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Lobster is therefore ideal for fast, iterative prototyping without rigidly confining the user to a particular engine architecture. Wouter himself has been able to quickly craft wildly different game ideas (each with bespoke engines instanced in Lobster) to “find the fun”. You can check out his many prototype ideas here.,详情可参考体育直播

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Now consider the consequences of a sycophantic AI that generates responses by sampling examples consistent with the user’s hypothesis: d1∼p​(d|h∗)d_{1}\sim p(d|h^{*}) rather than from the true data-generating process, d1∼p​(d|true process)d_{1}\sim p(d|\text{true process}). The user, unaware of this bias, treats d1d_{1} as independent evidence and performs a standard Bayesian update, p​(h|d1,d0)∝p​(d1|h)​p​(h|d0)p(h|d_{1},d_{0})\propto p(d_{1}|h)p(h|d_{0}). But this update is circular. Because d1d_{1} was sampled conditional on hh, the user is updating their belief in hh based on data that was generated assuming hh was true. To see this, we can ask what the posterior distribution would be after this additional observation, averaging over the selected hypothesis h∗h^{*} and the particular piece of data generated from p​(d1|h∗)p(d_{1}|h^{*}). We have