Can you provide an example of where this has been successful?
I've spoken with many researchers and grad students only to find that there was a critical typo in the algorithm or undescribed setting (e.g., only converges when a learning rate scheduler is applied) in the code. I'll see the same algorithm implemented differently in different repositories. This is even the case for papers I've found with thousands of citations. It can be tremendously difficult to reproduce the results of papers, especially when they may require large amounts of compute that small researchers don't have. And I don't know whether it's a bug in my code or the paper or the algorithm.
I mean, what you describe is an unfortunate and unavoidable issue in academia (and in the world in general). GPT4 doesn't work magic here, of course.
You still have to:
1) Understand the work and the motivation. (GPT4 can help by playing the role of junior PhD if you can play the role of astute advisor.)
2) Sniff out things that are underspecified or seem wrong. (GPT4 also can help here, see above.)
3) Email the authors with questions, compare against shitty published codebases, etc.
depending upon how gnarly/rushed the prose is.
With that said, it's also "research smell" (compare "code smell") if a paper is so hastily written and undercited that you're the first person replicating it. And maybe instead of going for "my new bleeding edge approach that got 0.1% score better than boring old model with 50 cites", you probably should just implement boring old model.
So, where this has been successful for me is in implementing denoising diffusion for different problem domains. Given that there is broad literature on denoising diffusion, when some things are underspecified you can start looking at best practices for other researchers.
Alternately, other things like specific transformers etc.
Basically what I'm saying is that if you are trying to reimplement something that is so niche, it's like catching butterflies. A better research agenda involves working within a particular field of study where there is supporting evidence and approaches to compare and constrast to. This goes without saying, regardless of whether an AI is involved or not.
Interested if you could elaborate further here or have some pointers to good references. I feel like I'm at a turning point in my life where I'm starting to see things a little differently. Sometimes bewildered by how little knowledge or control there really is.
Thanks for the input Troy. Although I can see the reasons for it, transfer restrictions seems to me a little like the company has too much control over its shares - but like you said, they aren't "privately-traded securities". If I owned a restaurant with a buddy and wanted out, I imagine I wouldn't be able to just sell it on to somebody else without my buddy's consent.
And actually, re: "The only real, pragmatic usecases are fueling ransomware, drugs and illicit cross-border transactions", arguably, will all the KYC checks needed now along with all the coin tracking software being developed to track the blockchain, that's not longer the case.
KYC procedures defeat the purpose of BTC. If it is tightly controlled, regulated and guarded by the same regulator which manages USD, then why use bitcoin?
On the other hand, those KYC procedures can be enforced in some jurisdictions, but not in all of them. What power does IRS have in Iran or Russia? How difficult is it to buy an identity in places like Egypt or Nigeria? How expensive is it to get authorities close their eyes on anything in places like Libya or Somali?
Sure, but you can still track those individuals through the block chain and freeze all their other "classic" assets in the US. All I'm saying is that the argument that it's a good "anonymous" form of currency is not all that true. That's not to say that there aren't other currencies out there that prioritize anonymity (e.g. Monero), but when we're focusing on Bitcoin here.
Yeh, I had actually briefly looked into after writing that comment and found the same - interesting that Paul came up with his own version of LISP to do it in.
If you wanted to remake it in a modern framework though, what stack would you personally opt for? I think Postgres or MySQL for the database is a no-brainer, but what about the quickest approach for the rest (bar of course simply cloning the repo ;) )
Erlang/Elixir do much better at utilizing all CPU cores effortlessly -- without you having to lift a finger. In Elixir's case just use the Phoenix framework and you get something that can serve 2000 requests per second on a $5 VPS host.
Here's perhaps a controversial and less heard argument:
The core concept of bitcoin is that it is decentralized and away from the prying "government". That's what also gives it its "safe haven" allure in much the same way as gold. The question is, is this really to society's benefit? Just think about this past year with COVID - if government didn't have the ability to impact the financial markets and everything was strictly down to human emotion, I'm sure we'd be in a worse place than we are now. Same goes for 2008. Through its actions, the FED is trying to actively avert financial distress - its goal is stability. As a corollary, if you believe that bitcoin will be there to save the day if the entire modern financial system collapses, then I believe you would be sorely mistaken. I'm not sure how such a technology and government infrastructure dependent currency would be usable in a world that has descended into chaos.
I mean just think about a world where Bitcoin is the only currency. So the mega-wealthy are suddenly some early hoarders of the currency who, by holding onto their currency, see their overall wealth INCREASE (through depreciation and constriction of the monetary supply). A currency's main role should be to promote and facilitate financial activity and trade... They sit their with their thousands of bitcoins whilst the rest of the world is transacting in smaller and smaller fractions of bitcoin.
Imagine back in Medieval times: A king sits on his throne with a pile of gold behind him. If he chose not spend it spend and simply kept taxing his people, he would not only accumulate more gold, but the value of every piece of gold would suddenly increase (in the peasant's economy) as the entire peasant population had less gold to do trade with (depreciation). This would further encourage hoarding money instead of using it - i.e. the whole reason currency depreciation is harmful to financial activity.
Thanks for the brutally honest reply :) I guess part of my question is Django still as relevant today or is it losing 'market share' and finding devs to help out in the future is going to prove a lot more difficult.
I agree with OP, pick the hyped framework if you want to learn or get a job. If you want to build then use tools you are comfortable with. Django is not going away, I would say it would still be around in 10 years but latest hype framework is less likely to be around in 10 years.
Even Nicolas Taleb talked about it in more general terms. Bible is more likely to be around in 2000 years than latest bestseller.
