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What are large language models exactly? What do they do? How do they work? Now I'm not going to go into the full details of this, but I want to give you just enough about how they work that will help you in thinking through designing prompts. There's a couple of things to know about them that will be useful when you're designing prompts. Now, though, fundamental thing that you want to know about these large language models is basically what they're doing is they're taking your input and they're trying to generate the next word. Then they'll take that word that they generate, they'll add it to you what you originally gave it, and they'll try to generate the next word. This is a way to think of it.

AI is a superpower and understanding it allows you to do things that very few people on the planet can. Let's summarize what you've seen in this course. In the first week, you learned about AI technology, what is AI and what is machine learning? What's supervised learning, that is learning inputs, outputs, or A to B mappings.

Every time there is a major technological disruption such as of AI, it gives us a chance to remake the world. AI is a very advanced technology, yes affecting both developed economies and developing economies. So, how could we make sure that even as AI creates tremendous wealth, that it uplifts all nations? Let's take a look. There's been a fairly predictable roadmap, almost a ladder, that many developing economies have successfully executed in order to help the citizens gain skills and climb to higher levels of wealth. All the nations started off with lower-end agricultural products, exporting crops, and then moved to low-end textile manufacturing, such as clothing manufacturing. Then as a population starts to gain a bit more wealth, become a bit more healthy, move on to low-end components manufacturing, such as making less expensive plastic parts. So, then moving on to low-end electronics manufacturing, to higher-end electronics manufacturing, maybe to automotive manufacturing, and so on. There's been this step-by-step progression by which developing economies can hope their citizens gain skill and become developed economies. One of the problems that AI could cause is that, a lot of the lower rungs on the ladder are particularly susceptible to automation through AI. For example, as factories become more automated, or as agriculture becomes more automated, there may be less needed and therefore lesser opportunities for large members of a population of some of these developing economies to get onto the lower rungs of the economic ladder from which they would then climb up. So, if we're knocking out some of the lower rungs of the ladder through AI, through AI providing automation on steroids, then it is incumbent on us to see if AI can also create a trampoline to hope some developing economies jump onto the trampoline and bounce maybe even more quickly to the highest rungs of this ladder. With the rise of earlier waves of technology, many economies have shown that they can leapfrog developed economies and jump straight to a more advanced technology. For example, here in the United States most of us had landlines. Phones that were connected via wire to the wall. Because so many of us had landline phones, that actually took a while to transition to wireless mobile phones. In contrast, many developing economies including India and China ,but many others as well, didn't bother to build nearly as many land lines, but skip straight to mobile phones. So, this was a leapfrog where developing economies jumped straight over the earlier generation of technology and didn't bother to lay so many physical cables at every person's house and instead jumped straight to mobile phones. We're seeing a similar thing with mobile payments where many developed economies have a mature credit card system and that actually is slowing down their adoption of mobile or cell phone payments compared to some developing economies which do not already have entrenched incumbents in the credit card industry. I'm also seeing rapid adoption of online education in developing economies. In countries that have not yet built all the many many physical schools and universities that they need, many educational leaders and governments are seeking ways to more quickly embrace online education compared to some of the developed economies that have this built-up physical infrastructure for in-person education. While developed economies are also rapidly embracing all of these technologies, one of the advantages of developing economies is that without an entrenched incumbents system, perhaps there are areas that they could build even faster. The US and China are leading, and the UK and Canada, and a few other countries also have vibrant AI communities. But because AI is still so immature today, I think all AI communities are still immature. This means that even though AI is creating tremendous economic value, most of the value to be created is still off in the future. This gives every nation an opportunity to be a large part of creating this value that hasn't been created yet, and even capturing a large piece of it. So, I hope every nation can figure out how to effectively use AI to continue to help his citizens. My advice to developing economies is to focus on the AI to strengthen a country's vertical industries. For example, I think most countries today should not try to build their own web search engine. There are already great web search engines and that was last decades competition. Instead, if a country has a very strong vertical industry in say coffee bean manufacturing, then that country is actually uniquely qualified to do work in AI for coffee manufacturing and building AI for coffee manufacturing will even further strengthen what that country is already good at. So, rather than needing for every country to compete with the US and China on AI in general, I would advice most countries to use AI to strengthen what that country is good at and what that country wants to do in the future. Finally, public-private partnerships, meaning governments and corporations working together, can really help accelerate a vertical industry's AI developments. In highly regulated sectors, ranging from healthcare to transportation like self-driving cars to finance, there are certain outcomes that we want and certain outcomes that we don't want. Governments that are thoughtful about crafting derived regulations to protect citizens while at the same time enabling the adoption of AI solutions to these industries, will see faster local economic growth as well as faster technology development within their country. Finally, developing economies should invest in education because AI is still so immature. There's still plenty of room for every nation to learn more about AI, maybe even build up its own AI workforce and participate in a significant way in this AI powered world that we're building. In moments of technological disruption, leadership matters. Here in the United States, we once trusted our governments to put a man on the moon and it worked. With the rise of AI, it creates a space, and in some countries a need for leadership, whether in the government levels, or in companies, or in education to help a country enter the AI era and embrace and adopt AI to keep on lifting up its citizens, and perhaps even keep on lifting up other people worldwide. In this video, we've touched briefly on the issue of AI and jobs. This is an important topic that is widely discussed in many countries right now. Let's go on to the next video, to take a deeper look at AI and jobs.

