this is Andrew Ning's Gen for Everyone course, and I took it for you. You're welcome. So, in this video, I'm going to cover everything you learn in this course but a lot faster by removing all of the fluff. So, no more procrastinating, okay? Watch this video to get a very good introduction to AI. Let's get straight into it. Generative AI for Everyone is a very good foundational course that covers three main topics, and they're supposed to be spread over three weeks of learning. The first topic is how generative AI technology works, including what it can and cannot do, and common use cases. The second topic is generative AI projects, so it's a very practical section which includes identifying and building AI use cases, as well as the technologies you need in order to build a project. And third is the impact on business and society. This topic is more theoretical, presenting how AI is currently and in the future going to be shaping the society that we live in, including which and how jobs will be augmented and potentially automated.

also does provide some practical tips on how the individual and team should be using AI and where it is that we should be focusing on. So first off, what is generative AI? Andrew defines generative AI as artificial intelligence systems that can produce high-quality content, specifically text, images, and audio. Generative AI is a subfield of the much larger artificial intelligence field as a whole, and actually, a large part of AI is focused on what is called supervised learning. Generative AI can actually trace its origins back to supervised learning. Supervised learning is just about providing an input and then getting an output, while generative AI, let's say like large language models, is an implementation of this but in the context of words. A large language model learns to repeatedly predict the next word. For example, to get the sentence my favorite food is a bagel with cream cheese, you have an input that is my favorite food is a, and the predicted next word could be bagel, and that becomes your next input, which is my favorite food is a bagel, and your

predicted output is with and so on and so forth. So when you train an AI system on a lot of data, like hundreds and billions of words and their predictions, you end up getting a large language model like OpenAI models that power ChatGPT. LLMs can be really useful as a thought partner. Like, for example, if you don't know which animes you want to watch, you could type some of your favorite animes and then ask the LM, What are some other animes that you would recommend me look at? They're also great as a writing partner. You can ask it to proofread the things that you write, summarize long documents that you don't feel like reading, and even write obscure little things like a tune about trucks to encourage a 3-year-old to brush his teeth. Andrew goes into more use cases later, so I'll cover more later in the video as well. But first, Andrew presents a simple framework that can help us understand for how it is we can come up with good practical use cases for AI.

technology. AI is called a general-purpose technology. Another example of a general-purpose technology is electricity. What it means is that it's a foundational technology that can have so many different use cases, just how like electricity is literally powering almost everything around you. AI is also something that will eventually be part of everything around you. When we consider the use cases of LLMs, we can divide them into two big categories. The first one is called web-based, and the second one is software-based. Web-based application interfaces would include things like ChatGPT or Bard or Bing chat, while software-based applications would include things like email routing and document searching. Many of you who are watching this video probably have only thought about LLMs as a web-based interactive platform, like if you're just talking to it through ChatGPT, asking it to help you brainstorm stuff, giving you summaries, writing stuff for you. But if you start thinking about it from a software-based application perspective, there is so much more that opens up to you. For example, you may be working with

a lot of review data, like maybe you work at a company that sells a lot of apparel, like t-shirts and hats and things like that. You get a lot of reviews that are coming in from social media. You can use a large language model in order to ingest that information and then figure out that maybe it's a complaint, and you can directly, automatically route that to a complaints department to deal with that. You can also gather insights from the reviews that are coming in. Instead of having to read through all of the reviews and try to figure out, the LLM can give you insights like maybe you should make a blue-colored shirt because that is what people are into these days. Chatbots are also a great example of software-based L applications. A lot of companies, I'm sure you've noticed, have some form of chatbot where the bot is able to answer questions for you. This is obviously very useful because then you don't have to spend a lot of money having to hire human representatives and then having to deal with like human

problem s when owed by L. These chat Bots get a lot better at answering people's personalized questions. If you own a burger joint and you have a good LM power chatbot, it's able to hold a conversation with a customer and take personalized orders, then pass it on to the chef, and then provide personalized notifications for when the order should be ready and when it's being delivered, and things like that. In addition to stages like chatting with chat gbt, there is so much more that you can do when you start thinking about how to incorporate llms from a software perspective on figure out what it is that LM can or cannot do. As a general rule of thumb, the question to ask yourself is can a fresh college graduate follow the instructions that I have provided in the prompt to complete the task. Some general assumptions to keep in mind: one, that this college graduate has no access to the internet or other resources; two, it has no specific training on your company and your business; and three, it has no memory of previous tasks that it

