Dr. Lance Eliot, AI Insider

AI Insider的Lance Eliot博士

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/]

[编辑 注意：如果读者对Eliot博士正在进行的有关自动驾驶汽车问世的业务分析感兴趣，请参阅他的在线《福布斯》专栏： https ： //forbes.com/sites/lanceeliot/ ]

The benefits of AI Machine Learning (ML) and Deep Learning (DL) has taken a slightly downbeat turn toward pointing out that there is a potential ecological cost associated with these systems. In particular, AI developers and AI researchers need to be mindful of the adverse and damaging carbon footprint that they are generating while crafting ML/DL capabilities.

AI机器学习(ML)和深度学习(DL)的优势已转为略微转淡，转向指出与这些系统相关的潜在生态成本。 尤其是，AI开发人员和AI研究人员在设计ML / DL功能时，需要注意他们正在产生的不利和破坏性的碳足迹。

It is a so-called “green” or environmental wake-up call for AI that is worth hearing, some refer to this as Green AI.

值得一听的是所谓的“绿色”或“环境唤醒”人工智能呼吁，有人将其称为绿色AI。

Let’s first review the nature of carbon footprints (CFPs) that are already quite familiar to all of us, such as the carbon belching transportation industry. A carbon footprint is usually expressed as the amount of carbon dioxide emissions spewed forth, including for example when you fly in a commercial plane from Los Angeles to New York, or when you drive your gasoline-powered car from Silicon Valley to Silicon Beach.

首先，让我们回顾一下我们所有人已经非常熟悉的碳足迹(CFP)的性质，例如碳排放运输行业。 碳足迹通常表示为所散发的二氧化碳排放量，包括例如当您乘坐商用飞机从洛杉矶飞往纽约时，或者当您驾驶汽油动力汽车从硅谷前往硅海滩时。

Carbon accounting is used to figure out how much a machine or system produces in terms of its carbon footprint when being utilized and can be calculated for planes, cars, washing machines, refrigerators, and just about anything that emits carbon fumes.

碳核算用于计算一台机器或系统在使用时产生的碳足迹，并且可以针对飞机，汽车，洗衣机，冰箱以及几乎所有排放碳烟的物质进行计算。

We all seem to now know that our cars are emitting various greenhouse gasses including the dreaded carbon dioxide vapors that have numerous adverse environmental impacts. Some are quick to point out that hybrid cars that use both gasoline and electrical power tend to have a lower carbon footprint than conventional cars, while Electrical Vehicles (EV’s) are essentially zero carbon emissions at the tailpipe.

现在我们大家似乎都知道，我们的汽车排放的各种温室气体，包括可怕的二氧化碳蒸气，会对环境造成许多不利影响。 一些人很快指出，既使用汽油又使用电力的混合动力汽车往往比传统汽车具有更低的碳足迹，而电动汽车(EV)的尾气排放基本上为零。

Calculating Carbon Footprints For A Car

计算汽车的碳足迹

When ascertaining the carbon footprint of a machine or device, it is easy to fall into the mental trap of only considering the emissions that occur when the apparatus is in use. A gasoline car might emit 200 grams of carbon dioxide per kilometer traveled, while a hybrid-electric might produce about half at 92 grams, and an EV presumably at 0 grams, per EPA and Department of Energy.

确定机器或设备的碳足迹时，很容易陷入仅考虑设备使用时产生的排放的精神陷阱。 根据EPA和能源部的数据，汽油车每行驶一公里可能会排放200克二氧化碳，而混合动力汽车可能会产生92克二氧化碳的一半，而电动汽车的排放量可能为0克。

See this U.S. government website for detailed estimates about carbon emissions of cars: https://www.fueleconomy.gov/feg/info.shtml#guzzler

有关汽车碳排放的详细估算，请访问此美国政府网站： https : //www.fueleconomy.gov/feg/info.shtml#guzzler

Though the direct carbon footprint aspect does indeed involve what happens during the utilization effort of a machine or device, there is also the indirect carbon footprint that requires our equal attention, involving both upstream and downstream elements that contribute to a fuller picture of the true carbon footprint involved. For example, a conventional gasoline-powered car might generate perhaps 28 percent of its total life-time carbon dioxide emissions when the car was originally manufactured and shipped to being sold.

