Someone did the math and realized we would need a 130% tariff on all goods to replace current income tax revenue.
People’s number one concern is inflation. If that tariff is created we will see 100% inflation over night!
You do realize that every posted on the Fediverse is open and publicly available? It’s not locked behind some API or controlled by any one company or entity.
Fediverse is the Wikipedia of encyclopedias and any researcher or engineer, including myself, can and will use Lemmy data to create AI datasets with absolutely no restrictions.
It took Hawking minutes to create some responses. Without the use of his hand due to his disease, he relied on the twitch of a few facial muscles to select from a list of available words.
As funny as it is, that interview, or any interview with Hawkins contains pre-drafted responses from Hawking and follows a script.
But the small facial movements showing his emotion still showed Hawking had fun doing it.
I am an LLM researcher at MIT, and hopefully this will help.
As others have answered, LLMs have only learned the ability to autocomplete given some input, known as the prompt. Functionally, the model is strictly predicting the probability of the next word^+^, called tokens, with some randomness injected so the output isn’t exactly the same for any given prompt.
The probability of the next word comes from what was in the model’s training data, in combination with a very complex mathematical method to compute the impact of all previous words with every other previous word and with the new predicted word, called self-attention, but you can think of this like a computed relatedness factor.
This relatedness factor is very computationally expensive and grows exponentially, so models are limited by how many previous words can be used to compute relatedness. This limitation is called the Context Window. The recent breakthroughs in LLMs come from the use of very large context windows to learn the relationships of as many words as possible.
This process of predicting the next word is repeated iteratively until a special stop token is generated, which tells the model go stop generating more words. So literally, the models builds entire responses one word at a time from left to right.
Because all future words are predicated on the previously stated words in either the prompt or subsequent generated words, it becomes impossible to apply even the most basic logical concepts, unless all the components required are present in the prompt or have somehow serendipitously been stated by the model in its generated response.
This is also why LLMs tend to work better when you ask them to work out all the steps of a problem instead of jumping to a conclusion, and why the best models tend to rely on extremely verbose answers to give you the simple piece of information you were looking for.
From this fundamental understanding, hopefully you can now reason the LLM limitations in factual understanding as well. For instance, if a given fact was never mentioned in the training data, or an answer simply doesn’t exist, the model will make it up, inferring the next most likely word to create a plausible sounding statement. Essentially, the model has been faking language understanding so much, that even when the model has no factual basis for an answer, it can easily trick a unwitting human into believing the answer to be correct.
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^+^more specifically these words are tokens which usually contain some smaller part of a word. For instance, understand and able would be represented as two tokens that when put together would become the word understandable.
This exact sticker at a different location still exists in my town. I'll take a photo of it and follow-up some time.
AFAIK, there’s nothing stopping any company from scraping Lemmy either. The whole point pf reddit limiting API usage was so they could make money like this.
Outside of morals, there is nothing to stop anybody from training on data from Lemmy just like there’s nothing stopping me from using Wikipedia. Most conferences nowadays require a paragraph on ethics in the submission, but I and many of my colleagues would have no qualms saying we scraped our data from open source internet forums and blogs.
This is done by combining a Diffusion model with ControlNet interface. As long as you have a decently modern Nvidia GPU and familiarity with Python and Pytorch it's relatively simple to create your own model.
The ControlNet paper is here: https://arxiv.org/pdf/2302.05543.pdf
I implemented this paper back in March. It's as simple as it is brilliant. By using methods originally intended to adapt large pre-trained language models to a specific application, the author's created a new model architecture that can better control the output of a diffusion model.
Small aircraft have a carbon equivalent to large cars. My plane is from 1961 and has a fuel economy of 15mpg as the crow flies (arguably closer to 25mpg because of straight line measurements versus winding roads that can almost double the distance), seats 4 people comfortably, and flies at 160 mph.
The only upside I can think of is they'd actually start caring about the planet instead of thinking they'll be dead in 100 years anyway.
This is the only response required. I'm quickly becoming exhausted of reading everyone's epiphany on "enshittification" as if it's some natural eventuality. Yes the money must eventually come, but not always at the expense of platform quality. If anything the results we see from "enshittification" are due to the fact that most businesses fail eventually due to poor leadership.
Just to echo what you have already said, money today is simply more expensive than it used to be. We even see the impacts of macro monetary decisions on households.
Buying a house or a car on loan is far more expensive than it would have been a year and half ago. A $500,000 house in 2021 would cost $2,000 a month at 2.75% interest and 20% down. Today same that payment is $2,800 or 40% more expensive at 7.75% interest.
Modern companies live on revolving debt, so if their suddenly gets 40% more expensive and that same amount of money is also less valuable at the same time (inflation), then they need to make up the difference somehow.
Corporations are trying to find the balance between squeezing more revenue to pay their ever increasing debt bills while also not destroying the environment that attracted the users (their products) in the first place. Twitter and Reddit are just going about it horrifically because of poor business leadership and decision making. Netflix's approach appears to be sustainable, and there is no doubt that YouTube will be fine in the long run.
This is not meant to be apologetic to the decisions made by Twitter and Reddit. They've made their bed through their own horrible decisions, and now they've got to sleep in it.
Lemmy today and Lemmy a week ago are night and day! Content has already reached 2014 reddit levels of activity. Besides the different UI, I wouldn't know I left.
Lemmy on 12 June was extremely sparse. Highest upvoted posts maybe had 50 votes and "active" and "hot" showed posts from up to 7 days prior. Now it's so much better with the amount of activity.
Valve is a unique company with no traditional hierarchy. In business school, I read a very interesting Harvard Business Review article on the subject. Unfortunately it’s locked behind a paywall, but this is Google AI’s summary of the article which I confirm to be true from what I remember:
According to a Harvard Business Review article from 2013, Valve, the gaming company that created Half Life and Portal, has a unique organizational structure that includes a flat management system called "Flatland". This structure eliminates traditional hierarchies and bosses, allowing employees to choose their own projects and have autonomy. Other features of Valve's structure include: