The funny thing is though, Microsoft has a market cap 58% larger than Alphabet, not just Google. We're left wondering why Microsoft continually takes these weird risks in the consumer space when they can just play it safe like most other big players. None of their (21st century) success has been due to quirky disruptions, it's usually been slow and steady progress (Surface, Office, Enterprise, Cloud, Consulting).

Yet with stuff like Edge, Windows 11, etc, it's been a mess. I'm not 12 anymore, I prefer stable products over the shinest new thing, and Windows 11 has been a collosal disappointment.

Microsoft is similar to Google; both like to experiment and make cool stuff, but Microsoft doesn't cut the fat and likes to put out products which are effectively trash under the guise of open beta. Heck, even their hardware is sometimes like that, while Google's products are typically solid, even if they have a short lifespan.

Going back to New Bing, it's genuinely innovative. It just sucks. That's not paradoxical, because a lot of new stuff does suck. We just rarely see it, because companies like Google are generally disciplined enough.

Most "deep" innovations are developed over decades. That development could be secretive (military tech), or open (SpaceX, Tesla), but it takes time nonetheless. Microsoft leans towards the latter, Google the former.

The latter is generally more efficient, if your audience is results-focused, not emotions-focused. AI is pretty emotionally charged, so maybe the former method is better.

Classical computing / engineering advances are good at repetitive actions. A human can never put in a screw 10,000x times with 0.01mm precision or calculate 5000 graphs by hand without quitting. But it's bad at actions that require flexibility and adaptation, like what chefs, dry cleaners, or software engineers do.

LLM and AI attempt to bridge that gap, by allowing for computers to be flexible and adapt. The issue is that we don't know how much they're actually capable of adapting, and how fast. We know humans have a limit; nobody in the world fluently speaks & reads & writes in more than 10 languages (probably not even >5). Do computers have a limit? How expensive is that limit? Because materials, manufacturing, and energy are finite resources.

What do you define as general use cases? Receptionist calls? (already done, one actually fooled me into thinking it was a human) Making a cup of coffee?

Anything repetitive will be automated, if it's economical to do so. You probably still make tea by hand, because it's a waste of money to buy a $100 tea maker (and they probably dont even exist because of how easy it is to make tea). But you probably have a blender, because it's a huge waste of time and energy to chop stuff yourself.

I think humans (on this subreddit especially) tend to underestimate how much finances & logistics play into tech. We've had flying cars since the 90s, yet they'll never "transform transportation" like sci-fi said, because it's dumb to have a car-plane hybrid.

We might get an impressive AGI in the next few years, but it might be so expensive that it's just used the same way we use robots: you get the cutting-edge stuff you'll never see cause it's in some factory, the entertaining stuff like the cruise ship robo-bartenders, and the consumer-grade crap like Roombas. AGI might also kill millions of humans but I know nothing about that side of AI so I won't comment.

Btw, I'm not an expert, I'm just a software engineer that likes talking to AI engineers.

I see. Even at a 5x reduction in parameter size, that's still not enough to run on consumer hardware (we're talking 10b vs. 500m) , but I recognize what you're trying to say.

Which part? LLM-capable hardware getting really really cheap, or useful LLMs not growing hugely in parameter size?

The thing is that in LLM training, memory and IO bandwidth are the big bottlenecks. If every GPU has to communicate via the internet, and wait for the previous person to be done first (because pipelined model parallel is still sequential, despite the name), it's gonna finish in like 100 years. Another slowdown is breaking up each layer into pieces that individual GPUs can handle. Currently they're being spread out to 2000-3000 huge GPUs and there's already significant latency. What happens if there's 20,000 small-sized GPUs? Each layer is gonna be spread out so thin the latency is gonna be enormous. The final nail in the coffin is that neural network architecture changes a lot, and each time the hardware has to be reconfigured too.

Crypto mining didn't have these problems because 1. bandwidth was important, but not the big bottleneck, 2. "layers" could fit on single GPUs, and if they couldn't (on a 1050ti for example), it was very slow, and 3. the architecture didn't really change, you just did the same thing over and over.

Cerebras is trying to make a huge chip that disaggregates memory from compute, and also bundles compute into a single chip, saving energy and time. The cost for the CS-2 system is around $3-10 million for the hardware alone. It's pretty easy for a medium-sized startup to offer some custom LLM. I mean there's already dozens, if not hundreds of startups starting to do that right now. It's expensive. All complex computing is expensive, we can't really get around that, we can only slowly make improvements.

LLMs will not be getting smaller. Getting better ≠ getting smaller.

Now, will really small models be run on some RTX 6090 ti in the future? Probably. Think GPT-2. But none of the actually useful models (X-Large, XXL, 10XL, etc) will be accessible at home.

Of course "regular" people will be able to use it, the same way regular people get access to state of the art quantum computers and supercomputers.

What TunaFish is saying is unlikely is for everyone to be able to run it in their own home. LLM engineers concur, moore's law isn't quite there anymore.

If you mean server time, that's obviously possible (I can run loads of GPT-3 right now for $5). But that's not exactly running it at home, if you know what I mean.

Language is a way of knowing, but it's not the only one. Reason, emotion, and sense perception are also ways of knowing.

