I think one of the results of this is that the concept of AI itself increasingly becomes more muddled until it becomes indistinguishable from a word like “technology” and therefore useless for describing a particular phenomenon. You can already see this with the usage of “AGI” and “super intelligence” which from the definitions I’ve been reading, are not the same thing at all. AGI is/was supposed to be about achieving results of the average human being, not about a sci-fi AI god, and yet it seems like everyone is using them interchangeably. It’s very sloppy thinking.

Instead I think the term AI is going to slowly become less marketing trendy, and will fade out over time, as all trendy marketing terms do. What will be left are actually useful enhancements to specific use cases - most of which will probably be referred to by a word other than AI.

Adobe is an obnoxious company that does a lot of bad things, but it's weird to me seeing them cast in a negative light like this for their approach to model training copyright. As far as I know they were the first to have a genAI product that was trained exclusively on data that they had rights to under existing copyright law, rather than relying on a free use argument or just hoping the law would change around them. Out of all the companies who've built AI tooling they're the last one I'd expect to see dragged out as an example of copyright misbehavior.

I can see how that checks the box of "policing", but you also made this claim:

> in order to grab content that is not theirs to train models they resell back to the people they stole content from

Did you not mean that to imply that Adobe is using images from customers to train generative AI? Because that's sure what it sounds like.

What happened to ML? With the relatively recent craze precipitated by chatgpt, the term AI (perhaps in no small part due to "OpenAI") has completely taken over. ML is a more apt description of the current wave.

Look at the google engineer who thought they had an AI locked up in the basement... https://www.theverge.com/2022/6/13/23165535/google-suspends-...

MS paper on sparks of AGI: https://www.microsoft.com/en-us/research/publication/sparks-...

The rumors that OpenAI deal with MS would give them everything till they got to AGI... A perpetual license to all new development.

All the "Safety people" have left the OpenAi building. Even musk isnt talking about safety any more.

I think the bet was that if you fed an LLM enough, got it big enough it would hit a tipping point, and become AGI, or sentient or sapient. That lines up nicely with the MS terms, and MS's on paper.

I think they figured out that the math doesn't work that way (and never was going to). A prediction of the next token being better isnt intelligence any more than weather prediction will become weather.

Gradually, as these LLM next-token predictors are set up recursively, constructively, dynamically, and with the right inputs and feedback loops, the limitations of the fundamental building blocks become less important. Might take a long time, though.

You say emergence is a real thing, and it is, but we have not one single example of it taking the form of sentience in any human-created thing of complexity.

The version of emergence that AI hypists cling to isn't real, though, in the same way that adding more NAND gates won't magically make the logic function you're thinking about. How you add the NAND gates matters, to such a degree that people who know what they're doing don't even think about the NAND gates.

An algorithm that can reason about the meaning of text probably isn't in the state space of GPT. Thanks to the https://en.wikipedia.org/wiki/Universal_approximation_theore..., we can get something that looks pretty close when interpolating, but that doesn't mean it can extrapolate sensibly. (See https://xkcd.com/2048/, bottom right.) As they say, neural networks "want" to work, but that doesn't mean they can.

That's the hard part of machine learning. Your average algorithm will fail obviously, if you've implemented it wrong. A neural network will just not perform as well as you expect it to (a problem that usually goes away if you stir it enough https://xkcd.com/1838/), without a nice failure that points you at the problem. For example, Evan Miller reckons that there's an off-by-one error in everyone's transformers. https://www.evanmiller.org/attention-is-off-by-one.html

If you add enough redundant dimensions, the global optimum of a real-world gradient function seems to become the local optimum (most of the time), so it's often useful to train a larger model than you theoretically need, then produce a smaller model from that.

> But isn't that what the training algorithm does?

It's true that training and other methods can iteratively trend towards a particular function/result. But in this case the training is on next token prediction which is not the same as training on non-verbal abstract problem solving (for example).

There are many things humans do that are very different from next token prediction, and those things we do all combine together to produce human level intelligence.

(Why spend a mouse? Just sit a strawberry in a library, and if the hypothesis holds that the quantity of data is the only thing that matters holds, you'll have a super intelligent strawberry)

That's the question though, do they? One way of looking at gen AI is as a highly efficient compression and search. WinRAR doesn't learn, neither does Google - regardless of the volume of input data. Just because the process of feeding more data into gen AI is named "learning" doesn't mean that it's the same process that our brains undergo.

Don't get me wrong, organic brains learn from far fewer examples than AI, there's a lot organic brains can do that AI don't (yet), but I don't really find the intellectual capacity of mice to be particularly interesting.

On the other hand, the question of if mice have qualia, that is something I find interesting.

But you should find their self-direction capacity incredible and their ability to instinctively behave in ways that help them survive and propagate themselves. There isn't a machine or algorithm on earth that can do the same, much less with the same minuscule energy resources that a mouse's brain and nervous system use to achieve all of that.

This isn't to even mention the vast cellular complexity that lets the mouse physically act on all these instructions from its brain and nervous system and continue to do so while self-recharging for up to 3 years and fighting off tiny, lethal external invaders 24/7, among other things it does to stay alive.

All of that in just a mouse.

No, why would I?

Depending on what you mean by self-direction, that's either an evolved trait (with evolution rather than the mouse itself as the intelligence) for the bigger picture what-even-is-good, or it's fairly easy to replicate even for a much simpler AI.

The hard part has been getting them to be able to distinguish between different images, not this kind of thing.

> and their ability to instinctively behave in ways that help them survive and propagate themselves. There isn't a machine or algorithm on earth that can do the same,

https://en.wikipedia.org/wiki/Evolutionary_algorithm

> much less with the same minuscule energy resources that a mouse's brain and nervous system use to achieve all of that.

Is nice, but again, this is mixing up the intelligence of the animal with the intelligence of the evolutionary process which created that instance.

