把 Jev 理解为「自定义小世界」的下一步预测器
作者认为 Jev 的价值在于让人自己定义问题与解空间，用 FPS 游戏和自动驾驶举例说明输入输出受限时预测更可靠，并表示很想上手试。
my take on Jev: i think Jev is filling the gap and complements what current LLMs do. it lets you define a smaller, well-defined world, and the model just needs to work and predict the next move according to the context in this environment. this could simplify the next move prediction a lot. and you are in control of defining the world the way you want. LLMs on the other hand are built for a huge world with a huge number of possible outputs. this makes them versatile, but also leaves more room for predicting the wrong next step. consider a first person shooter game. the inputs could be much more limited, like your position, health, ammo and distance to enemies. the output is also well defined: move, aim, shoot or reload. same idea for a self driving car. the world could be represented by object positions, car speed, direction, etc. and the output could be the next steering and acceleration change. obviously driving itself is still hard, but the interface can be much simpler. the ability to define the world, both the problem and solution space, feels huge. super excited to try it.
这条是引用着另一条写的：「ChatGPT 联合发明人发布新模型 Jev」