
English: 
♪ (intro music) ♪
I want to talk to you about something
that's very important to me.
And that's, how will civilization
power itself for the next 100 years?
So, in 2100,
the projected world's population
is 11.2 billion.
If all 11.2 billion people
want to enjoy the same
power usage that we do now,
in the United States,
that's going to require burning
around 0.2 yottajoules of energy
over the next 100 years.
So, that's a whole lot.
To put that into perspective--
if we wanted to do that with oil alone,
we'd have to ramp up oil production
by a factor of 10 for the next 100 years.
So there's no way that's going to happen.
So, besides being infeasible,

Chinese: 
我想和你谈谈对我而言非常重要的事情。
那就是，未来 100 年文化将如何发展？
因此，在 2100 年，
预计的世界人口为 112 亿。
如果所有 112 亿人
都想享受与我们现在所用的相同电量，
那么在美国，
这将需要在未来的 100 年内
燃烧大约 0.2 兆焦耳的能量。
所以，这真的很多。从这个角度来看 -
如果我们只想用石油做到这一点，
那么在接下来的 100 年里，
我们不得不提高石油产量 10 倍。
所以这没有办法会发生。
除了不可行之外，

English: 
that would contribute
to catastrophic climate change.
If we want to keep climate change
to a level that's not ideal,
but at least reasonable--
say, under 2 degrees
of temperature increase--
then only 8% of that 0.2 yottajoules
can come from fossil fuels,
like coal or oil.
So where does the other 92% come from?
One possible source
would be nuclear fusion.
Fusion involves pushing together
two smaller nuclei,
and what you get out
is a whole lot of energy,
and no greenhouse gas.
So, right now, the sun
runs on nuclear fusion.
And the reaction is so energy-dense
that the 0.2 yottajoules would require
say, a trivial amount
of deuterium found in seawater.

Chinese: 
这将导致灾难性的气候变化。
如果我们想把气候变化保持在
一个不理想的水平上，但至少是合理的 -
比如少过 2 摄氏度的温度升高 -
那么只有 0.2 焦耳的 8％
可以来自化石燃料，如煤或石油。
那么其他 92％ 来自哪里呢？
一种可能的来源是核聚变。
聚变包括将两个较小的原子核聚集在一起，
你得到的是很多能量，
没有温室气体。
现在，太阳运行在核聚变上。
而且这种反应的能源密度非常高，
以至于 0.2 焦耳焦耳就需要
大概，海水中含有的微量的氘。

English: 
Or say, just seven months
of the world's current boron production--
so a very trivial amount of boron.
So far it sounds
like some sort of miracle fuel.
Well, what's the catch?
The difficulty is that people
have been trying this for 70 years,
and so far, no one has gotten
out more energy than they put in.
So, to understand this,
you have to imagine that the--
well, the reaction takes place
inside of a plasma.
And a plasma is a million plus
degree swarm of charged particles.
And these particles
don't want to stay in place.
The sun uses a gravitational force
to keep everything in place.
We can't do that;
so instead, we use magnets.
Now, magnets--
you try to squeeze it with magnets
and they can pop out the end.
And you can get little turbulent ripples,
and what happens is the plasma breaks up,

Chinese: 
或者说，目前世界上仅有的 7 个月的硼产量 -
因此硼的数量非常微不足道。
到目前为止，这听起来像是某种奇迹般的燃料。
那么，有问题是什么？
困难在于人们已经尝试了 70 年，
迄今为止，没有人能达到比能源投入更多的释放。
因此，要理解这一点，你必须想象 -
反应发生在等离子体内部。
等离子体是百万度以上的群体的带电粒子。
而这些粒子不想留在原地。
太阳使用引力将一切都保持着位子上。
我们不能那样做;反之，我们使用磁铁。
现在，磁铁 -
你试图用磁铁挤压它们，它们会在尾端弹出。
你会得到小小的湍流波纹，
然后等离子体会破裂，

English: 
it gets unstable, it gets cooler,
and then the reaction stops.
And that's what's been happening
for 70 years.
So this is the kind
of problem that I like:
it combines physics, probability,
computation, mathematics.
And so, that was like,
"I want to work on this
and how can we accelerate progress?"
Google is not building a fusion reactor.
(laughter)
What we have done is we've partnered
with TAE Technologies.
And this is the world's largest
private fusion energy company.
And we've been working
with them since 2015.
So, pictured here
is their 5th-generation
plasma generation device.
And this thing is huge--
it would fill up
a large part of this room.
And then in the center
is where the plasma is kept.
This is an elongated toroid,