I as a human have no knowledge of the evolutionary process which lets me enjoy the flavour of coriander, and my understanding of the Krebs cycle is "something about vitamin C?" rather than anything functional, and while my body knows these things it is unconventionable to claim that my body knowing it means that I know it.

The evolutionary processes behind the mouse being capable of all that are a part of the long distant past, up to the present, and their results are manifest in the physiology and cognitive abilities (such as they are) of the mouse), but this means that these abilities, conscious, instinctive and evolutionary only exist in the physical body of that mouse and nowhere else. No man-made algorithm or machine is capable of anything remotely comparable and its capacity for navigating the world is nowhere near as good. Once again, this especially applies when you consider that the mouse does all it does using absurdly tiny energy resources, far below what any LLM would need for anything similar.

Mediocre, or even excellent, Python and rap lyrics in Latin are easy stuff, just like chess and arithmetic. Humans just are really bad at them.

Isn't this distinction more about "language" than "intelligence". There are some fantastically intelligent animals, but none of them can do the tasks you mention because they're not built to process human languages.

But this is besides the point; I have no doubt that if one were to make a mouse immortal and give it 50,000 years experience of reading the internet via a tokeniser that turned it into sensory nerve stimulation and it getting rewards depending on how well it can guess the response, it would probably get this good sooner simply because organic minds seem to be better at learning than AI.

But mice aren't immortal and nobody's actually given one that kind of experience, whereas we can do that for machines.

Machines can do this because they can (in some senses but not all) compensate for the sample-inefficient by being so much faster than organic synapses.

In terms of spoken language they are limited, but they surprise me all the time with terms they have picked up over the years. They can definitely associate a lot of words correctly (if it interests them) that we didn't train them with at all, just by mere observation.

A LLM associates bytes with other bytes very well. But it has no notion of emotion, real world actions and reactions and so on in relation to those words.

A thing that dogs are often way better than even humans is reading body language and communicating through body language. They are hyper aware of the smallest changes in posture, movement and so on. And they are extremely good at communicating intent or manipulate (in a neutral sense) others with their body language.

This is a huge, complex topic that I don't think we really fully understand, in part because every dog also has individual character traits that influence their way of communicating very much.

Here's an example of how complex their communication is. Just from yesterday:

One of our dogs is for some reason afraid of wind. I've observed how she gets spooked by sudden movements (for example curtains at an open window).

Yesterday it was windy and we went outside (off leash in our yard), she was wary and showed subtle fear and hesitated to move around much. The other dog saw that and then calmly got closer to her, posturing towards the same direction she seemed to go. He made small very steps forward, waited a bit, let her catch up and then she let go of the fear and went sniffing around.

This all happened in a very short amount of time, a few seconds, there is a lot more to the communication that would be difficult and wordy to explain. But since I got more aware of these tiny movements (from head to tail!) I started noticing more and more extremely subtle clues of communication, that can't even be processed in isolation but typically require the full context of all movements, the pacing and so on.

Now think about what the above example all entails. What these dogs have to process, know and feel. The specificity of it, the motivations behind it. How quickly they do that and how subtle their ways of communications are.

Body language is a large part of _human_ language as well. More often than not it gives a lot of context to what we speak or write. How often are statements misunderstood because it is only consumed via text. The tone, rhythm and general body language can make all the difference.

The alternative I'm considering is that It might just be that it's just a dataset problem, feeding these llms on words makes the lack a huge facet of embodied axistance that is needed to get context.

I am a nobody though, so who knows....

They do seem to do generalisation, to at least some degree.

If it was literal memorisation, we do literally have internet search already.

("You think before you speak". That thinking of course does not stop at "sounding" proper - it has to be proper in content...)

If an LLM can predict the next word without doing a critical evaluation, then it raises the question of what the intelligent people are doing. They might not be doing a critical evaluation at all.

Well certainly: in the mind ideas can be connected tentatively by affinity, and they become hypotheses of plausible ideas, but then in the "intelligent" process they are evaluated to see if they are sound (truthful, useful, productive etc.) or not.

Intelligent people perform critical evaluation, others just embrace immature ideas passing by. Some "think about it", some don't (they may be deficient in will or resources - lack of time, of instruments, of discipline etc.).

And who says LLM are not able to do that (eventually)?

They are not _good_ at it right now, and they are totally bad at making generalizations. But who says it's not just an artifact of the limited context?

It's cyclical. The term "ML" was popular in the 1990s/early 2000s because "AI" had a bad odor of quackdom due to the failures of things like expert systems in the 1980s. The point was to reduce the hype and say "we're not interested in creating AGI; we just want computers to run classification tasks". We'll probably come up with a new term in the future to describe LLMs in the niches where they are actually useful after the current hype passes.

Eventually ML got pretty good and a lot of the industry forgot the AI winter, so we're calling it AI again because it sounds cooler.

Well for one, five years ago, anyone doing machine learning was doing their own training -- you know, overseeing the actual learning part. Now, although there is learning involved in LLMs, you as the consumer aren't involved in that part. You're given API access (or at best a set of pre-trained weights) as a black box, and you do what you can with it.

Maybe if you go by article titles it has. If you look at job titles, there are many more ML engineers than AI engineers.

I don't know what AI is, and nobody else does, that's why they're selling you it.

A generative tool can’t Hallucinate! It isn’t misperceiving its base reality and data.

Humans Hallucinate!

ARGH. At least it’s becoming easier to point this out, compared to when ChatGPT came out.

Humans sometimes hallucinate, but still have direct sensory input against which to evaluate inferential conclusions against empirical observation in real time. So we can refine ideas, whatever their origin, against external criteria of correctness -- this is something LLMs totally lack.

We want reliable tools - they have to give reliable results; humans are limited and can be unreliable.

That is why we need the tools - humans are limited, we want tools that overcome human limitation.

I really do not see where you intended to go with your post.

I took his point to mean that hallucinate is an inaccurate verb to describe the phenomenon of AI creating fake data, because the word hallucination implies something that is separate from the “real world.”