AI is automation on steroids. Before the rise of modern AI, automation had already had a huge impact on a lot of jobs. With the rise of AI, the set of things we can now automate is suddenly much bigger than before, and so this is also having an accelerating impact on jobs. How many jobs will be displaced? How many new jobs will be created? I don't think anyone has a firm answer to these questions yet, but let's take a look at some studies to try to understand what maybe ahead. McKinsey Global Institute did a study in which they estimated that 400 to 800 million jobs will be displaced by AI automation by 2030. These are very large numbers. On the flip side though, the same report also estimates that the number of jobs created by AI maybe even larger. There have been many studies other than the McKinsey Global Institute's one. There is a range in these estimates of numbers of jobs displaced and numbers of jobs created. For example, just focusing on the United States, the numbers on this slide are worldwide, but just focusing on the United States, PwC estimates about 16 million jobs displaced by 2030. Bank of England estimates 80 million jobs displaced by 2035. So no one can predict with certainty exactly what will happen in 2030, but there is a sense that the impact of jobs worldwide will be significant. I hope you find it as encouraging as I do though that AI is creating many jobs even as it is displacing some. I think many of the jobs of the future, we may not even have names for yet, be it drone traffic optimizer or 3D-printed clothing designer or as in healthcare, we'll have custom DNA-based drug designers. So even though there is concern about AI displacing jobs, there is also hope of many new jobs, maybe even more new jobs being created in the future. Now you might wonder, how do we estimate how many jobs are likely to be displaced? One typical way that these studies are carried out would be to take a job and think of the task that make up the job. For example, you might look at the task of the radiologist does or look at all the tasks that a taxi driver does. Then for each of the task, estimate how amenable it is to automation through AI and if a job comprises mainly task that are highly automatable, then the risks of the job being displaced will be higher. Most AI engineers find it more useful to think of AI being applied to task rather than to people's jobs. But this framework allows us to use AI's ability to automate tasks to estimate how many jobs are likely to be displaced. So which are the jobs that are most likely or least likely to be displaced through AI and automation? The OECD, a well-respected intergovernmental body, looked at the number of job types to estimate which of them are most and least likely to be automated. The future is hard to predict with certainty, but perhaps not surprisingly, many other jobs that comprise more routine repetitive work are more amenable to automation, whereas many of the tasks that are less repetitive, less routine or that involve more social interaction with people maybe less susceptible to automation. How do we hope citizens and nations navigate the coming impact of AI on jobs? Here are some solutions. First, conditional basic income. You may have heard of universal basic income in which a government would pay citizens with no strings attached. I think people do deserve a safety net. For individuals that are unemployed but are able to learn, I think a more effective version maybe conditional basic income in which we do provide the safety net but incentivize them to keep on learning and keep on investing in their own development. By providing a structure to help people that can learn do so, this will increase the odds that those individuals can re-enter the workforce, contribute to themselves, their families and to society, as well as to the tech space that is paying for all this. Second, building a lifelong learning society. By virtue of your taking this course right now, you may already be part of this lifelong learning society. The old model of education where you go to college for four years and then cost for the remaining 40, that just does not work anymore into these rapidly changing world. Through governments, companies, and individuals realizing that all of us need to keep on learning, this increases the odds that everyone will be better position, even as jobs may go away. So, take advantage of the new jobs being created as well. In the future, even after completing college, I think most individuals should keep on learning throughout their whole lives. Finally, there are political solutions being explored as well. Everything from incentivizing or helping with new job creation to legislation to make sure that people are treated fairly. I hope that society will figure out the right political solutions to navigate the coming impact of AI on jobs as well. One question now sometimes asked is what should you do if you want to work in AI? Recently, a radiologist resident served radiologists near the start of his career. He actually asked me. He said, "Hey, Andrew, I'm hearing a lot about the coming impacts of AI on radiology." He said, "Should I quit my profession and just learn AI and do AI instead?" My answer to him was no. You could do that. You can actually quit whatever you are doing and pick up AI from scratch. It is entirely possible to do that. Many people have done that. This one other alternative that you could consider though, which is, I said to this radiology resident, consider doing work in AI plus radiology because with your knowledge of radiology, if in addition you learned something about AI, you would be better positioned to do work at the intersection of radiology and AI than most other people. So, if you want to do more work in AI, it is possible in today's world to learn AI from scratch through online courses and other resources. But if you take whatever you are already knowledgeable in and learn some AI and do your area plus AI, then you might be more uniquely qualified to do very valuable work by applying AI to whatever area you are already an expert in. So, I hope this video helps you navigate the coming impacts of AI in jobs. Let's go on to the next and final video of this course.

