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If you look at our current technological systems they are obviously nowhere near where our minds are.
看一下我们现在的技术体系 它还远远达不到我们的期望水平
They are very different.
跟想象的完全不同
And one of the biggest questions for me is:
我最大的一个问题是：
What’s the difference between where we are now and where we need to be if we want to build minds—
如果我们想建造大脑 那我们的现状和我们需要达到水平还差多远
If we want to build systems that are generally intelligent and self-motivated and maybe self-aware?
如果我们想创建基本智能 主动甚至是有自我意识的体系？
And, of course, the answer to this is 'we don’t know' because if we knew we’d have already done it.
当然 这个问题的答案是“我们不知道” 因为如果我们知道差距的话 那我们早就能把它补起来了
But there are basically several perspectives on this.
不过在这一点上有几种说法
One is our minds as general learning systems that are able to model arbitrary things, including themselves,
一种是 我们的思想是一个综合学习体系 它可以模拟任何东西 包括思想本身
and if there are this, they probably need a very distinct set of motivations, needs; things that they want to do.
如果真是这样 它们可能需要一些非常清晰的动机 需求；需要它们想做的事
I think that humans get their specifics due to their particular needs.
我觉得人类各有特性就是因为他们有不同的需求
We have cognitive and social and physiological needs and they turn us into who we are.
我们有认知 社交和生理需求 正是这些需求造就了我们每个人的样子
Our motivations determine where we put our attention, what we learn and what we actually do in the world—
我们的动机决定了我们把注意力放在哪里 学习什么内容 以及我们在这世界上做什么——
what we model, how we perceive, what we are conscious of.
我们模拟什么 如何认知 能感知到什么
In a similar sense, it might be that it’s sufficient to build a general learning architecture and combine this with a good motivational system.
同样地 构造一个综合学习结构 并把它与良好的动机系统结合起来就足够了
And we are not there yet in building a general learning architecture.
但我们还没达到构造综合学习结构的程度
For instance, our minds can learn and create new algorithms that can be used to write code and invent code, programming code for instance,
比如 我们的大脑可以学习并创造新的算法 用来书写编码或发明编码 比如程序码
or the rules that you need to build a shop and run that shop if you’re a shopkeeper,
或者是你建商店和经营商店所需的规则 如果你是一名店主的话
which is some kind of programming task in its own right.
这其实是一种独立的编程任务
We don’t know how to build a system that is able to do this yet.
现在我们还不知道怎么去构建一个能做到这一点的系统
It involves, for instance, that we have systems that are able to learn loops and we have some techniques to do this,
它包括 比如 我们已经有了能学习循环的系统 也有一些技术能实现这一点
for instance, a long- and short-term memory and a few other tricks,
比如 长期或短期记忆 或其他技巧
but they’re nowhere near what people can do so far.
但它们与人类的能力相比还相差甚远
And it’s not quite clear how much work needs to be done to extend these systems into what people can do.
目前还不知道我们仍需做多少工作才能把这些系统发展的像人脑一样
It could be that it’s very simple.
有可能很简单
It could be that it’s going to take a lot of research.
也有可能需要做大量研究
The dire view, which is more the traditional view, is that human minds have a lot of complexity,
比较惨也是比较传统的想法是 人类的思想具有复杂性
that you need to build a lot of functionality into it, like in Minsky's society of mind,
你需要给它输入很多函数 就像明斯基的思想社会一样
to get to all the tricks that people are up to.
才能让它学会人脑能做的那些技巧
And if that is the case then it might take a very long time until we have re-created all these different functional mechanisms.
如果真是这样 那可能需要很长一段时间 我们才能重新创造这些不同的函数机制
But I don’t think that it’s going to be so dire, because our genome is very short and most of that codes for a single cell.
但我觉得其实没这么惨 因为我们的基因组很短 而且多数基因组都只对单细胞指定遗传密码
Very little of it codes for the brain.
极少数的基因组会对大脑指定遗传密码
And I think a cell is much more complicated than a brain.
而且我觉得细胞要比大脑复杂得多
A brain is probably largely self-organizing and built not like clockwork but like a cappuccino—
大脑很大程度上都是自我组织 它并不是有规律地构建 而是像杯卡布奇诺一样——
so you mix the right ingredients and then you let it percolate and then it forms a particular kind of structure.
你把正确的原料混在一起 让它过滤 然后它会形成某种特定的结构
So I do think, because nature pulls it off pretty well in most of the cases, that even though a brain probably needs more complexity than a cappuccino—dramatically more—
所以我相信 因为大自然在多数情况下都做的很完美 即便大脑比一杯卡布奇诺还要复杂——复杂很多——
it’s going to be much simpler than a very complicated machine like a cell.
它也比细胞之类的单个非常复杂的机器要简单很多