completed. You essentially get a fresh, different college graduate every single time. You should also keep in mind that each model has a known cut-off date, so if you're asking questions after its cut-off date, it's not going to know the answer. Like, for example, if a model's cut-off date is October 5th, 2024, it would have no knowledge that the second season of Blue Lock has just dropped and cannot provide you any information about that. I am very excited to watch that season after I make this video. Hallucinations, another consideration, otherwise known as making stuff up. Apparently, this is like a real incident in which a California lawyer used ChatGPT in order to come up with this argument, and he cited two different court cases that did not actually exist. So, that was pretty awkward, and you can see that if this wasn't caught, it could potentially lead to very serious consequences in court. There are some techniques to reduce hallucinations, and I'll actually include them in the resource companion of this video, but it is very important to

know that this can be a very serious problem. Another consideration is that large language models have an input and output length that is limited, so you cannot provide an infinite amount of information in the prompt, and you can't expect it to provide an infinite amount of information back as well. Like, for example, if you're trying to, like, feed it a 10,000-page essay and tell it to summarize everything, it's not going to be able to do that. Or if you're telling it to, like, write your entire book for you, it probably can't do that either. And finally, generative AI does not work that well with structured tabular data. Like, for example, if you give it a table and tell it to, like, query things and then give you information about it, it's actually not that good at doing that. But it is very good with unstructured data, like unstructured text, images, audios, and videos. If you tell it to tell you information about these things, it's very good at doing that. One more thing, bias.

and toxicity. So, an LLM can reflect the biases that exist in the text it was learned from. So, if, for example, the training data keeps indicating that doctors are referred to as he, then the LM would want to refer to a doctor as he as well. While, on the other hand, LM could have internalized that nurses are often she, so it would just automatically, when told it's a nurse, it would just be like she did this. Compared to other AI courses, this course's approach towards prompting is a lot more conceptual. So, instead of giving you a bunch of different patterns to work on and different exercises, it much more just helps you understand how it is that you should think about prompting. And there's a big emphasis on telling you that prompting is an iterative process where you need to kind of build up your prompts and just build up that intuition for how to prompt. With that being said, though, Andrew does have a few tips. The first one is to be detailed and specific by first giving it specific context on

what it is he wants you to do to describing what the task is in detail and including what it is that you want the end result to look like. Another tip is to guide the model to think through its answers. For example, if you want the LLM to help you write an email to be assigned to a new project, you could write just something like help me write an email asking to be assigned to the legal documents project. But you would get a very generic result. So, to make it better, you can provide it with context, such as I'm applying for a job on the legal documents project, which will check legal documents using LMS. I have ample experience prompting LM to generate accurate text and a professional tone. Then you can provide it details for what it is you want it to do, the exact task in detail: write a paragraph of text explaining why my background makes me a strong candidate for this project and advocates for my candidacy. The result of this is an email that's going to be a lot more tailored and also with a lot

more nuance. Remember earlier how Andrew said that you should think about the LLM as a college student that has the abilities but does not have any specific knowledge or information about whatever it is that you're trying to do. Since the LLM has generalized abilities and does not have any specific information or details when you have a task to give it, it's best to break it down into subtasks so it's easier for the LLM to follow and perform each step correctly. For example, if you want to brainstorm five new names for a cat toy that you're making, you can split this task into three different steps. Step one is to generate five fun, joyful words that relate to cats, and the LLM will come up with things like purr, whisker, and feline. Then step two is to ask for rhyming names for a toy based on those words. And step number three would be to add a fun, relevant emoji for each toy name. Does that make sense? So you're kind of guiding it on a step-by-step to reach

kind of like the end result that you're looking for, as opposed to just being like, come up with cool cat names for me. And the third tip is to experiment and to iterate. Andrew actually explicitly says that he doesn't really like it when companies, where like resources would just be like, here's the 10 perfect prompts to achieve this task. And the reason is because prompting is more of a skill set, as opposed to just like memorizing prompts themselves. I guess it's similar to saying that if you're trying to learn to code, instead of trying to memorize syntax and memorize like other people's chunks of code, it's much better for you to understand how to write good code yourself. Just write the prompt, check what the results are, and then just iteratively build upon that. His process is: one, be clear and specific in prompt; two, think about why the result isn't giving the desired output; three, refine your prompt; and then four, repeat the process until your desired prompt and output couple cavas is that be careful about confidential information.

