Facebook keeps showing me ads (in this case – a vest) and occasionally chooses very “successful” spots for a freeze frame that serves as a video preview in the feed. But, I must say, it achieves its goal and I click to see what kind of madness this is.
Let’s be honest about the art market (and why the prices there are insane).
Actually, there’s no mystery to it. It works somewhat like NFTs, only with a longer history and a better reputation.
The scheme is simple:
You take an item that hasn’t been on the market in this form yet (a painting, sculpture, installation — doesn’t matter). You call it an “important artifact”. It helps if you have connections — galleries, auction houses, billionaire collectors. If you don’t have connections, then find someone who does and sell the cow to them. Since uniqueness is required, there will be no paintings of bears in a pine forest, no matter how brilliantly they are done. There will be something distinct.
The very notion of “this is a painting/sculpture” — is just a convenient formality. The main thing is that the object can be incorporated into the already established art trading system.
Art is such — one of the most convenient ways to “optimize” taxes and move large sums of money. Paid 18 million euros for someone else’s work, and then someone “on your side” bought some of your work for the same 18 million. Virtually no money was really lost (just taxes), and now in catalogs and rankings, two works are each priced at 18 million. The price can be pushed up by selling them cascadingly. Win-win. Auctions are just in on the deal. Further, if you donate this work to a museum for charity, you can even cut more taxes. But it can also be sold. And here’s why.
Currently, there are simply too many free funds in the world. The number of billionaires and their wealth is growing faster than the availability of truly rare assets (real estate, companies, gold, etc. have all been divided already).
Art is one of the few markets where “scarcity” can still be created literally out of nowhere.
If you have access to a hundred such wealthy simpletons and you can tell stories (“this is an investment for 20-30 years, it will only increase”), then selling is purely a technical matter. Two or three interested parties = bidding already starts, and already you see +50-100% to the price.
Over time, real cases appear: someone bought in 2000 for 2 million, sold in 2024 for 80 million.
These cases are used to convince the next buyers. New buyers with their money confirm and amplify these cases. The cycle is closed.
Result: the rise in prices in the top segment of art is directly tied to the increase in the number of super-rich and their capital. As soon as a serious global crisis occurs and the extra trillions stop being printed/earned, and the pyramid collapses, the market will very quickly show where there was real cultural value, and where it was just a beautiful financial scheme.
P.S. This doesn’t mean at all that all contemporary art is a bubble. There are works that are really important historically and culturally. It’s just that at the very top of the price pie, cultural value has long ceased to be the main driver.
But at the summit of Olympus of the most expensive paintings of classic genius solitaries, there will never be, because galleries and dealers need artists who can produce 20-50 works a year to satisfy demand, organize exhibitions in five capitals simultaneously, and maintain turnover. Artists like Lopez Garcia, Odd Nerdrum, or Ron Mueck make unique pieces that will become especially valuable only after the artist dies.
What a delight, Windows 95 in the browser copy.sh/v86.
This must be about my tenth staging of Le Nozze di Figaro. And I still can’t understand why no one ever bothers to make modern, well-constructed subtitles instead of something that resembles a product of prehistoric “Google translate”. Every single line is translated from Italian in such a barbaric way that it’s about to make one’s eyes bleed. And for what reason? The Italian is certainly not modern either, but if you undertake to translate, then do so in a way that the meaning can be grasped within those tens of milliseconds when your eyes dart to the screen. Now, you land on a text that takes a minute to chew over… Every woman makes me change colour… Instead of make me blush or make me pale… If you are fain to dance… And all that stuff.
Cloudflare broke down. Comrade asked a very valid question
A cryptic post today. While writing a book on RecSys, I caught myself thinking that modern data science is essentially the alchemy of the 21st century. Half of the “best practices” in algorithms lack a solid mathematical framework. It’s a set of heuristics that “just work”. Much like in the 17th century where they mixed everything indiscriminately, it happens now, and if something works better, everyone else starts doing the same. There’s just no answer to the question “why”.
Take, for example, the NCF/NeuMF (Neural Collaborative Filtering) algorithm. The logic goes like this. Say, there are a million movie ratings by users. And 100 million ratings by users yet given – users can’t watch every movie in the world. But out of these 100 million, you need to choose candidates for advertising for a particular user. The algorithm, of course, has a training phase, where weights are calculated, and a prediction stage, where these weights are used on the incoming data.
(What the algorithm does. Essentially, it’s an ensemble of three sub-algorithms, two of which generate their own conclusions, and then their decisions go to a new neural network, the third algorithm, which provides the final recommendation. Smartly, it’s a hybrid of GMF (matrix factorization) and MLP (Multi-Layer Perceptron). The first of these two is based on matrix decomposition, and the second represents a neural network with multiple layers. Weights are adjusted on training data.)
For one positive example, it takes 4 negative ones. Why four? Just because it’s “not too many and not too few”. Would 8 be better? Unknown, but it would definitely take longer to learn.
Why are embedding dimensions 32? or 64? There’s no formula. It’s the “golden mean” between a “dumb” model (few k) and an “overtrained” (many k).
