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" ... In a traditional relational database, information is stored in tables, where the rows of each table correspond (roughly) to a particular thing or resource, and the columns correspond to the properties acting on those resources. This distinction is somewhat fuzzy in that tables and classes of objects aren’t completely synonymous but it works to a first approximation. The values where a particular row and column intersect can essentially be of one of three broad types: a scalar value (such as a revenue number or the text contents of an article), a primary key, or a foreign key reference to a primary key. ... "
" ... In addition to that, we’re assuming that the laws of General Relativity are still perfectly accurate for describing the dynamics of space at a quantum level: we are assuming that the quantum effects that create Hawking radiation are important, but that any quantum effects that arise because treating space as a classical and continuous background can be ignored. Researchers who work on this call this approach a “semi-classical approximation,” and the suspicion is that something about it must break down. ... "
" ... Regardless, the NBA is trying to do its best to put out some kind of representative approximation of fair basketball to decide a 2019-2020 season with as few people traveling with the team as possible, as Charania reported on Tuesday. ... "
" ... To illustrate this framework, I’ll use a simplified version of Netflix’s recommendation engine (the one that suggests what movies you might like). The first component of this framework is a clearly defined qualitative goal. In the case of Netflix, that goal is to suggest relevant movies to users. Next, and perhaps most important to AI problems, is converting that qualitative goal into a quantitative approximation. In the case of the Netflix recommendation engine, a simplified way to do that is to minimize the sum-squared of the number of stars predicted minus the number of stars actually given. For example, if Netflix predicted you would rank The Crown as 3-stars and you actually gave it 5-stars, and Netflix also thought you would give Arrested Development 5-stars but you gave it 4-stars, the score would be (5-3)^2 + (5-4)^2 = 5. This function is called the “objective function” (in this case it could also be called the “error function”) and Netflix would want to minimize it so that it can optimally predict customer taste. Next, we formulate a model as a “guess” of what structure might naturally exist. In the case of Netflix, the model considers how other similar users rate a movie and uses those ratings to predict your taste. The model is then optimized in the pursuit of minimizing (or maximizing) the objective function. Finally, we need data, which in the case of Netflix is star rankings of all users. ... "
" ... You can see that the model accuracy is at the very end of a chain of not-so-perfect connections. Understanding that models can at best produce an approximation of outcomes is crucial to make the most of them. ... "