Chinese: 
变得不稳定，变凉，然后反应会停止。
这就是 70 年来发生的事情。
所以这就是我喜欢的那种问题：
它结合了物理，概率，
计算，数学。
我就说 “我想做这方面的工作，
我们如何能够加速进展？
”谷歌不是要建造一座聚变反应堆。
我们所做的是我们与
TAE Technologies 的合作。
这是世界上最大的私人聚变能源公司。
我们从 2015 年开始一直与他们合作。
因此，这里展示的是
他们的第五代等离子体生成装置。
而且这个东西很大 -
它会填满这个房间的很大一部分。
然后在中心是等离子体保存的地方。
这是一个拉长的超环面，

English: 
and the goal, really, is to keep
this in its place and prevent turbulence.
And if it gets out of place,
then the reaction stops.
So there's magnets and neutral beams,
and a host of other technologies
to keep it in place.
Now, what's Google's job specifically?
Well, our goal is to take the measurements
that come from this experimental reactor.
And every time the physicists
do an experiment,
within five minutes we want to tell them
the plasma density, temperature,
and magnetic field
on a three-dimensional grid.
So, how hard is that?
Well, first of all,
the plasma is very, very hot.
So you can't just poke
it with a thermometer, like a turkey.
The thermometer would melt,
and you would disrupt the plasma
and ruin the experiment.
So what you do have
are measurements along the boundary.

Chinese: 
目标实际上是保持它在一定的位置并防止湍流。
如果它离开位置，那么反应就会停止。
所以有磁铁和中性束，
还有其他一些技术来保持它在一定的位置。
现在，谷歌的具体工作是什么？
那么，我们的目标是采取
来自这个实验反应堆的测量结果。
每次物理学家做一个实验，
在五分钟之内，我们想要告诉他们
三维网格上的等离子体密度，温度和磁场。
那么，这有多困难？
首先，等离子非常非常热。
所以你不能像火鸡一样用温度计捅它。
温度计会融化，
你会破坏等离子体并破坏实验。
所以你有的是沿边界的尺寸。

English: 
But there's only so many
measurements you can take,
because you can't cut that many holes
on the side of this device.
So, let's look closely at one--
let's look at measuring
of electron density,
and that's done with a device
known as an interferometer.
An interferometer shines lasers
through the plasma,
and then the phase shift is proportional
to the average electron density
along that ray.
So, we have 14 lasers shining through
the center of the plasma.
So we know the average density
along 14 lines.
And from that, we want
to know the density everywhere.
So clearly, there's no one
unique solution to this problem.
And instead, we'll have
a distribution over possible solutions.
So, we do this in a Bayesian sense,
and our final output
will be a probability density function

Chinese: 
但是你只可以进行一个特定次数的测量，
因为你无法在该设备的侧面切割多个孔。
所以，让我们仔细看一看其中一个 -
我们来看看电子密度的测量，
这是用一种称为干涉仪的设备完成的。
干涉仪通过等离子体照射激光，
然后相移与该射线的平均电子密度成比例。
所以，我们有 14 个激光穿过等离子的中心。
所以我们知道 14 条线的平均密度。
由此，我们想知道每一处的密度。
很显然，这个问题没有一个独特的解决方案。
相反，我们将对可能的解决方案进行分布。
所以，我们以贝叶斯的方法来做这件事，

Chinese: 
并且在给定测量的情况下，
我们的最终输出将是电子密度的概率密度函数。
我们可以用一个图形来显示它 -
你有平均值和一些误差线。
TensorFlow 如何为此提供帮助？
所以，第一步是将测量物理学转化为代码。
所以，我们来考虑相​​机测量的分布。
所以，相机测量光子。
并假设我们有一些从等离子体发射的光子。
到达相机的平均光子数
由 tf.sparse_tensor_dense_matmul 给出。
但是我们并没有真正意识它的平均值 -
而是我们意识到的是一个嘈杂的平均值。
因此，有限数量的光子会产生噪声 - 
即泊松噪声。
我们也有离散化噪声，
因为我们离散空间。
因此，TensorFlow 分布库

English: 
for the density of the electrons,
given the measurements.
And we can visualize that with a graph--
where you have the mean
and some error bars.
How does TensorFlow help with this?
So, the first place is translating
measurement physics into code.
So, let's consider the distribution
for the camera measurement.
So, the cameras measure photons.
And say we have some photons
that are being emitted from the plasma.
The mean number of photons
reaching the camera is given
by a tf.sparse_tensor_dense_matmul.
But we don't actually realize the mean--
instead what we realize is a noisy mean.
So there's noise due to a finite number
of photons-- that's Poisson noise.
We also have discretization noise,
because we discretize space.
So, the TensorFlow distributions library

English: 
gives you access
to this normal distribution object,
so that this noisy flux
represents a normal distribution--
it has a mean, you can draw samples,
you can compute the PDF, and so on.
Then, that's not all, though.
We also have analog
to digital conversion process
that we model as passing
this normal distribution
through a non-linear response curve,
and then digitizing it to 8 bits.
So, at the end, this digitized_charge
is another distribution object
that has an ability to take samples,
you can compute the probability
mass function because it's discrete,
and so on.
And since we want to be Bayesian,
we can reassemble
a number of these distributions,
giving us a likelihood
and a prior, and so on.
It's with a goal of producing a posterior.
And then we do Bayesian inference--

