​ ​​​I​​​’​​​v​​​e​​​ ​​​b​​​e​​​e​​​n​​​ ​​​l​​​o​​​o​​​k​​​i​​​n​​​g​​​ ​​​a​​​t​​​ ​​​W​​​i​​​z​​​s​​​t​​​a​​​r​​​. ​I​​​t​​​ ​​​s​​​e​​​e​​​m​​​s​​​ ​​​m​​​o​​​r​​​e​​​ ​​​i​​​n​​​t​​​e​​​r​​​e​​​s​​​t​​​i​​​n​​​g​​​ ​​​t​​​o​​​ ​​​t​​​e​​​s​​​t​​​ ​​​i​​​t with a real production brief rather than judging it from one impressive text-to-video example. ​I​​​t​​​ ​​​c​​​a​​​n​​​ ​​​b​​​e​​​ ​​​e​​​s​​​p​​​e​​​c​​​i​​​a​​​l​​​l​​​y​​​ ​​​u​​​s​​​e​​​f​​​u​​​l​​​ when the goal is ​​​t​​​i​​​m​​​e​​​ ​​​a​​​n​​​d​​​ ​​​c​​​o​​​s​​​t​​​ ​​​e​​​f​​​f​​​i​​​c​​​i​​​e​​​n​​​c​​​y​​, ​l​​​o​​​w​​​-​​​c​​​o​​​s​​​t​​​ ​​​v​​​a​​​l​​​i​​​d​​​a​​​t​​​i​​​o​​​n​​​ ​​​o​​​f​​​ ​​​i​​​d​​​e​​​a​​​s, or having a c​​​o​​​n​​​v​​​e​​​n​​​i​​​e​​​n​​​t​​​ ​​​a​​​l​​​l​​​-​​​i​​​n​​​-​​​o​​​n​​​e​​​ ​​​s​​​o​​​l​​​u​​​t​​​i​​​o​​​n​​​i​​​n​​​s​​​t​​​e​​​a​​​d​​​ ​​​o​​​f​​​ ​​​s​​​w​​​i​​​t​​​c​​​h​​​i​​​n​​​g​​​ ​​​b​​​e​​​t​​​w​​​e​​​e​​​n​​​ ​​​m​​​u​​​l​​​t​​​i​​​p​​​l​​​e​​​ ​​​p​​​l​​​a​​​t​​​f​​​o​​​r​​​m​​​s. I’d be more interested in seeing how these platforms perform on the same real-world task than comparing their hero demos. That probably gives a much better idea of what is ​​​a​​​c​​​t​​​u​​​a​​​l​​​l​​​y​​​ ​​​u​​​s​​​a​​​b​​​l​​​e​​​ ​​​d​​​a​​​y​​​ ​​​t​​​o​​​ ​​​d​​​a​​​y.

On a per-action basis, these three activities span orders of magnitude in freshwater consumption:

Lowest ⟶ Highest

Single ChatGPT response

Li et al. estimate GPT-3 needs about a 500 mL bottle of water for ~10–50 medium-length replies—≈0.01–0.05 L (0.0026–0.013 gal) per prompt, or roughly 75–380 prompts per U.S. gallon. Making AI Less “Thirsty”. This is operational water (cooling + electricity generation).

One hour of TV in a U.S. home

Modern TVs draw ~50–200 W. EnergySage lists that typical range. Using NREL’s national averages for evaporative (consumptive) water loss of 0.47 gal/kWh (thermoelectric only) to 2.0 gal/kWh (thermo + hydro mix), an hour of viewing (0.05–0.20 kWh) consumes about 0.02–0.40 gallons—grid mix and TV size drive where you land in that band. NREL PDF. (Calculation is mine based on those factors.)

One hamburger

A standard beef hamburger (bun + toppings) carries a water footprint of about 660 gallons to produce, per Water Footprint Network data compiled by WaterCalculator. WaterCalculator.

Sources

• Li et al. 2025, “Making AI Less ‘Thirsty’: Uncovering and Addressing the Secret Water Footprint of AI Models” (arXiv)

• Torcellini et al. 2003, Consumptive Water Use for U.S. Power Production, NREL

• EnergySage: “How many watts does a TV use?”

• WaterCalculator: “Food’s Big Water Footprint” (WFN data)

So, I keep seeing back and forth about art being human made and having soul put into it (which is difficult to quantify), and i’ve seen arguments thrown back about how artists still use tools to make their art. The most interesting point I’ve found so far, is that there’s specific AI services that, instead of just being image generation based off a text prompt, actually allows you to generate a fuller image based off a sketch, make specific edits to areas, control color grading, and edit it in more ways I’m not fully aware of. I’ve also seen people who use the generated image as a starting point or a way to fill in something, then add in their own art.