Now about the neural network. Why is the MLP block built as a “tower” (64 -> 32 -> 16)? Why not (50 -> 25 -> 10)? Why ReLU between them (and not tanh for example)? Pure empiricism. The number of layers in the tower is also adjusted.
Why do GMF and MLP parts have different embeddings at the input? Because the authors of the paper tried it, and it “worked out better”. No mathematical proof. Why do they go to the final layer with equal weights? Because they just do.
Why are the outputs of the two paths “concatenated” (concat), and not added or multiplied? “Experience showed that this way the result is more accurate.”
And so it is with everything, up to the choice of optimizer Adam or the “magical” learning_rate=0.001, although at least these have some mathematical basis.
That is, at least a dozen parameters of one algorithm are empirically chosen, with no clear confidence that they are independent of each other. But many of them depend on the dataset, but no one knows how 😉
In general, alchemy.
I was reading Metchnikoff’s biography (don’t ask why I ended up there) and thought about how much can fit into one life. He wasn’t just a scientist, but rather like a saga:
His elder brother Ivan was the prototype for Leo Tolstoy’s “The Death of Ivan Ilyich.” Another brother, Lev, was a prominent anarchist, sociologist and fought in Italy alongside Garibaldi. Metchnikoff himself tried to end his life twice: the first time after the death of his first wife (who, sick with tuberculosis, was carried to the church on a chair). He took morphine but survived. The second time was when his second wife Olga fell critically ill with typhus. He deliberately inoculated himself with relapsing fever. Fortunately, both survived. However, the Grim Reaper with his scythe only came after his third consecutive heart attack.
The dude graduated from university at 19 as an external student. I.M. Sechenov himself recommended him for a professorship. But Metchnikoff was “blackballed” (rejected) by one vote. In protest, Sechenov resigned along with him.
He founded the first bacteriological station in the country at that time in Odessa. But due to an employee mistake (they spoiled the anthrax vaccine) an entire flock of sheep died. After this scandal, he left Russia. The station — on Leo Tolstoy Street.
In Paris, he was immediately taken under the wing of Louis Pasteur (the father of pasteurized milk), who supported his theory and gave him a lab in his institute. There, Metchnikoff worked for 28 years, becoming the deputy director.
While studying cholera at the Pasteur Institute, Metchnikoff proposed a theory that not everyone who comes into contact with the pathogen gets sick. He suggested that it’s all about… (of course) the gut flora. To prove it, he deliberately drank a culture with cholera vibrios. Nothing happened (it would have surely happened to you, Metchnikoff thought)
In the end, he received the Nobel Prize for the discovery of phagocytosis (cellular immunity). He is also “the father of gerontology” — Metchnikoff was the one who proposed the theory that to achieve longevity, one must combat bad bacteria in the gut with probiotics. Now, they say, gerontologists around the world drink sour milk on May 15th remembering Metchnikoff.
He died in Paris, and his ashes are kept in the library of the Pasteur Institute.
Also, in the English Wikipedia he’s Élie Metchnikoff. Not easy to guess.
In the photo, Metchnikoff and Leo Tolstoy are discussing immunology.
Instagram advertises accounts to teenagers with a girl strongly resembling a young Lena Katina from t.A.T.u 😉
I’ve launched an electronic open source application for my book Recommender Algorithms! It’s a “sandbox” where you can “run” various recommendation algorithms with different settings, and view specific visualizations for each algorithm that help understand how it works. For instance, for algorithms like ItemKNN, SLIM, or EASE, a key visualization is a heatmap of the learned similarity matrix (item-item similarity matrix). This allows you to see which pairs of items the model considers “similar” (or “influencing” each other). For SLIM, for example, a useful “Sparsity Plot” shows that the similarity matrix indeed turned out to be sparse. For associative rule algorithms (Apriori, FP-Growth, Eclat) the visualization is not a graph, but interactive tables with found “Frequent Itemsets” and generated “Association Rules,” which can be filtered and sorted.
Additionally, there is a parametric mechanism for creating a “game dataset” — Dataset Wizard. It works like this – there are template datasets that describe items through characteristics. For example, recipes through flavors. Or movies through genres. The system generates random users with a random set of characteristics from the same set — and there are many sliders to make this distribution more contrasted or complex. Next, a matrix of user ratings of items is created – conditionally, if the characteristics of the user and the item match, then the rating will be higher because “tastes match”; conversely, if they differ, then the rating will be lower. Here too, sliders add noise and scarcity – randomly removing part of the matrix. The characteristics of products and users are not fed into the recommendation algorithm; they are hidden, but they are used to visualize the results.
The third component of the application is the tuning of hyperparameters. Essentially, it’s an auto-configurator for a specific dataset. An iterative approach is used, which is much more efficient than a full search (Grid Search) or random search (Random Search). In short, the system analyzes the history of past runs (trials) and builds a probability “map” (surrogate model) of which parameters will likely yield the best result. Then, it uses this map to smartly choose the next combination to test. This method is called Sequential Model-Based Optimization (SMBO).
The code is open source and will be further supplemented with new algorithms and new visualizations.
Link to the code in the comments.
Link to the site where the code is deployed and where you can check out the application is also in the comments.
My apples are almost ripe #pictured
Hi, I'm Rauf Aliev. 