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!
CodeInvasion
joined 1 year ago
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.
To add to this insight, there are many recent publications showing the dramatic improvements of adding another modality like vision to language models.
While this is my conjecture that is loosely supported by existing research, I personally believe that multimodality is the secret to understanding human intelligence.
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.
Agreed.
Nevertheless, the Federal regulators will have an uphill battle as mentioned in the article.
Neither "puffery" nor "corporate optimism" counts as fraud, according to US courts, and the DOJ would need to prove that Tesla knew its claims were untrue.
The big thing they could get Tesla on is the safety record for autosteer. But again there would need to be proof it was known.
I am a pilot and this is NOT how autopilot works.
There is some autoland capabilities in the larger commercial airliners, but autopilot can be as simple as a wing-leveler.
The waypoints must be programmed by the pilot in the GPS. Altitude is entirely controlled by the pilot, not the plane, except when on a programming instrument approach, and only when it captures the glideslope (so you need to be in the correct general area in 3d space for it to work).
An autopilot is actually a major hazard to the untrained pilot and has killed many, many untrained pilots as a result.
Whereas when I get in my Tesla, I use voice commands to say where I want to go and now-a-days, I don’t have to make interventions. Even when it was first released 6 years ago, it still did more than most aircraft autopilots.
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.
The victims have beed identified as the owner of the flight school, a flight instructor, and a student.
https://www.wtnh.com/new-england-news/no-survivors-after-plane-crash-in-greenfield/
You are absolutely right on all accounts. I'm sorry you've had shitty landlords, I wish there was a better way to weed those people out, because as it stands, the balance of power is heavily in the favor of the landlord due to the micro-monopolistic nature of renting a place for years at a time.
Renting vs Buying is very dependent on your local market. I have friends in Ottawa that I've run the numbers for and it would literally never be profitable to purchase a home compared to continuing to rent. Some areas two years is the break even point. These days with high interest rates, the break even on buying vs renting is after about 5 or 6 years. I encourage anyone to check it out for themselves! :)
https://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html
(For anyone stuck behind the paywall, install this chrome extension to get past it: https://github.com/iamadamdev/bypass-paywalls-chrome)
I could've have been clear, but my situation has a very slight net benefit for me, and since my tenants only plan to live in the are for two years, they are getting the better end of the deal. In the end though, there is a mutual benefit and that's what a competitive market should tend towards (as opposed to the monopolistic nature of corporate apartment housing which encourages the opposite).
My point is that the people who hate all landlords instead of just the bad ones don't understand the economic realities of housing. It's actually the mom and pops that rent out their homes for a short period that make renting cheaper on average for the market as a whole. Mostly because they are imperfect businessmen/women and don't understand the full cost of being a landlord before it's too late. Instead, most mom and pop landlords are just hoping to break even.
It's clear there is a fundamental misunderstanding in the amount of capital required to own an investment property without first living in it as a primary residence for a few years.
If one were to purchase a property with the expressed intent of immediately renting it, most banks will require at least 25% down with no option to pay PMI to cover the difference. That's an insane amount of money to put down just so the landlord can make a negative cash flow for the first 10 years. If an investor has that kind of money, and still want to be involved in real estate, they should buy a share in an apartment complex where the margins are more favorable, and the property actually has a positive cash flow.
Thus nearly ever single family home was purchased initially as a primary residence, with the intent to live there. But then by some circumstance one way or another they needed toove away. Selling a home will cost you 10% of the home's value in fees. So if that person has any intent to return to the home in the future, it's better to eat the temporary loss and rent out the property.
You do realize that being a landlord is typically a negative cashflow business, meaning they lose money every year? The only upside they get from renting out that property if the possible growth in equity, which is typically less than that of investing in the stock market.
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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: