Petropolitan

Karma: 158

Petropolitan 3 Dec 2025 12:26 UTC
1 point
0
in reply to: pier’s comment on: debating buying NVDA in 2019
Thanks, I have seen that and thought about that. The best explanation I have come up with is that Meta is not a really a competitor to Google anymore, they lag way behind (there’s also a hypothesis of “offloading” TPU depreciation from GCP to clients, but that seems less important than profits from the GCP, and a possibility of Meta promising to integrate TPUs and PyTorch).
SemiAnalysis reported that a similar deal might have been considered with OpenAI and Anthropic is actually buying the hardware (in parallel to renting it), but this looks dubious for me, why would they do that, eating into the profit of their cloud services and competing with them? SemiAnalysis offers no explanation

Petropolitan 19 Nov 2025 10:32 UTC
1 point
0
in reply to: Carl Feynman’s comment on: Buck’s Shortform
TLDR; you probably already know that 2^10=1024, use this to derive powers of 10 instead of memorizing!
https://en.wikipedia.org/wiki/Renard_series, which were designed in the 1870s to be convenient for the officers and engineers of the French Army without a slide rule or a log table, are based on 5th and 10th roots of 10. 1024 being quite close to 1000 means that $\sqrt[3]{2}$ is very close to $10^{0.1}$ , and this allows you to quickly derive R10 numbers without pen and paper.
I have used the algorithm for so long that it has become almost unconscious so I had Gemini write it out:
Mental Algorithm for R10 Numbers (sorry for poor formatting, it doesn’t copypaste neatly, I only fixed manually where it doesn’t read well)
In the R10 series, every step increases the value by a factor of $\approx 1.26$.
However, since $2^{10} \approx 10^{3}$ :
$10^{3 / 10} \approx 2$
This gives you the Golden Rule of R10:
* Add 3 to the Index $\to$ Multiply Value by 2
* Subtract 3 from the Index $\to$ Divide Value by 2
### The Algorithm: The Three Strands
To find any R10 number mentally, you don’t calculate them sequentially. Instead, you split the numbers 0–10 into three “strands” based on the anchors you already know: **1**, **8**, and **10**.
#### Strand A: The Powers of 2 (Indices 0, 3, 6, 9)
Start at **1** and double it every 3 steps.
* R10(**0**) = **1.0**
* R10(**3**) = **2.0**
* R10(**6**) = **4.0**
* R10(**9**) = **8.0**
#### Strand B: The Halving from 10 (Indices 10, 7, 4, 1)
Start at **10** and halve it every 3 steps (going backwards).
* R10(**10**) = **10.0**
* R10(**7**) = **5.0**
* R10(**4**) = **2.5**
* R10(**1**) = **1.25**
#### Strand C: The “80% Rule” (Indices 8, 5, 2)
This is the hardest strand because it doesn’t land on a clean integer.
We derive this by starting at R10(9), which we know is **8.0**, and going **down 1 step**.
Mathematically, going down 1 step is dividing by $1.2589...$, which is almost exactly multiplying by **0.8**.
* Start at R10(9) = 8.0.
* **R10(8)** $\approx 8.0 \times 0.8 =$ **6.4** (Anchor)
* Now, apply the “Subtract 3 is Half” rule:
* **R10(5)** $\approx 6.4 / 2 =$ **3.2**
* **R10(2)** $\approx 3.2 / 2 =$ **1.6**
### Summary Table (Mental vs Actual)
By using this mental model (Doubling, Halving, and the 0.8 factor), your approximations are incredibly close to the standard values.
| Index | Mental Derivation | Approx Value | Actual R10 Value |
| :--- | :--- | :--- | :--- |
| **0** | Base | **1.00** | 1.00 |
| **1** | $10 \div 8$ | **1.25** | 1.25 |
| **2** | $3.2 \div 2$ | **1.60** | 1.60 |
| **3** | $1 \times 2$ | **2.00** | 2.00 |
| **4** | $5 \div 2$ | **2.50** | 2.50 |
| **5** | $6.4 \div 2$ | **3.20** | 3.15 |
| **6** | $2 \times 2$ | **4.00** | 4.00 |
| **7** | $10 \div 2$ | **5.00** | 5.00 |
| **8** | $8 \times 0.8$ | **6.40** | 6.30 |
| **9** | $4 \times 2$ | **8.00** | 8.00 |
| **10** | Base | **10.00** | 10.00 |
### Quick Reference for Your Brain
1. **0, 3, 6, 9:** Just say **1, 2, 4, 8**.
2. **1, 4, 7:** Start at 10 and halve backwards ($10 \to 5 \to 2.5 \to 1.25$).
3. **2, 5, 8:** Remember **6.4** (from $8 \times 8$), then halve backwards ( $6.4 \to 3.2 \to 1.6$ ).
For the 5th R10 number you can also use the coincidence that square root of 10 is close to pi (used in antiquity to approximate pi), or for the 8th number you can use ${2.5}^{2} = (2 + 0.5) * (3 - 0.5) = 2 * 3 + 1.5 - 1 - 0.25 = 6.25$ (I personally have just memorized it in middle school), but neither is really necessary for mental calculations

