This is an element of the delusion. The koolaid drinkers will all tell you that we're on the edge of AGI, but if you ask them for simple examples of breakthroughs made by current AI, they can name none. The entire thing absolutely wreaks of mass psychosis.
Prepare for moving goalposts. In 50 years people will still doubt that AI can create anything novel and worthwhile, while they are going to rely mostly on the things that didn't exist before AI, in nutrition, medicine, technology, communication, entertainment. They will see them as, normal, common and simple extensions of the previous developments, pushed mindlessly a bit forward by stochastic parrots.
Claiming that AI will be valuable in 50 years is you moving the goalposts back 50 years. It’s a strawman argument, no one is saying it won’t be valuable in 50 years
They're saying it's obviously valuable and useful now, but some people absolutely refuse to acknowledge it. And in 50 years, there will still be a contingent who insists that AI is useless garbage, even as it's responsible for creating and maintaining every aspect of their daily lives.
The results we've been seeing internally on our physics and circuit design environments are expert-level and beyond-expert-level results from models that Astra completely outclasses across the board on our evaluation suite (Fable 5+/Opus 5/Grok 4.6 were all worthy of being called AGI in my opinion). That's hard tech that will translate to real product innovation.
But you don't need any kind of insider information to see how fast the world is changing. ChatGPT launched less than 4 years ago and the advances in robotics, unsolved maths, and software are all riding the steepest exponential improvement curve any of us have seen. Interesting times we live in.
I mean honestly, that's the problem. I'm actually not seeing the world changing. What specific advances in robotics, unsolved maths, and software have LLMs provided? What is the finished result that affects everyday life? In all categories, it's been hype with little actual real results. The robots are still doing the things they did before 2022. The maths are a handful of fairly insignificant proofs that have no significant applications. Software seems to be buggier than ever, but that aside, we certainly aren't seeing a lot of new innovative applications. We're using the same applications as ever. The same operating systems. They've all changed very little.
I'm not trying to be a pain here, but I keep seeing people saying "look at the massive change all around us" and back here in reality, there is none. Give me concrete, real world examples. Name software. Name products. Name the breakthroughs specifically. This should be easy.
There are several hundred thousand mathematicians producing hundreds of thousands of new results in math each year. Why can't most people name any human contributions to mathematics from the past decade? What is the finished result that affects everyday life? Do you consider all those human mathematicians to be useless?
Most of the biggest breakthroughs in mathematics, breakthroughs that win Fields Medals like sphere packing in dimensions 8 and 24, have no applications in everyday life. Probably the only new mathematics results that people notice affecting their daily lives are the ones that enabled AI.
Nevermind mathematicians. What about the millions of programmers? Are they all hype too because people pre-2023 were griping on hn that software is buggier than ever and people are still using the same operating systems as always? Why couldn't the 30 million human programmers make something better in the past decade?
You set your bar so high that all the world's human experts in math and programming combined would fail to meet it.
The top LLMs in 2024 were Sonnet 3.5 and GPT 4o. You couldn't have expected those much weaker models to be making breakthroughs in math. The models that are making breakthroughs haven't been around very long.
1. I didn't say LLMs have made any breakthroughs in math, not because they haven't, but because it's irrelevant to my point. The parent comment is using the same argument academic research opponents have long used against research. The vast majority of research fails to meet their bar. How would your daily life be different if we had no humanities papers published since 2023? Or math?
2. You can google this in 10 seconds and see a dozen results in math. This is not a good-faith demand.
Maybe AI isn't that incredible, the more you use it, the more you realize it's a tool, like a VCR, maybe that's why?
What was sold as AI was basically a "computer person". Maybe that isn't the reality so when people are like, "here's the self coding machine" everyone is a bit disappointed because it's not C3PO?
The number of bug fixes to important programs has skyrocketed. Look at what Google are saying about how many Chrome security bugs they've been fixing lately. Other big software firms have been doing the same thing - AI has been finding and fixing a ton of bugs. I know of one big program where thousands and thousands of security bugs are being found and fixed.
It may not feel like this to you because a lot of the dollars right now are going into security bugs which you can't perceive. But it's definitely happening.
At the company I own I've got AI employees autonomously triaging backlogs and fixing long tail bugs. The software is definitely getting better, although by definition long tail bugs aren't ones you are likely to encounter. The subjective "feel" of how robust the software is won't change quickly.