AI is incredibly powerful and the vast majority of users of AI are making people, companies, countries, society better off. But there are a few adverse uses of AI as well. Let's take a look at some of them and discuss what we can do about them. AI technology has been used to create deep fakes and that means to synthesize video of people doing things that they never actually did. The website BuzzFeed created a video of former US President Barack Obama saying things that he never did. BuzzFeed was transparent about it and when they publish the video, it was really obvious because he told everyone that this is a fake video. But if this type of technology is used to target an individual and make others think they said or did things they never actually did, then these individuals could be harmed and left to defend themselves against fake video evidence of something they never actually did. Similar to the war of spam versus anti-spam, there is AI technology today for detecting if a video is a deep fake. But in today's world of social media, where a fake could spread around the world faster than the truth can catch up, many people are concerned about the potential of deep fakes to harm individuals. There's also a risk of AI technology being used to undermine democracy and privacy. For example, many governments around the world are trying to improve their citizens' lives, and have a lot of respect for the government leaders that are uplifting their citizens. But there are also some oppressive regimes that are not doing the right things by their citizens, that may seek to use this type of technology to carry out oppressive surveillance of their citizens. While governments have illegitimate need to improve public safety and reduce crime, there are also ways of using AI that feel more oppressive than uplifting of its own citizens. Closely related to this, is the rise of fake comments that AI can generate. Using AI technology is now possible to generate fake comments. Either on the commercial side, fake comments of products, or in political discourse, fake comments about political matters in the public discourse, and to generate fake comments much more efficiently than if you only had humans writing them. So, detecting such fake comments and weeding them out, is an important technology for maintaining trust in comments that we might read online as well. Similar to the battles of spam versus anti-spam and fraud verses anti-fraud, I think that for all of these issues, there may be a competition on both sides for quite some time to come. Similar to the battles of span versus anti-spam, fraud versus anti-fraud, I'm optimistic about how these battles will play out. Because if you take spam filter as an example, there are a lot more people that are motivated to make sure spam filters do work, that anti-spam does work. Then there are the smaller number of spammers trying to get this spam into your inbox.

Even though modern AI is incredibly powerful, one of the limitations of modern AI technologies especially deep learning is that sometimes it can be fooled. In particular, modern AI systems are sometimes susceptible to adversarial attacks, if someone else sets out deliberately to fool your AI system. Let's take a look.