Petropolitan 16 Nov 2025 20:02 UTC
1 point
0
in reply to: jimrandomh’s comment on: Everyone has a plan until they get lied to the face
As a side note, Secret Hitler is actually a tabletop game which is about convincingly lying and identifying lies, but also counting probabilities like in poker. If the post author hasn’t tried it, could recommend

Petropolitan 9 Nov 2025 14:31 UTC
1 point
0
on: A country of alien idiots in a datacenter: AI progress and public alarm
They are used for cybercrime and rumored to be deployed for state-funded espionage.
To make it a little more substantial, web browsing agents with some OSINT skills (and multimedia models already geolocate photos made in Western urban areas comparably with human experts) offer prospects of automatizing or at least significantly speeding up (making much cheaper) targeted attacks like spearphishing

Petropolitan 9 Nov 2025 12:39 UTC
3 points
0
in reply to: Ben Pace’s comment on: Legible vs. Illegible AI Safety Problems
A quick side note: in the 17 years which have passed since the post you cite had been written historiography of connectionism moved on, and we now know that modern backpropagation was invented as early as 1970 and first applied to neural nets in 1982 (technology transfer was much harder before web search!), see https://en.wikipedia.org/wiki/Backpropagation#Modern_backpropagation and references thereof

Petropolitan 9 Nov 2025 12:26 UTC
1 point
0
in reply to: jmh’s comment on: EU explained in 10 minutes
I think it does, among other things, actually investigate cross-border crime but just on a small scale due to limited resources, check this: https://www.frontex.europa.eu/what-we-do/operations/operations

Petropolitan 26 Oct 2025 21:57 UTC
3 points
2
on: EU explained in 10 minutes
police force
Actually, since 2016 EU has a relatively small (~3700 officers as of this writing, which is about ¹⁄₆ larger than police of Luxembourg) border police force called Frontex! EU president would like to increase it an order of magnitude in a few years, but member states are not very enthusiastic

Petropolitan 12 Aug 2025 17:57 UTC
3 points
0
in reply to: Thomas Kwa’s comment on: Thomas Kwa’s Shortform
Why would anyone want to pay a fortune for a system that is expected to let ~40 warheads through (assuming ~99% overall interception rate which will require average rate of 99.99+%), about the same as the number of ICBMs the Soviet Union had in service during the Cuban Missile Crisis? Unacceptable damage is the cornerstone of the nuclear deterrence, MAD or not (there is no MAD between India and Pakistan, for example).
The RV separation distance is normally around ~100 km (even up to 300 km in some cases) not 10 km, and the decoy dispersal might be expected on the same order of magnitude. It will be easy to ramp it up BTW with a cheap modernization.
None of the US adversaries really practice counterforce targeting, so the silo protection is moot.

Petropolitan 12 Aug 2025 17:35 UTC
1 point
0
in reply to: Kaj_Sotala’s comment on: Kaj’s shortform feed
lower EQ
I don’t think it’s relevant here: judging by the EQ-Bench leaderboard, GPT-5 is on par with GPT-4o and has far higher EQ than any of the Anthropic models!
Even if it has some influence, it should be much less than the emoji usage (remember the scandal about the Llama 4 on LMSys) and certainly incomparable to the sycophancy

Petropolitan 11 Aug 2025 9:31 UTC
7 points
4
in reply to: Adam Newgas’s comment on: Adam Newgas’s Shortform
I like to imagine the whole GPT-5 launch from the perspective of a cigarette company.
OpenAI is Philip Morris over here. Realized they make a product that addicts and hurts people. Instead of feeding it, they cut it off. The addicts went insane and OpenAI unfortunately caved.
— u/ohwut at https://www.reddit.com/r/OpenAI/comments/1mlzo12/comment/n7uko9n

