What may be added is that some people have a hard time reading words by their 'total shape'. I can imagine that for them, the difference between pattern matching symbols and strings of letters is even more profound.
Comparable to what I read someone say about AI the other day: we're living in the small sliver of history where smart-glasses with cameras are technically feasible yet are still (kind of) detectable.
There's no reason why stealth technology should have to advance faster than detection technology. In fact, in many applications with strong incentives to advance both stealth and detection capabilities, the modern world has trended towards being increasingly transparent.
If we culturally/economically wanted it, I'm sure we could all have cheap nonlinear junction detectors in our pockets.
My mom has hearing aids, I only get all the technical info through her, so it's a bit blurry, but she complained about very unpleasant scratchy noises, for instance when my dad was watching videos on his iPad (for himself) elsewhere in the room. Settings were changed but now she has a harder time understanding us. We don't have to scream but if we don't speak 'clearly', she misses a lot, especially when we are with a larger group (say 10 people at a dinner). She says she has some friends that she understands very clearly, in contrast to others (admittedly, me and one of my sisters are not the best examples of how to speak crystal clear).
Perhaps this is just the limit of her hearing capacity. Or do you think she should not settle for this and push for something better?
> We don't have to scream but if we don't speak 'clearly', she misses a lot, especially when we are with a larger group (say 10 people at a dinner). She says she has some friends that she understands very clearly, in contrast to others (admittedly, me and one of my sisters are not the best examples of how to speak crystal clear).
This is where my normal hearing is now. My assumption is the "some I understand clearly" is base very much on what frequency their speech is in.
I'm meeting with a hearing aid doctor this week, actually.
Just as a tip, shouting or even just speaking more loudly is rarely necessary if someone has (properly configured) hearing aids. Just speak clearly. Ensuring proper enunciation so they can read lips is usually more important. Hearing aids will make it "loud enough" but they won't clean up the information.
Does she have primarily high frequency loss? High frequency loss is the most typical in the elderly and also from damage from noise exposure.
Different people have different voices. I usually find it easier to understand men because I hear the lower frequencies better, especially without my hearing aids but also with them. And it's always easier to understand people you know well compared to strangers.
I find small speakers to be awful. The high frequencies are distorted and tinny. Scratchy is a good way to put it. I have a very hard time understanding anything played through a smartphone or tablet speaker. The speaker is too small to reproduce the bass frequencies I can hear the best, and so it's just TSSST TSSSSSZZT sounds through my hearing aids.
I cannot wear my hearing aids at full volume at the dinner table or while working in the kitchen for this reason. Metal, plastic and paper are also common offenders. CLINK. CLINK. CRSZZST. It's almost painful and headache inducing if I'm tired.
Unfortunately those high frequencies are what carry speech sounds like sh, t, ch and so on. Without those it's like the adults talking in the Charlie Brown cartoons. "wah-womp-wah-wah-mhuh??"
Hearing aids can do two things for this: one is to take some of that high frequency information and remap it to lower frequencies. This is part of why they say you won't like wearing your hearing aids when you first get them. They're systematically distorting what you hear -- but in a way you might eventually learn to interpret.
The other is just to make it loud enough that it can be heard. And as you suggest that may be the limit of the hearing capacity if there's very little at the high frequencies the only way to make something high frequency to be perceived, is to hammer the ear with a 90 or 100 dB level of sound.
It's absolutely worth having them adjusted a bit. Also every manufacturer uses a different algorithm for speech frequency remapping. Some people have strong preferences by brand as to the hearing aid sound. (Phonak and Oticon certainly have different "feels" in my experience.)
Almost all hearing aids allow multiple "configuration profiles" where you can switch through them with the app or buttons. I have four: general, lecture, comfort, music. Comfort mode just nukes the high end and cranks up the noise reduction. That's what I use if I'm just reading alone in the living room, or when out at the grocery store, etc.
As to large groups, personally I've simply conceded I can't do large groups. If I try I will feel left out and get depressed over it. If people want to see me at a family reunion, for example, after I do a brief tour to say hi to everyone, they'll have to join me for a small group chat in the den or whatnot.
It's not just you. The generated stuff - in my opinion - doesn't make any sense at all, with regard to structure or meaning. Unless, perhaps, the aim was to generate some kind of badly designed Ikea store.
I used to be in (molecular biology) research. At some point my supervisors were already working towards a paper in their mind, while I was still doubting (the statistical significance of) my findings.
In the end I left my Phd track before actually finishing it. My conclusion is that I like research(ing stuff) as a verb, but I don't like research as an institute.
This reminds me of my interactions lately with ChatGPT where I gave into its repeated offer to draw me an electronics diagram. The result was absolute garbage. During the subsequent conversation it kept offering to include any new insights into the diagram, entirely oblivious to its own incompetence.
I can understand how/that this works, but it still feels like a 'hack' to me. It still feels like the LLM's themselves are plateauing but the applications get better by running the LLM's deeper, longer, wider (and by adding 'non ai' tooling/logic at the edges).
But maybe that's simply the solution, like the solution to original neural nets was (perhaps too simply put) to wait for exponentially better/faster hardware.
This is exactly how human society scaled from the cavemen era to today. We didn't need to make our brains bigger in order to get to the modern industrial age - increasingly sophisticated tool use and organization was all we did.
It only mattered that human brains are just big enough to enable tool use and organization. It ceased to matter once our brains are past a certain threshold. I believed LLMs are past this threshold as well (it has not 100% matched human brain or ever will, but this doesn't matter.)