A group at Microsoft found this remarkable result that when AI learns from text file on the internet, it can learn unhealthy stereotypes. To the credit, they also proposed technical solutions for reducing the amount of bias in this type of AI system.

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.

AI is having a huge impact on society and on so many people's lives. So, for all of us to make good decisions, it is important that we have a realistic view of AI and be neither too optimistic nor too pessimistic. Here's what I mean. Did you ever read the story of Goldilocks and the three bears maybe when you were a kid.

You also learned about the roles and responsibilities of large AI teams, and maybe what it's like to build a large AI team, and saw the AI transformation playbook for helping a great company become a great AI company. In case some of this seems daunting, because some of these will take maybe two or three years to execute.

There are a lot of AI and machine learning techniques today. And while supervised learning, that is learning A to B mappings, is the most valuable one, at least economically today, there are many other techniques that are worth knowing about. Let's take a look. The best-known example of unsupervised learning is clustering, here's an example. Let's say you run a grocery store that specializes in selling potato chips. And you collect data on different customers, and keep track of how many different packets of potato chips a single customer buys, as well as what's the average price per package that person paid for their potato chips.

I hope you'll be able to use AI to build exciting and valuable projects either for yourself or for your company and make life better both for yourself and for others. Along the way, I hope you also manage to avoid some of the pitfalls I've seen some AI teams fall into. Let's go over five don'ts and dos for if you're trying to build AI for your company. First one, don't expect AI to solve everything. You already know that AI can do a lot but there's also lots AI cannot do. Instead, you should be realistic about what AI can or cannot do, given the limitations of technology, data, and engineering resources. That's why I think technical diligence in addition to business diligence is important for selecting feasible and valuable AI projects. Second, don't just hire two or three machine learning engineers and count solely on them to come up with use cases for your company. Machine learning engineers are a scarce resource but you should instead air the engineer talents with business talent and work cross-functionally to find feasible and valuable projects. It is often the combination of the machine-learning talents worked to business talent that can select the most valuable and feasible projects. Third, don't expect AI project to work the first time. As you've already seen, AI development is often an iterative process so should plan for it through an iterative process with multiple attempts needed to succeed. Fourth, don't expect traditional planning processes to apply without changes. Instead, you should work with the AI team to establish timeline estimates, milestones, KPIs, or metrics that do make sense. The types of timeline estimates, milestones, and KPIs or metrics associated with AI projects are a bit different than the same things associated with non-AI projects. So, hopefully working with some individuals knowledge about AI can help you come up with better ways of planning AI projects. Finally, don't think you need superstar AI engineers before you can do anything. Instead, keep building the team and get going with a team you have realizing that there are many AI engineers in the world today including many that have learned primarily from online courses. They can do a great job building valuable and feasible projects. If you can avoid these AI pitfalls, you already be ahead of the game compared to many other companies. The important thing is to get started. You're second AI project would be better than your first. Your third AI project would be better than your second. So, the important thing is to get started and to attempt your first AI project. In the final video for this week, I want to share with you some concrete first steps you can take in AI. Let's go on to the next video.

In the last video, you learned how to execute pilot projects to gain momentum for the in-house AI team and provide broad AI training. But if you want your business to not just gain momentum in the short-term using AI, but in the long term be a very valuable and maybe even defensible business. What can you do? Let's talk about AI strategy as well as perhaps important for some companies, internal and external communications relative to AI.

How can you help your company become good at AI? Based on my experience, starting leading the Google Brain Team as well as Baidu's AI group which were respectively the leading forces for helping Google and Baidu become good and deeper in AI. I've spent a lot of time thinking about what it takes to help a great company become a great AI company and I wound up writing an AI transformation playbook to help other companies on this journey. In this video, I'd like to share with you the details of the AI transformation playbook so that you can better understand what it might take for your company to become good at AI.

You saw from the last two videos that some AI products may require a large AI team, maybe you have a 100 engineers or sometimes many more than a 100 to build. What I would like to do in this video is share with you the typical roles and responsibilities of a large AI team like this, so you can better understand the types of work needed to build these complex AI products.