尽管直接碳足迹方面确实涉及机器或设备在使用过程中发生的情况，但是间接碳足迹也需要我们同等关注，涉及上游和下游元素，有助于更全面地了解真实碳。涉及的足迹。 例如，传统的汽油动力汽车在最初制造并运输到出售时，可能会产生其总使用寿命二氧化碳排放量的28％。

You might at first be normally thinking like this:

您最初可能通常会这样想：

• Total CFP of a car = CFP while burning gasoline汽车的总CFP =燃烧汽油时的CFP

But it should be more like this:

但是应该更像这样：

• Total CFP of a car = CFP when the car is made + CFP while burning gasoline汽车的总CFP =制造汽车时的CFP +燃烧汽油时的CFP

Let’s define “CFP Made” as a factor about the carbon footprint when a car is manufactured and shipped, and another factor we’ll call “CFP FuelUse” that represents the carbon footprint while the car is operating.

让我们将“ CFP Made”定义为制造和运输汽车时碳足迹的一个因素，另一个我们称为“ CFP FuelUse”的因数代表了汽车运行时的碳足迹。

For the full lifecycle of a car, we need to add more factors into the equation.

对于汽车的整个生命周期，我们需要在方程中添加更多因素。

There is a carbon footprint when the gasoline itself is being generated, I’ll call it “CFP FuelGen,” and thus we should include not just the CFP when the fuel is consumed but also when the fuel was originally processed or generated. Furthermore, once a car has seen its day and will be put aside and no longer used, there is a carbon footprint associated with disposing or scrapping of the car (“CFP Disposal”).

生成汽油本身时会产生碳足迹，我将其称为“ CFP FuelGen”，因此在消耗燃料时，我们不仅应包括CFP，还应在最初处理或生成燃料时也包括CFP。 此外，一旦汽车停顿了一天，将被搁置并且不再使用，就会产生与汽车处置或报废相关的碳足迹(“ CFP处置”)。

This also brings up a facet about EV’s. The attention of EV’s as having zero CFP at the tailpipe is somewhat misleading when considering the total lifecycle CFP since you should also be including the carbon footprint required to generate the electrical power that gets charged into the EV and then is consumed while the EV is driving around. We’ll assign that amount to the CFP FuelGen factor.

这也带来了关于电动汽车的一个方面。 在考虑整个生命周期的CFP时，EV的注意是排气管的CFP为零，这在某种程度上会引起误解，因为您还应该包括产生电能的碳足迹，该电能会被充入EV，然后在EV行驶时被消耗周围。 我们将该金额分配给CFP FuelGen因子。

The expanded formula is:

扩展公式为：

• Total CFP of a car = CFP Made + CFP FuelUse + CFP FuelGen + CFP Disposal汽车的总CFP = CFP制造+ CFP燃料使用+ CFP FuelGen + CFP处理

Let’s rearrange the factors to group together the one-time carbon footprint amounts, which would be the CFP Made and CFP Disposal, and group together the ongoing usage carbon footprint amounts, which would be the CFP FuelUse and CFP FuelGen. This makes sense since the fuel used and the fuel generated factors are going to vary depending upon how much a particular car is being driven. Presumably, a low mileage driven car that mainly sits in your garage would have a smaller grand-total over its lifetime of the CFP consumption amount than would a car that’s being driven all the time and racking up tons of miles.

让我们重新安排因素，以将一次性碳足迹量分组，这将是CFP Made和CFP处置，并将当前使用的碳足迹量分组，这将是CFP FuelUse和CFP FuelGen。 这是有道理的，因为所使用的燃料和燃料产生的因素将根据特定汽车的行驶量而变化。 据推测，主要位于您车库中的低里程驾驶汽车在其CFP消费量的整个生命周期中的总和要比一直行驶并累积数英里的汽车要小。

The rearranged overall formula is:

重新排列的总体公式为：

• Total CFP of a car = (CFP Made + CFP Disposal) + (CFP FuelUse + CFP FuelGen)汽车的总CFP =(CFP制造+ CFP处置)+(CFP燃料使用+ CFP FuelGen)

Next, I’d like to add a twist that very few are considering when it comes to the emergence of self-driving autonomous cars, namely the carbon footprint associated with the AI Machine Learning for driverless cars.

接下来，我想补充一点，关于自动驾驶自动驾驶汽车的出现，很少有人考虑，即与无人驾驶汽车的AI机器学习相关的碳足迹。

Let’s call that amount as “CFP ML” and add it to the equation.

让我们将该金额称为“ CFP ML”并将其添加到公式中。

• Total CFP of a car = (CFP Made + CFP Disposal) + (CFP FuelUse + CFP FuelGen) + CFP ML汽车的总CFP =(CFP制造+ CFP处置)+(CFP燃料使用+ CFP FuelGen)+ CFP ML

You might be puzzled as to what this new factor consists of and why it is being included. Allow me to elaborate.

