I have an android phone which means I use android auto fairly often. The sheer quality destruction it's experienced since transitioning to Gemini is incredible.
I experience this mostly when asking for music. Before gemini, mistakes were common but deterministic. It was easy to understand where the query had gone wrong and so how to fix it. Example:
"Hey google, play Blackstar"
(Plays the album blackstar by David Bowie, not what I wanted)
"Hey google, play "Blackstar by Radiohead"
(Plays the right thing).
Now:
"Hey Google, play Blackstar by Radiohead" can result in playing... something vaguely semantically related with no way to course correct. In this exact instance (happened yesterday!) it played an album by the hip hop due Black Star.
I will admit that there are some superpowers hidden in Gemini that were not present in the previous AI assistant. I recently discovered that Gemini can manipulate the navigation app, and a prompt like "Mute alerts" works, which is kind of cool. However like OP said, it's incredibly verbose, which is super annoying.
Your daily reminder that there is no scenario in which putting data centers in space is easier than putting them in Texas, or Morocco, or literally anywhere else.
The only problem that "data centers in space" solves is the problem of trying to scale a rocket company where the potential demand for rocket launches is simply not that big.
This is correct. The only problem that "data centers in space" solves is the problem of trying to scale a rocket company where the potential demand for rocket launches is simply not that big.
> The question will be: how much of the current compute capacity craze will local hosting give the kiss of death to and what that means for the market.
This will depend on how much inference happens for consumer (desktop, local) vs enterprise ("cloud"), vs consumer mobile (probably also cloud).
I would assume that the proportion of "consumer, local" is small relative to enterprise and mobile.
There are some people that believe that writing is an act of creative expression. In other words, that writing is primarily about the act (and as such, it's a quite selfish activity). Editing destroys the expressive act and must be avoided.
These people's writing is usually incoherent and they are very proud of it. If you've ever read a bad new-age self-help book you've probably encountered writing like this.
Good writers understand that writing is about communication. The initial act of writing (ie, word puke) is worthless. What matters most is a piece of writing's ability to communicate clearly.
This writing is usually pleasant, concise, and clear.
> Compare that to ~30% of all energy use for transportation. So approximately 40%*4% = 1.6% vs 30%. I find your correction to be more wrong that the initial statement.
I don't follow. The comparison is 30% of energy use for transportation vs 4% for AI, and soon 30% for transportation vs 10% for AI.
What is the "ELI5" summary of the practical limits & scaling laws that govern robotics?
The current "futurist" vision is one of humanoid robots taking over many/most jobs done by humans today, but - as someone that routinely hires human welders & assemblers - the dexterity required for most ad-hoc tasks seems many many decades (if not more?) away from what I see robots do--yes, even the fancy chinese jumping ones.
This has led me to think one of two things:
1. The robotics revolution will not come. It's predicated on the idea that advances in robotics will follow a curve of the same shape as advances in compute/ai, which will not happen. OR...
2. There has been some paradigm-shift or some breakthrough that has put robotics improvement on a new curve.
To an outsider, what I see in robots is not categorically different than like, the sony AIBO dog in 1999. It's significantly better of course, but is it really that different? (Whereas what we can do in compute-land today is categorically diffrent because of the transformer model breakthrough).
So:
1. Have there been any breakthroughs that would lead us to believe that a robot will be able to like, look under a table to adjust a screw?
2. What are the scaling laws & practical limits to present-day robotic dexterity? Is it materials? Energy density? What?
3. What is the real rate of improvement along these key dimensions? Are robots improving linearly? Geometrically? Exponentially?
4.Or should I keep discounting robotics until we get our first robots that are made of meat? That I'd believe would result in exponential change!
If by "survival" you mean surviving against a bloodthirsty regime that killed 10,000 people in January alone, then yes: the people of Iran are fighting for survival.
If those numbers are correct, then my assertion that "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is incorrect.
And I admit that I made that assertion from my gut without actually knowing if it's true or not.
