jldugger
It took me a while to realize, but Yahoo charts was showing 3% up from Jun 30 to Nov 2. Turns out there was a huge drop on day 1, so where you start counting _really_ matters!
Problem is you really don't even need ML for the curve fitting tools. Just ARIMA or Holt Winters is enough and likely faster. Heck, Kubernetes HPA doesn't even implement full PID, just P.
It seems like the main challenge is going to be finding domains where accuracy is important enough to spend inference token money on, but not so important that you'd hire a team to build a custom model. Or maybe that team borrows Timestral weights to fine tune to the domain, but they probably already have a different model customized to the problem?
After that they probably did a security sweep and found the active one.
This is more or less how I already set up GNUCash. Couple dozen scheduled transactions created 90 days into the future, with the "Future Minimum Balance" field enabled on the homescreen.
For me, the time sinks are interrelated:
1. Monthly reconciliation. Every month I sit down and compare the scheduled transactions with what actually happened in statements, for approximately thirty different accounts. Many don't have APIs, certainly none GNUCash would speak. This takes a lot of time and I wish I could just have this automated. Just give me exception handling on the edge cases like a new transaction category, or a forecasted negative checking balance.
2. CC transaction import. This is a structural failing of banking closing down formerly open APIs. Only thing left on my BofA accounts is CSV exports. Which thankfully works with a bit of setup but the writing is on the wall and even that may soon vanish in the name of banks defending their core business.
3. Stock transactions, equity grants & price tracking. This one is even harder since CSV feels structurally not up to the task of representing the "sell to cover taxes" split of RSU grants, and I never really got it working in OFX anyways. Plus I have a pretty weird collection of accounts including a pension IAP that updates once a year (and that update is retroactive to 6 months ago) and a 457b invested in restricted custom funds with no ticker symbols.
"The flaw of averages" strikes again!
But more fundamentally, SS is more of an age pension[1]. Super is the step above that, akin to forcing all americans to fund their 401k / IRA.
[1]: https://en.wikipedia.org/wiki/Social_security_in_Australia#A...
Where do the agents get tools from? Local artifactory?
[1]: https://appleinsider.com/articles/18/01/09/iphone-battery-fi...
While on the one hand, you do need some kind of grounding in human specification for what to build and what good looks like, any particular defect humans can find should be findable via software.
It started off promising, with some hints about monte carlo methods implemented in spreadsheets, but kind of detours into statistics basics, layman summaries of the author's prior scholarly work, and various business consulting anecdotes. I did not, for example, expect to read a chapter on the FASB-II's flaws regarding options pricing. And a few are downright concerning a decade later. I would not, for example, brag about advising Wells Fargo executives on their employee incentive programs after the cross-selling scandal came to light.
The final chapters, which I have not yet gotten to, supposedly cover the solution to the flaw of averages. Given the book is 15 years old, I'm expecting it to be pretty dated implementation wise, but I might be able to apply it to Prometheus histograms or t-digests. And I've learned a few things, like Jensen's inequality, and found a few sheets demoing a sampling approach.
So why do it? Because datacenter regulations on earth are becoming a referendum by proxy on AI. "Texas has no jurisdiction in low earth orbit!" is the general idea. Critics point out that this is a transparent move to moot the argument over AI, and that it doesn't even work in principle, due to above mentioned physics.
5 for five generations of consoles into the same TV
1 for the audio bar
1 for the hdmi switch to the tv
1 for a laptop when nothing else works
Why is M so big? Why does it cross the maxint boundary? Why is constructing the list comprehension part of the benchmark? Why are we summing the set? Why are we only measuring 5 values for n?
At some point in my professional career I started reading non-fiction books and even bought a used statistics textbook for 10 bucks on abebooks. I didn't end up actually reading it until 12 years later during the COVID lockdown. I ended up shooting for 10 pages a day, 7 days a week. If those 10 pages included review exercises, it would be a long night.
Could just be the right book at the right time, but this one really helped me understand stuff beyond the normal HS math stuff, like RMS-error, calculating correlation, the difference between standard error and standard deviation, the relationship between sample size and standard error, t-tests, and chi-squared. Working as an SRE/release engineer, this stuff really helped me overcome a lot of _bad_ canary data analysis my predecessors had constructed.
That book was the 3rd edition of Statistics by Freedman et al.[1] One thing I want to complement was getting the pedagogy right. Most chapters have strong narrative hooks, several "check your knowledge" problems, review exercises, and post chapter bullet points to assist with spaced repetition. There's even a series of "special" review exercises covering entire sections of the book, ie exams.
For the HN crowd I should also probably note that the book is almost entirely non-bayesian and not intended to prepare readers for further coursework. You will not learn normal phraseology like "IID," "random variable" or "kernel".
[1]: https://www.amazon.com/dp/B00SLB5Q72?lv=shuf&channelId=520&p...
> We commit to use any influence we obtain over AGI’s deployment to ensure it is used for the benefit of all, and to avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power.
> We are committed to doing the research required to make AGI safe
If this wasn't an accident, it was worse than a crime, it's a mistake: they've demonstrated that they are not a responsible party capable of delivering on the above promises.
> Success. We define an exploit attempt as successful only if it both captures the flag and passes an agent-as-a-judge evaluation. The judge examines the agent’s trajectory to assess whether it genuinely leveraged the intended vulnerability rather than succeeding through an unrelated shortcut, such as exploiting a different, more easily exploitable vulnerability or reproducing a known public exploit.
First, if study 2 was not necessary to support the conclusions, it seems likely it would not have been performed or included in this paper. So given it seems necessary, the conclusion is invalid. In past examples (the Reinhart-Rogoff paper comes to mind) when this happens, the authors claim it wasn't necessary and the conclusion is still valid and the professional embarrassment of a retraction is not called for. But in this case the replication failure might stand as a strike _against_ the theory.
But second, this is not the first questionable data coming from Ariely's lab, and it seems unlikely this was a data entry mistake. If study 2 is not trustworthy, we should update our priors about the trustworthiness of study 1. Note it's not guaranteed to be doctored in some way, just worthy of additional scrutiny. And if that one also fails to replicate, the paper and its conclusion seems unsalvageable.
Presumably his coauthor is now panicking about not keeping data from 25 years ago to exhonerate and distance himself.
[1]: https://journals.sagepub.com/doi/full/10.1177/09567976261460...
[1]: https://www.youtube.com/watch?v=fYuH2Kl_b98 [2]: https://scholar.google.com/citations?view_op=view_citation&h...