I prefer the 18th century Carl Von Clausewitz's definition of war:

War Is politics by other means

https://en.m.wikipedia.org/wiki/Carl_von_Clausewitz

Shitposters ride for free

This is actually most helpful to the little guys that own $20,000 airplanes.

I have a small airplane and it's always bothered me that my name and address are publicly accessible through the FAA registry.

Most pilots I know are careful about photos they publish online showing their tail number printed in large bold letters on either side of the aircraft. This registration number can be entered into websites like flightaware.com and someone is literally two clicks from seeing my full name and home address.

Sell it to whom, Ben?

Ugh... This is all based on Dan Gryder's YouTube channel. He's not exactly the most trustworthy person. His channel is full of criticisms of pilots that have died flying that basically amount to "the pilot was dumb" and then when he causes a nearly mutli-fatal accident in a Lockheed 12A because he forgot to lock the tail wheel before landing, he feuds with the YouTuber that pointed this out.

He constantly derides the FAA and government in general, stating they are incompetent. He has stolen aircraft accident investigation evidence from a crash scene, a violation of federal statute. There's the defamation lawsuit he lost against a pilot in 2023 to the tune of $1 million. And he was arrested in 2009 at an airport in Georgia.

Dan Gryder is an attention seeker who will say anything to receive publicity.

Edit: here's the analysis on his Lockheed 12A crash: https://youtu.be/sQhA-R2kKbo?si=fWg9EwLirkXwBDOh

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: 

• Self-allocated time: Employees have complete control over how they allocate their time 
• No managers: There is no managerial oversight 
• Fluid structure: Desks have wheels so employees can easily move between teams, or "cabals" 
• Peer-based performance reviews: Employees evaluate each other's performance and stack rank them 
• Hiring: Valve has a unique hiring process that supports recruiting people with a variety of skills

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!

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.

—-

^+^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.

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.

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.