AI 与机器学习2021storage.googleapis.com
每个人都想做模型工作，而不是数据工作
Everyone wants to do the model work, not the data work
入门 · 约 45 分钟读完前置知识：机器学习基础、数据标注流程
导读
这篇 Google 研究团队的论文探讨机器学习领域中一个普遍现象：从业者普遍热衷于建模工作，却轻视数据收集、标注和维护等数据工作。论文分析了这种重模型轻数据的倾向如何影响 ML 系统的质量与可持续发展，呼吁重新审视数据工作在 ML 研究与实践中的地位。
HN 关注点：是因为论文点出了业界普遍存在但少被正面讨论的问题——数据工作枯燥、不被认可却至关重要，评论者纷纷分享亲身经历印证这一现象。
后来怎么样
这篇论文推动了 ML 社区对数据中心化 AI（data-centric AI）运动的关注，与吴恩达同期的倡导相互呼应，使数据质量与数据工程在研究和工业实践中获得更系统的重视。
HN 评论精选
这几条评论里：支持 1 补充 1 亲历 2
亲历数据采集公司亲历：单独的数据工作难以盈利，必须打通整条价值链才能生存
我从另一个角度分享一下经验。我领导一家做“数据工作”的公司，每天采集数 TB 的摄影和激光雷达数据。这个行业一直很艰难，似乎唯一的盈利方式是提供整条价值链，这也是我们正在转型的方向。单纯的摄影和激光扫描对谁都没多大价值，而建立高质量数据流的成本极高——传感器设备、人员培训、运营都要巨额投入，这一切只是为了比派人去现场检查省几块钱。如果做整条价值链：采集数据、加工成信息、再转化为可行动的报告，那意味着我们除了运营，还要做建模工作，并提供行业专业知识。客户付费是为了知道什么东西坏了、坏在哪、缺陷影响多大。我相信我们最终会成功，但那是因为我们的投资人愿意陪我们走完全程，而许多其他公司早已放弃。
tinco查看英文原文
So, I don't have time to read the entire paper, but I thought it would be interesting to share my experience from the other side. I lead the engineering for a company that does the "data work", we gather terabytes of data, mostly photography and lidar, every day. So far it's been a crappy industry to be in, it seems the only way to make money is to provide the entire value chain, and this is the business model we're pivoting to. Just a bunch of photography and lidar scans isn't worth much to anyone, and it's very costly to establish a quality data stream. We had enormous capex on sensor gear, training personnel, running the operations. All this to save a couple bucks on sending an inspector physically to the site. If you do the entire value chain, you gather the data, you process it into information, and then you turn that information into actionable reports. So that means that besides running our operations, we're also doing the modeling work, and we're providing the industry specific expertise for the actual inspection. So our customers pay for knowing what is broken, where it's broken, and what the impact of that defect is. In the end, I believe we'll be very successful, but it will be because our investors were willing to take us all the way, where many other companies have simply given up on the concept.
亲历亲历案例：真实数据分布与公开数据集脱节，数据工作被学术界低估
真实经历。我曾为移动端实时视频聊天应用做裸露检测（成功了）。本以为很简单：标注数据，从 ImageNet 预训练网络（我们用 SqueezeNet）微调即可。问题是 ImageNet 是照片而非视频帧，移动视频帧的分布很不一样——大量帧要么是黑屏（摄像头被挡）要么是天花板（摄像头朝向不对）。我们最终显式标注了大量黑屏和天花板数据，结果模型成了出色的天花板检测器，连没见过的天花板都能识别。为什么没有移动视频帧的天花板数据集？我不信只有我一个人遇到这个问题。为什么天花板数据集不配发 CVPR？它对移动端视频深度学习的价值超过大多数 CVPR 论文。这是严肃的问题。补充：我理解为什么小众数据集留在工业界而非学术界，但天花板数据集对所有处理手机视频帧的人都有普遍价值。
sanxiyn查看英文原文
Real story. I worked on nudity detection for mobile realtime video chat app with on-device deep learning model. (It was successful.) Should be easy: label some data, start from a good network pretrained on ImageNet (we used SqueezeNet), fine tune. The problem was that ImageNet is photos, not video frames, and distribution of video frames from mobile video is somewhat different. Incredibly large proportion of frames are either blank (camera is blocked) or ceiling (camera is misdirected). We ended up adding lots of data for blank and ceiling explicitly labeled as blank and ceiling. It became an excellent ceiling detector, detecting ceilings it never have seen before. Is there a mobile video frames dataset for labeling ceilings? Why not? I can't believe I am the only one who experienced this. Why is not a ceiling dataset worthy of, say, CVPR? It will improve mobile video on-device deep learning more than most of CVPR papers. This is a serious problem. Edit: I understand why niche datasets remain in industry and not academia. Public datasets are better if they are of general interest. But ceiling dataset is of general interest to anyone who wants to process video frames originating from blocked or misdirected camera aka smartphones, and it's hard to imagine topics of more general interest once you have obvious things like face.
支持顾问视角：客户只想“做 AI”却完全不了解自己的数据和产品
是的，这极其普遍也极其令人沮丧。公司、政府、个人不断来问我如何“做 AI”，却对他们的产品、数据来源、测试计划、状态可观测性一无所知。你当然可以用现成的视觉或 NLP 框架和现成数据做个玩具问题，但那有什么意义？没有新颖性，也不会真正改变你的产品功能。Chomsky 说年轻人最重要的是“认识你自己”。想做 AI 的人需要先“认识你的数据和产品架构”。我去年就此做过一个演讲。
AndrewKemendo查看英文原文
Yes, this is extremely common and extremely frustrating. Companies, governments, individuals come to me all the time asking how to "Do AI" without knowing anything about their product, data provenance, testing plans, state observability etc... Sure, you can do a toy problem with some interesting vision or NLP framework and some off the shelf data that isn't ready for production or anything serious. Who cares? That doesn't do anything novel, it doesn't actually change your product function. Chomsky said the most important thing to do as a young person is to follow the phrase "Know Thyself." People who want to do AI need to "Know Thy-data and product architecture" first. I did a talk last year on exactly this: https://www.youtube.com/watch?v=jK-SBu1iiHo
补充术语批判：“人工智能”这一命名本身美化了算法、掩盖了数据的重要性
这可能源于“人工智能”这一根本性的误导性命名（美化了算法），相比之下“数据驱动的算法”更恰当地强调了两方面。AI 在规则式专家系统时代还算说得过去，但“AI”和“ML”用在现代数据密集型方法上尤其误导，导致了文章指出的后果。此外，淡化数据重要性而突出算法并非偶然，这源于该学科的计算机科学根基和推广话术。那些对数据采集成本有清醒认识的子领域，情况则不同。
ssivark查看英文原文
Probably stems from the foundational misnomer of “artificial intelligence” (which glamorizes the algorithm) compared with, say “data-driven algorithms” which emphasizes both aspects suitably. AI might have been a passable term in the era of rule-based expert systems, but both “AI” and “ML” are particularly misleading terms (when applied to modern data-heavy methods), leading to consequences like that pointed out in the article. Further, glossing over the importance of data to emphasize the algorithms is not accidental, given the subject’s roots in CS, and the rhetoric used to promote the field. The sub fields where it plays out differently are those which have a healthy respect for the prices of data gathering.
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历次讨论
- 2021Hacker News 讨论帖358 分 · 112 评
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