Interesting. I used Apple II's in elementary school (early 1980's) and then some Macs but I had never even seen a Lisa in person until going to a computer museum about 5 years ago.
It was a fascinating idea - programs were hidden behind a document template metaphor. It was not as neat as Windows “New” menu and its templates folder.
While Jobs thought it wasn't going to work, a lot of folks on Apples board disagreed at the time. A controversial character at times, yet both Jobs and Woz provably understood their customers better than most. =3
That makes the bite less damaging - if everyone hax "Co-authored-by AI" in their commits less shame for it, just a normal fact of life now, not a sign of low quality.
When classifying resumes it is better to use the LLM as a feature extractor, think of 10-20 features you base your decision on, and extract them by LLM. The LLM only needs to do lower level task of question answering. Then you fit a classical ML model (xgboost for example) on the extracted features, based on company triage data points. This way you don't rely on the biases in the model, you can decide what criteria to use and how to judge cases without retraining the LLM. The feature extractor is generic, and the actual triage model is a toy you can retrain in seconds on new data points. It is also much more explainable, you can see how features influence decisions.
Rather than talking about consciousness which we can't even define or observe in others directly, why not focus on something more concrete - cost. A process or pattern that pays its costs, or gains to offset its costs. Why cost? Because cost decides what can be. It shapes what we can be, and what we need, including the need to learn from experience and act serially - to channel that parallel brain activity in a serial stream of actions.
So, how does AI stand? Humans pay their costs. AI is beginning to. It does not matter what we think about it, as long as it can self sustain and reacts to cost gating pressure. Of course not alone, it depends on us too, like we do individually also depend on society.
> Weird, I thought AI was going to create so much economic surplus that we wouldn’t know what to do with it. What happened?
The surplus is converted in new structure and becomes baseline. Even if a company does not change, their competition does (with AI), and their customers & investors change their values as well. So the structure in which the company exists has changed.
That forces everyone to adapt, but most importantly surplus cannot be captured. Everyone is working harder just to stay in place. AI is like internet, Google search and MS office - everyone has them. They provide no competitive advantage, no moat for using them.
I think they're in a win-win situation. Big AI companies would love to see local computing die in favour of the cloud because they are well aware the moment an open model that can run on non ludicrous consumer hardware appears, they're screwed. In this situation Nvidia, AMD and the like would be the only ones profiting from it - even though I'm not convinced they'd prefer going back to fighting for B2C while B2B Is so much simpler for them
If you want to run AI models at scale and with reasonably quick response, there's not many alternatives to datacenter hardware. Consumer hardware is great for repurposing existing "free" compute (including gaming PCs, pro workstations etc. at the higher end) and for basic insurance against rug pulls from the big AI vendors, but increased scale will probably still bring very real benefits.
Currently, yes. But I don't find it hard to imagine that in a while we could get reasonably light open models with a level of reasoning similar to current opus, for instance. In such a scenario how many people would opt to pay for a way more expensive cloud subscription? Especially since lots of people are already not that interested in paying for frontier models nowadays where it makes sense. Unless keep on getting a constant, never ending stream of improvements we're basically bound to get to a point where unless you really need it you are ok with the basic, cheaper local alternative you don't have to pay for monthly.
I think average users are already okay with the reasoning level they'd get with current open models. But the big AI firms have pivoted their frontier models towards the enterprise: coding and research, as opposed to general chat. And scale is quite important for these uses, ordinary pro hardware is not enough.
This is really just a question of product design meeting the technology.
Today, lots of integer compute happens on local devices for some purposes, and in the cloud for others.
Same is already true for matmul, lots of FLOPS being spent locally on photo and video processing, speech to text, …
No obvious reason you wouldn’t want to specialize LLM tasks similarly, especially as long-running agents increasingly take over from chatbots as the dominant interaction architecture.
At a consistent amount of usage, datacenters are at least an order of magnitude more hardware efficient. I'm sure Nvidia and AMD would be fine fighting for B2C if it meant volume would be 10+x.
Now, given they can't satisfy current volume, they are forced to settle for just having crazy margins.
The problem with B2C is that you need to have leverage of some kind (more demanding applications, planned obsolescence, ...) in order to get people to keep on buying your product. The average consumer may simply consider themselves satisfied with their old product they already own and only replace it when it breaks down. On the contrary, with the cloud you can keep people hooked on getting the latest product whether they need it or not, and get artificial demand from datacentres and such.
I think businesses running datacenters are much less likely to frivolously buy the latest GPUs with no functional incentive than general consumers are...
Future upgrade cycles on phones and laptops, PCs, will be driven by SOCs that embed some type of ASIC that run a specific model. Every 6 months there will be a new, better version to upgrade to, which will require a new device. This is how Apple will be able to reduce cycles from 3 years to 6-12 months.
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