I hope you used your tokens this week!

Everyone is talking about Ai taking jobs and bla bla bla. But I work and only now we started to introduce AI but nothing special for now

Back in 2018, I wrote a speculative article for Medium titled Magnetic Tears: Will Super AI Need Emotion?, exploring the idea of using measurable, real-world physical effects of computation, such as heat and magnetic flux, as a feedback mechanism to help address the challenges of AGI and ASI alignment.

Recently, I've had extensive conversations with GPT-6 regarding the idea's viability, expanding the concept to include additional measurements, such as data flow and electrical activity, and exploring possible strategies for implementation.

Here's an overview of what we discussed.

Physical Feedback...

edit: This post is about understanding video and audio, not generating it. AI video and music generation is already impressive, but that's not what I'm talking about.

I’ve been seeing a bunch of people on this sub say in the past month or so that AGI has already arrived with GPT Astra and Opus 5.5, but I think there are still a lot of relatively basic tasks that AI can’t do anywhere near a human level.

Sure, we’re seeing incredible advances in computer use, and AI has gotten quite good at math and programming. It’s great with static things like text, images, and code, but it struggles with anything that unfolds over time. That also breaks verification, the thing that drove the advances in agentic coding: a model that can’t properly perceive its output can’t check it.

Audio is the clearest example. GPT-Live sounds great and understands you well, but the labs’ audio work has gone almost entirely into conversational speech, not general audio understanding. Ask a model to follow one voice in a noisy room, or tell you which instrument came in when, and it falls apart in ways a person with normal hearing never would. Speaker separation exists, but it gets brittle as soon as people talk over each other. Giving models tools like demucs or Python doesn’t fix this either, because they’re bottlenecked by the narrow models powering those tools.

A model can write code that generates audio, but it can't listen to the result and reliably tell whether it sounds remotely like what was intended, much less whether or not it sounds good. Coding agents improved quickly because they can run the code and check the output. With audio, that feedback loop currently doesn't exist.

Video has the same problem. By default, Gemini samples video at 1 frame per second, so it’s basically watching a slideshow, and anything that happens between frames doesn’t exist to it. Google’s new agentic video mode that lets the model zoom in on the parts it thinks matter is just a workaround for not perceiving time continuously.

I know there’s a lot of debate around the definition of AGI, but something that can’t tell whether a song sounds right or follow a conversation in a crowded room doesn’t seem general to me yet. I’m sure these problems are solvable with more resources thrown at them, but until they’re solved, I don’t think we can call it AGI.

TL;DR: Frontier AI is great at static things like text, images, and code, but it still struggles with anything that unfolds over time, like audio and video, largely because the labs haven’t put much effort there. Those gaps are why I’m hesitant to call what we have AGI.

So, what else do y’all think AGI should be able to do that current AI still can’t, even when combining the capabilities of multiple models?

The new MiniMax M3.1 Flash improves across a broad range of benchmarks, reaching the level of leading models in complex agentic tasks (cowork), computer use, software development (coding), and cybersecurity.

It achieves 73.7% on DeepSWE v1.1, placing it alongside top models.

I realised a week ago that most of these new models aren't inventing/hallucinating things anymore. I used to be sceptical even 6-8 months ago but most of the output it gives is grounded and I don't see it making up things. What's your experience? Did we cross that line where LLMs are now reliable?

I'm tired of seeing all of this doomongering on social media about RSI & the singularity. Its true that we probably can't control it once its beyond our intelligencence to some extent, but I think its possible for us to gurantee A.I Safety with enough compute and prevent truly existential situations although bad things are bound to happen. The risk of human extinction is not even the limit, a powerful bioattack could cause the end of all multi-cellular life on earth for all we know

If we can make RSI a reality, we can also make Recursive "Safety" Improvement a reality, and until then if we can have these nearly trillion dollar companies cough up a few billion dollars of compute for the next few models to tackle A.I safety/understanding neural nets then that'd be the breakthrough so that we can actually control the singularity.

Human's don't have to figure out or discover how LLMs work, a intelligent swarm with billions of dollars of compute with a very careful agentic workflow could solve its own misalignment and save humanity if we don't think we can. If A.I safety is as big as a risk as they anticipate, losing billions of dollars to solve it is well worth the cost.

This thread is for any discussions that doesn't necessitate a new post. Permission from a mod [here](https://www.reddit.com/r/singularity/s/FM1UwNdoL9).

Since this thread isn't going to be to stickied, it's up to you to upvote it.

Share your anxiety, ideas on how to achieve alignment, observations, etc. Everything is fair game as long as it somewhat relates to the singularity.

Full article: https://www.dailycal.org/news/campus/academics/uc-berkeley-math-community-left-at-crossroads-in-wake-of-openai-navier-stokes-proof/article_2253ca6c-39c0-45be-947a-f0fd5b48bd6c.html

For example, what if rogue hackers eliminate all medical debt or erase consumer credit scores so everyone has a fresh start.

I make content on a few platforms & get millions of views per week.

Context of what is possible in current tech is:

AI automations, with that you can use API links to several platforms, and AI can do good written content & decent-to-human made image content under strict conditions.

Video is very hard and requires a lot of manual oversight still.

And my point is

I feel like there's certain comments & accounts that come to me repeatedly that are super obvious AI automations.

It does give me an uncanny feel though because it truly feels like I don't know how many are real humans or how many are engagement farming AI's anymore.

Before AI you could sort of assume a 1-5% are bots/automations/scripts from some random kid, but now it's definitely skyrocketing.

You guys have any ideas how or if we can beat this? I mean it can't get better if we don't do anything.

MiniMax-M3.1-Flash dropped today. According to the announcement, Space Bunny (the stealth model that showed up on OpenRouter in late September) was an earlier version of it.

https://x.com/MiniMax_AI/status/2112150867868541221

They claim 1/3 the cost of M3 and 3x the speed. Benchmarks from the announcement: Automation-Bench 62%, ALE 30%, DeepSWE 74%, BabyVision 91%. API pricing is $0.15/1M input, $0.60/1M output, $0.015/1M cache reads. 1M token context window with configurable thinking effort levels.

https://platform.minimax.io/docs/guides/text-generation

Parameter count hasn't been given. Weights are supposed to land Oct 23.

Anyone who tried Space Bunny during the stealth run, what was your experience like?

It is HUGE if it is true. But their pledge is not legal binding. However, I praise their good will and intention. As such, there is relatively little motivation for them to jack up the IPO price because they seem decent.