Petropolitan 11 Aug 2025 8:59 UTC
1 point
0
in reply to: Foyle’s comment on: Thomas Kwa’s Shortform
one or more warheads are blown up at limits of interceptor range
Not range but height. You blow up a warhead high enough the drones can’t intercept it, and all the drones below fall out of the air

Petropolitan 11 Aug 2025 8:35 UTC
3 points
0
in reply to: Thomas Kwa’s comment on: Thomas Kwa’s Shortform
You seem to believe that radars and infrared cameras can somehow distinguish between the decoys and the warheads, but they can’t. In space, no radar and no IR camera can differentiate between a conical foil balloon with a small heater inside and a reentry vehicle with a nuke.
Another problem of ballistic missile defense is that once you are dealing with nukes and not conventional warheads, you can’t afford, say, a 97% average interception rate, it must be 99.999+%^[1]. To put this in context, Israel, which currently has the best BMD system in the world, couldn’t even reliably achieve 90% against Iranian MRBMs (and those are pretty unsophisticated, e. g. they lack MIRVs and decoys).
Now calculate how many interceptors your plan requires for a plausible probability of an interception with a single drone, and you will see it’s entirely unworkable. Note that both arguments are based on simple physics and math so don’t depend on the progress in technology at all.
If you are interested in the topic, I strongly recommend reading on the Soviet response to SDI for more expensive anti-ABM options that were considered but ultimately not pursued: https://russianforces.org/podvig/2013/03/did_star_wars_help_end_the_col.html
1. ^
  When this seemingly waterproof probability is raised to the power of the Russian warhead count it still results in ~4% (basically 1e-5 times ~4k) of at least one RV not intercepted, and in reality hundreds of warheads will be harder to intercept than the average one you accounted for when calculating your probability. E. g., drones work poorly in bad weather, and it’s almost always bad weather above at least some of American cities

Petropolitan 7 Jul 2025 12:35 UTC
4 points
1
in reply to: Tom Davidson’s comment on: The Industrial Explosion
Normal timescales for building new mines using mature, already well-established technologies is over a decade for exploration to feasibility, 1.8 years of construction planning and getting environmental permits (unlike a robot factory, which you may built almost whenever you like, you have to build a mine on the actual minerals) and 2.6 of construction and production set up: https://www.statista.com/statistics/1297832/global-average-lead-times-for-mineral-resources-from-discovery-to-production This is the timescales on which commodity cycles boom and bust.
I do not assume that “innovation may lose”, I actually do not consider new technologies for mining resources whatsoever in my reasoning, only the application of existing technologies to the deposits formerly uneconomical to extract. I try to explain that all these processes are just much slower than the timescales you are discussing.
And you seem to have missed my argument about capital: if the interest rates skyrocketed due to transformative AI (see, e. g., https://www.lesswrong.com/posts/k6rkFMM2x5gqJyfmJ/on-ai-and-interest-rates), how would you finance all these mines?

Petropolitan 28 Jun 2025 14:20 UTC
5 points
−5
on: The Industrial Explosion
Downvoted the post (which I do very rarely) because it considers neither the Amdahl’s Law nor the factors of production, which is Economics 101.
Fully automated robot factories can’t make robot factories out of thin air, they need energy and raw materials which are considered secondary factors of production in economics. As soon as there appears a large demand for them, their prices will skyrocket.
These are called so because they are acquired from primary factors of production, which in classical economics consist of land, labor and capital. Sure, labor is cheap with robots but land and capital will become very costly because they are complements. These primary and secondary factors will become bottlenecks, making the discussion of theoretical doubling rates moot.
Note that during most of European 2nd millennium, including times of Adam Smith and Karl Marx, labor was the most abundant and cheap primary factor, so that reversal from the now-expensive labor would not be something extraordinary.
P. S.
The following might not apply to the “post-AGI” world but this post gives a hint how hard automating manufacturing actually is: https://www.lesswrong.com/posts/r3NeiHAEWyToers4F/frontier-ai-models-still-fail-at-basic-physical-tasks-a