"Any conversation about token costs devolves into an ad-hoc, informally-specified, bug-ridden implementation of half of generally accepted accounting principles."
We have a way of determining if Anthropic is, or has the capability of being profitable, and what the levers to that may be. AI may be world-changing, but the accounting principles behind AI labs are no different than those behind a Pizza Hut.
Even if the cost of "inference + serving" is lower than the cost of selling a token, the relevant question is what is the depreciation schedule of the cost of training. ie, if I spend $1 on training, how long do I have before I have to spend $1 again?
Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable. So the question is:
What can be done to make training depreciate more slowly? Perhaps users can be persuaded to stick around using non-fronteir models for longer, although then there's a shift in the competitive landscape.
If users cannot be persuaded (forced?) to use legacy models, then the entire business model is thrown into question, because there's no reason why training frontier models would ever get cheaper: even if it gets cheaper on the margin, surely that will result in more compute used to generate an even "better" model, resulting in more spend in the aggregate.
This doesn't mean that the AI industry is "doomed". A couple things could happen, and this is where the fronteir labs should be focusing their attention:
1. They could find a way to climb up the value chain and capture more of the consumer surplus.
2. There could be a paradigm shift in compute architecture/compute cost.
3. We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.
Edit: My assertion of "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is made with no real information, just a gut feeling, and should not be taken seriously.
Elon's superpower is commanding insane valuation premiums. The trouble with this is that "the bill eventually comes due", so to speak, which forces Elon's companies to take wilder and wilder bets, or to make wilder and wilder promises.
With telsa it was robotaxis, and when that failed to materialize, humanoid robots (fucking LOL).
SpaceX is an even more insane example. They are eyeing an IPO at a 1.5 trillion valuation. And yet the market for satellite launches is simply not that big. (What would you do with a satellite, if I gifted you one for free?). Estimates have SpaceX doing about $3B in annual earnings, which would give them a 500x earnings multiple at a 1.5T valuation (Apple: 35).
And so SpaceX/Elon had to invent the absolutely idiotic idea of "data centers in space" to sell some future vision of tens of thousands of launches per year.
He keeps upping the ante (and the ridiculousness of the vision), and so far investors keep funding it.
Me? I've realized that this madness is entirely "opt-in" and I choose to simply...not opt-in.
You'll find those claims in sibling comments to yours, so they are clearly pretty real!
(At the time of writing this comment there's a sibling claiming that the comment cannot possibly understand this POV because they are not "an American POC.")
Voting is not a monolithic process. It's actually a combination of 3 things:
- How votes are cast
- How votes are counted
- How votes are custodied
In order for an election to be trusted, all three steps must be transparent and auditable.
Electronic voting makes all three steps almost absolutely opaque.
Here's how Mexico solves this. We may have many problems, but "people trust the vote count" is not one of them:
1. Everyone votes, on paper, in their local polling station. The polling station is manned by volunteers from the neighborhood, and all political parties have an observer at the station.
2. Once the polling station closes, votes are counted in the station, by the neighborhood volunteers, and the counts are observed by the political party observers.
3. Vote counts are then sent electronically to a central system. They are also written on paper and the paper is displayed outside the poll both for a week.
The central system does the total count, but the results from each poll station are downloadable (to verify that the net count matches), and every poll station's results are queryable (so any voter can compare the vote counts displayed on paper outside the station to the online results).
Because the counting is distributed, results are available night-of in most cases.
Elections like this can be gamed, but the gaming becomes an exercise in coercing people to vote counter to their preference, not "hacking" the system.
**
Edit: Some people are confused about what I mean by "coerced." Coerced in this case means "forced to vote in some way."
The typical way this is done is as follows:
- The "coercer" obtains a blank ballot (for example, by entering the ballot box and hiding the ballot away).
- The blank ballot is then filled out in some way outside the poll station.
- A person is given the pre-filled ballot and threatened to cast it, which they will prove by returning a blank ballot.
- Rinse and repeat.
This mode of cheating is called the "revolving door" for obvious reasons.