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AI for Everyone
Beginner
6 hours 54 mins
Topics
Deep Learning
Machine Learning
AI today is being successfully applied to image and video data, to language data, to speech data, to many other areas. In this video, you see a survey of AI applied to these different application areas, and I hope that this may spark off some ideas of how you might be able to use these techniques someday for your own projects as well. Let's take a look. One of the major successes of deep learning has been computer vision. Let's take a look at some examples of computer vision applications. Image classification and object recognition refer to taking as input a picture like that and telling us what is in this picture. In this case, it'd be a cat. Rather than just recognizing cats, I've seen AI algorithms able to recognize specific types of flowers, AI able to recognize specific types of food, and the ability to take as input a picture and classify it into what type of object it is, is being used in all sorts of applications. One specific type of image classification that has had a lot of traction is face recognition. This is how face recognition systems today work. A user might register one or more pictures of their face to show the AI what they look like. Given a new image, the AI system can then say, is this the same person? Is this you? Or is this a different person? So that it can decide if it should unlock the door or unlock the cell phone, unlock the laptop, or something else based on the identity of the person. Of course, I hope face recognition will only be used in ways that respect individuals' privacy. We'll talk more about AI and society next week as well. A different type of computer vision algorithm is called object detection. So rather than just trying to classify or recognize an object, you're trying to detect if the object appears at all. For example, in building a self-driving car, we've seen how an AI system can take as input a picture like this and not just tell us, yes or no, is there a car? Yes or no, is there a pedestrian? It will actually tell us the position of the cars as well as the positions of the pedestrians in this image. An object detection algorithm can also take as input a picture like that and just say, nope, I'm not finding any cars or any pedestrians in that image. So rather than taking a picture and labeling the whole image, which is image classification, instead an object detection algorithm will take as input an image and tell us where in the picture different objects are, as well as what are the types of those objects. Image segmentation takes this one step further. Given an image like this, an image segmentation algorithm may output that, where it tells us not just where are the cars and pedestrians, but tells us for every single pixel, is this pixel part of this car or is this pixel part of a pedestrian? So it doesn't just draw rectangles around the objects it detects, instead it draws very precise boundaries around the objects that it finds. So in reading x-rays, for example, it would be an image segmentation algorithm that could look at an x-ray scan or some other image of a human body and carefully segment out where is the liver or where is the heart or where is the bone in this image. Computer vision can also deal with video, and one application of that is tracking. In this example, rather than just detecting the runners in this video, it is also tracking in a video where the runners are moving over time. So those little tails below the red boxes show how the algorithm is tracking different people running across several seconds in a video. So the ability to track people and cars and maybe other moving objects in the video helps the computer figure out where things are going. If you're using a video camera to track wildlife, for example, say birds flying around, a tracking algorithm would also be able to help track individual birds flying across the frames of your video. These are some of the major areas of computer vision, and perhaps some of them will be useful for your projects. AI and deep learning specifically is also making a lot of progress in natural language processing. Natural language processing, or NLP, refers to AI understanding natural language, meaning the language that you and I might use to communicate with each other. One example is text classification, where the job of the AI is to input a piece of text, such as an email, and tell us what is the class or what is the category of this email, such as is this spam or non-spam email. There are also websites that would input a product description. For example, you might write, I have a secondhand cell phone for sale, and automatically figure out what is the product category in which to list this product. So that would go under cell phones or electronics. Or if you write, I have a new t-shirt to sell, then it would list it automatically under clothing. One type of text classification that has had a lot of attention is sentiment recognition. For example, a sentiment recognition algorithm can take as input a review like this of a restaurant, the food was good, and automatically try to tell us how many stars this review might get. The food was good is a pretty good review, maybe that's a 4 out of 5 star review. Whereas if someone writes service was horrible, then a sentiment recognition algorithm should be able to tell us that this corresponds maybe to a 1 star review. A second type of NLP, or natural language processing, is information retrieval. Web search is perhaps the best known example of information retrieval, where you type in a text query and you want the AI to help you find relevant documents. Many corporations will also have internal information retrieval systems where you might have an interface to help you search just within your company's center documents for something relevant to a query that you might enter. Name entity recognition is another natural language processing technology. Let's illustrate it with an example. Say you have this sentence, and you want to find all the people names in this sentence. So Queen Elizabeth II is a person, Sir Paul McCartney is a person. So in the sentence, Queen Elizabeth II knighted Sir Paul McCartney for his service of music at Buckingham Palace, it would be a name entity recognition system that can find all the people's names in a sentence like this. If you want to find all the location names, all the place names in a sentence like that, a name entity recognition system can also do so. Name entity recognition systems can also automatically extract names of companies, phone numbers, names of countries. And so if you have a large document collection, and you want to find automatically all the company names, or all the company names that occur together, all the people's names, then a name entity recognition system would be the tool you could use to do that. Another major AI application area is machine translation. So for example, if you see this sentence in Japanese, AI wa aratana denki da, then hopefully a machine translation system can input that and output the translation, AI is the new electricity. The four items on this slide, text classification, information retrieval, name entity recognition, and machine translation are four major categories of useful NLP applications. Modern AI, specifically deep learning, has also completely transformed how software processes audio data, such as speech. How is speech represented in a computer? This is an audio waveform of one of