視頻地址：https://www.coursera.org/learn/ai-for-everyone/lecture/5TPFo/machine-learning
英文字幕：
The rise of AI has been largely driven by one tool in AI called machine learning. In this video, you'll learn what is machine learning, so that by the end, you hope we will start thinking how machine learning might be applied to your company or to your industry. The most commonly used type of machine learning is a type of AI that learns A to B, or input to output mappings. This is called supervised learning. Let's see some examples. If the input A is an email and the output B one is email spam or not, zero one. Then this is the core piece of AI used to build a spam filter. Or if the input is an audio clip, and the AI's job is to output the text transcript, then this is speech recognition. More examples, if you want to input English and have it output a different language, Chinese, Spanish, something else, then this is machine translation. Or the most lucrative form of supervised learning, of this type of machine learning maybe be online advertising, where all the large online ad platforms have a piece of AI that inputs some information about an ad, and some information about you, and tries to figure out, will you click on this ad or not? By showing you the ads you're most likely to click on, this turns out to be very lucrative. Maybe not the most inspiring application, but certainly having a huge economic impact today. Or if you want to build a self-driving car, one of the key pieces of AI is in the AI that takes as input an image, and some information from their radar, or from other sensors, and output the position of other cars, so your self-driving car can avoid the other cars. Or in manufacturing. I've actually done a lot of work in manufacturing where you take as input a picture of something you've just manufactured, such as a picture of a cell phone coming off the assembly line. This is a picture of a phone, not a picture taken by a phone, and you want to output, is there a scratch, or is there a dent, or some other defects on this thing you've just manufactured? And this is visual inspection which is helping manufacturers to reduce or prevent defects in the things that they're making. This set of AI called supervised learning, just learns input to output, or A to B mappings. On one hand, input to output, A to B it seems quite limiting. But when you find a right application scenario, this can be incredibly valuable. Now, the idea of supervised learning has been around for many decades. But it's really taken off in the last few years. Why is this? Well, my friends asked me, "Hey Andrew, why is supervised learning taking off now?" There's a picture I draw for them. I want to show you this picture now, and you may be able to draw this picture for others that ask you the same question as well. Let's say on the horizontal axis you plot the amount of data you have for a task. So, for speech recognition, this might be the amount of audio data and transcripts you have. In lot of industries, the amount of data you have access to has really grown over the last couple of decades. Thanks to the rise of the Internet, the rise of computers. A lot of what used to be say pieces of paper, are now instead recorded on a digital computer. So, we've just been getting more and more data. Now, let's say on the vertical axis you plot the performance of an AI system. It turns out that if you use a traditional AI system, then the performance would grow like this, that as you feed in more data is performance gets a bit better. But beyond a certain point it did not get that much better. So it's as if your speech recognition system did not get that much more accurate, or your online advertising system didn't get that much more accurate that's showing the most relevant ads, even as you show the more data. AI has really taken off recently due to the rise of neural networks and deep learning. I'll define these terms more precise in later video, so don't worry too much about what it means for now. But with modern AI, with neural networks and deep learning, what we saw was that, if you train a small neural network, then the performance looks like this, where as you feed them more data, performance keeps getting better for much longer. If you train a even slightly larger neural network, say medium-sized neural net, then the performance may look like that. If you train a very large neural network, then the performance just keeps on getting better and better. For applications like speech recognition, online advertising, building self-driving car, where having a high-performance, highly accurate, say speech recognition system is important, enable these AI systems get much better, and make speech recognition products much more acceptable to users, much more valuable to companies and to users. Now, a few couple of implications of this figure. If you want the best possible levels of performance, your performance to be up here, to hit this level of performance, then you need two things: One is, it really helps to have a lot of data. So that's why sometimes you hear about big data. Having more data almost always helps. The second thing is, you want to be able to train a very large neural network. So, the rise of fast computers, including Moore's law, but also the rise of specialized processors such as graphics processing units or GPUs, which you'll hear more about in a later video, has enabled many companies, not just a giant tech companies, but many many other companies to be able to train large neural nets on a large enough amount of data in order to get very good performance and drive business value. The most important idea in AI has been machine learning, has basically supervised learning, which means A to B, or input to output mappings. What enables it to work really well is data. In the next video, let's take a look at what is the data and what data you might already have? And how to think about feeding this into AI systems. Let's go on to the next video.
