Not until I found something hidden in the BIOS interface.

后来发现基本输入输出系统接口上隐藏有东西。

That just describes the input-output relation, and not any theory or wishful thinking about what the computer is doing to be able to achieve that.

它只是描述输入输出关系，而不是计算机如何做到这一点的任何理论或妄想。

Imagine that we have a large database with these geometries and images, and we can search and compare them with arbitrary inputs and outputs.

试想有一个很大的图像和几何形状数据库，然后可以搜索数据库中的模型并和任意的输入输出进行比较。

So, we type output, plus equals.

所以，输入输出，加上等于。

So in machine learning, traditionally, again, going to benchmarks, I mean, if you have some input-output, right, like that is just a single input and a single output, you generally, when you train these systems, you see reasonably smooth

因此，在传统机器学习中，再次回到基准测试的话题， 的意思是，如果你有一个输入输出对，就像单一的输入和单一的输出那样，通常在训练这些系统时， 当你分析数据集大小如何影， 或者模型大小如何影， 或者训练系统的时间长短如何影时，你会看到相当平滑的曲线。

And so we absolutely look at evals and R1 benchmark and figure out, wow, DeepSeek figured out a way to deploy a model at the 35X reduction for input-output tokens on a comparative basis to OpenAI's reasoning models.

因此，确实会查看评估和R1基准，并发现，哇，深度求索找到了一种方法， 在输入输出令牌上以相对于OpenAI推理模型35倍的减少量部署模型。

For example, if the 10-bit address were this, then only the bit lines 4784 through 4791 would be connected to the I-O wires, and the rest of the 8000-ish bit lines would be connected to nothing.

例如， 如果10位地址是这个值，那么只有第4784到4791位的位线会连接到输入输出线，而其余大约8000条位线则不会连接到任何地方。

Now for the local case, because the authorization is defined on on the transport layer, and so requires framing, which means like headers effectively, this does not work in standard IO.

对于本地情况而言， 由于授权是在传输层定义的，因而需要进行帧封装， 这实质上意味着需要添加报头，这在标准输入输出（IO）中是无法实现的。

Many predict that speech technologies will become as common a form of interaction as screens, keyboards, trackpads and other physical input-output devices that we use today.

许多预测语音技术将成为像屏幕、键盘、触控板和其他今天使用的物理输入输出设备一样常见的交互方式。

And so a lot of being fast, not all, but a good chunk of being fast is trying to be smart about how you do IO.

因此，速度的提升，并非全部，但相当一部分，在于如何巧妙地处理输入输出。

(laughing) I love how it has complete in and out ports to support all kinds of data transmission and display output.

（笑）喜欢它完整的输入输出端口，支持各种数据传输和显示输出。

Uh, there's just not, but the USB access input and output, then there's no way of getting input from you guys.

嗯， 只是没有， 但有USB接口的输入输出功，这样就无法从你那里获取输入了。

You've got all these different tools, databases, models, lab scripts, maybe totally different inputs, outputs, ways of working.

你手头有各种不同的工具、数据库、模型、实验脚本，输入输出方式可完全不同，工作方式也各不相同。

Such data offers gross support of contemporary models of memory that assume an input-output balance.

这样的数据为假设输入输出平衡的当代记忆模型提供了粗略的支持。

Well, it's to be suggestive of a certain way to visualize this input output relationship.

这是一种将输入输出关系可视化的方法。

So I wrote something called, it was an I-O package, right?

所以写了一个叫做输入输出包的东西，对吧？

But in standard IO, you run locally, and you can do whatever you want anyway, and you might just pop open a browser and deal with it that way.

但在标准输入输出中， 你在本地运行，无论如何都可以随心所欲地操作，你甚至可以直接打开浏览器， 通过这种方式来处理问题。

Like even if you look at rough and UV, Because in UV, we're doing way more IO.

就像即便你看看Ruff和UV，因为在UV中，处理了更多的输入输出操作。

So actually they have to use DirectIO.

所以实际上他不得不使用直接输入输出（DirectIO）。

And so for supervised learning, we try to gather lots of such data and we train these machine learning models to imitate the input output.

因此，对于监督学习，试图收集大量这样的数据，并训练这些机器学习模型来模仿输入输出。