This term is thus not an accurate label, because that’s not how LLMs work. There is no distinction between “real” and “imagined” data to an LLM - it’s all just data. And so this metaphor is one that is misleading and inaccurate.

The problem with AI, as perfectly clear outlined in this article, is the same as the problem with the blockchain or with other esotheric grifts: It drains needed resources from often already crumbling systems¹.

The people falling for the hype beyond the actual usefulness of the hyped object are wishing for magical solutions that they imagine will solve all their problems. Problems that can't be fixed by wishful thinking, but by not fooling yourself and making technological choices that adequately address the problem.

I am not saying that LLMs are never going to be a good choice to adequately address the problem. What I am saying is that people blinded by blockchain/AI/quantum/snakesoil hype are the wrong people to make that choice, as for them every problem needs to be tackled using the current hype.

Meanwhile a true expert will weigh all available technological choices and carefully test them against the problem. So many things can be optimized and improved using hard, honest work, careful choices and a group of people trying hard not to fool themselves, this is how humanity managed to reach the moon. The people who stand in the way of our achievements are those who lost touch with reality, while actively making fools of themselves.

Again: It is not about being "against" LLMs, it is about leaders admitting they don't know, when they do in fact not know. And a sure way to realize you don't know is to try yourself and fail.

¹ I had to think about my childhood friend, whose esotheric mother died of a preventable disease, because she fooled herself into believing into magical cures and gurus until the fatal end.

This topic needs careful consideration and I should use more brain cycles on it. Please insert another coin.

You could make the user interface display the output more slowly "when it is unsure", but that'd show you the wrong thing: a tie between "brilliant" and "excellent" is just as uncertain as a tie between "yes" and "no".

It's only now that everybody's used to natural language interfaces that I think we're becoming far less forgiving of things like this nonsense:

---

- "How do I do (x)."

- "You do (A)."

- "No, that's wrong because reasons."

- "Oh I'm sorry you're 100% right. Thank you for the correction. I'll keep that in mind in the future. You do (B)."

- "No that's also wrong because reasons."

- "Oh I'm sorry you're 100% right. Thank you for the correction. I'll keep that in mind in the future. You do (A)."

- #$%^#$!!#$!

---

Sort of, kind of. Those natural language interfaces that Actually Work aren't even 2 years old yet, and in that time there weren't many non-shit integrations released. So at best, some small subset of the population is used to the failure modes of ChatGPT app - but at least the topic is there, so future users will have better aligned expectations.

Not working well to this day. Siri is mostly a joke and a meme, much like Alexa, Cortana and Google whatever-they-call-it-now.

Other stuff: yeah, you can get away with a lot using some formal grammar, a random number generator, and a lot of effort railroading the user so they won't even think to say anything that'll break the illusion. I've had people not realize for weeks that my IRC bot is a bot, even if all it did was apply a bunch of regular exceptions to input (the trick was that the bot would occasionally speak unprompted and react to other people's conversations, and replied to common emoticons and words indicating emotion).

No, only in the last two years we can say that there exist Speech-to-Text that works reliably, Text-to-Speech that sounds natural, and a ML model that can parse arbitrary natural language text and reliably infer what you want, even if you never directly stated what you want, and handling for it was never explicitly coded.

I think you are presenting a gradual incremental change as some sort of binary transition, and it isn't. This is at best disingenuous and misleading and at worst it's a flat out lie.

Text-only natural language interfaces were working in the 1960s and working well by the 1980s.

Live real-time speech recognition with training was working by the turn of the century, and following a hand injury, I was dictating my work into freeware for a while by 2000 or so. It was bundled with later versions of IBM OS/2 Warp.

Real-time speaker-independent speech recognition started working usefully well some 15 years ago, and even as a sceptic who dislikes such things, I was using it and demonstrating it a decade ago. It's been a standard feature of mainstream commercial desktop OSes as well as smartphones for about 8-9 years. Windows 10 (2015) included Cortana; macOS Sierra (2016) included Siri.

In fact, after I posted my previous comment, this morning Facebook reminded me that 8Y ago today I was installing Win10 on my Core 2 Duo Thinkpad.

I don't allow any of these devices in my home but they're a multi-billion dollar in domestic voice-controlled hardware.

This is mainstream used by a double-digit percentage of humanity.

You seem to have been deceived by the LLM bot fakery of "intelligence" that it's achieved some quantum leap in smarts recently. This is illusory.

Agreed, but I think we'll soon start to discover that interacting with systems as if they were text adventure games of the 80's is also going to get pretty weird and frustrating, not to mention probably inefficient.

If you tell ChatGPT that your name is Dan, and give it some rules to follow, after enough exchanges, it will simply forget these things. And sometimes it won't accurately follow the rules, even when they're clearly and simply specified, even the first prompt after you give them. (At least, this has been my experience.)

I don't think anyone really wants to play "text adventure game with advanced Alzheimer's".

I was reading one famous book about investing some times ago (I don't remember which one exactly, I think it was a random walk into wall st, but don't quote me on that) and one chapter at the beginning of the book talk about the .com bubble and how companies, even ones who had nothing to do with the web, started to put .com or www in their name and were seeing an immediate bump in their stock price (until it all burst, as we know now).

And every hype cycle / bubble is like that. We saw something similar with cryptocurrencies. For a while, every tech demos at dev convention had to have some relation to the "blockchain". We saw every variation of names ending in -coin. And a lot of company, that where not even in tech, had dumb project related to the blockchain, which for anyone slightly knowledgeable with the tech it was clear that it was complete BS, and they almost all the time were quietly killed off after a few month.

To a much lesser extent, we saw the same with "BigData" (who even use this word anymore?) and AR/VR/XR.

And now its AI, until the next recession and/or the next shiny thing that makes for amazing demos pops-out.

It is not to say that it is all fake. There is always some genuine business that have actual use case with the tech and will probably survive the burst (or get brought up and live on has MS/Google/AWS Thingamajig). But you have to be pretty naïve if you think 99% of the current AI company will live in the next 5 years, and believe their marketing material. But it doesn't matter if you manage to sell before the bubble pop, and so the cycle continue.