中文字幕：
人工智能的興起主要是由人工智能中的一種稱爲機器學習的工具驅動的。在本視頻中，您將瞭解什麼是機器學習，因此最終，您希望我們將開始考慮如何將機器學習應用於您的公司或您的行業。最常用的機器學習類型是一種學習A到B或輸入到輸出映射的AI。這稱爲監督學習。我們來看一些例子。如果輸入A是電子郵件而輸出B是電子郵件垃圾郵件，則爲零。然後，這是用於構建垃圾郵件過濾器的核心AI。或者，如果輸入是音頻剪輯，並且AI的工作是輸出文本記錄，則這是語音識別。更多的例子，如果你想輸入英語並輸出不同的語言，中文，西班牙語等等，那麼這就是機器翻譯。或者最有利可圖的監督學習形式，這種類型的機器學習可能是在線廣告，其中所有大型在線廣告平臺都有一塊人工智能輸入一些廣告信息，以及一些關於你的信息，並試圖想象你會點擊這個廣告嗎？通過向您展示您最有可能點擊的廣告，結果證明這是非常有利可圖的。也許不是最鼓舞人心的應用程序，但今天肯定會產生巨大的經濟影響。或者，如果你想要製造一輛自動駕駛汽車，人工智能的一個關鍵部分就是在人工智能中輸入圖像，從雷達或其他傳感器獲取一些信息，並輸出其他車輛的位置，所以你的自動駕駛汽車可以避開其他車。或者在製造業。我實際上已經在製造方面做了很多工作，你可以把你剛剛製造的東西的圖片作爲輸入，例如手機下線的照片。這是一張手機的照片，而不是手機拍的照片，你想要輸出，是否有劃痕，或者是否有凹痕，或者你剛製造的這個東西還有其它缺陷？這是視覺檢查，幫助製造商減少或防止他們正在製造的東西的缺陷。這組AI稱爲監督學習，只是學習輸出到輸出，或A到B映射。一方面，輸入到輸出，A到B似乎非常有限。但是當您找到合適的應用場景時，這可能非常有價值。現在，監督學習的想法已經存在了幾十年。但它在過去幾年裏真的起飛了。爲什麼是這樣？好吧，我的朋友問我，“嘿安德魯，爲什麼現在有人監督學習起飛？”有一張我爲他們畫的照片。我現在想給你看這張照片，你也可以把這張照片畫給那些問你同樣問題的人。假設您在水平軸上繪製了任務的數據量。因此，對於語音識別，這可能是您擁有的音頻數據和成績單的數量。在許多行業中，您可以訪問的數據量在過去幾十年中確實增長了。由於互聯網的興起，計算機的興起。過去常被說成紙的很多東西現在被記錄在數字計算機上。所以，我們剛剛獲得越來越多的數據。現在，讓我們說在縱軸上繪製AI系統的性能。事實證明，如果你使用傳統的AI系統，那麼性能會像這樣增長，因爲當你輸入更多數據時，性能會變得更好。但是超過某一點它沒有那麼好。因此，就好像您的語音識別系統沒有那麼準確，或者您的在線廣告系統沒有那麼準確，顯示最相關的廣告，即使您顯示的數據更多。由於神經網絡和深度學習的興起，人工智能最近真正起飛。我將在以後的視頻中更準確地定義這些術語，所以不要過於擔心它現在意味着什麼。但是對於現代AI，神經網絡和深度學習，我們看到的是，如果你訓練一個小型神經網絡，那麼性能就像這樣，在你爲它們提供更多數據時，性能會持續變得更好。如果你訓練一個更大的神經網絡，比如說中型神經網絡，那麼性能可能就像那樣。如果你訓練一個非常大的神經網絡，那麼性能就會越來越好。對於語音識別，在線廣告，構建自動駕駛汽車等應用，具有高性能，高精度的語音識別系統非常重要，使這些AI系統變得更好，並使語音識別產品更易被用戶接受，對公司和用戶更有價值。現在，這個數字有幾個含義。如果你想要最好的性能水平，你的性能在這裏，達到這個性能水平，那麼你需要兩件事：一個是，它確實有助於擁有大量的數據。這就是爲什麼有時你會聽到大數據的原因。 擁有更多數據幾乎總是有幫助。 第二件事是，你希望能夠訓練一個非常大的神經網絡。 因此，包括摩爾定律在內的快速計算機的興起，以及圖形處理單元或GPU等專用處理器的興起，在後來的視頻中你會聽到更多，這使得許多公司，而不僅僅是一家大型科技公司 但是，許多其他公司能夠在足夠大量的數據上訓練大型神經網絡，以獲得非常好的性能並提高業務價值。 AI中最重要的想法是機器學習，基本上是監督學習，即A到B，或輸入到輸出映射。 使它能夠很好地工作的是數據。 在下一個視頻中，讓我們看一下數據是什麼以及您可能擁有哪些數據？ 以及如何考慮將其投入AI系統。 讓我們繼續下一個視頻。