twayt

This is actually pretty useful

Also checkout www.askYouTube.ai

Which does essentially this but requires no indexing of videos!

Yea I think this is the most reasonable take.

You can always check information before believing or acting on it.

However it’s often super difficult to even get started and know what it is that you should be reading more about.

This description is wrong on so many levels.

Amphetamine is primarily absorbed through the plasma membranes and not DAT since it’s lipid soluble.

Dopamine does not and thus relies on DAT. By inhibiting DAT (as both amphetamine and Modafinil do), you increase extracellular dopamine.

Next, DAT does not “transport amphetamine around the brain”. It only transports it from the post synaptic cleft into the cell.

Also Modafinil is a much weaker DAT inhibitor than amphetamine itself as far as we know.

Lastly, drugs don’t “antagonize” other drugs. That word is a specific term used for the action on the receptor level.

The dopaminergic effects of Modafinil aren’t well known and are thought to be mild. It’s simply misinformation to claim that modafinil was somehow blocking the effects of amphetamine on the dopamine receptors.

You get the same result in a short span of time, heck you may even get a reliable error bound.

Where this falls apart is that error accumulates over time and not just for one heap of sand but for many such heaps of sand that also interact with other heaps of sand.

Predicting weather for the next hour is trivial. Aviation runs on the fact that you can forecast fairly accurately into the next hour most of the time.

The difficulty scales superlinearly over time due to the error accumulation over predictions

We need to bring back Kazaa

Also try www.askYouTube.ai for q&a across multiple videos!

Its fixed now unfortunately the email you provided on the contact form bounces.

Despite the rude comments you left on my contact form, I have noted your point and am making changes to fix this.

Also try www.askYouTube.ai, not exactly pure text search but it can help you find videos that answer your query using LLMs

Thinly veiled attempt to advertise bentoml, their startup.

You can just use huggingface and Automatic111 for this, not sure why this is needed at all.

Did you just completely ignore the basis of my comment and instead specifically respond to what I explicitly explained was not the point I was making?

I find your statement about the Edison quote disingenuous. I did some reading about the origin of the quote and it isn’t as cut and dry as you seem to claim.

Also, the quote is meaningful because Edison said it, not because the quote has merit in and of itself.

It’s inspiring because someone of Edison’s acclaim attributed his success to hard work. Anyone else could have said it and it wouldn’t have been a meaningful quote because they weren’t as successful as him.

Note that my statement doesn’t presuppose that he was successful solely due to his own efforts or that he isn’t a fraud etc. But rather that the significance of the quote relies on his perception as being successful in the public eye.

Installed it, can't even get through the tedious intro and stuff

People have a visceral reaction to the image of them being in a self driving car and hurtling to their death with no control.

What they don’t have a visceral reaction is to be saved from a collision with a drunk driver or their own error.

Nothing else to discuss here, move along.

Until you get models with completely disentangled feature spaces such that you know that the influence of a piece of data is completely removed (at the limit this is something like an embedding DB), there is absolutely no way you can claim you’ve removed the data from the model.

At most, these efforts will amount to data laundering where it will be impossible to prove that a piece of data was used to train the model, not provide conclusive proof that it was removed.

They probably can:

No, actually they probably can’t. There is no verifiable way to remove the data from the model apart from completely removing all instances of information from the training data. The project you linked only describes a selective finetuning approach.

If you recite enough small snippets, you make a large one.

Especially with ChatGPT you can probe the model by asking certain questions about the material at hand to see if it has seen the entire book.

Also you don’t have to be able to recite the book verbatim for it to have been in your training set. The snippets I am referring to are on the side of the training data

Libgen / Scihub or not, if the model can provide details about the book other than just high level info like the summary and no explicit deal with the publisher has been made, you can make a strong argument that it is plagiarism.

Even if bits and pieces of the book text are distributed across the internet and you end up picking up portions of the book, you still read the book.

It is extremely sad but ChatGPT will be taken down by the end of this year and replaced by a highly neutered model next year.

Sounds like an FB shill

Yea you can create the nicest office of all time, still wouldn't want to live there or trade it for my own private personal space.

Way happier to work hard and contribute to the company when I feel like I don't have to be forced to work in an office that isn't conducive to my life.

This is a pretty simplistic take tbh.

