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Cake day: 2023年7月1日

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  • he was forced to release it quickly to coincide with the film’s release. For comparison, it used to take a team of devs a couple of months to make a game. He had 6 weeks.

    Also, if you read the manual, this essentially never happened to you. It was easy to avoid.

    You also needed to read the manual. The game did stuff that other games at the time didn’t, for example, a contextual button. You couldn’t know what would happen unless you read the manual to learn what the icons meant. A lot of people never did and so decided that the game was bad.


  • when climbing out of the pit, it was very easy to immediately fall back down (due to the pixel-perfect collision detection).

    And here is an excerpt from the manual: “Even experienced extraterrestrials sometimes have difficulty levitating out of wells. Start to levitate E.T. by first pressing the controller button and then pushing your Joystick forward. E.T.'s neck will stretch as he rises to the top of the well (see E.T. levitating in Figure 1). Just when he reaches the top of the well and the scene changes to the planet surface (see Figure 2), STOP! Do not try to keep moving up. Instead, move your Joystick right, left, or to the bottom. Do not try to move up, or E.T. might fall back into the well.”







  • you wouldn’t be “freezing” anything. Each possible combination of input tokens maps to one output probability distribution. Those values are fixed and they are what they are whether you compute them or not, or when, or how many times.

    Now you can either precompute the whole table (theory), or somehow compute each cell value every time you need it (practice). In either case, the resulting function (table lookup vs matrix multiplications) takes in only the context, and produces a probability distribution. And the mapping they generate is the same for all possible inputs. So they are the same function. A function can be implemented in multiple ways, but the implementation is not the function itself. The only difference between the two in this case is the implementation, or more specifically, whether you precompute a table or not. But the function itself is the same.

    You are somehow saying that your choice of implementation for that function will somehow change the function. Which means that according to you, if you do precompute (or possibly cache, full precomputation is just an infinite cache size) individual mappings it somehow magically makes some magic happen that gains some deep insight. It does not. We have already established that it is the same function.


  • the fact that it is a fixed function, that only depends on the context AND there are a finite number of discrete inputs possible does make it equivalent to a huge, finite table. You really don’t want this to be true. And again, you are describing training. Once training finishes anything you said does not apply anymore and you are left with fixed, unchanging matrices, which in turn means that it is a mathematical function of the context (by the mathematical definition of “function”. stateless, and deterministic) which also has the property that the set of all possible inputs is finite. So the set of possible outputs is also finite and strictly smaller or equal to the size of the set of possible inputs. This makes the actual function that the tokens are passed through CAN be precomputed in full (in theory) making it equivalent to a conventional state transition table.

    This is true whether you’d like it to or not. The training process builds a markov chain.





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