人工智能ai发展前景
An off-shelf Artificial intelligence language processing system can generate as much as 1,400 pounds’ carbon emission [1]. An AI-based language system can create around 80,000 pounds’, which is twice of what a human being breathes in a lifetime [1]. The amount of power for searching and training a neural network architecture can be anywhere around 6,16,000 pounds [2]’ — roughly five times what an average U.S. car emits!
现成的人工智能语言处理系统可以产生多达1,400磅的碳排放量[1]。 基于AI的语言系统可以产生大约80,000磅的体重，这是人类一生中呼吸的两倍[1]。 搜索和训练神经网络体系结构所需的能量大约为6,16,000磅[2]'-大约是美国普通汽车排放量的五倍！
You heard it right; the massive carbon footprint that comes with artificial intelligence is posing risks for the technology landscape.
您没听错； 人工智能带来的巨大碳足迹给技术格局带来了风险。
Right, A.I. continues to rise exponentially and is here to stay — but with environmental concerns at stake. A.I. has not only become a focal point of ethical concerns; it is also contributing to around 3% of total carbon dioxide globally[3].
没错，人工智能将继续呈指数级增长，并且将继续存在，但是环境问题已成问题。 人工智能不仅成为道德关注的焦点； 它也占全球二氧化碳总量的3％左右[3]。
Question yourself — Machine-driven society for who?
问问自己-机器驱动的社会是谁的？
Technology and society — these two are at crossroads. Think of roads with bots, human beings with machines and everything else driven by numbers — What do you see? Do you see smart cities or engineered ideologies with biased views splashed over them, or do you imagine a world where social good has converted into social justice?
技术与社会-两者正处于十字路口。 想想带机器人的道路，带机器的人类以及其他一切由数字驱动的事物-您看到了什么？ 您是否看到智慧城市或工程意识形态的观点views贬不一，或者您想象一个社会福利已转变为社会公正的世界？
Think.
认为。
Engineered education is what we like to call it, and surprisingly, it doesn’t come from the foundational knowledge. It’s only confined to the education that is imparted from the school and community. Teaching something over the education system is only helping people with clouded thoughts about how everything will switch to a data-driven ecosystem with absolutely no implications on the environment. Dr. Erin A Cech [4], in 2013 spoke about how the U.S. is trying to emphasize the importance of training ethical, socially conscious engineers but in contrary engineering education itself is failing to encourage the neophytes to look upon public welfare as their professional responsibility — and thus giving a pseudo-reality with A.I. and uninformed opinions, in equal parts.
工程教育是我们想要称呼的，而且令人惊讶的是，它并非来自基础知识。 它仅限于学校和社区提供的教育。 在教育系统上教些东西只会帮助那些对一切都将如何切换到数据驱动的生态系统，而对环境完全没有影响的想法云雾people绕的人。 Erin A Cech博士[4]在2013年谈到美国如何试图强调培训具有道德意识，具有社会意识的工程师的重要性，但相反，工程教育本身却未能鼓励新手将公益视为其职业责任-从而在同等程度上利用AI和无知的见解给出了伪现实。
There is more to it.
还有更多。
Depoliticization — It states that cultural and social concerns of the society don’t hold good in the ‘real’ engineering work and hence should be cast out. So to say, somewhere we do bring a notion of only technological advancement, but how it will impact the human or environment around us, we are least concerned about that. Engineering work done without any thought around the people in major is just the technology of no good. As technology and society are seen as two different pillars, that makes public welfare difficult for engineers to understand what they are doing, how it will impact society. We do need metrics, which can be brought into the picture crafted by ‘socio- techno’ guys.
非政治化-它指出，社会的文化和社会问题在“实际”工程工作中并不令人满意，因此应予以消除。 可以这么说，在某个地方，我们确实带来了仅技术进步的概念，但是它会如何影响我们周围的人或环境，我们对此最不用担心。 未经专业人士的考虑而完成的工程工作只是一项不好的技术。 由于技术和社会被视为两个不同的Struts，这使工程师难以理解他们的所作所为及其对社会的影响，从而使公共福利变得更加困难。 我们确实需要度量标准，这些度量标准可以纳入“社交技术”专家制作的照片中。
There are a lot of things the engineered minds talk about including data-privacy, IoT, ease of accessibility, 5G, so on and so forth — At the same time, it opens a world that is close to being easily exploited. One of the best instances for this could be cybersecurity in the education system. Due to increased surveillance, policing, etc. in today’s society for better justice, it also opened a backdoor for unethical means. In modern society, even though we talk about smart cities, most of the funding does come in from the government and business people.