Reddit makes money from ads having reach and they're hoping that only a small percentage get premium so that they can continue to make money off of ads.

Why would they reduce the reach their ads are having by making it easy for people to opt-out?

Their API pricing probably reflects the money they are losing by not getting the users on their own platform and showing them ads.

Their hope is that people would switch over to the main app so that their advertisers can pay them more.

It's this perception that assumes that CEOs can do no right by the people if there is a financial interest in not doing so.

Whether you believe this is the case or not, you have to agree that all nuance goes out the window and the mob is only satisfied when it gets what it asks for, not what is in its best interests.

Yea the CEO's response was tone deaf and not tactful at all.

However, it seems insane that people are complaining about this for the following reason:

1. Reddit is not profitable, it is literally bleeding money. 2. No Reddit = No 3P apps to access Reddit. 3. The discussion that should be had is whether it is sustainable for Reddit to keep running it's servers and whether the recent decisions are made in favor of additional growth or survival.

I suspect you have adopted the speech patterns of people you respect criticizing LLMs of lacking “reasoning” and “understanding” capabilities without thinking about it carefully yourself.

1. How would you define these concepts so that incontrovertible evidence is even possible. Is “reasoning” or “understanding” even possible to measure? Or are we just inferring by proxy of certain signals that an underlying understanding exists?

2. Is it an existence proof? I.e we have shown one domain where it can reason, therefore reasoning is possible. Or do we have to show that it can reason on all domains that humans can reason in?

3. If you posit that it’s a qualitative evaluation akin to the Turing test, specify something concrete here and we can talk once that’s solved too.

Just because it makes mistakes on a domain that may not be part of it's data and/or architectural capabilities doesn't mean it can't do what humans consider "reasoning".

Once again, I implore you to come up with a working definition of "reasoning" so that we can have a real discussion about this.

Many undergraduates also confidently regurgitate incorrect proofs of linear algebra theorems, do you consider them completely lacking in reasoning ability?

If you understand transformers, you’d know that they’re doing precisely that.

They’re taking a sequence of tokens (symbols), manipulating them (matrix multiplication is ultimately just moving things around and re-weighting - the same operations that you call symbol manipulations can be encoded or at least approximated there) and output a sequence of other tokens (symbols) that make sense to humans.

You use the term “ascertain truth” lightly. Unless you’re operating in an axiomatic system or otherwise have access to equipment to query the real world, you can’t really “ascertain truth”.

Try using ChatGPT with gpt4 enabled and present it with a novel scenario with well defined rules. That scenario surely isn’t present in its training data but it will able to show signs of making inferences and breaking the problem down. It isn’t just regurgitating memorizing text.

Any program you write is encoded reasoning. I’d argue if-then statements are reasoning too.

Even if you do write a garbage next word predictor, it would still be reasoning. It’s just a qualitative assessment that it would be good reasoning.

Again, what exactly is your definition of reasoning? It seems to be not well defined enough to have a discussion about in this context.

I don’t think they’re mutually exclusive. Next word prediction IS reasoning. It cannot do arbitrarily complex reasoning but many people have used the next word prediction mechanism to chain together multiple outputs to produce something akin to reasoning.

What definition of reasoning are you operating on?

Ok I actually thought about this a fair bit a few days ago and I think I have a good answer for this.

You’ve probably heard of the cheap bar trick that goes something like: “And what does a cow drink? Milk!”.

Irrespective of intelligence, humans tend to make silly cognitive errors like this because we are fundamentally pattern marchers.

In order to become a forerunner in a field, you necessarily have to be good at abstract pattern matching.

What happens as you age is that you no longer have the need to question assumptions because you know what’s real and what’s not. There’s also the decrease of white matter and an increase of grey matter which doesn’t help this.

As time goes on, certain assumptions change, essentially deprecating certain chunks of your crystallized learnings.

Some chunks of your thinking are still valid, so when you think something can be done, it most likely can be done.

However, if something falls outside your crystallized learning, you get a strong sense it’s wrong, when it might be because of your outdated assumptions.

You can try to hotswap the assumptions you have, but it becomes like Jenga the more years of experience you have in your field.

You either have to start from scratch and rebuild your lifetimes worth of learnings from the ground up or be super careful in reassessing everything you know