These 3 AI Papers Save Human Lives!
Summary
TLDR在这段视频中,主持人介绍了他在访问了包括谷歌、OpenAI实验室、NVIDIA等在内的多个科技巨头后,发现的三项令人惊叹的研究。这些研究包括:一种新的AI系统,它能够利用美国等国家的现有数据,帮助那些缺乏数据收集能力的国家预测洪水;一种基于扩散模型的AI技术,用于从历史天气数据中预测极端天气事件,这种方法比传统方法更准确且计算成本更低;以及一种利用AI优化飞行路线以减少飞机尾迹对气候影响的技术,这项技术在与美国航空的试验中显示,只需额外0.3%的燃油,就能减少54%的尾迹产生,从而减少全球变暖效应。这些研究不仅令人兴奋,而且具有实际应用价值,能够拯救生命并保护我们的地球。
Takeaways
- 🛫 视频作者为了与旧金山的科学家们交流,花费了超过50小时的往返时间,并幽默地提出下次可能需要AI克隆技术来节省时间。
- 🤖 作者访问了Google、OpenAI实验室、NVIDIA等公司,并与一些传奇的研究科学家进行了交谈。
- 🌍 视频中提到的三个研究论文都与拯救人类生命有关,涉及洪水检测、天气预报和飞行可持续性。
- 💧 Google开发了一种新的AI系统,用于洪水预测,它比全球洪水预警系统更准确,对非先进国家尤其有帮助。
- 🌐 该AI系统能够重新利用美国等国家的现有数据,帮助那些没有能力收集训练数据的国家进行洪水预测。
- ❄️ Google还开发了一种新的AI技术用于天气预报,它使用历史天气数据和基于扩散的AI模型来预测极端天气事件。
- 🌡️ 这种新的天气预报方法在计算成本上只有以前技术的一小部分,但准确性更高,有助于预测极端事件并拯救生命。
- ✈️ 另一个研究是关于使用AI使飞行更加可持续,特别是通过预测飞机尾迹(contrails)的形成来减少其对气候的影响。
- 🌟 研究发现,只有大约5%的飞机尾迹会长时间存在并对天气产生显著影响,而AI可以帮助预测哪些飞行路线会产生这些尾迹。
- 🛰️ 通过与美国航空的试验,AI建议的微小航线调整仅增加了0.3%的燃料消耗,却减少了54%的尾迹形成,显著降低了热捕获效应。
- 🌱 这项技术的实际应用表明,它比现有技术对环境和地球更有利20倍,作者希望更多人能了解到这些能够拯救生命的研究工作。
Q & A
作者提到了哪些公司和实验室?
-作者提到了OpenAI实验室、Google、NVIDIA等公司和实验室。
作者为什么提到可能需要AI克隆技术来缩短旅行时间?
-作者提到了需要50多个小时的往返旅行时间,这是为了幽默地表达下一次旅行时希望能够使用先进的AI克隆技术来减少实际旅行时间。
为什么说能够进入Google的研究大楼是一种荣誉?
-因为Google通常不允许外部人员进入他们的研究大楼,作者能够进入并与著名的研究科学家交谈,这显示了对作者的一种特别认可。
作者提到的三个研究领域分别是什么?
-作者提到的三个研究领域是洪水检测、天气预报以及使用AI使飞行更加可持续。
为什么当前的洪水预测系统不够完善?
-当前的洪水预测系统不够完善是因为预测降雨量是天气预报中最不确定的因素,而降雨量又是预测洪水的关键。
新的AI系统如何帮助那些没有足够训练数据的国家进行洪水预测?
-新的AI系统能够通过重用美国和其他国家的现有数据,并将其应用到那些不幸的国家,从而帮助他们进行洪水预测。
作者提到的天气预测的新技术与传统方法相比有何优势?
-新技术的优势在于它不需要大量的物理计算来生成模拟天气数据,而是使用基于扩散的AI模型直接从历史天气数据中进行预测,这大大降低了计算成本,并且提高了准确性。
为什么飞机尾迹(contrails)对地球的温度有影响?
-飞机尾迹能够随着时间的推移扩散开来,并且能够捕获热量,因此它们对地球的温度有可测量的影响。
AI如何帮助减少飞机尾迹对地球温度的影响?