您可能会对这个新因素组成以及为什么要包括在内感到困惑。 请允许我详细说明。

AI Machine Learning As A Carbon Footprint

AI机器学习成为碳足迹

In a recent study done at the University of Massachusetts, researchers examined several AI Machine Learning or Deep Learning systems that are being used for Natural Language Processing (NLP) and tried to estimate how much of a carbon footprint was expended in developing those NLP systems (see the study at this link here: https://arxiv.org/pdf/1906.02243.pdf).

在马萨诸塞州大学进行的一项最新研究中，研究人员检查了几种用于自然语言处理(NLP)的AI机器学习或深度学习系统，并试图估计开发这些NLP系统所消耗的碳足迹量(请在以下链接中查看研究： https : //arxiv.org/pdf/1906.02243.pdf )。

You likely already know something about NLP if you’ve ever had a dialogue with Alexa or Siri. Those popular voice interactive systems are trained via a large-scale or deep Artificial Neural Network (ANN), a kind of computer-based model that simplistically mimics brain-like neurons and neural networks, and are a vital area of AI for having systems that can “learn” based on datasets provided to them.

如果您曾经与Alexa或Siri进行过对话，那么您可能已经对NLP有所了解。 那些流行的语音交互系统通过大规模或深度的人工神经网络(ANN)进行训练，这是一种基于计算机的模型，可以简单地模拟类似脑的神经元和神经网络，并且对于AI具有重要意义的系统领域来说，可以根据提供给他们的数据集“学习”。

Those of you versed in computers might be perplexed that the development of an AI Machine Learning system would somehow produce CFP since it is merely software running on computer hardware, and it is not a plane or a car.

那些精通计算机的人可能会感到困惑，因为AI机器学习系统的开发将以某种方式产生CFP，因为它仅仅是在计算机硬件上运行的软件，而不是飞机或汽车。

Well, if you consider that there is electrical energy used to power the computer hardware, which is used to be able to run the software that then produces the ML model, you could then assert that the crafting of the AI Machine Learning system has caused some amount of CFP via however the electricity itself was generated to power the ML training operation.

好吧，如果您考虑到有电能用来为计算机硬件供电，该电能用来运行可以产生ML模型的软件，那么您可以断言AI机器学习系统的设计导致了然而，通过电力本身产生的CFP数量为ML训练操作提供了动力。

According to the calculations done by the researchers, a somewhat minor or modest NLP ML model consumed an estimated 78,468 pounds of carbon dioxide emissions for its training, while a larger NLP ML consumed an estimated 626,155 pounds during training. As a basis for comparison, they report that an average car over its lifetime might consume 126,000 pounds of carbon dioxide emissions.

根据研究人员所做的计算，某种较小或适中的NLP ML模型在训练过程中估计消耗了78,468磅二氧化碳排放量，而较大的NLP ML在训练过程中估计消耗了626,155磅二氧化碳排放量。 作为比较的基础，他们报告说，一辆普通汽车在其整个使用寿命期间可能会消耗126,000磅的二氧化碳排放量。

A key means of calculating the carbon dioxide produced was based on the EPA’s formula of total electrical power consumed is multiplied by a factor of 0.954 to arrive at the average CFP in pounds per kilowatt-hour and as based on assumptions of power generation plants in the United States.

计算产生的二氧化碳的一种关键方法是，根据EPA的总电力消耗公式乘以0.954，得出以磅/千瓦时为单位的平均CFP，并基于该电厂的假设。美国。

Significance Of The CFP For Machine Learning

CFP对机器学习的意义

Why should you care about the CFP of the AI Machine Learning for an autonomous car?

您为什么要关心自动驾驶汽车的AI机器学习的CFP？

Presumably, conventional cars don’t have to include the CFP ML factor since a conventional car does not encompass such a capability, therefore the factor would have a value of zero in the case of a conventional car. Meanwhile, for a driverless car, the CFP ML would have some determinable value and would need to be added into the total CFP calculation for driverless cars.

大概，常规汽车不必包含CFP ML因子，因为常规汽车不具备这种能力，因此，在常规汽车的情况下，该因子的值为零。 同时，对于无人驾驶汽车，CFP ML将具有一些可确定的价值，并且需要将其添加到无人驾驶汽车的CFP计算中。

Essentially, it burdens the carbon footprint of a driverless car and tends to heighten the CFP in comparison to a conventional car.

