Hey, I’m Owen.
I think rationality is pretty rad.
As a defi user / Ethereum dev, there are two things that seem relevant:
Many of the use-cases for which Kleros seems good for (e.g. arbitrating social contracts, prediction markets, etc.) is not where most of the capital is on Ethereum. Most capital is engaged in various overcollateralized lending/borrowing, LP, derivatives, synthetics, etc. So until we see additional applications which run on the social layer, I don’t think the utility of Kleros will be made that apparent.
Partially related to the above, the lack of social-based applications I think has to do with a combination of generally poor UX and high latency for Ethereum. We’re seeing some exciting updates on this front with rollup solutions like Optimism and zkSync which can provide ~10x increases to start, but again, the ecosystem hasn’t really caught up.
More broadly, I think that attention in crypto markets shifts often, and valuations are kind of all over the place. I think I believe that scalable protocols for dispute arbitration if done well can be worth several multiples of Kleros’ market cap, but also it’s unclear if Kleros is that specific protocol? I personally need to dive into this more.
ah yes, the proof of stake bridge is faster.
i guess it depends if you’re running this strategy with size. e.g. for over $100,000, 10% returns means you’d earn back gas fees in ~3 days.
fyi you can get around half these returns on aave on ethereum mainnet without having to mess with matic at all.
while i don’t think the matic team is untrustworthy, it’s worth pointing out their entire network is currently secured by an upgradeable multisig wallet.
there is also a ~1 week period to move back from matic to ethereum mainnet which can be irksome if you e.g. want to sell quickly back to fiat via some centralized exchange.
Just chiming in here to say that I completely forgot about Intercom during this entire series of events, and I wish I had remembered/used it earlier.
(I disabled the button a long time ago, and it has been literal years since I used it last.)
Thanks for this summary of our post.
I think one other sub-field that has seen a lot of progress is in creating somewhat competitive models that are inherently more interpretable (i.e. a lot of the augmented/approximate decision tree models), as well as some of the decision set stuff.
Otherwise, I think it’s a fair assessment, will also link this comment to Peter so he can chime in with any suggested clarifications of our opinions, if any.
Ah, I didn’t mean to ask about the designing part, but moreso about how you use the word optimize in your definition when it comes to ‘optimizing from scratch’, which might get a little recursive.
Your definition of optimizer uses “optimizing that function from scratch” which might need some more unpacking.
You may be interested in this prior discussion on optimization which shares some things with your definition but takes a more control theory / systems perspective.
I have not read the book, perhaps Peter has.
A quick look at the table of contents suggests that it’s focused more on model-agnostic methods. I think you’d get a different overview of the field compared to the papers we’ve summarized here, as an fyi.
I think one large area you’d miss out on from reading the book is the recent work on making neural nets more interpretable, or designing more interpretable neural net architectures (e.g. NBDT).
Thanks! Didn’t realize we had a double entry, will go and edit.
buy defipulse index
For even higher variance crypto:
buy defi small cap
get eth, turn it into st-eth/eth LP on curve.fi, and then stake into the harvest.finance st-eth pool for ~30% APY on your eth
In case you haven’t seen, similar projects exist:
See also previous discussion here
Thanks for the feedback Emiya! I hope it ends up being useful for helping you get what you want to get done, done.
I never got the chance to update here, but I cleaned up some of the essays in the years since writing this series.
They can now be found here. Of note is that I massively edited Habits 101, and I think it now reads a lot tighter than before.
The extent to which this app is used and to which people bond over the assistant.
my friend from china says this is likely sensationalized.
agree w/ gwillen about being skeptical.
Seconding the Boox Note as being a very good device I’m overall pleased with.
(I have the large 13 inch Boox Note Max which makes reading papers very bearable, and it can do file drop via local wifi.)
The way I did this for a specific ordering of cards (used for a set of magic tricks called Mnemonica) was to have some sort of 1 to 1 mapping between each card and its position in the deck.
Some assorted examples:
5 : 4 of Hearts because 4 is 5 minus 1 (and the Hearts are just there).
7 : Ace of Spades because 7 is a lucky number and the Ace of Spades is a lucky card.
8 : 5 of Hearts because 5 looks a little like 8.
49 : 5 of Clubs because 4.9 is almost 5 (and the Clubs are just there).
This is a good point, and this is where I think a good amount of the difficulty lies, especially as the cited example of human interpretable NNs (i.e. Microscope AI) doesn’t seem easily applicable to things outside of image recognition.
My understanding is that the OpenAI Microscope (is this what you meant by microscope AI?) is mostly feature visualization techniques + human curation by looking at the visualized samples. Do you have thoughts on how to modify this for the text domain?
Same here. I am working for a small quant trading firm, and the collective company wisdom is to prefer CDFs over PDFs.