-AI可以通过预测哪些飞机在哪些航线上会产生尾迹,并建议微小的航线调整,从而减少尾迹的产生,同时对燃油使用的影响非常小。
作者提到的与美国航空公司的试验结果如何?
-与美国航空公司的试验结果显示,通过使用AI的结果进行微小的航线调整,额外的燃油使用仅增加了0.3%,但减少了54%的尾迹产生,从而将热捕获效应减少了一半。
作者希望视频能够达到什么效果?
-作者希望视频能够让更多的学者了解到这些能够拯救生命的研究工作,并且激发他们对这些研究的兴趣和讨论。
Outlines
🌐 人工智能在洪水预测和天气预测中的应用
在第一段中,视频作者介绍了他访问了包括OpenAI、Google、NVIDIA在内的多个知名科技公司,并与那里的科学家进行了交流。他特别提到了Google的三项令人难以置信的研究:洪水检测、天气预测和飞行可持续性。Google的研究团队提出了一种新的AI系统,能够利用现有数据预测洪水,甚至比全球洪水预警系统还要准确。此外,他们还开发了一种新的基于历史天气数据的AI模型,用于预测极端天气事件,这种方法比传统的需要大量物理计算的方法更加准确且计算成本更低。最后,作者提到了一项关于使用AI来减少飞机尾迹对气候影响的研究,这项研究通过微小的航线调整,成功减少了54%的尾迹产生,同时仅增加了0.3%的燃料消耗。
🛫 利用AI优化飞行路径减少尾迹影响
第二段详细讨论了如何利用AI预测飞机飞行路径上可能产生的尾迹,并提出了一种解决方案。尾迹,也称为凝结尾迹,是由飞机排放的废气在高空中形成的,它们会随着时间扩散并捕获热量,对地球的温度产生可测量的影响。研究团队面临的挑战是如何区分尾迹和类似的卷云,但他们已经找到了一种可靠的区分方法。通过与美国航空合作的试验,他们证明了通过AI建议的微小航线调整,可以在几乎不影响燃料消耗的情况下,显著减少尾迹的产生。这项技术不仅有助于减少飞机对气候的影响,而且其净效果比现有技术好20倍,为航空业的可持续发展提供了一种新的思路。
Mindmap
Keywords
💡人工智能
💡洪水预测
💡Global Flood Awareness Systems
💡天气预报
💡NVIDIA's FourCastNet
💡DeepMind’s GraphCast
💡扩散模型
💡飞行可持续性
💡飞机尾迹
💡cirrus clouds
Highlights
作者前往旧金山与OpenAI实验室、谷歌、英伟达等机构的科学家们交流。
作者提到可能需要AI克隆技术来缩短50多小时的往返旅行时间。
作者获得了进入谷歌大楼并与传奇研究科学家交谈的荣誉。
作者将介绍三篇能够拯救人类生命的研究论文。
谷歌注意到全球每年有数亿次关于洪水的搜索,意识到这是一个重要问题。
目前洪水预测系统无法准确预测,因为降雨是最难预测的变量之一。
提出了一个创新的想法:完全不考虑降雨,而是从其他已知因素预测洪水。
这项新技术可以帮助缺乏训练数据的国家利用美国等国家的数据进行洪水预测。
新的AI系统比全球洪水预警系统更准确,对非发达国家是巨大帮助。
一些预测系统成本越来越低,甚至可以在手机中运行。
谷歌还开发了一种新的AI天气预测技术,与现有的NVIDIA和DeepMind技术相比。
新的天气预测技术避免了大量物理计算生成模拟天气数据的需求。
使用基于扩散的AI模型从历史天气数据中预测,提高了准确性并降低了计算成本。
AI技术可以帮助预测极端天气事件,从而拯救许多人的生命。
作者对使用AI使航班更可持续的研究感到惊讶。
飞机尾迹(contrail)对地球温度有可测量的影响。
只有5%的尾迹会持续存在并对天气产生显著影响。
科学家们开发了一种方法来区分尾迹和卷云,使问题变得可学习。
通过与美国航空公司的70次航班试验,AI预测技术减少了54%的尾迹,减少了一半的热量捕获效果。
这项技术只需额外0.3%的燃油,但对地球的影响比现有技术好20倍。
作者希望这个视频能让更多的人了解到这些能拯救生命的研究工作。
Transcripts
I flew San Francisco to talk to scientists at the OpenAI lab, Google in Mountain View,
NVIDIA and more. My travels took 50+ hours round trip, so I think I might some advanced
AI cloning technology to get there next time. Now here are some of the incredible things
I’ve seen at Google. They said they don’t let in anyone external in that building for any reason,
and I could get in there to talk to some legendary research scientists,
so that was an incredible honor. Thank you! And now, I’ll show you about 3 absolutely incredible
research papers that do the greatest thing any papers can do. And that is saving human lives.