从本质上讲，与传统汽车相比，它增加了无人驾驶汽车的碳足迹，并倾向于提高CFP。

For those of you that might react instantly to this aspect, I don’t think though that this means that the sky is falling and that we should somehow put the brakes on developing autonomous cars, you ought to consider these salient topics:

对于可能会对这方面立即做出React的那些人，我不认为这意味着天空正在坠落，我们应该以某种方式制止开发自动驾驶汽车，您应该考虑以下重要主题：

• If the AI ML is being deployed across a fleet of driverless cars, perhaps in the hundreds, thousands, or eventually millions of autonomous cars, and if the AI ML is the same instance for each of those driverless cars, the amount of CFP for the AI ML production is divided across all of those driverless cars and therefore likely a relatively small fractional addition of CFP on a per driverless car basis.如果将AI ML部署在无人驾驶汽车的车队中，也许部署在成百上千，甚至最后几百万的无人驾驶汽车中，并且如果AI ML对于每个无人驾驶汽车都是相同的实例，则CFP的数量AI ML生产分布在所有这些无人驾驶汽车上，因此，每辆无人驾驶汽车的CFP添加量可能相对较小。
• Autonomous cars are more than likely to be EVs, partially due to the handy aspect that an EV is adept at storing electrical power, of which the driverless car sensors and computer processors slurp up and need profusely. Thus, the platform for the autonomous car is already going to be significantly cutting down on CFP due to using an EV.无人驾驶汽车很有可能是电动汽车，部分原因是电动汽车擅长存储电力，这是方便的方面，无人驾驶汽车的传感器和计算机处理器非常耗电，需要大量使用。 因此，由于使用电动汽车，用于自动驾驶汽车的平台已经大大减少了CFP。
• Ongoing algorithmic improvements in being able to produce AI ML is bound to make it more efficient to create such models and therefore either decrease the amount of time required to produce the models (accordingly likely reducing the electrical power consumed) or can better use the electrical power in terms of faster processing by the hardware or software.在能够产生AI ML方面正在进行的算法改进势必使它更有效地创建此类模型，因此可以减少产生模型所需的时间(从而有可能减少所消耗的电能)或可以更好地利用电能在硬件或软件的更快处理方面。
• For semi-autonomous cars, you can expect that we’ll see AI ML being used there too, in addition to the fully autonomous cars, and therefore the reality will be that the CFP of the AI ML will apply to eventually all cars since conventional cars will gradually be usurped by semi-autonomous and fully autonomous cars.对于半自动驾驶汽车，您可以期望除了全自动驾驶汽车之外，我们还将看到AI ML的使用，因此现实是AI ML的CFP最终将应用于所有汽车，因为传统汽车将逐渐被半自动和全自动驾驶汽车所取代。
• Some might argue that the CFP of the AI ML ought to be tossed into the CFP Made bucket, meaning that it is just another CFP component within the effort to manufacture the autonomous car. And, if so, based on preliminary analyses, it would seem like the CFP AI ML is rather inconsequential in comparison to the rest of the CFP for making and shipping a car.有人可能会争辩说，应该将AI ML的CFP扔进CFP Made铲斗中，这意味着它只是制造自动驾驶汽车的另一个​​CFP组件。 而且，如果是这样的话，根据初步分析，与制造和运输汽车的CFP其余部分相比，CFP AI ML似乎无关紧要。

For those of you interested in trying out an experimental impact tracker in your AI ML developments, there are various tools coming available, including for example this one posted at GitHub that was developed jointly by Stanford University, Facebook AI Research, and McGill University: https://github.com/Breakend/experiment-impact-tracker.

对于那些有兴趣在您的AI ML开发中尝试实验性影响跟踪器的人来说，可以使用多种工具，例如，由斯坦福大学，Facebook AI Research和麦吉尔大学联合开发的在GitHub上发布的工具： https ：//github.com/Breakend/experiment-impact-tracker 。

As they say, your mileage may vary in terms of using any of these emerging tracking tools and you should proceed mindfully and with appropriate due diligence for applicability and soundness.

正如他们所说，使用这些新兴跟踪工具中的任何一个，您的里程可能会有所不同，因此您应谨慎行事并进行适当的尽职调查，以确保其适用性和可靠性。

Conclusion

结论

There’s an additional consideration for the CFP of AI ML.

AI ML的CFP还需要考虑其他因素。

You could claim that there is a CFP AI ML for the originating of the Machine Learning model that will be driving the autonomous car, and then there is the ongoing updating and upgrading involved too.

您可以声称存在CFP AI ML，用于将要驾驶自动驾驶汽车的机器学习模型的产生，然后还涉及正在进行的更新和升级。

Therefore, the CFP AI ML is more than just a one-time CFP, it is also part of the ongoing grouping too.