Lower density is expensive. You're spreading out infrastructure costs to less households and businesses, increasing commute times (sure, cars are faster than the bus, but in urban areas is usually the same speed as the subway and walking and cycling), and generally taking up a larger environmental footprint.

Of course I don't want to live in Hong Kong or Singapore core, that's way too crammed. But if we look at Tokyo, which is second in urban development size only to NYC, and is the same size as the state of Connecticut, their population density is not high at all. 200-400 US cities would have higher population density than the Greater Tokyo Area.

Correct me if I'm wrong, but what you want is a medium density, larger urban area kinda like Greater Tokyo or NYC, with everyone free to drive wherever they want with manageable traffic.

This exists in reality, except car ownership is pretty low for Tokyo, and in NYC's car ownership is mostly centred in the suburbs. https://edc.nyc/article/new-yorkers-and-their-cars

So when people are free to choose, only 10-40% of households in your ideal metro area (can't say city cause it's too sparse) choose car ownership. That's not out of poverty, Tokyo and NYC are among the highest earning cities in the world.

What makes those two cities livable and world-class is the public transit that connects the various boroughs together. NYC needs to improve in this regard because they don't have a ring line yet, but Tokyo is pretty good at it. I also picked two random spots in Tokyo, and though car is faster by 10 minutes when it's quiet, it's an hour slower if there's traffic.

The Greater Toronto Area is an example of what happens when you have a medium density, large metro area without good inter-borough public transit (and mediocre intra-borough PT outside of Toronto proper). You have all the high costs of urban living (you gotta pay for each borough's budgets as a separate city, as well as the huge road infrastructure costs) with few of the benefits (suburbs are isolated, you get this very Americanized feel of restrictive zoning and stroads, etc).

You can't pick and choose what aspects of our reality to address; the current price of Self-Driving (nevermind it's not SAE Level 5 yet) matters because it's real life. Just because the topic is Singularitarianism doesn't mean we are allowed to toss money and resource scarcity out the window. Or else I can say hey, singularity, cities won't be necessary anymore because we can live in underground pods and interact in Web 5.0.

Because driving doesn't count as commuting; it's driving, the best joy ever!

Let's assume owning and using a car only costs a few hundred euros a month. Let's assume that parking/traffic is hassle-free (nevermind in Paris, lightly hitting another car's bumper while parking is considered routine as everyone parks in neutral).

I still don't understand what point you're trying to make. I recognize car commuting to be perfectly normal. I'm just under no illusions that it's somehow an efficient and effective commute method for everybody in a dense city like Manhattan/Paris/Tokyo. Tragedy of the commons will occur, and it seems you recognize that too: "quieter roads will induce more people to drive."

Also the cost of self-driving cars is relevant, because that was your response to the whole working-while-commuting point.

-The cost of using a car is way beyond the fuel cost, it's insurance, cost of the car itself, maintenance, taxes (registration), parking, and depreciation. It's way more than the 75 euros for a monthly pass in Paris.

-Free time has value. So does working more hours to afford that car.

-Self driving cars won't come free. Tesla FSD is 15k USD and counting and that's the minimum sophistication level needed for true self driving. Stuff like Waymo is nice, though it would be way too expensive if the actual cost of mapping streets and r&d is factored in (like Tesla)

-It takes more than a year for the "invisible hand" to materialize. 4% is also pretty consequential, especially from a congestion perspective.

I'm not against cars at all; in my college town, I drive practically everywhere even though my public transportation is free. But that's because the latter is very limited in scope and reliability. In the main city I live in, I take public transit within the urban areas and only drive when going out to the suburbs.

I'm not saying to ditch cars obviously, but shaving off 30 minutes doesn't really mean that much for the average person. Money is just a way to value time, and even if you're an out of touch multimillionaire, it's easy to understand why people want a more stress-free transportation matrix.

And how much more expensive is it to drive in Paris than take public transit?

The goal isn't for public transit to be lightning fast, though that would be great. The goal is for public transit to be easy, economical, and effective enough that we reduce the number of cars on the road to the essential amount, making everything more efficient.

Cars will be faster mode of transportation for decades. This is a totally normal side effect. It means those who actually need a car (ambulances, police, handicapped, running late to meetings) benefit more.

This tradeoff is fine because you can read on a train/tram/bus, you can't while driving. And if you have a chauffeur, I don't think this subject is too relevant anyways.

I keep hearing the concern of ads getting brought up. Am I the only one who feels it's way down the list of concern?

I can find any academic article without encountering a single ad. Ads are annoying, sure, and the whole tracking thing is unsettling, but it doesn't significantly destroy my ability to use the internet the same way the proliferation of deep web (reddit, twitter, facebook, tiktok etc.) does.

It is on the chatGPT page.

I believe davinci003 is pretty close to ChatGPT, and that's a full API. It is paid but is relatively cheap.

I think there's a distinction between AI and ML that needs to be made here. For example Deep Blue, the chess machine that beat Kasparov, was certainly AI, but had relatively little ML involved. Earlier chess computers were AI, but had no ML.

The FDA has found that anti-microbial soap is equally as effective as regular soap. But the main anti-microbial ingredients can actually be harmful to the environment, so it's worse if you care about the environment.

It's different in a hospital setting, I think they have some more unique reasons for needing to use anti-microbial soap.