and whether you trust the lm's output or not. Like, don't be like that lawyer from before. If it's something that's high stakes, then don't rely on llm to be sure that it's giving you a correct answer. All right, let's now move on to generative AI projects. This portion of the video is brought to you by Posit, whose mission is to create open-source software for data science, scientific research, and technical communication. They build a lot of cool open-source free products that I've talked about a lot in the past, including Shiny, to quickly and easily deploy interactive web applications; Cal, which is great for programmatically creating documents showcasing your projects and dashboards; and just displaying all sorts of information in an organized fashion. I'm actually also making a company resource to this video using Cordal, since I'm going to be sharing a lot of information this video, so it's linked in the description. Anyways, another product I'd like to highlight from Posit is Posit Connect for Enterprise, a very common challenge, especially if you work with proprietary

or sensitive information, and strict it is that after you build a project, you then have to share it with other people, but you have to do it in a way that the data and the analytics stay secure. That is where Posit Connect comes in to help you deploy, share, and manage all of the analytics products that you and your team build all-in-one, a secure, scalable, and easy-to-manage location. With Posit Connect, you can rapidly and securely deploy what you build in Python with Streamlit, Dash, Bouquet, FastAPI, Shiny, Flask, Quarter Reports, and APIs. It comes with all the bells and whistles to support it and enterprise requirements. You can try Posit Connect for free for 3 months with this link over here, also linked in the description. Now back to the video. Remember that simple framework we talked about, the first one being interactive web applications, like directly talking to ChatGPT, versus software-based applications where you have applications that are powered by large language models. So, this entire section of the course is focused on these software-based applications, which I

think from Andrew's perspective is like the next step from just interacting directly with the web interface in the course itself. He does include a couple of optional sections where you can go in and actually play around with the code. I think he's trying to show that it's not that hard to actually build an application that's powered by LMMS. So, Clif, no version of your traditional AI models and their applications versus LM-powered prompt-based applications. When you have a traditional supervised learning AI application, you need to have a lot of data to feed the AI. For example, if you're looking at restaurant reviews, you need to have labeled data that would have a review and then have the output of being like positive or negative. For example, best soup dumplings I've ever eaten. You have to label that as a positive output, while not worth the 3-month wait for reservation would be labeled as a negative output. So, you need to have all of this different data to train your AI, which the training itself is also like a huge pain in the

ass and quite expensive as well. Um, in order to create a model that's able to do that. But on the other hand, if you have prompt-based development, all you have to do is programmatically prompt the LM like classify the following review as having either a positive or negative sentiment. And then you write the review, which is the banana pudding was really tasty. Then you would get the response output, which is that it's a positive review. That's it. No need to have labeled data, no need to feed it to anything, no need to train anything. So, here is a comparison workflow for supervised learning. It takes like a month to get labeled data, another 3 months to train the AI model on the data, another 3 months to deploy and to run a model. While for prompt-based AI, it takes minutes to hours to specify the prompt and hours to days to deploy and run the model. There is the caveat though that building generative AI products, there is a lot of experimentation and a lot of testing that's involved to catch the

mistakes and to fix them since it is based upon prompting the results that you're getting back. Um, you're also not exactly sure what kind of results they're going to look like in order to improve the results from the LM. There's a few tools that can be used. The first one is just better prompting, like learning to be a better prompt engineer, essentially. The second one is called retrieval augmented generation, otherwise known as RAG. This is a technique where you can provide specific information to an LLM that it would otherwise not have known about. For example, for a specific company, you might be interested in asking, Is there parking for employees? The LM is not going to know that answer because it doesn't know any specific information about that specific company. But you can provide a company document, for example, a document that can include where it is that employee parking is. Then the LLM would be able to reference that document and to answer your question. So, it could tell you that according to the onboarding policy, employees are supposed to park on

parking levels 1 and 2. Another technique that researchers and developers use in order to improve an L's results is called fine-tuning. When an L is created, it's first pre-trained on a lot of information, like all of the information on the internet. This makes it really good at predicting word after word after word and developing its general reasoning abilities. But maybe you wanted to behave in a very specific way. For example, maybe I wanted to speak exactly the way that I speak. This is pretty hard to do purely through prompting, because you could be like, Talk like Tina, but there's like one does Tina talk like, right, where I can start trying to specify, Oh, like whenever Tina uses this word, then you got to do this. Like, Tina uses the word 'um' a lot. Tina likes to use these specific words, but not those words. And she likes to throw in random anime jokes. As you can probably tell, the list can get really long, and it's hard to be very specific. So, a solution to this is fine-tuning.