New tech is always applied in apparently boring ways because we are imagination constrained and people harvest the low hanging fruits first. Remember claims there was worldwide demand for only about four computers? When Gates said he wanted a computer on every desk and in every home people laughed at him. What would people do with all those computers, they asked. But he was right about where the world was heading.
We're not going to suddenly have new robots or operating systems. Those things take time. Just because there's massive change afoot, doesn't mean it's widely adopted or applied at lower-level products. ChatGPT is a product, and software, and a breakthrough. You can have a freeform conversation with your computer about anything, in human language, and ask it to do or make stuff and it will at least try, sometimes with surprising results. That wasn't possible until recently. The robots and products are coming, rest assured.
toy example but spending 1.50 in openrouter to create a 23KB APK that can trigger my cat feeder without all the bloat is something that I would not have been arsed to code ever.
And you can check my comments, I'm no AI shill. If in 3 years we don't see positive change I'll go myself and put a wet finger in altmans ears.
Constructed human life is just more complicated than the AI capitalists would want you to believe. For example, even if an AI model can design a circuit-board, does that mean it's inherently useful? You need to source the wafers, cut them, package them, advertise them, etc. Given LLMs by their nature are confined to language and language-adjacent tasks, that is a very small percentage of the overall reasoning needed to make changes in the real world. In reality, LLMs are the intended way to extract maximal surplus-value from white-collar workers. We may see an increase in innovation as a result of that, but not because AI necessarily did it, in the same way that the power loom didn't create computers because its textiles clothed the computer scientists.
>AI is grown more than designed
>Teaching machines to love
Do these guys ever look in the mirror and recognize how utterly ridiculous and contrived this appears to the general public? They are clearly trying to convince us all that LLMs are just like humans. They grow like people do. They can love like people do. The language in these essays is utterly laden with the intention to engineer perception.
>There are two things SOTA LLMs fundamentally cannot do.
I would say there’s a third thing. They seem to be very bad at being creative. Maybe they will eventually fix that, but if you ask it to come up with a list of business names or business ideas, for example, what you’ll get is the most generic, boring answer you could think of. They seem to be terrible at extrapolating outside of their training data. To me, this is the most significant difference.
It’s because you do less memorable things, so there are less milestones to judge the time by. When you’re young, you don’t have to try so hard as many things are new and exciting. As you get older, you get a career, fall into autopilot mode, and maybe just take a vacation once in a while. Your options are to either figure out how to free yourself of this system so you can spend your time exactly how you want, or to rediscover the wonder and reward in small, seemingly everyday things.
I don’t understand who so many people seem surprised by this. 2023 was when we first started experimenting with linking two LLMs together to have them chat back and forth. Nothing really interesting there - they don’t care if they’re talking to an actual human or another LLM.
They’ve been able to send HTTP requests since the beginning as well, as long as any guardrails preventing this are disabled. Also not interesting.
Tell an LLM to do something and it tries its best to come up with the solution. They are not designed to go “I dunno”.
Why does it seem interesting that when you spawn 500 of them, they do the same things they’ve always done, just at a larger scale since there’s…more? Why are we treating this like a new discovered behavior? It’s how they’ve behaved from the beginning. It only required an organization reckless enough to try it at scale and without safeguards, and that’s something OAI excels in.
>I can't play an instrument. I have tried more than once, and each time I got as far as being able to make roughly the right noises without ever understanding why they were the right noises. The problem was never the practice. It was that every explanation of music theory seemed to miss out the fundamental reasons for how and why things are the way they are.
You can’t play an instrument because you failed to practice. Playing an instrument doesn’t require knowing theory. It certainly helps with being a musician, however, which is a completely different thing.
That aside, the way the author goes about explaining “music theory” here is exactly how one prevents themselves from ever producing an actual piece of music. Yes, it’s good to understand the mechanics of how sounds and chords work under the hood, but one thing I’ve learned in my nearly 30 years of making and recording music is that thinking is the enemy of creating art. This framework introduces far too much cognitive overhead and gets in the way of what we’re really trying to do, which is communicating emotion. What is written in this article is has been known for a very, very long time, but we still use the more common version of music theory because it abstracts much of this away so that it doesn’t get in the way.
You can also do a volumetric taper if you don’t have an accurate milligram scale (and none of the ones on Amazon are accurate). Dissolve a larger, known amount of medicine in a known amount of water or propylene glycol, depending upon solubility of the medicine. Then dose the liquid by volume. For example, 1 gram of medicine in 1 liter of water = 1 mg per ml. You can easily dose even microgram amounts this way.
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