When we build something we do not intend to build something that just achieves results «of the average human being», and a slow car, a weak crane, a vague clock are built provisionally in the process of achieving the superior aid intended... So AGI expects human level results provisionally, while the goal remains to go beyond them. The clash you see is only apparent.

> I think the term AI is going to ... will fade out over time

Are you aware that we have been using that term for at least 60 years?

And that the Brownian minds of the masses very typically try to interfere while we proceed focusedly and regarding it as noise? Today they decide that the name is Anna, tomorrow Susie: childplay should remain undeterminant.

> Are you aware that we have been using that term for at least 60 years?

Yes, and for the first ±55 of those years, it was largely limited to science fiction stories and niche areas of computer science. In the last ±5 years, it's being added to everything. I can order groceries with AI, optimize my emails with AI, on and on. It's become exceptionally more widespread of a term recently.

https://trends.google.com/trends/explore?date=today%205-y&q=...

> And that the Brownian minds of the masses very typically try to interfere while we proceed focusedly and regarding it as noise? Today they decide that the name is Anna, tomorrow Susie: childplay should remain undeterminant.

You're going to have to rephrase this sentence, because it's unclear what point you're trying to make other than "the masses are stupid." I'm not sure "the masses" are even relevant here, as I'm talking about individuals leading/working at AI companies.

> In the last ±5 years, it's being added to everything // I'm not sure "the masses" are even relevant here, as I'm talking about individuals leading/working at AI companies

Who has «added to everything» the term AI? The «individuals leading/working at AI companies»? I would have said, the onlookers, or relatively marginal actors (e.g. marketing) who have an interest in the buzzword. So my point was: we will go on using he term in «niche [and not so niche] areas of computer science» irregardless of the outside noise.

And this delay between people's mental images of what an 'intelligent' product can do and the actual benefits they get for their money once a new generation reaches the market creates this bullwhip effect in mood. Hence the 'AI winters'. And guess what, another one is brewing because tech people tend to think history is bunk and pay no attention to it.

It's obvious BS from anyone in tech, but the people throwing money aren't in tech.

>I think the term AI is going to slowly become less marketing trendy, and will fade out over time, as all trendy marketing terms do. What will be left are actually useful enhancements to specific use cases - most of which will probably be referred to by a word other than AI.

it'll die down, but the marketing tends to stick, sadly. we'll have to deal with if AI means machine learning or LLMs or video game pathfinding for decades to come.

They're still on r/Metaverse_blockchain. Every day, a new "meme coin". Just in:

"XXX - The AI-Powered Blockchain Project Revolutionizing the Crypto World! ... Where AI Meets Web3"

"XXX is an advanced AI infrastructure that develops AI-powered technologies for the Web3, Blockchain, and Crypto space. We aim to improve the Web3 space for retail users & startups by developing AI-powered solutions designed explicitly for Web3. From LLMs to Web3 AI Tools, XXX is the go-to place to boost your Web3 flow with Artificial Intelligence."

This is a synergistic, paradigm-shifting piece of content that leverages cutting-edge, scalable innovation to deliver a robust, value-added user experience. It epitomizes next-gen, mission-critical insights and exemplifies best-in-class thought leadership within the dynamic ecosystem

This isn't really a new phenomenon, though. The only thing new about it is that the marketing buzzword of the day is "AI". For a little while prior it was "machine learning". History is littered with examples of marketers and salespeople latching onto whatever is popular and trendy, and using it to sell, regardless if their product actually has anything to do with it.

Next up: Synthetic Consciousness, "SC"

Prediction: We will see this press release within 24 months:

"Introducing the Acme Juice Squeezer with full Synthetic Consciousness ("SC"). It will not only squeeze your juice in the morning but will help you gently transition into the working day with an empathetic personality that is both supportive and a little spunky! Sold exclusively at these fine stores..."

“GPP feature?” said Arthur. “What's that?”

“Oh, it says Genuine People Personalities.”

“Oh,” said Arthur, “sounds ghastly.”

A voice behind them said, “It is.” The voice was low and hopeless and accompanied by a slight clanking sound. They span round and saw an abject steel man standing hunched in the doorway.

“What?” they said.

“Ghastly,” continued Marvin, “it all is. Absolutely ghastly. Just don't even talk about it. Look at this door,” he said, stepping through it. The irony circuits cut into his voice modulator as he mimicked the style of the sales brochure. “All the doors in this spaceship have a cheerful and sunny disposition. It is their pleasure to open for you, and their satisfaction to close again with the knowledge of a job well done.”

»

Everything that is magic will be labeled under AI for now, until it gets seated into their proper terms and are only closely discussed by those who are actually driving innovation in the space or are just casually using the applications in business or private.

(Ask Office Copilot in PowerPoint to create you a slide. I dare you! I double dare you!!)

The problem with demos is that they're staged, they showcase integrations that are never delivered, and probably never existed. But you know what's not hype and fluff? The models themselves. You could hack a more useful Copilot with AutoHotkey, today.

I have GPT-4o hooked up as a voice assistant via Home Assistant, and what a breeze that is. Sure, every interaction costs me some $0.03 due to inefficient use of context (HA generates too much noise by default in its map of available devices and their state), but I can walk around the house and turn devices on and off by casually chatting with my watch, and it work, works well, and works faster than it takes to turn on Google Assistant.

So no, I honestly don't think AI advances are oversold. It's just that companies large and small race to deploy "AI-enabled" features, no matter how badly made they are.

In a large sense, Microsoft lied. But they didn't lie about capability of the technology itself - they just lied about being able to afford to deliver it for free.

--

[0] - Extrapolated to a hypothetical subscription, this would be ~$27 per month. I've seen more expensive and worse subscriptions. Still, it's a big motivator to go dig into the code of that integration and make it use ~2-4x fewer tokens by encoding "exposed entities" differently, and much more concisely.

