中文字幕无码av不卡一区,亚洲综合AV永久无码精品一区二区,黑人巨大白妞出浆,黄色高清无码免费看,久久久久免费精品国产,久久无码人妻丰满熟妇区毛片,欧美日韩国产综合草草,久久福利网站,亚洲一区二区三区中文字幕在线,av国内精品久久久久影院

全球人工智能教育及研究領導者、DeepLearning.AI創始人吳恩達，4月21日于知乎發布本文。

Dear friends,

The competitive landscape of large language models (LLMs) is evolving quickly. The ultimate winners are yet to be determined, and already the current dynamics are exciting. Let me share a few observations, focusing on direct-to-consumer chat interfaces and the LLM infrastructure and application layers.

First, ChatGPT is a new category of product. It’s not just a better search engine, auto-complete, or something else we already knew. It overlaps with other categories, but people also use it for entirely different purposes such as writing and brainstorming. Companies like Google and Microsoft that are integrating LLMs into existing products may find that the complexity of switching not only technologies but also product categories raises unique challenges.

OpenAI is clearly in the lead in offering this new product category, and ChatGPT is a compelling direct-to-consumer product. While competitors are emerging, OpenAI’s recent move to have ChatGPT support third-party plugins, if widely adopted, could make its business much more defensible, much like the app stores for iOS and Android helped make those platforms very defensible businesses.

Second, the LLM infrastructure layer, which enables developers to interact with LLMs via an API, looks extremely competitive. OpenAI/Microsoft leads in this area as well, but Google and Amazon have announced their own offerings, and players such as Hugging Face, Meta, Stability AI, and many academic institutions are busy training and releasing open source models. It remains to be seen how many applications will need the power of the largest models, such as GPT-4, versus smaller (and cheaper) models offered by cloud providers or even hosted locally, like gpt4all, which runs on a desktop.

Finally, the application layer, in which teams build on top of LLMs, looks less competitive and full of creativity. While many teams are piling onto “obvious” ideas — say, building question-answering bots or summarizers on top of online content — the sheer diversity of potential LLM-powered applications leaves many ideas relatively unexplored in verticals including specialized coaching and robotic process automation. AI Fund, the venture studio I lead, is working with entrepreneurs to build applications like this. Competition feels less intense when you can identify a meaningful use case and go deep to solve it.

LLMs are a general-purpose technology that’s making many new applications possible. Taking a lesson from an earlier era of tech, after the iPhone came out, I paid $1.99 for an app that turned my phone into a flashlight. It was a good idea, but that business didn’t last: The app was easy for others to replicate and sell for less, and eventually Apple integrated a flashlight into iOS. In contrast, other entrepreneurs built highly valuable and hard-to-build businesses such as AirBnB, Snapchat, Tinder, and Uber, and those apps are still with us. We may already have seen this phenomenon in generative AI: Lensa grew rapidly through last December but its revenue run appears to have collapsed.

Today, in a weekend hackathon, you can build a shallow app that does amazing things by taking advantage of amazing APIs. But over the long term, what excites me are the valuable solutions to hard problems that LLMs make possible. Who will build generative AI’s lasting successes? Maybe you!

One challenge is that the know-how for building LLM products is still evolving. While academic studies are important, current research offers a limited view of how to use LLMs. As the InstructGPT paper says, “Public NLP datasets are not reflective of how our language models are used. . . . [They] are designed to capture tasks that are easy to evaluate with automatic metrics.”

In light of this, community is more important than ever. Talking to friends who are working on LLM products often teaches me non-intuitive tricks for improving how I use them. I will continue trying to help others wherever I can.

Keep learning!

Andrew

親愛的朋友們，

大型語言模型 (LLMs) 的競爭格局正在迅速打開。最終贏家尚未出爐，但目前的形勢已經令人興奮。我想分享一些觀察結果，重點關注直接面向消費者的聊天接口以及LLMs基礎設施和應用程序層。

首先，ChatGPT是一個新的產品類別。它不僅僅是一個更好的搜索引擎——能自動完成檢索，及其他我們已經知道的功能。ChatGPT與其他類別有一些重疊，但人們也將其用于了完全不同的目的，如寫作和頭腦風暴。谷歌和微軟等公司正在將LLMs集成到現有產品中，這樣做可能不僅需要轉換技術，還要轉換產品類別，這就帶來了獨特的挑戰。

OpenAI在提供這種新的產品類別方面顯然處于領先地位，ChatGPT就是一種引人注目的直接面向消費者的產品。雖然競爭對手不斷涌現，但OpenAI最近讓ChatGPT支持第三方插件的舉措——一旦被廣泛采用，可能會使其業務更具防御性——會像iOS和Android的應用商店使這些平臺的業務更具防御性一樣。

其次，LLMs的基礎設施層使開發人員能夠通過API與LLMs進行交互，這看起來極具競爭力。OpenAI和微軟在這一領域也處于領先地位，谷歌和亞馬遜也爭相發布了自己的產品，而Hugging Face, Meta, Stability AI等公司和許多學術機構都在忙著訓練和發布開源模型。有多少應用程序需要用到像GPT-4這樣的最大型模型，而不是云提供商提供的更?。ǜ阋耍┑哪Ｐ?，甚至是本地托管的模型（比如運行在桌面上的gpt4all）還有待觀察。

最后是應用程序層。開發團隊建立在LLMs的基礎上，看起來競爭不那么激烈，且充滿創造力。雖然許多團隊都在嘗試“顯而易見”的想法——比如在在線內容的基礎上構建問答機器人或摘要器。但LLMs支持的潛在應用程序的多樣性，使得許多想法在專業指導和機器人過程自動化等垂直領域還未被充分探索。我領導的風投公司AI Fund正在與企業家合作開發這樣的應用程序。當你能夠確定一個有意義的用例并深入解決它時，競爭的感覺就不那么激烈了。

LLMs是一種通用技術，它使許多新的應用成為可能。在iPhone問世后，我從早期科技時代吸取了教訓花費1.99美元購買了一個能把手機變成手電筒的應用程序。這是個好主意，但這筆生意沒能持續多久：這款應用很容易被其他人復制，售價也更低，最終蘋果將手電筒集成到了iOS系統中。相比之下，其他企業家建立了價值更高和開發難度更大的業務，如AirBnB、Snapchat、Tinder和Uber，這些應用程序至今仍在被使用。我們可能已經在生成式人工智能中看到了這種現象：Lensa（一款火爆的照片編輯器）在去年12月的使用量增長迅速，但收入卻不盡如人意。

現在，你可以在一個周末進行的黑客馬拉松中構建一個簡單的應用程序，通過利用厲害的API來實現驚人的結果。但從長遠來看，令我興奮的是LLMs能為解決難題提供有價值的解決方案。誰將打造生成式人工智能的長期成功？也許就是你！

我們面臨的一個挑戰是，構建LLMs產品的技術訣竅仍在不斷發展。雖然學術研究很重要，但目前的研究對如何使用LLMs只提供了有限的幫助。正如InstructGPT論文所說，“公共NLP數據集并不能反映我們的語言模型是如何被使用的……（它們）被設計用來捕捉那些容易用自動指標進行評估的任務。”

鑒于此，社群的作用比以往任何時候都更加重要。與從事LLMs產品開發工作的朋友交談能帶給我一些直覺以外的技巧來改進對這些產品的使用。我將繼續盡我所能去幫助別人。

請不斷學習！

吳恩達