工程师头脑中涉及很多事情，包括数据隐私性，物联网，易访问性，5G等，同时，它打开了一个几乎容易被利用的世界。 最好的例子之一可能是教育系统中的网络安全。 由于当今社会越来越多的监视，维持治安等目的，以实现更好的司法公正，这也为不道德手段打开了后门。 在现代社会中，即使我们谈论的是智慧城市，大部分资金的确来自政府和商人。
The foundation for everything? Trust. And that’s where machines lack.
一切的基础？ 相信。 那就是机器缺乏的地方。
Well, the solution? A consortium, which can be trusted by people who are nurtured in different circumstances so that everyone can put faith in them. At least, we can look upon somebody who can make decisions regarding the price that human beings will be paying now so to correct the old deeds and for the brighter future of humankind. There is a need for social justice-based society. Emergence in research around decolonizing research methodologies, do show we have started, and we are moving towards the better world. The new generation does know what we lost and the current situation we are facing.
好吧，解决方案？ 一个财团，可以由在不同情况下受过养育的人们信任，以便每个人都可以对他们充满信心。 至少，我们可以找一个可以为人类现在付出的代价做出决定的人，以便纠正旧的行为和人类的美好未来。 需要建立以社会正义为基础的社会。 围绕非殖民化研究方法的研究的出现确实表明我们已经开始，并且我们正在朝着更美好的世界前进。 新一代确实知道我们失去了什么以及我们目前面临的情况。
The big question — is the technology just smarter, not greener?
最大的问题-技术是否更智能，而不是绿色？
Businesses are scurrying to assimilate data and derive insights, which is giving A.I. an impetus to become stronger and, therefore, deliver better results. But the pylon holding up the burden is the environment. More extensive models, more consumption, and a negative impact on the environment.
企业急于吸收数据并获取见解，这为AI带来了强大的动力，因此可以提供更好的结果。 但是承受重担的塔架是环境。 更广泛的模型，更多的消耗以及对环境的负面影响。
When the ‘data-hungry’ machines speak, the world listens.
当“数据饥渴”的机器说话时，全世界都在聆听。
Carbon emissions. Energy consumption. Reduction in greenhouse gas emissions. –They all veil themselves under the influence of shiny insights that machines offer.
碳排放量。 能源消耗。 减少温室气体排放。 –他们都在机器提供的闪亮见解的影响下掩盖自己。
From 2012–2018, the energy required for the computation of deep learning has increased around 3,00,000 times [5]. Machine learning models usually require more data and are prone to consume more power. To make those models even more skilled and accurate, one needs more training and execution it becomes a never-ending consumption process.
从2012年至2018年，深度学习计算所需的能量增加了约300万倍[5]。 机器学习模型通常需要更多数据，并且倾向于消耗更多功率。 为了使这些模型更加熟练和准确，人们需要更多的培训和执行，这是一个永无止境的消费过程。
Case in point — OpenAI recently launched its biggest AI-based language model — GPT3, trained on around 500bn words dataset against the previous GPT2 model that was trained on a dataset of 40bn words [6]. Before this, in 2018, the BERT, the best NLP model, was trained on a dataset of 3bn words and BERT outperformed by XLNet, which was trained on 32bn words [7]. — These numbers sound very optimistic at the outset, but the risk they come with includes lengthy training sessions translating into more energy consumption, and eventually a significant carbon emission.
一个很好的例子-OpenAI最近推出了最大的基于AI的语言模型-GPT3，该模型在约5000亿个单词的数据集上进行了训练，而之前的GPT2模型在400亿个单词的数据集上进行了训练[6]。 在此之前，2018年，最佳的NLP模型BERT在30亿个单词的数据集上进行了训练，而BERTNet则优于XLNet，后者在320亿个单词上进行了训练[7]。 —这些数字一开始听起来很乐观，但随之而来的风险包括冗长的培训课程，这转化为更多的能源消耗，最终导致大量的碳排放。
Is there some light at the end of this data tunnel?
该数据隧道的末端是否有亮灯？
Definitely, there is. A.I. is unarguably saving the environment and boosting the country’s economy to provide transparency in governance. A.I. for environmental applications has the potential to ramp the GDP of a nation by 3.1–4.4% if a recent report by PwC has to be believed [8].
肯定有 。 毫无疑问，人工智能正在保护环境并促进该国经济的发展，从而提供治理的透明性。 如果必须相信普华永道的最新报告，用于环境应用的人工智能有可能使一个国家的GDP增长3.1–4.4％[8]。
It can reduce global greenhouse gas emissions by around 1.5–4.0% by 2030 if the business is done as usual, while raising the GDP by a significant margin [8]. The early GDP gains are visible in a few parts of the world like Europe, North America, and East-Asia, accounting for around 1trillion USD [8] For the energy and transport sector, there could be a cut of about 2% and 1.7% respectively in total greenhouse emissions [8]. Having said that, more focus is still needed towards the water and agriculture in particular as they play a significant role in the environment in a broader sense.
