nfcampos

Even if I take the most charitable view, ie. that everything they did was above board etc, there’s still a lesson here about diversification -

a startup (ie. a private company which represents the majority of the founders assets) is already a massively concentrated, ie. not diversified, financial position -

no need to compound it by making a myriad bets (through Alameda or otherwise) in in other tokens/companies in the same market, which are ultimately incredibly correlated assets.

If there was no fraud, that will ultimately be the explanation for their downfall.

Abstract: Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties.

>The only fair model here is the one where 100% of apple's income comes from hardware sales alone. That is the only situation where you don't set up the user to be exploited by erecting these walls around what they can and cannot run on their own hardware.

And that is what we call the slippery slope.

Apple has created a successful product, therefore developers want to make money from it, therefore apple cannot charge a fee for access, therefore apple cannot charge for developer tools, therefore apple cannot charge for hardware (lest it makes the developers market smaller than it could be if phones were free)

New API inspired by ggplot2 / vega-lite

Yes although in this specific case you’d lose the order of the results with exists

This sounds rather like how random forests and other ensemble models work

I’d personally have a hard time calling real-time something that doesn’t react to changes, ie if you need to re-run the query in trino on a schedule to get updates then it’s necessarily near real time (as near as your schedule interval allows). But other people may use it to mean something else

Interesting results, would be even more interesting if the importance of the features in the trained model had been analysed with a method that considers the interaction between them, like SHAP values