17年老狗六年前准确预测🐶家当下困境

我贴了chatgpt summary，但建议阅读全文。不得不感慨，狗家不是缺人才，问题还是org太臃肿反应太慢，同时genAI对狗家business model太颠覆了。


chatgpt总结：
Eric Lehman predicts that deep machine learning (ML) will outdo Google's search relevance algorithms soon, potentially within months. He highlights the recent success of BERT in web answers as an example of ML's rapid advancement. Lehman urges his team to prepare for this shift, noting that competitors could also develop superior ML systems. He recalls how Google Translate had to adapt when ML transformed translation, suggesting that web search relevance could face a similar disruption. Despite current systems' complexity, Lehman believes traditional methods will eventually fall behind as ML progresses, and he considers this shift to be almost inevitable. He encourages reflection on the future implications of this technological evolution.




原文：
On Wed, Dec 26, 2018 at 4:48 PM, Eric Lehman wrote:


I'd like to offer a thought for contemplation over the break: Within the near future, a deep ML system will clearly outperform Google's 20-year accumulation of relevance algorithms for web search. Here, I'm just talking about relevance; that is, determining whether a document and query are talking about the same thing. There is a lot more to web ranking for which ML seems much less appropriate. But I think basic relevance is the major task in web ranking and probably "objective" enough to go after pretty effectively with ML. None of us can see the future, but my bet is that this is nearly certain to be true within 5 years and could be true even within 6 months. One problem after another that is similar in flavor to web ranking has fallen, and there is little reason to think that web ranking is somehow exceptional. Indeed, this holiday thought stems from recent advances in web answers, where deep ML (in the form of BERT) abruptly subsumed essentially all preceding work.


For the web answers team, the tidal wave of deep ML that arrived in the last few weeks was a complete shock. With this warning, we should not allow ourselves to be caught off-guard again; rather, we should start thinking through the implications now. And now is really the time, because in the new year I expect a lot of web ranking engineers to reflect on BERT and start thinking along these same lines.


One consideration is that such a deep ML system could well be developed outside of Google—at Microsoft, Baidu, Yandex, Amazon, Apple, or even a startup. My impression is that the Translate team experienced this. Deep ML reset the translation game; past advantages were sort of wiped out. Fortunately, Google's huge investment in deep ML largely paid off, and we excelled in this new game. Nevertheless, our new ML-based translator was still beaten on benchmarks by a small startup. The risk that Google could similarly be beaten in relevance by another company is highlighted by a startling conclusion from BERT: huge amounts of user feedback can be largely replaced by unsupervised learning from raw text. That could have heavy implications for Google.


.--Relevance in web search may not fall quickly to deep ML, because we rely on memorization systems that are much larger than any current ML model and capture a ton of seemingly-crucial knowledge about language and the world. And there are lots of performance challenges, specialized considerations, etc. Still, my guess is that the advantages of our current approach will eventually crumble; ML is advancing very fast, and traditional techniques are not.


I don't know how others think about this. Maybe this prospect was already obvious to you. Or you might think this view of the future is just wrong. Personally, I'm inclined to think that this future is near-inevitable, but—despite that—I hadn't taken the next step of thinking through implications.

google现在的窘境有且只有一个原因，就是劈柴的超低效执行力和策略能力。
这人搞了将近10年的AI，全花在了搞各种politics上面了，
一个缩影事件：AI还没做出东西之前先hire了一群AI ethics team整天搞这个team搞那个team说这个AI产品不能launch 因为ethics没达标之类的。搞的研发速度砍了一大截。
另外一个缩影事件：整天在喊being thoughtful，说白了无非就是“搞不定内部politics”还有就是“对于自己策略的准确性没有信心” 还有“没有强大的执行能力”。所以只能用being thoughtful 来搪塞。

匿名用户 发表于 2024-02-11 20:20:48
同意，劈柴做的决定在疫情之间大放水招了近7万人，按小渣的话说，most of them don‘t belong here. 这么说很残酷，但是是劈柴把Goog

Pichai把google搞成了microsoft— 醒醒，msft现在不是你狗能碰瓷的

匿名用户 发表于 2024-02-11 14:17:15
Search转型是必然，狗家Competititve analysis早就预测到传统推荐算法被AI被取代这点了。早在几年前就在YouTube, Cloud, A

感觉很难。现在狗选择大范围降薪而不是大规模裁员，就说明内部politics很难搞。想要做到你说的这种转型，太多org要伤筋动骨了。

想问下能贴下原文链接吗? 搜了首页没搜到

匿名用户 发表于 2024-02-11 13:39:29
最大的问题不是“org太臃肿反应太慢”， 而是现有的技术跟不上。
. From 1point 3acres bbs现有的大模型，用一个词形容就是难堪大用。 准确读不够， 实时性不够，query一次花费巨大

Relevance这个问题难度是基本固定的，但ml model的能力是不断增强的，价格是不断下降的。

Search转型是必然，狗家Competititve analysis早就预测到传统推荐算法被AI被取代这点了。🐶早在几年前就在YouTube, Cloud, Android针对生成式AI有布局，但是传统Search业务何去何从的难题比想象中的来得更快势头更猛。接下来就看Gemini Advanced面向B2B用户能不能发力了。

delta42 发表于 2024-02-11 14:30:02
感觉很难。现在狗选择大范围降薪而不是大规模裁员，就说明内部politics很难搞。想要做到你说的这种转型，太多org要伤筋动骨了。

狗家产品大趋势是各自做各自的。现在把AI研究开发团队集中起来开发底层模型Gemini，从招聘到管理把公司层面的决策下沉到各个部门，意图很明显就是部门各玩各的了。我其实看好Cloud和YouTube，这两个PA五六年前就在布局GenAI了, Neal 和Thomas 决策能力还是蛮强的。Search PA目前前景很不乐观，就看那个P 的svp 能不能把PA带出沼泽了。

不是很懂在说什么 search不是一直都在用AI么…难道之前lr svm不算ai？只有nn算ai？

同意，劈柴做的决定在疫情之间大放水招了近7万人，按小渣的话说，most of them don‘t belong here. 这么说很残酷，但是是劈柴把Google搞成了Microsoft。