如果照常开展业务，到2030年，它可以将全球温室气体排放量减少1.5％至4.0％，同时将GDP大幅提高[8]。 早期的GDP增长在欧洲，北美和东亚等世界各地可见，约合1万亿美元[8]对于能源和交通运输业，可能分别削减约2％和1.7分别占温室气体总排放量的百分比[8]。 话虽如此，但仍然需要更多地关注水和农业，因为它们在更广泛的意义上在环境中发挥着重要作用。
While A.I. can help make the right decisions, improve climate predictions, and work on allocating renewable resources — there have to be a few solutions to mitigate the risks.
尽管人工智能可以帮助做出正确的决定，改善气候预测并致力于分配可再生资源，但必须有一些解决方案来减轻风险。
● A.I. model training sessions can be moved to the cloud and hosted near the location where there is a more significant consumption of renewable resources. Since a cloud can store more datasets, it’s also easier to leverage data from different locations.
●可以将AI模型培训课程移至云中，并在可再生资源消耗更多的位置附近进行托管。 由于云可以存储更多数据集，因此利用来自不同位置的数据也更加容易。
● Making efficient A.I. algorithms can help — A recent study carried out by the Stanford group evaluated different algorithms for the same task. Results revealed that the difference in the electricity consumption of tuned and un-tuned algorithms was nearly about 880 kilowatt-hours, which is a typical consumption of American households for a month [1]. If we write better code or better models, we can make a daunting impact to reduce the carbon footprint of the application.
●制定有效的AI算法可以有所帮助-斯坦福大学小组最近进行的一项研究评估了针对同一任务的不同算法。 结果显示，调整后的算法和未调整后的算法的耗电量差异接近880千瓦时，这是美国家庭一个月的典型耗电量[1]。 如果我们编写更好的代码或更好的模型，则会对减少应用程序的碳足迹产生巨大影响。
● Evaluating programs based on the default configuration, and fine-tune the model once it’s fixed. As we know, not only training of the machine learning model demands high energy, but consuming such an A.I. system consumes far more power than training. Evolution not only from algorithmic side needed but also infrastructure built on sustainable energy will also be required to facilitate such a vast A.I. dependent application.
●根据默认配置评估程序，并在修复模型后对其进行微调。 众所周知，不仅机器学习模型的训练需要高能量，而且消耗这样的AI系统比训练还要消耗更多的动力。 不仅需要从算法方面进行进化，而且还需要基于可持续能源的基础设施来发展，以促进如此庞大的依赖于AI的应用程序。
Well, let’s hope for the A.I. pipeline to become environmentally sustainable — wouldn’t that be the literal best of both worlds?
好吧，让我们寄希望于AI管道在环境上可持续发展-这难道不是两全其美吗？
[1] https://hai.stanford.edu/blog/ais-carbon-footprint-problem[2] https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/[3] https://www.seai.ie/data-and-insights/seai-statistics/key-statistics/co2/[4] https://journals.sagepub.com/doi/abs/10.1177/0162243913504305[5] https://www.sciencedaily.com/releases/2020/05/200518144908.htm[6] https://www.forbes.com/sites/robtoews/2020/06/17/deep-learnings-climate-change-problem/#2bd06ae56b43[7] Yang, Zhilin and Dai, Zihang and Yang, Yiming and Carbonell, Jaime and Salakhutdinov, Russ R and Le, Quoc V, Xlnet: Generalized autoregressive pretraining for language understanding (2019), Advances in neural information processing systems[8] https://www.pwc.co.uk/sustainability-climate-change/assets/pdf/how-ai-can-enable-a-sustainable-future.pdf
[1] https://hai.stanford.edu/blog/ais-carbon-footprint-problem [2] https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai -model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes / [3] https://www.seai.ie/data-and-insights/seai-statistics/key- statistics / co2 / [4] https://journals.sagepub.com/doi/abs/10.1177/0162243913504305 [5] https://www.sciencedaily.com/releases/2020/05/200518144908.htm [6] https ：//www.forbes.com/sites/robtoews/2020/06/17/deep-learnings-climate-change-problem/#2bd06ae56b43 [7]杨志林和戴，子行和杨，宜明和卡博乃尔，海梅和Salakhutdinov，Russ R和Le，Quoc V，Xlnet：用于语言理解的广义自回归预训练(2019年)，神经信息处理系统的进展[8] https://www.pwc.co.uk/sustainability-climate-change/assets / pdf /如何启用一个可持续的未来.pdf
翻译自: https://towardsdatascience.com/ai-for-a-sustainable-society-731fe5116471
人工智能ai发展前景