Petropolitan 4 Jun 2025 14:15 UTC
9 points
0
on: Have LLMs Generated Novel Insights?
Does this count? A new mathematical result, particularly a proof of what a professional mathematician was intuitively sure of, but only a small part of an actual paper (literally just one proposition), and also took some prompt engineering https://x.com/bremen79/status/1927768299271496008
What links here?
- LLM Daydreaming (gwern.net) by Noosphere89 (21 Jul 2025 16:50 UTC; 18 points)
- Cole Wyeth's comment on LLM Daydreaming (gwern.net) by Noosphere89 (21 Jul 2025 21:32 UTC; 2 points)

Petropolitan 31 May 2025 15:41 UTC
1 point
0
on: Meditations on Doge
Poland and Estonia, ended up much better off than those that chose a gradual approach, like Bulgaria and Ukraine
The main reason of the hardness of the crash of Ukrainian is the large share of advanced defense industry (which almost ceased to exist) in the GDP in 1990, as well as advanced civilian industries which were reliant on their partners in other Soviet republics. Belarus, which had similar economy structure but smaller share of defense industry in particular, which maintained economic ties to Russia and which implemented even less reforms and even more gradually than Ukraine, weathered better in the 1990s and might provide a counterexample to this thesis (even if the end point long-term is awful).
Also, both Bulgaria and Ukraine are further from rich Western/Northern European markets. Poland in particular borders Germany, which provides all kinds of benefits. Even then, Polish and Bulgarian GDP per capita were similar at the moment of their EU ascension (2004 and 2007 respectively). I do not exclude that Bulgaria had their own self-inflicted problems but you have to compare against Romania and Hungary to demonstrate that!

Petropolitan 10 May 2025 10:01 UTC
7 points
0
in reply to: Thane Ruthenis’s comment on: Short Timelines don’t Devalue Long Horizon Research
P. P. S.
In the month since writing the previous comment I have read the following article by @Abhishaike Mahajan and believe it illustrates well why the non-tech world is so difficult for AI, can recommend: https://www.owlposting.com/p/what-happened-to-pathology-ai-companies

Petropolitan 28 Apr 2025 9:18 UTC
2 points
0
in reply to: Tobias H’s comment on: Accountability Sinks
I guess it was usually not worth bothering with prosecuting disobedience as long as it was rare. If ~50% of soldiers were refusing to follow these orders, surely the Nazi repression machine would have set up a process to effectively deal with them and solved the problem

Petropolitan 22 Apr 2025 13:49 UTC
1 point
0
on: Training AGI in Secret would be Unsafe and Unethical
Continuing the analogy to the Manhattan Project: They succeeded in keeping it secret from Congress, but failed at keeping it secret from the USSR.
To develop this (quite apt in my opinion) analogy, the reason why this happened is simple: some scientists and engineers wanted to do something so that no one country could dictate its will to everyone else. Whistleblowing project secrets to the Congress couldn’t have solved this problem but spying for a geopolitical opponent did exactly that

Petropolitan 17 Apr 2025 11:36 UTC
3 points
0
on: Surprising LLM reasoning failures make me think we still need qualitative breakthroughs for AGI
In my experience, this is a common kind of failure with LLMs—that if asked directly about how to best a solve problem, they do know the answer. But if they aren’t given that slight scaffolding, they totally fail to apply it.
The recent release of o3 and o4-mini seems to indicate that diminishing returns from scaling are forcing OpenAI into innovating with scaffolding and tool use. As an example, they demonstrated o3 parsing an image of a maze with an imgcv and then finding the solution programmatically with graph search: https://openai.com/index/thinking-with-images
I believe that it won’t be hard to help reasoning models with the scaffolding you discuss and RL them to first think about which tools are most suitable, if any, before going on with actually tackling the problem. Afterwards any tasks which are easily solvable with a quick Python script won’t usually be a problem, unless there’s some kind of “adversarialness”, “trickyness”.
P. S.
And on the topic of reliability, I would recommend exploring PlatinumBench, which is a selection of hundreds of manually verified reasonably easy problems on which SOTA LLMs still don’t achieve 100% accuracy. The amount of mistakes correlates very well with the actual performance of the model on real-world tasks. I personally find the commonsense reasoning benchmark Winograd WSC the most insightful, here’s an example of puzzling mistakes SOTA LLMs (in this case Gemini 2.5 Pro) make in it sometimes:
**Step 6:** Determine what logically needs to be moved first given the spatial arrangement. If object A (potatoes) is below object B (flour), and you need to move things, object A must typically be moved first to get to object B or simply to clear the way.