[1] - Maybe Llama 3 could, but IIRC license prevents it, plus it's how many days old now?

But they never said it'll be free - I'm pretty sure it was always advertised as a paid add-on subscription. With that being the case, why would they not just offer multiple tiers to Copilot, using different models or credit limits?

1) Add OpenAI Conversation integration - https://www.home-assistant.io/integrations/openai_conversati... - and configure it with your OpenAI API key. In there, you can control part of the system prompt (HA will add some stuff around it) and configure model to use. With the newest HA, there's now an option to enable "Assist" mode (under "Control Home Assistant" header). Enable this.

2) Go to "Settings/Voice assistants". Under "Assist", you can add a new assistant. You'll be asked to pick a name, language to use, then choose a conversation model - here you pick the one you configured in step 1) - and Speech-to-Text and Text-to-Speech models. I have a subscription to Home Assistant Cloud, so I can choose "Home Assistant Cloud" models for STT and TTS; it would be great to integrate third party ones here, but I'm not sure if and how.

3) Still in "Settings/Voice assistants", look for a line saying "${some number} entities exposed", under "Add assistant" button. Click that, and curate the list of devices and sensors you want "exposed" to the assistant - "exposed" here means that HA will make a large YAML dump out of selected entities and paste that into the conversation for you[0]. There's also other stuff (I heard docs mentioning "intents") that you can expose, but I haven't look into it yet[1].

That's it. You can press the Assist button and start typing. Or, for much better experience, install HA's mobile app (and if you have a smartwatch, the watch companion app), and configure Home Assistant as your voice assistant on the device(s). That's how you get the full experience of randomly talking to your watch, "oh hey, make the home feel more like a Borg cube", and witnessing lights turning green and climate control pumping heat.

I really recommend everyone who can to try that. It's a night-and-day difference compared to Siri, Alexa or Google Now. It finally fulfills those promises of voice-activated interfaces.

(I'm seriously considering making a Home Assistant to Tasker bridge via HA app notification, just to enable the assistant to do things on my phone - experience is just that good, that I bet it'll, out of the box, work better than Google stuff.)

--

[0] - That's the inefficient token waster I mentioned in the previous comment. I have some 60 entities exposed, and best I can tell, it generates a couple thousand token's worth of YAML, most of which is noise like entity IDs and YAML structure. This could be cut down significantly if you named your devices and entities cleverly (and concisely), but I think my best bet is to dig into the code and trim it down. And/or create a synthetic entities that stand for multiple entities representing a single device or device group, like e.g. one "A/C" entity that combines multiple sensor entities from all A/C units.

[1] - Outside the YAML dump that goes with each message (and a preamble with current date/time), which is how the Assistant know current state of every exposed entity, there's also an extra schema exposing controls via "function calling" mechanism of OpenAI API, which is how the assistant is able to control devices at home. I assume those "intents" go there. I'll be looking into it today, because there's a bunch of interactions I could simplify if I could expose automation scripts to the assistant.

Here's an example: I mentioned PowerPoint in my earlier comment. You know what's the correct way to use AI to make you PowerPoint slides? A way that works? It's to not use the O365 Copilot inside PowerPoint, but rather, ask GPT-4o in ChatGPT app to use Python and pandoc to make you a PowerPoint.

I literally demoed that to a colleague the other day. The difference is like night and day.

Theoretically future lenses may make it possible, but the visible light metamaterials needed are still very early research stage.

“and now hopes to release a more standard headset with fewer abilities by the end of next year.

I don't think it will ever become the mainstream everyday carry proponents want it to be. But only time will tell...

In my eyes, it's exactly the same as AI. The demos work. You can play around with it, and its impressive for an hour. But there's just very little value.

The R&D required to get to that point is vast though.

In most applications, it then would need to compete on price with multiple high resolution displays, and undercut them quite significantly to break the inertia of the old tech (and other various advantages - like not wearing something all day and being able to allow other people to look at what you have on your screen).

I do think we're 4+ years until it gets to the 'iPhone 1' level of utility though, so we'll see how committed Apple are to it.

It would be valuable if it could do multimonitor, but it can't. It would be valuable if it could run real apps but it only runs iPad apps. It would be valuable if Apple opened up the ecosystem, and let it easily and openly run existing VR apps, including controllers - but they won't.

In fact the hardware itself crosses the threshold to where the value could be had, which is something that couldn't be said before. But Apple deliberately crimped it based on their ideology, so we are still waiting. There is light at the end of the tunnel though.

It's in a strange place, because Apple definitely also crimped it by not even writing enough software for it inhouse.

Why can't it run Mac apps? Why can't you share your "screen configuration" and its contents with other people wearing a Vision Pro in the same room as you?

Even w/ a 20% failure it's better than not having the classifications

It doesn't matter whether that's reasonable or not, there are a lot of people who expect software systems to be totally reliable at what they do, and don't want to accept less.

I think that even the simple LLM's are very well suited for classification-tasks, where very little prompting is needed.

So you had a list of products (what sort - I am thinking like widgets from a wholesaler and you want to have a three tier menu for an e-commerce site?)

I am guessing each product has a description - like from Amazon, and chatgpt read the description and said “aha this is a Television/LCD/50inch or Underwear/flimsy/bra

I assume you sent in 200,000 different queries - but how did you get it to return three tiers? (Maybe I need to read one of those “become a ChatGPt expert” blogs

You could plainly ask the LLM something like this as the query goes on:

"Please provide 3 categories that this product could exist under, with increasing specificity in the following format:

  {
     "broad category": "a broad category that would encompass this product, as well as others, for example 'televisions' for a 50" OLED LG with Roku integration",
     "category": "a narrower category that describes this product more aggressively, for example 'Smart Televisions'",
     "narrow category": "an even narrower category that describes this product and its direct competitors, for example OLED Smart televisions"
  }

"Given a product categorization of a specific level of specificity, please provide a list of words and phrases that mean the same thing".