One of the most exciting products of the AI era is the self-driving car. Self-driving cars are also one of the most mysterious things you hear about in AI these days. In this video what I want to do is share with you a somewhat simplified description of a self-driving car so that you understand how you can piece together multiple AI components in order to build these amazing things. Let's get started.

What does it feel like to work on a complex AI product, where isn't just using a single machine-learning algorithm to map from A to B, but that learning algorithm is part of a bigger more complex project or product. I want to start it this week with two case studies of building complex AI products. First, building a smart speaker so that you can start to understand what it might feel like to maybe someday work on a complex AI product within your own company. Let's get started.

When you work with AI teams, you may hear them refer to the tools that they're using to build these AI systems. In this video, I want to share with you some details and names of the most commonly used AI tools, so that you'd be able to better understand what these AI engineers are doing.

Say you found an exciting project that you want to try to execute on, how do you work with an AI team on this project? In this video, you learn how AI teams think about data and therefore how you can interact with AI teams to help them succeed on a project. Now, there is one caveat which is, whether you have a cool idea but you don't have access to an AI team, you don't have any access to any AI engineers.

In this video, you see a framework for brainstorming potentially exciting AI projects to pursue. Let's say you want to build an AI project for your business. You've already seen that AI can't do everything, and so there's going to be a certain set of things that is what AI can do. So let's let the circle represent the set of things that AI can do.

Maybe you have a lot of ideas for possible AI projects to work on. But before committing to one, how do you make sure that this really is a worthwhile project? If it's a quick project that might take you just a few days maybe just jump in right away and see if it works or not, but some AI projects may take many months to execute. In this video, I want to step you through the process that I use to double-check if a project is worth that many months of effort. Let's take a look. Before committing to a big AI project, I will usually conduct due diligence on it. Due diligence has a specific meaning in the legal world. But informally, it just means that you want to spend some time to make sure what you hope is true really is true.

Data is transforming many different job functions, whether you work in recruiting or sales or marketing or manufacturing or agriculture, data is probably transforming your job function. What's happened in the last few decades is the digitization of our society.