因此，CFP AI ML不仅仅是一次性CFP，它也是正在进行的分组的一部分。

Let’s split it across the two groupings:

让我们将其分为两个组：

• Total CFP of a car = (CFP Made + CFP Disposal + CFP ML1) + (CFP FuelUse + CFP FuelGen + CFP ML2)汽车的总CFP =(CFP制造+ CFP处置+ CFP ML1)+(CFP燃料使用+ CFP FuelGen + CFP ML2)

You can go even deeper and point out that some of the AI ML will be taking place in-the-cloud of the automaker or tech firm and then be pushed down into the driverless car (via Over-The-Air or OTA electronic communications), while some of the AI ML might be also occurring in the on-board systems of the autonomous car. In that case, there’s the CFP to be calculated for the cloud-based AI ML and then a different calculation to determine the CFP of the onboard AI ML.

您可以更深入地指出一些AI ML将在汽车制造商或技术公司的云端进行，然后被推入无人驾驶汽车中(通过Over-The-Air或OTA电子通讯) ，而某些AI ML也可能出现在自动驾驶汽车的车载系统中。 在这种情况下，需要为基于云的AI ML计算CFP，然后再进行其他计算以确定车载AI ML的CFP。

There are some that point out that you can burden a lot of things in our society if you are going to be considering the amount of electrical power that they use, and perhaps it is unfair to suddenly bring up the CFP of AI ML, doing so in isolation of the myriad of other ways in which CFP arises due to any kind of computer-based system.

有一些指出，如果您要考虑它们使用的电量，可能会给我们的社会带来很多负担，而突然提起AI ML的CFP也许是不公平的，这样做隔离了由于任何一种基于计算机的系统而导致CFP出现的其他多种方式。

In the case of autonomous cars, it is also pertinent to consider not just the “costs” side of things, which includes the carbon footprint factor, but also the benefits side of things.

就自动驾驶汽车而言，不仅要考虑物联网的“成本”方面，其中包括碳足迹因子，还要考虑物联网的利益方面。

Even if there is some attributable amount of CFP for driverless cars, it would be prudent to consider what kinds of benefits we’ll derive as a society and weigh that against the CFP aspects. Without taking into account the hoped-for benefits, including the potential of human lives saved, the potential for mobility access to all and including the mobility marginalized, and other societal transformations, you get a much more robust picture.

即使无人驾驶汽车的CFP可归因于一定数量，但谨慎地考虑一下我们将为社会带来什么样的好处，并将其与CFP的各个方面进行权衡。 如果不考虑希望获得的利益，包括挽救生命的潜力，所有人的出行潜力，包括边缘化的出行能力以及其他社会变革，您的情况就会更加稳健。

In that sense, we need to figure out this equation:

从这个意义上讲，我们需要弄清楚以下等式：

• Societal ROI of autonomous cars = Societal benefits — Societal costs无人驾驶汽车的社会投资回报率=社会效益–社会成本

We don’t yet know how it is going to pan out, but most are hoping that the societal benefits will readily outweigh the societal costs, and therefore the ROI for self-driving driverless autonomous cars will be hefty and leave us all nearly breathless as such.

我们还不知道它会如何发展，但是大多数人希望社会收益会很快超过社会成本，因此，自动驾驶无人驾驶自动驾驶汽车的投资回报将非常可观，使我们所有人都感到喘不过气来。这样。

For free podcast of this story, visit: http://ai-selfdriving-cars.libsyn.com/website

有关此故事的免费播客，请访问： http : //ai-selfdriving-cars.libsyn.com/website

The podcasts are also available on Spotify, iTunes, iHeartRadio, etc.

播客还可以在Spotify，iTunes，iHeartRadio等上获得。

More info about AI self-driving cars, see: www.ai-selfdriving-cars.guru

有关AI自动驾驶汽车的更多信息，请参见： www.ai-selfdriving-cars.guru

To follow Lance Eliot on Twitter: https://twitter.com/@LanceEliot

要在Twitter上关注Lance Eliot： https ： //twitter.com/@LanceEliot

For his Forbes.com blog, see: https://forbes.com/sites/lanceeliot/

有关他的Forbes.com博客，请访问： https ： //forbes.com/sites/lanceeliot/

For his AI Trends blog, see: www.aitrends.com/ai-insider/

有关他的AI趋势博客，请访问： www.aitrends.com/ai-insider/

For his Medium blog, see: https://medium.com/@lance.eliot

有关其Medium博客，请访问： https : //medium.com/@lance.eliot

For Dr. Eliot’s books, see: https://www.amazon.com/author/lanceeliot

有关艾略特博士的书，请参见： https : //www.amazon.com/author/lanceeliot

Copyright © 2020 Dr. Lance B. Eliot

版权所有©2020 Lance B.Eliot博士