my friends saying the phrase, machine learning. The x-axis here is time, and the vertical axis is what a microphone is recording. What a microphone is recording is little variations, very rapid variations in air pressure, which your ear and your brain then interpret as sound. And this plot shows, as a function of time, the horizontal axis, how the air pressure is changing very, very rapidly in response to someone saying the word, machine learning. The problem of speech recognition, also known as speech to text, is the problem of taking as inputs a plot like this and figuring out what were the words that someone said. A lot of speech recognition's recent progress has been due to deep learning. One particular type of speech recognition is trigger word detection or wake word detection, and you saw this in the earlier video with having an AI system detect a trigger word such as Alexa or Hey Google or Hey Device. Speaker ID is a specialized speech problem where the task is to listen to someone speak and figure out the identity of the speaker. Just as face recognition helps verify your identity by taking a picture, speaker ID can also help verify your identity by listening to you speak. Finally, speech synthesis, also called text-to-speech or TTS, is also getting a lot of traction. Text-to-speech is a problem of inputting a sentence written in text and turning that into an audio file. Interestingly, whereas text-to-speech is often abbreviated TTS, I don't often see speech-to-text abbreviated STT. For a quick example, let's take the sentence, the quick brown fox jumps over the lazy dog. This is a fun sentence that you often see NLP people use because this sentence contains every single letter from A to Z, so that's A, B, C, all the way up to X, Y, and Z. You can check all 26 letters appear in this sentence, some letters appear more than once. If you pass this sentence into a TTS system, then you might get an audio output like this. The quick brown fox jumps over the lazy dog. Modern TTS systems are increasingly sounding more and more natural and human-like. You've heard me mention generative AI a few times so far in this course. Generative AI is a collection of AI systems that can produce high-quality media, specifically text, images, or audio. Let's take a look at these applications of generative AI in more detail. Large language models are great at text generation tasks, including writing content from scratch, writing summaries, copy editing, that is, editing text to improve grammar, clarity, and so on, and chatting. For example, you can give one of these models an instruction like, suggest three funny creative names for a line of chocolate ice cream, and the model will generate some creative-sounding names like these. Note that this input text here is known as a prompt, and writing prompts to generate the output you want is becoming a useful skill for many jobs. I find that having a large language model as a brainstorming partner makes me more productive, and if you're able to write prompts effectively, perhaps you find it a useful tool at work or in your personal life as well. In fact, I think large language models are now at a point where almost all knowledge workers can get at least a bit of a productivity boost by learning and using them in their day-to-day workflow. Generative AI can also create new images from scratch. Software like McJourney, Dally, Adobe Firefly, and Stable Diffusion have learned how to generate images from text descriptions by learning from millions of images on the internet. So with one of these image generation models, you can input an example prompt like, a purple friendly robot eating ice cream, and the model will generate a high-quality image for you that matches your prompt. Oh, what a cute robot! Lastly, generative AI is also capable of generating audio. Previously, we saw how speech synthesis models can convert text to speech audio. Software also exists like Stable Audio or Meta's AudiCraft. They can generate music and sound effects from a text prompt. So by writing a prompt like, drum solo, 140 bpm or beats per minute, you can use a music generation model to create an audio track like this one. So, generative AI is capable of creating several types of content. This is affecting many industry sectors and we'll learn more about the impact of AI, including generative AI, on jobs next week. AI is also applied to many applications in robotics, and you've already seen one example in the self-driving car. In robotics, the term perception means figuring out what's in the world around you based on the sensors you have, be it cameras or radar or LiDAR. Shown on the right is the 3D laser scan or the LiDAR scan of a self-driving car, as well as the vehicles that this self-driving car in the middle has detected in the vicinity of your car. Motion planning refers to finding a path for your robot to follow. So if your car wants to make a left turn, the motion planner might plan a path as well as a speed for the car to make a left turn that way. And finally, control refers to sending commands to the motors, such as your steering wheel motor as well as your gas pedal and brake motors, in order to make the car smoothly follow the path that you want. On this slide, I focus on the software and the AI aspects of robotics. Of course, there's also a lot of important work being done to build hardware for robotics as well. But a lot of the work of AI on perception, motion planning, and control has focused on the software rather than the hardware of robotics. In addition to these major application areas, machine learning is also very broadly used. The examples you've seen in this video relate mainly to unstructured data, such as images, audio, and text. Machine learning is applied at least as much to structured data, and that means these tables of data, some of which you saw in the earlier videos. But because unstructured data, such as images, is so easy for humans to understand, there's something very universal, very easy for any person to understand and empathize with when we talk about an AI system that recognizes a cat. And so the popular press tends to cover AI progress on unstructured data much more than it does AI on structured data. Structured data also tends to be more specific to a single company, and so it's hard to cover for people that write about or understand, but AI on structured data, or machine learning on structured data, is creating tremendous economic value today, as well as AI on unstructured data. I hope this survey of AI application areas gives you a sense of the wide range of data that AI is successfully applied to today, and maybe this will even inspire you to think of how some of these application areas may be useful for your own projects. Now so far, the one AI technique we've spent the most time talking about is supervised learning. That means learning input-output or A-to-B mappings from labeled data, where you give the AI system both A and B. But that's not the only AI technique out there. In fact, the term supervised learning almost invites the question of what is unsupervised learning. Or you might also have heard from media articles from the news about reinforcement learning. So what are all these other techniques? In the next video, the final optional video for this week, we'll do a survey of AI techniques, and I hope that through that, maybe you'll see if some of these other AI techniques and supervised learning could be useful for your projects as well. Let's go on to the final optional video for the week.
