"Anti-hype LLM reading list" - this is the actual title of the github page.<p>"Anti-AI Hype" seems to be a very bad editorializing. It corrupts the meaning of the title, and makes it ambiguous.
A solid reading list is useful because it's important to understand a technology before either hyping it or criticizing it, Too much description of LLMs both from the "hey this is cool and will revolutionize the world" crowd and this "this is scammy crap like NFTs and not AI" crowd seems to oversimplify the method making it sound like a simple Markov chain. We had Markov chains in the 1980s on hardware less powerful than what's probably powering your microwave today. For better or ill, LLMs are considerably more powerful than that.
There has been a lot of push back lately that the current hype cycle is completely un-warranted, in the process of fading away, and generally that LLM's aren't that good really.<p>I thought that was what this post was about.<p>Really, this is just trying to 'cut through the hype'.<p>When we get beyond the hype, here is a list of articles to explain current state?<p>Am I correct here?
This is a double edged sword. While not being anything like crypto as it will have a real world impact almost immediately the important thing to understand and recognize is that AI is imperfect as it is formed from imperfect data as humans are imperfect. However this does mean that AI can for a lack of better judgement replace a lot of imperfect work.<p>The problem with this replacement is that it rips out a financial ecosystem a heavily interpersonal society depends on in the form of classes. Businesses can not make money without losing money and definitely can not continue to make more money without money to be made. It has almost like a hydrological diagram where instead it is money. The problem is that without a circular flow of income within a currency we experience what we call stagnation which will cause hyper inflation , money printed to meet demand and the depreciated value of a currency and increase in the cost of goods.There was a particularly good reason for having income tax brackets up to 70%.<p>AI needs laws and regulation. It will like keep progressing and ripping out key stones in the foundation of our economy. Less people working eventually means less profits for all businesses and industry.
Nice list, many are resources I used to get up to speed on this. I'm currently competing on kaggle on an NLP competition and I feel this is honestly great way to see the limitations of LMs and LLMs. Kaggle's community is like nothing else out there. They share incredible resources and experiments constantly.
Adding to the bits about deploying and training models: <a href="https://news.ycombinator.com/item?id=37121384">https://news.ycombinator.com/item?id=37121384</a><p>If anyone has anything to add, that would be great!
I thought it would be philosophy nonsense like consciousness is not computable/ consciousness need a body etc, but turn out a legit introduction booklist into deeping learning.
Great list by Vicki Boykis. I just saw this posted by someone in Mastodon.<p>So far, my favorite linked article is on why you may want to self-host your own open LLMs.