It’s a capitalist invention and, therefore, will be used for whatever capitalists deem it profitable to be. Once the money for AI home assistants starts rolling in, then you’ll see it adopted for that purpose.
It’s a free market invention and, therefore, will be used by whatever a free market decides it should be used for.
The people already with the money have orders of magnitude more freedom on average to decide and pursue opportunities.
Free market inventions do not guarantee persistent and open access.
You can’t turn a spicy autocorrect into anything even remotely close to Jarvis.
It’s not autocorrect, it’s a text predictor. So I’d say you could definitely get close to JARVIS, especially when we don’t even know why it works yet.
You’re just being pedantic. Most autocorrects/keyboard autocompletes make use of text predictors to function. Look at the 3 suggestions on your phone keyboard whenever you type. That’s also a text predictor (granted it’s a much simpler one).
Text predictors (obviously) predict text, and as such don’t have any actual understanding on the text they are outputting. An AI that doesn’t understand its own outputs isn’t going to achieve anything close to a sci-fi depiction of an AI assistant.
It’s also not like the devs are confused about why LLMs work. If you had every publicly uploaded sentence since the creation of the Internet as a training reference I would hope the resulting model is a pretty good autocomplete, even to the point of being able to answer some questions.
Yes, autocorrect may use text predictors. No, that does not make text predictors “spicy autocorrect”. The denotation may be correct, but the connotation isn’t.
Text predictors (obviously) predict text, and as such don’t have any actual understanding on the text they are outputting. An AI that doesn’t understand its own outputs isn’t going to achieve anything close to a sci-fi depiction of an AI assistant.
There’s a large philosophical debate about whether we actually know what we’re thinking, but I’m not going to get into that. All I’m going to elaborate on is the thought experiment of the Chinese room that posits that perhaps AI doesn’t need to understand things to have apparent intelligence enough for most functions.
It’s also not like the devs are confused about why LLMs work.
Yes they are. All they know is that if you train a text predictor a ton, at one point it hits a bottleneck of usability way below targets, and then one day it will suddenly surpass that bottleneck for no apparent reason.
Training good models requires lots of training data and computational resources, so the only ones who can afford to train them are big corporations with access to both. And the only objective they have is to increase their profit.
Well, as long as we ensure training data needs to be paid for and can’t just be scraped from the web, we will ensure that only large corporations with deep pockets can train models.
That is the reason there is a big “grassroots” push to stop AI from training on all our web content: it’s a play to ensure no small players can make AI, and that AI is dominated by a few big players.
Lots of technologies could be used to improve things, but corporations just look at profit, not improving the human condition. Just like Ford patenting the system to listen to you in the car and serve you better ads, AI will trend toward making more ad sales, and models trained will lean to this always.
It is why OpenSource stuff is so important, its the unpaid or low paid people doing cool stuff to solve actual problems that innovate to a goal of solving , not to goal of monotizing.
Like Windows 11 is ad bloatware. The amount of tech and money MS could leverage and instead they build an ad OS, that they are now backporting to Windows10.
Meanwhile OpenSource devs build a linux distro that turned my 13 year old laptop (that choked and died on running W10 (was OK on W7)) into a peppy machine that handles web streaming, zoom calls, and opening files as fast as a brand new laptop. When money is not the end goal lots of good things happen
The best way to ensure AI is used for good purposes is to make sure AI is in as many hands as possible. That was the original idea behind OpenAI (hence the name), which was supposed to be a nonprofit pushing open-source AI into the world to ensure a multipolar AI ecosystem.
That failed badly.