So, data science projects have a different workflow than machine learning projects. Let's take a look at one of the steps of a data science project. As our running example, let's say you want to optimize a sales funnel. Say you run an e-commerce or an online shopping website that sells coffee mugs and so for a user to buy a coffee mug from you, there's a sequence of steps they'll usually follow. First, they'll visit your website and take a look at the different coffee mugs on offer, then eventually, they have to get to a product page, and then they'll have to put it into their shopping cart, and go to the shopping cart page, and then they'll finally have to check out. So, if you want to optimize the sales funnel to make sure that as many people as possible get through all of these steps, how can you use data science to help with this problem? Let's look at the key steps of a data science project. The first step is to collect data. So, on a website like the one we saw, you may have a data set that stores when different users go to different web pages. In this simple example, I'm assuming that you can figure out the country that the users are coming from, for example, by looking at their computers' address, called an IP address, and figuring out what is the country from which they're originating. But in practice, you can usually get quite a bit more data about users than just what country they're from. The second step is to then analyze the data. Your data science team may have a lot of ideas about what is affecting the performance of your sales funnel. For example, they may think that overseas customers are scared off by the international shipping costs which is why a lot of people go to the checkout page but don't actually check out. If that's true then you might think about whether to put part of shipping costs into the actual product costs ,or your data science team may think there are blips in the data whenever there's a holiday. Maybe more people will shop around the holidays because they're buying gifts or maybe fewer people will shop around the holidays because they're staying home rather than sometimes shopping from their work computers. In some countries, there may be time-of-day blips wherein countries that observe a siesta, so a time of rest like an afternoon rest, there may be fewer shoppers online and so your sales may go down. They may then suggest that you should spend fewer advertising dollars during the period of siesta because fewer people will go online to buy at that time. So, a good data science team may have many ideas and so they try many ideas or will say iterate many times to get good insights. Finally, the data science team will distill these insights down to a smaller number of hypotheses about ideas of what could be going well and what could be going poorly as well as a smaller number of suggested actions such as incorporating shipping costs into the product costs rather than having it as a separate line item. When you take some of these suggested actions and deploy these changes to your website, you then start to get new data back as users behave differently now that you advertise differently at the time of siesta or have a different check-out policy. Then your data science team can continue to collect data and we analyze the new data periodically to see if they can come up with even better hypotheses or even better actions over time. So the key steps of a data science project are to collect the data, to analyze the data, and then to suggest hypotheses and actions, and then to continue to get the data back and reanalyze the data periodically. Let's take this framework and apply it to a new problem, to optimizing a manufacturing line. So we'll take these three steps and use them on the next slide as well. Let's say you run a factory that's manufacturing thousands of coffee mugs a month for sale and you want to optimize the manufacturing line. So, these are the key steps in manufacturing coffee mugs. Step one is to mix the clay, so make sure the appropriate amount of water is added. Step two is to take this clay and to shape the mugs. Then you have to add the glaze, so add the coloring, a protective cover. Then you have to heat this mug and we call that firing the kiln. Finally, you would inspect the mug to make sure there aren't dents in the mug and it isn't cracked before you ship it to customers. So, a common problem in manufacturing is to optimize the yield of this manufacturing line to make sure that as few damaged coffee mugs get produced as possible because those are coffee mugs you have to throw away, resulting in time and material waste. What's the first step of a data science project? I hope you remember from the last slide that the first step is to collect data. So for example, you may save data about the different batches of clay that you've mixed, such as who supplied the clay and how long did you mix it, or maybe how much moisture was in the clay, how much water did you add. You might also collect data about the different batches of mugs you made. So how much humidity was in that batch? What was the temperature in the kiln and how long did you fire it in the kiln? Given all this data you would then ask the data science team to analyze the data and they would, as before, iterate many times to get good insights. So, they may find that, for example, that whenever the humidity is too low and the kiln temperature is too hot that there are cracks in the mug or they may find out that because it's warmer in the afternoon that you need to adjust the humidity and temperature depending on the time of day. Based on the insights from your data science team you get suggestions for hypotheses and actions on how to change the operations and manufacturing line in order to improve the productivity of the line. When you deploy the changes, you then get new data back that you can reanalyze periodically so they can keep on optimizing the performance of your manufacturing line. To summarize, the key steps of a data science project are to collect the data, to analyze the data, and then to suggest hypotheses and actions. In this video and the last video you saw some examples of machine learning projects and data science projects.

Machine learning algorithms can learn input to output or A to B mappings. So, how do you build a machine learning project? In this video, you'll learn what is the workflow of machine learning projects. Let's take a look. As a running example, I'm going to use speech recognition. So, some of you may have an Amazon Echo or Google Home or Apple Siri device or a Baidu DuerOS device in your homes. Some years back, I've done some work on Google's speech recognition system that also led Baidu's DuerOS project.