In OpenAI's backend - and many of them, I think, you can have the api run the query multiple times and get back multiple answers. enumerate over those answers, build the graph, and you can have all that data in an easy to read and follow format!

It might not be perfect, but it should be pretty good!

Text similarity works well in this case. You can just use cosine similarity and merge ones that are very close or ask GPT to compare for those on the edge

Unless you need to have some "reasoning" to classify the documents correctly, a much more lightweight BERT-like model (RoBERTa or DistilBERT) will perform on par in accuracy while being a lot faster.

LLMs can operate as a very, very *very* approachable natural language processing model without needing to know all the gritty details of NLP.

If you already have the answers to verify the LLM output against why not just use those to begin with?

Thinking being that you'd compare outputs of the two, and under assumption of the results being statistically independent from each other and of similar quality, say 1% difference between the two in said comparison, would suggest ~ 0.5% error rate from "ground truth".

GP was talking about something else though, the 90:90 rule is related to an extremely common planning optimism fallacy around work required to demo, and work required to productise.

The last part of any semi-difficult project nearly always takes much longer than the officially difficult “main problem” to solve.

It leads to the last 10% of the deliverables costing at least 90% of the total effort for the project (not the planned amount; the total ad calculated after completion, if that ever occurs)

This seems to endlessly surprise people in tech, but also many other semi-professional project domains (home renovations are a classic)

  "It’s been obvious even to casual observers like myself for years that Waymo/Google was one of the only groups taking the problem seriously and trying to actually solve it, as opposed to pretending you could add self-driving with just cameras in an over-the-air update (Tesla), or trying to move fast and break things (Uber), or pretending you could gradually improve lane-keeping all the way into autonomous driving (car manufacturers). That’s why it’s working for them. (IIUC, Cruise has pretty much also always been legit?)"

One thing it is good at is scaring people into paying to feed it all the data they have for a promise of an unquantifiable improvement.

This is _always_ the problem with these things. Voice transcription was a great tech demo in the 1990s (remember DragonDictate?), and there was much hype for a couple of years that, by the early noughties, speech would be the main way that people use computes. In the real world, 30 years on, it has finally reached the point where you might be able to use it for things provided that accuracy doesn't matter at all.

- hearing people next to you speaking to the computer would be tiring and annoying. Though remote work might be a partial solution to this

- hello voice extinction after days of using a computer :-)

I get requests all the time from colleagues to have discussions via telephone instead of chat because they are bad at typing.

(MS had a bit of a fetish for alternate interfaces at the time; before voice they spent a few years desperately trying to make Windows for Pen Computing a thing).

Color me surprised. Maybe we should ask Mira Murati to step aside from her inspiring essays about the future of poetry and help us figure out why the world spent trillions on nvidia equity and how to unwind this pending disaster...

> help us figure out why the world spent trillions on nvidia equity and how to unwind this pending disaster..

There are many documented examples of the market being irrational.

So I really don't actually know what the OP wants to do, besides brutalize idiots searching for a golden calf to worship. AI will progress regardless of how you gatekeep the public from percieving it, and manipulative thought-leaders will continue to schiester idiots in hopes of turning a quick buck. These cycles will operate independently of one-another, and those overeager idiots will move onto the next fad like Metaverse agriculture or whatever the fuck.

It reminds me of every past generation of focusing on the technology, not the underlying hard work + literacy needed to make it real.

Decades ago I saw this - I worked at a hardware company that tried to suddenly be a software company. Not at all internalizing - at every level - what software actually takes to build well. That leading, managing, executing software can't just be done by applying your institutional hardware knowledge to a different craft. It will at best be a half effort as the software craftspeople find themselves attracted to the places that truly understand and respect their craft.

There's a similar thing happening with data literacy where the non data literate hire the data literate, but don't actually internalize those practices or learn from them. They want to continue operating like the always have, but just "plug in AI" (or whatever new thing) without changing fundamentally how they do anything

People want to have AI, but those company's leaders struggle with basic understanding of statistical significance, basic fundamentals of experimentation, and thus essentially destroy any culture needed to build the AI-thing.

I'm in a similar situation with my own 'C-suite' and it's impossible to try and make them understand, they just don't care. I can't make them care. It's a clash of cultures, I guess.

I think we should do a HN backed project, crowd funded style.

1. Identify the best P-hackers in current science with solid uptake on their content (citations).

2. Pay them to conduct a study proving that C levels who eat crayons have higher something... revenue, pay, job satisfaction, all three.

3. buy stock in crayons

4. Publish and hype, profit.

5. Short crayons and out your publication as fraud.

6. Profit

Continue to work out of spite, always with a crayon on hand for when every someone from the C-suite demands something stupid and offer it to them.

A man can dream... I feel like this is the plot to a Hunter S Thompson writes Brave new World set in the universe of Silicon Valley.

I should be a prompt engineer.

> it's impossible to try and make them understand, they just don't care. I can't make them care. It's a clash of cultures, I guess.

That seems to be what OP's cathartic humor is about. It's also (probably) a deliberate provocation since that sub-culture doesn't deal well with this sort of humor.

If that's the case, you can see it working in this thread. Some of the commenters with the clearest C-suite aspirations are reacting with unironic vitriol as if the post is about them personally.

I think most of those comments already got flagged, but some seemed genuine in accusing OP of being a dangerously ill menace to society, e.g. "...Is OP threatening us?"

In a sense, OP is threatening them, but not with literal violence. He's making fun of their aspirations, and he's doing so with some pretty vicious humor.

It's that the whole conversation around machine learning has become "tainted" - mention AI, and the average person will envision that exact type of an evil MBA this post is rallying against. And I don't want to be associated with them, even implicitly.

I shouldn't feel ashamed for taking some interest and studying machine learning. I shouldn't feel ashamed for having some degree of cautious optimism - the kind that sees a slightly better world, and not dollar signs. And yet.