Chinese: 
可让你访问此正态分布物体，
以便此噪声通量能以正态分布的形式呈现 -
它具有平均值，你可以采样，
可以计算 PDF 等等。
然而，这还不是全部。
我们也有模拟到数字的转换过程，
我们在通过非线性响应曲线传递此正态分布时
模拟它，然后将其数字化为 8 位。
因此，最后，这个 digitized_charge 是
另一个可以抽样本的分布对象，
可以计算概率质量函数，因为它是离散的，
等等。
既然我们想成为贝叶斯，
我们可以重新组合这些分布，
以便可以给我们一个可能性和一个先验，等等。
它的目标是产生一个后验。
然后我们做贝叶斯推理 -

Chinese: 
我们以两种不同的方式来进行推理。
第一种方法是变分推理，
这相当于使损失函数最小化。
通过最小化损失函数，
你可以近似得到真正的后验。
因此，这种最小化的做法
与其他 TensorFlow 最小化一样 -
例如，我们使用 AdamOptimizer。
第二种方法是使用哈密顿蒙特卡罗。
因此，TensorFlow 概率库
可让你访问多个蒙特卡罗采样器，
而哈密尔顿蒙特卡洛则使用渐变
来帮助你更快地采样。
注意，在这两种情况下，
我们都在这里使用自动分化，
无论我们是否采用这损失的渐变 -
或者我们是否采用渐变
来进行哈密顿蒙特卡罗采样。
所以，更上一级，你会注意到
我们没有继续深入学习。

English: 
so now to do inference,
we do this in two different ways.
The first way is variational inference,
which amounts
to minimizing a loss function.
And by minimizing a loss function
you get an approximation
of the true posterior.
So, this minimization is done
just like any other
TensorFlow minimization--
for example, we use AdamOptimizer.
The second way is using
Hamiltonian Monte Carlo.
So, the TensorFlow probability library
gives you access to a number
of Monte Carlo samplers,
and the Hamiltonian Monte Carlo
is one that uses gradients
in order to help you take samples faster.
Notice, in both cases,
we're using auto differentiation here,
whether we're taking
gradients of this loss--
or whether we're taking gradients
to do Hamiltonian Monte Carlo sampling.
So, popping up a level, you'll notice
we're not doing deep learning.

English: 
Instead, we're doing an inverse problem
whereby measurements,
given to us from physicists,
are translated into a reconstruction
of some physical state.
So, there's a few differences
I want to highlight.
First of all, there are no labels
that are given to us.
The natural label here
would be a three-dimensional image
of the actual plasma.
But we're the ones who are telling people
what the plasma looks like,
so we're the ones
actually producing the labels.
Given that there's no labels,
you might be tempted to say,
"Well, this is an unsupervised
learning technique, like word clustering."
There's a key difference, though,
that here there really is a right answer,
there really was a plasma out there,
and if that plasma doesn't fall
within our error bars,
we've made a mistake.
And also, you'll notice
that our graph here models physics
rather than generic functions.
So, it's a bit more constrained
than these deep neural networks,

Chinese: 
反之，我们正在做一个逆向问题，
由物理学家给我们的测量结果
转化为某种物理状态的重构。
所以，我想强调一些差异。
首先，我们并没有被给予任何的标签。
这里的自然标签
将是实际等离子体的三维图像。
但我们是那些告诉人们等离子体是什么样子的人，
所以我们是真正生产标签的人。
鉴于没有标签，你可能会很想说：
“好吧，这是一种无监督学习技术，
就像词汇聚类。”
然而，这里有一个关键的区别，
那就是在这里真的有一个正确的答案，
确实有一个等离子体这东西，
如果这个等离子体不在我们的误差线里，
就代表我们犯了错误。
而且，你会注意到我们的图形模拟物理
而不是泛型函数。
因此，它比这些深度神经网络更受约束，

Chinese: 
但这使我们能够在没有标签的情况下
得到正确答案。
总而言之，TensorFlow
尽管不是深度学习，
TensorFlow 增加了 TensorFlow 分布
和概率库的价值。
我们有自动分化来做推论。
为了一次过给许多测量提供答案，
GPU 和分布式计算非常重要。
所以，非常感谢你们。

English: 
but that allows us to get
the right answer with no labels.
At the end of the day, TensorFlow,
despite it not being deep learning,
TensorFlow adds value
with the TensorFlow distributions
and probability library.
We have auto differentiation
to do inference.
And in order to provide answers
to many measurements at once,
GPUs and distributed
computing is very important.
So, thank you very much.
(applause)
♪ (outro music) ♪