You will see what’s new in flood detection, weather prediction, and something about this
too that really surprised me. I’ll note that this video is not sponsored by them,
and they will see this video for the first time together with you Fellow Scholars when it appears.
Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Now, one, they are Google, so they see that people around the world search about floods
several hundred million times each year, so this is a super important problem. However, current
systems are not up to the task. Why is that? Well, one of the hardest variables to predict
in weather is rainfall, it is the main source of uncertainty, and you need that to predict floods.
However, here is an insane idea: just skip it entirely. Don’t even think about the rain. Okay,
but what now? Well, now, try to predict floods from everything else that we know instead.
But there is a problem. A huge problem. The countries that need these predictions
the most are the places that don’t have training data for the learning algorithm
as they don’t have the means to collect it. And this new technique one helps them
reuse data from the USA and other countries and apply it to less fortunate countries.
Is that even possible? Well, get this, this new AI system is more accurate
than the Global Flood Awareness Systems used around the world. This is absolutely amazing,
a great help for non-advanced nations. And not only for their governments,
but for citizens too. You see, some of these are getting cheaper and cheaper to run,
so much so that some of these even run on your phone in your pocket.
Two, they also have a crazy new AI technique for weather prediction. However, wait,
there are already real good ones out there, for instance,
NVIDIA's FourCastNet and DeepMind’s GraphCast. So what is going on here?
Well, there is a problem - most techniques need lots of physics computation to generate simulated
weather data to be able teach the AI. So here is once again, a crazy idea: don’t do that. Instead,
take a bunch of historical weather data, and use diffusion-based AI models to predict from
historical data. Yes, these are the AI models that create images from your text where they start out
from noise, and over time, they reorganize this noise to resemble your text input more and more
over time. It can generate videos too. Except that here, it generated plausible weather data
to provide the AI with a textbook to learn the intricacies of extreme weather events.
And as a result, it has so much more data to work with, and is more accurate than previous
methods at a fraction of the computational cost of previous techniques. Loving this one. This will,
once again, be able to predict extreme events and save many-many human lives over time.
Now, three. I was really surprised by this. This one is about using AI to make flights a little
more sustainable. Did not know that aircraft exhaust lines called contrails are very good
at spreading out over time, and trapping heat. Yes, so much so that they have a measurable
impact on the temperature of the planet. Not huge, but measurable and meaningful.
This I found very surprising because as we have tons of flights around the world,
there are lots of contrails, but only few of them stay for long,
probably only 5% of them. And that 5% has a meaningful effect on our weather.
Okay, so now what? Well, it would be great to have some sort of simulation that would
be able to predict which planes on which routes are going to create these trails.
However, there is a problem here too. To have the AI learn the evolution of these trails and
how they behave over time, we would need tons and tons of training data. So, just pull up lots of
satellite imagery and off you go, right? Well, no. Not at all. And therein lies the problem.
Contrails are very easily confused by these famously wispy cirrus clouds. But, scientists
found a way to tell them apart reliably, so this problem is now learnable. Fantastic.
Now, this is nice, but this is numbers on paper. Does this prediction technique actually work?
Does it actually help us? Well, get this. They really put their money where their papers are,
because they had a trial with American Airlines for 70 flights, where they proposed tiny-tiny
changes to their routes by using the AIs results, the additional fuel usage was very little,
about 0.3% difference, and that led to a 54% reduction of these trails. Cutting the heat
trapping effect by half. Yes! If planes intentionally avoid flying through these
regions, then they can avoid creating warming contrails, with minimal impact on fuel usage.
Now, wait, 0.3% of extra fuel usage is still quite meaningful. Yes, that is true, however, the net
effect of this technique is still 20x better for us and the planet than what we have now.
And I hope that with this video more of you Fellow Scholars will hear about these incredible
research works that will save lives. What a time to be alive! There are many many more out there,
and unfortunately, I found that almost no one is talking or reporting on them.
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