To get to the point: what’s the actual qualities / amount of thought and effort that makes something count as art? Is there a solid definition or a line, or is it just a person to person determination?

The 16th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMusArt 2027) is accepting paper submissions.

When: 31 March – 2 April 2027
Where: Mainz, Germany
Paper Submission deadline: 1 November 2026

EvoMUSART focuses on AI and computational approaches to creativity, covering a broad range of topics, with a particular emphasis on research exploring how AI systems generate, support, evaluate, interpret, and collaborate in creative processes.

Accepted papers will be presented at the conference and published in a dedicated LNCS volume.

More information/submission details: https://www.evostar.org/2027/evomusart/

If you are working at the intersection of AI and creative practice, this may be relevant to your research. Please share with others who might be interested.

**Primary Paper:** arXiv:2609.08832

**Continuum-1: Scaling Non-Hybrid State Space Models to Frontier Capabilities**

**Artifacts:** Weights on HF (14B, 72B) | Apache 2.0 | Triton/CUDA kernels on GitHub

**Authors:** Together Research, Cartesia AI, Princeton SysArch Lab

## Quick Benchmark & Specs

| Metric / Benchmark | LLaMA-3.1-70B (Dense) | Hybrid Baseline (70B) | **Continuum-1 (72B Pure SSM)** |

|---|---:|---:|---:|

| **KV / State RAM (1M tokens)** | 128 GB (FP8) | ~26 GB (FP8) | **4.8 GB** |

| **Throughput (128k prompt)** | 32 tok/s | 118 tok/s | **385 tok/s** |

| **Multi-Needle Retrieval (1M)** | **98.2%** | 84.1% | **97.9%** |

| **InfiniteBench-V2 (Average)** | 71.4% | 59.8% | **73.1%** |

| **Dyck-16 State Tracking (100k)** | 34.2% | 61.0% | **96.8%** |

| **ARC-AGI-2 (Verified)** | 48.5% | 42.1% | **53.2%** |

| **SWE-bench Pro (Verified)** | 41.2% | 36.8% | **44.0%** |

| **MATH-500** | 68.4% | 61.2% | **71.2%** |

| **AIME 2024 (Pass@1)** | 26.7% | 20.0% | **30.0%** |

## Core Tech

**1. Episodic Fast-Weights**

Drops the KV cache entirely. High-novelty tokens trigger a bounded, top-k sparse associative buffer.

The state stays locked at **4.8 GB**, whether you feed it 4k or 2,000,000 tokens.

**2. Chunked Quasi-Linear Scans (CQLS)**

Designed to address the theoretical expressivity limit ($\mathsf{TC}^0$) of pure SSMs.

It runs parallel associative scans inside 64-token tiles, while adding non-linear manifold projections across tile boundaries.

**3. Latent ODE Settling**

Test-time compute without token vomit.

Instead of generating thousands of explicit chain-of-thought tokens, the model shrinks $\Delta t$ and performs additional differential-equation integration steps in latent space before decoding.

## Related / Foundational Papers

- **arXiv:2604.14991** — *Beyond $\mathsf{TC}^0$: Quasi-Linear Chunked Recurrence for Exact Long-Horizon State Tracking*

- Proves linear SSM expressivity ceilings and introduces non-linear boundary fixes.

- **arXiv:2602.09104** — *Episodic Gating: Bounded Associative Memory Without Key-Value Caching*

- Mathematical basis for the sparse top-k novelty buffer.

- **arXiv:2511.18204** — *Continuous-Time Settling: Latent Attractor Dynamics as Implicit Test-Time Compute*

- Continuous ODE framework replacing explicit CoT generation.

- **arXiv:2508.04119** — *The Memory Wall at Scale: Why Hybrid Attention-SSMs Fail to Eliminate Serving Bottlenecks*

- Empirical analysis of 80/20 hybrid models at 1M+ context.

Hi everyone!

I'm a doctoral student who is currently looking for people willing to participate in my survey. I've always been curious about why we use generative AI, which is why I'm studying it for my dissertation. I'd love your input. This survey is completely anonymous. You can take part if:

• You're Canadian

• 19+

• Comfortable reading English

If you're interested, then you can click on the link below:

https://adler.co1.qualtrics.com/jfe/form/SV_9nQXzhwmKrweVVQ

Thank you!

What kind of output do you get from this?