The author here draws pretty clear lines in what they're talking about - but most readers won't care or even read that far. And the degree of how emotionally charged it is does lead me to think that there's a degree of further discontent, not just the C-suite rhetoric that almost everyone but the actual C-suites can get behind.

I think part of the problem is that it's generally futile to judge the mental state or hidden motivations of some random person on the internet based solely on something they've written about a particular topic. And yet, we keep trying to do that, over and over and over, and make our own (usually incredibly flawed) judgments about authors based on that.

The post left a bit of a sour taste in my mouth too, mainly because as I've gotten older I don't really enjoy "violence humor" all that much anymore. I think a big part of that is experience: experiencing violence myself (to a fairly minor degree, even), and knowing people who have experienced violence makes joking about violence just not feel particularly funny to me.

But if I step back a bit, my (probably flawed) judgment is pretty mild: I don't think the author is a violent person or would ever actually threaten or bring violence upon colleagues. I'm not even sure the author is even anywhere near as angry about the topic as the post might lead us to believe. Violence humor is just a rhetorical device. And just like any rhetorical device, it will resonate with some readers but not with others.

Is that because I added a TL;DR line, or my entire post?

> I shouldn't feel ashamed for taking some interest and studying machine learning. I shouldn't feel ashamed for having some degree of cautious optimism - the kind that sees a slightly better world, and not dollar signs. And yet.

I agree with this in general. I didn't mean to criticize having interest in it.

> And the degree of how emotionally charged it is does lead me to think that there's a degree of further discontent

Do you mean the discontent outside the C-suite? If so, yes, I agree with that too. But if we start discussing that, we'll be discussing the larger context of economic policy, what it means to be human, what art is, etc.

The TL;DR was a fine summary of the post, I was talking about the whole of it. Though, now that I re-read it, I see that you were cautious to not make complete generalizations - so my reply was more of a knee-jerk reaction to the implication that most people who oppose the author's style are just "temporarily embarrassed C-suites", unlike the sane people who didn't feel uncomfortable about it.

> I didn't mean to criticize having interest in it.

I don't think you personally did - I was talking about the original post there, not about yours. The sentiment in many communities now is that machine learning itself (or generative AI specifically) is an overhyped, useless well that's basically run dry - and there's no doubt that the dislike of financial grifters is what started their disdain for the whole field.

> Do you mean the discontent outside the C-suite?

Yes.

There is a decent chance that, yes, this rant is quite literally aimed at the people that frequent Hacker News. Where else are you going to find a more concentrated bunch of people peddling AI hype, creating AI startups, and generally over-selling their capabilities than here?

Here, if you strip away the guff about how smart the author is and how badly he wants to beat up people who disagree, I have no idea what he's trying to say. The rest reads like "these companies who want to use AI are bad, and instead of using AI they should try not being bad", and such. Ok?

I remember ~6 years ago wondering if I was going to be able to remain relevant as a software engineer if I didn't learn about neural networks and get good with TensorFlow. Everyone seemed to be trying to learn this skill at the same time and every app was jamming in some ML-powered feature. I'm glad I skipped that hype train, turns out only a minority of programmers really need to do that stuff and the rest of us can keep on doing what we were doing before. In the same way, I think LLMs are massively powerful but also not something we all need to jump into so breathlessly.

But that's all it is, hype. It'll die down like web3, Big Data, cloud, mobile, etc. It'll probably help out some tooling but it's not taking our jobs for decades (it will inevitably cost some jobs from executives who don't know better and ignore their talent, though. The truly sad part).

At least half of those the promise was realised though - mobile is substantially bigger than the market for computers and cloud turned out to be pretty amazing. AWS is not necessarily cost effective but it is everywhere and turned out to be a massive deal.

Big Data and AI are largely overlapping, so that is still to play. Only web3 hasn't had a big win - assuming web3 means a serious online use case for crypto.

"Die down" in this context means that the hype will come, go and then turn out to be mostly correct 10 years later. That was largely what happened in the first internet boom - everyone could see where it was going, the first wave of enthusiasm was just early. I don't think any technology exists right now that will take my job, but I doubt that job will exist in 20 years because it looks like AI will be doing it. There are a lot of hardware generations still to land.

I dunno, I think there might be different sets of "promises" here.

For example, "cloud infrastructure" is now a real thing which is useful to some people, so one could claim that "the promise of cloud infrastructure" was fulfilled.

However that's not really the same promises as when consultants preached that a company needed to be Ready For The Cloud, or when marketing was in a slapping "Cloud" onto existing product marketing, or unnecessary/failed attempts to rewrite core business logic into AWS lambda functions, etc.

At a point in time the database was a bleeding edge technology.

Ingres (Postgres)... (the ofspring of Stonebreaker), Oracle, ... Db2? MSSQL? (Heavily used but not common)... So many failed DB's along the way, people keep trying to make "new ones" and they seem to fade off.

When was the last time you heard someone starting a new project with Mongo, or Hadoop? Postgres and Maria are the go to for a reason.

Postgres adopted a lot of mongos features when it released the JSON data type and support for path expressions

Front-end web devs might not write SQL all day, but they probably won't get very far without some comprehension. I see AI/ML becoming something as common. Maybe you need to know some outline of what gradient descent is. Maybe you just need some understanding of prompt engineering. But a reasonable grasp of the priciples is still going to be useful to a lot of people after all the hype moves to other topics.

Worth noting that I don't think you need to train the models or even touch the PyTorch level, but you do need to understand how LLMs work and learn how (if?) they can be applied to what you work on. There are big swaths of technology that are becoming obsolete with generative AI (most obviously/immediately in the visual creation and editing space) and IMO AI is going to continue to eat more and more domains over time.

> I work on all aspects of human language: the words, the structures, across lots of languages. I mostly works [sic] with behavioral data: what people say, and how they perform in simple experiments.

(I find it ironic to see a grammatical error in his bio. Probably because of a mass find/replace from "He" to "I" but still...)

[1] http://tedlab.mit.edu/ted.html

Im not sure its "unnecessary".