An individual LLM call might lack domain knowledge, context and might hallucinate. The solution is not to scale the individual LLM and hope the problems are solved, but to direct your query to a team of LLMs each playing a different role: planner, designer, coder, reviewer, customer rep, ... each working with their unique perspective & context.
I get that feeling too - the underlying tech has plateaued, but now they're brute force trading extra time and compute for better results. I don't know if that scale anything but, at best, linearly. Are we going to end up with 10,000 AI monkeys on 10,000 AI typewriters and a team of a dozen monkeys deciding which one's work they like the most?
This is an interesting point. If this ends up working well after being optimized for scale it could become the dominant architecture. If not it could become another dead leaf node in the evolutionary tree of AI.
Isn't that kinda why we have collaboration and get in room with colleagues to discuss ideas? i.e., thinking about different ideas, getting different perspectives, considering trade-offs in various approaches, etc. results in a better solution than just letting one person go off and try to solve it with their thoughts alone.
Not sure if that's a good parallel, but seems plausible.
…like what? I thought the consensus was that humans exhibit truly general intelligence. If LLMs require access to very specific tools to solve certain classes of problems, then it’s not clear that they can evolve into a form of general intelligence.
Specifically, which portions of the brain are “very specialized”? I’m not aware of any aspect of the brain that’s as narrowly applied to tasks as the tools LLMs use. For example, there’s no coding module within the brain - the same brain regions you use when programming could be used to perform many, many other tasks.
They are, but I think the keyword is "generalization". Humans do very well when innovation is required, because innovation needs generalized models that can be used to make very specialized predictions and then meta-models that can predict how specialized models relate to each other and cross reference those predictions. We don't learn arithmetic by getting fed terabytes of text like "1+1=2". We only use text to communicate information, but learn the actual logic and concept behind arithmetic, and then we use that generalized model for arithmetic in our reasoning.
I struggle to imagine how much further a purely text based system can be pushed - a system that basically knows that 1+1=2 not because it has built an internal model of arithmetic, but because it estimates that the sequence of `1+1=` is mostly followed by `2`.
They have somewhat an internal model of arithmetic, with lookup tables and separate treatment of digits. I'm conscious you might have seen this already and not interpret it like that, but in case you haven't section 6 on addition in this Anthropic interpretability paper goes into it.
Keep in mind that is a basic level of understanding of what is going on in quite a small model (Claude 3.5 Haiku). We don't know what is happening inside larger models.
This nicely describes where we're at with LLM's as I see it: they are 'fancy' enough to be able to write code yet at the same time they can't be trusted to do stuff which can be solved with a simple hook.
I feel that currently improvement mostly comes from slapping what to me feels like workarounds on top of something that very well may be a local maximum.
> they are 'fancy' enough to be able to write code yet at the same time they can't be trusted to do stuff which can be solved with a simple hook.
Humans are fancy enough to be able to write code yet at the same time they can’t be trusted to do stuff which can be solved with a simple hook, like a simple formatter or linter. That’s why we still run those on CI. This is a meaningless statement.
The machine just needs to be coded to run stuff (as shown in this very post). My coworkers can’t be coded to follow procedures and still submit PRs failing basic checks, sadly.
Claude Code is an agent, not an LLM. Literally this is software that was released 4mo ago. lol.
1y ago - No provider was training LLMs in an environment modeled for agentic behavior - ie in conjunction with software design of an integrated utility.
'slapped on workaround' is a very lazy way to describe this innovation.
Someone described LLMs in the coding space as stone soup. So much stuff is being created around then to make them work better that at some point it feels like you'll be able to remove the LLM part of the equation
We cant deny the LLM has utility. You cant eat the stone but the LLM can implement design patterns for example.
I think this insistance on near autonomous agents is setting the bar too high, which wouldnt be an issue if these companies werent then insisting that the bar is set just right.
These things understand language perfectly, theyve solved NLP because thats what they model extremely well. But agentic stuff is modelled by reinforcement learning and until thats in the foundation model itself (at the token prediction level) these things have no real understanding of state spaces being a recursive function of action spaces and such stuff. And they cant autonomously code or drive or manage a fund until they do
Humans use tools, so does AI. Does us make any less valuable as humans because we use bicycles and hammers? Why would it be bad for an AI to use tools?
Here in the Netherlands the impact of Nitrogen coming from cattle excretions (mostly through ammonia I believe) is paralizing the entire country (due to the impact on the environment, it's now blocking the building of - very much needed - housing. So there could be a win/win/win/win there.
Interesting. First time I've heard of this outside the UK. In my local area, there's a near total moratorium on new-builds. The reasons are complex, but it's a mixture of agriculture having poisoned all the rivers, housing which is not connected to mains waste water (and people just not maintaining their private waste water systems, which are often just tanks of excrement mixed with chemicals, overflowing into nature) and, even if houses connected to mains, those constantly overflow into storm drains and make the rivers and coasts dangerous to swim in.
All of that while it's completely clear that if we need one thing, it's more housing. Quite a predicament we find ourselves in.
California is like this, for different reasons. Mostly the leaders think nature > humanity so the more they cap the knees of civilization, more for nature, and that's a good and meaningful legacy in their minds. Of course this is a politically dangerous thing to speak up about publicly, so it's more along the lines of "uhhh we need to make sure the house you build is safe, so you need 50,000 pages describing how safe it is, must be evaluated by an army of Phds, and rejections take 5 years"