One of the challenges of becoming good at recognizing what AI can and cannot do is that it does take seeing a few examples of concrete successes and failures of AI. If you work on an average of say, one new AI project a year, then to see three examples would take you three years of work experience and that's just a long time. What I hope to do, both in the previous video and in this video is to quickly show you a few examples of AI successes and failures, or what it can and cannot do so that in a much shorter time, you can see multiple concrete examples to help hone your intuition and select valuable projects. So, let's take a look at a few more examples. Let's say you're building a self-driving car, here's something that AI can do pretty well, which is to take a picture of what's in front of your car and maybe just using a camera, maybe using other senses as well such as radar or lidar. Then to figure out, what is the position, or where are the other cars. So, this would be an AI where the input A, is a picture of what's in front of your car, or maybe both a picture as well as radar and other sensor readings. The output B is, where are the other cars? Today, the self-driving car industry has figured out how to collect enough data and has pretty good algorithms for doing this reasonably well. So, that's what the AI today can do. Here's an example of something that today's AI cannot do, or at least would be very difficult using today's AI, which is to input a picture and output the intention of whatever the human is trying to gesture at your car. So, here's a construction worker holding out a hand to ask your car to stop. Here's a hitchhiker trying to wave a car over. Here is a bicyclist raising the left-hand to indicate that they want to turn left. So, if you were to try to build a system to learn the A to B mapping, where the input A is a short video of our human gesturing at your car, and the output B is, what's the intention or what does this person want, that today is very difficult to do. Part of the problem is that the number of ways people gesture at you is very, very large. Imagine all the hand gestures someone could conceivably use asking you to slow down or go, or stop. The number of ways that people could gesture at you is just very, very large. So, it's difficult to collect enough data from enough thousands or tens of thousands of different people gesturing at you, and all of these different ways to capture the richness of human gestures. So, learning from a video to what this person wants, it's actually a somewhat complicated concept. In fact, even people have a hard time figuring out sometimes what someone waving at your car wants. Then second, because this is a safety-critical application, you would want an AI that is extremely accurate in terms of figuring out, does a construction worker want you to stop, or does he or she wants you to go? And that makes it harder for an AI system as well. So, today if you collect just say, 10,000 pictures of other cars, many teams would build an AI system that at least has a basic capability at detecting other cars. In contrast, even if you collect pictures or videos of 10,000 people, it's quite hard to track down 10,000 people waving at your car. Even with that data set, I think it's quite hard today to build an AI system to recognize humans intentions from their gestures at the very high level of accuracy needed in order to drive safely around these people. So, that's why today, many self-driving car teams have some components for detecting other cars, and they do rely on that technology to drive safely. But very few self-driving car teams are trying to count on the AI system to recognize a huge diversity of human gestures and counting just on that to drive safely around people. Let's look at one more example. Say you want to build an AI system to look at X-ray images and diagnose pneumonia. So, all of these are chest X-rays. So, the input A could be the X-ray image and the output B can be the diagnosis. Does this patient have pneumonia or not? So, that's something that AI can do. Something that AI cannot do would be to diagnose pneumonia from 10 images of a medical textbook chapter explaining pneumonia. A human can look at a small set of images, maybe just a few dozen images, and reads a few paragraphs from medical textbooks and start to get a sense. But actually don't know, given a medical textbook, what is A and what is B? Or how to really pose this as an AI problems like know how to write a piece of software to solve, if all you have is just 10 images and a few paragraphs of text that explain what pneumonia in a chest X-ray looks like. Whereas a young medical doctor might learn quite well reading a medical textbook at just looking at maybe dozens of images. In contrast, an AI system isn't really able to do that today. To summarize, here are some of the strengths and weaknesses of machine learning. Machine learning tends to work well when you're trying to learn a simple concept, such as something that you could do with less than a second of mental thought, and when there's lots of data available. Machine learning tends to work poorly when you're trying to learn a complex concept from small amounts of data. A second underappreciated weakness of AI is that it tends to do poorly when it's asked to perform on new types of data that's different than the data it has seen in your data set. Let me explain with an example. Say you built a supervised learning system that uses A to B to learn to diagnose pneumonia from images like these. These are well pretty high-quality chest X-ray images. But now, let's say you take this AI system and apply it at a different hospital or different medical center, where maybe the X-ray technician somehow strangely had the patients always lie at an angle or sometimes there are these defects. Not sure if you can see the lost structures in the image. These little other objects lying on top of the patients. If the AI system has learned from data like that on your left, maybe taken from a high-quality medical center, and you take this AI system and apply it to a different medical center that generates images like those on the right, then it's performance will be quite poor as well. A good AI team would be able to ameliorate, or to reduce some of these problems, but doing this is not that easy. This is one of the things that AI is actually much weaker than humans. If a human has learned from images on the left, they're much more likely to be able to adapt to images like those on the right as they figure out that the patient is just lying on an angle. But then AI system can be much less robust than human doctors in generalizing or figuring out what to do with new types of data like this. I hope these examples are helping you hone your intuitions about what AI can and cannot do. In case the boundary between what it can or cannot do still seems fuzzy to you, don't worry. That's completely normal, completely okay. In fact even today, I still can't look at a project and immediately tell is something that's feasible or not. I often still need weeks or small numbers of weeks of technical diligence before forming strong conviction about whether something is feasible or not. But I hope that these examples can at least help you start imagining some things in your company that might be feasible and might be worth exploring more. The next two videos after this are optional and are a non-technical description of what are neural networks and what is deep learning. Please feel free to watch those. Then next week, we'll go much more deeply into the process of what building an AI project would look like. I look forward to seeing you next week.