He is, very clearly venting into an open mic. He starts with his bonfides (a Masters, he's built the tools not just been an API user). He adds more through out the article (talking about peers).

His rants are backed by "anecdotes"... I can smell the "consulting" business oozing off them. He cant really lay it out, just speak in generalities... And where he can his concrete examples and data are on point.

I dont know when angry became socially unacceptable in any form. But he is just that. He might have a right to be. You might have the right to be as well in light of the NONSENSE our industry is experiencing.

Maybe its time to let the AI hate flow though you...

Cause getting angry at a problem and spending 2 days coding to completely replace a long standing issue isnt something that happens...

People need to be less precious. You cant be happy all the time. In fact you probably should not be (life without contrast is boring). A little anger about the indignities and bullshit in the world is a good thing. As long as you're still rational and receptive, it can be an effective tool for communicating a point.

Being able to control negative emotions isn't a nice-to-have trait or something that can be handled later. There is an urgent social pressure that men only get angry about things that justify it - a class of issues which includes arguably nothing in tech. Maybe a few topics, but not many.

Anger isn't a bad thing in itself (and can be an effective motivator in the short term). But people get very, very uncomfortable around angry people for this obvious reason.

What emotions do you really control?

We expect men to suppress this emotion. And there's is 400k years of survival and reproductive success tied up with that emotion. We didn't get half a percent of the population with Ghegis Khans Y chromosome with a smile, balloons and a cake.

It's not like violence doest exist. But we seem to think that we can remove it just like the murder in the meat case. Are we supposed to put anger on a foam tray and wrap it in plastic and store it away like a steak because the reality of it upsets people?

It's to the point where words murder, suicide, rape and porn are "forbidden words"... were saying unlike, grape and corn. So as not to offend advertisers a peoples precious sensibilities. Failing to see this behavior is a major plot point in 1984.

I think we all need to get bad to the reality of the world being "gritty" and having to live in it.

2) Violence != anger [0]. I don't know much about him, but Ghengis Khan could have been an extremely calm person. It is hard to build an empire that large and win that many campaigns for someone prone to clouded thinking which is a point in favour of him being fairly calculating.

> What emotions do you really control?

3) In terms of what gets expressed? Nearly all of them. Especially in a written setting, there is more than enough time to take a deep breath and settle.

> We expect men to suppress this emotion.

4) As an aside, I advise against suppressing negative emotions if that means trying to hold them back or something. That tends to lead to explosions sooner or later. It is better to take a soft touch, let the emotion play out but disconnect it from your actions unless it leads to doing something productive. Reflect on it and think about it; that sort of thing.

[0] Although maybe I should not that angery violence is a lot more dangerous than thoughtful violence; angry violence tends to be harder to predict and lead to worse outcomes.

You cant posit this and then go on to try and claim Violence != anger.

> The man was a calamity clothed in flesh Nice, well said!!! He was also likely brilliant. Its rare stupid people make it to the top!

I hope that Ghengis Kahn NEVER happen again...But I think society is just a thin veil between us and those monsters. The whole idea of pushing down anger is just moving us one more steep from that reality!

I propose the alternate theory that as in-person interaction becomes a smaller portion of most people's social experience, many have gotten worse at handling even mild interpersonal conflict without the kind of impersonal mediating forces that are omnipresent online, and this kneejerk aversion reaction can rationalize itself with the aid of this whole weird gilded age revivalist-ass cartoon notion of "rationality" that's become popular among a certain flavor of influential person of late and, especially in a certain kind of conversation with a certain kind of smug obnoxious person, seems kind of like classic Orwellian doublespeak

Also this position that "arguably almost nothing" in tech warrants anger seems super tonedeaf in a context where most of the world has become a panopticon in the name of targeting ads, you need a mobile phone owned by a duopoly to authenticate yourself to your bank, and large swaths of previously functional infrastructure is being privatized and stripmined to function as poorly as the companies that own them can get away with while the ancillary benefit of providing employees with subsistence and purpose wherever possible, while still managing to nickel and dime you for the privilege with all manner of junk fees, and offer poorly-designed phone trees in place of any meaningful documentation or customer service

And these issues are just minor compared to all the terrible stuff that happens routinely. If we're ranking issues from most to least important things like "you need a mobile phone owned by a duopoly to authenticate yourself to your bank" are just so far down it is laughable (the wry type, like "why do I even care"). The fact that you need a bank at all is a far more crippling issue. Let alone all the war, death, cruelty and disinterest in suffering that is just another day in a big world.

Epictetus wrote of concerning oneself more with that which one may be able to control than that which one can't, and people who aren't familiar with the Enchiridion have nonetheless internalized this wisdom. It pops up in lots of places, like in various schools of therapy, or in the serenity prayer. My career is in computers, and this website is a nexus wherein people who do computers for a living gather to discuss articles. Therefore, the shared context we have is disproportionately about issues surrounding computers. We are all of us likely better positioned to enact or at least advocate for change in how computer things are done in the world, and in each of the last 7 decades this has become a larger share of the problems affecting the world, and anger is difficult to mask when talking about problems precisely because one of the major ways we detect anger in these text conversations devoid of body language or vocal tone is expressing a belief that something is unacceptable and needs to be changed

But it's liberating to be angry at bullshit (and God knows China is the bullshit Mecca), and AI is the top bullshit these days. We're not anti innovation because we say chatgpt is not gonna maintain our trading systems or whatever you work on. It's a funny silly statistical text generator that uses hundreds of thousands of video cards to output dead http links.

We're far from anything intelligent but it's at least indeed very artificial.

To be honest, this is kind of nice, because my girlfriend recently told me that I'm "extremely unthreatening" because of all the improv theater, fencing, music, reading, and writing. Now I know at least a few people on the internet are threatened by me. I'm a loose cannon, on the verge of totally unrestrained violence. I'm two steps removed from a dinosaur, and if someone looks at me funny, who KNOWS what'll happen.