where you can provide additional examples, like for example how Tina speaks, like lots of transcriptions of Tina talking, and then you're getting that model to adjust itself based upon those examples. And these don't need to be like the billions of words that it was trained on, was pre-trained on. You only need like maybe even 100, a few thousand examples of this in order to tweak the model so that it could sound exactly like me. Another example for why you want to use fine-tuning is if you wanted to gain specific knowledge. For example, medical notes can look something like this, and that kind of looks like gibberish to the average viewer and your average large language model. So you can actually fine-tune the model by showing it these medical notes, so it's able to summarize using this style. All right, the final topic of this course is the impact on business and society. Because AI is a foundational technology, its use is widespread and will grow even more widespread in the future. He gives examples for people in

all different jobs can use it for a variety of different ways. Like a marketer can be using it for marketing strategy, a recruiter can use it for summarizing resumés, a computer programmer can use it as a coding companion, etc., etc. I really like that the course does also address the idea of augmentation and automation of jobs. So, the argument presented is that AI doesn't automate jobs; it automates tasks. So, how do we identify automation opportunities? When we think of a job, it's actually made up of a lot of different tasks. If you're a customer service representative, your tasks can include answering inbound phone calls from customers, answering customer chat queries, checking the status of customer orders, keeping records of customer interactions, etc., etc., etc. So, it's much better to think about AI automation on the level of tasks. Then, you can go through these tasks and label how likely it is that AI will impact that specific task. For example, for answering customer chat queries, the ability for an AI to do well in this is very high, but

for example, in terms of answering inbound phone calls for customers, at least right now, the ability of an AI to do that is relatively low. So, next time you're thinking about, Oh, like, is this job going to be impacted by AI in the future? think about it in terms of what are the tasks that comprise that job, and then look at the tasks and see if AI has the ability to automate or impact those specific tasks. And if more of those tasks can be impacted and done by AI, then the likelihood of the entire job being impacted by AI is also higher. There's also a difference between augmentation versus automation. When an AI is augmenting something, it means that it's helping a human complete this task. For example, if you're a customer service agent, augmentation using AI could be the AI recommending a response for the agent, but the agent having the final say whether it wants to edit that response or approve it. Automation, on the other hand, is automatically completing that task without the intervention of the human. For example, in customer service, automation would be transcribing and

summarizing records of a customer interaction. What's probably going to happen for most tasks is that we'll start off with augmentation and still keep a human in the loop, but over time, some of these augmentations could turn into automations. In the future, if you are an engineer or someone who's looking to build an AI product, a good place to start for you is actually also to go and look at these tasks and see which are the tasks that AI can feasibly do and also has high return on investment. It's a pretty good place to start. A tip is that don't define a job purely on what is considered the main task at that job. For example, if you're a software engineer, people would think, Oh, like a software engineer does programming, but actually, like a software engineer's main job, like the most kind of prominent thing, could be programming, but there's a lot of other things that a software engineer does as well, such as communicating with stakeholders, producing product specs, reviewing other people's code, gathering

requirements and writing documentation, and actually writing documentation, is a huge part of a software engineer's job, even though it's not like the main thing that people think of. So, it may be the case that AI could be really useful for tasks related to a job, say like writing documentation, and it can be very impactful to build an AI tool that's done, is that even though it's not the main task of a job itself. For a lawyer, maybe the main thing of a lawyer is, you know, being part of a court case, but you know, a large part of what a lawyer actually does is referencing a lot of legal documents and having to go through that. So, if you can create an AI application or a tool that can help lawyers get through a lot of documents a lot faster, that could be a very valuable tool to create. And even better than that, if you can identify a task where there's a lot of overlap from a lot of different professions, the impact of what you create can be even greater. There's a research study from 2023 by Eleno and

all uh which showcases that gen will overall impact higher-paid jobs more than lower-paid jobs because uh most of the impact will be on knowledge workers. A Mackenzie report shows that 75% of the total annual impact of generative AI is just on six different functional roles, which are sales, marketing, product R&D, software engineering for product development, software engineering for corporate IT, and customer operations. Another concern is amplifying humanity's worst impulses. Since LLS are trained on the text on the Internet by humans, the biases and toxicities of human beings are also going to be present in that of large language models. So, it's really important to be aware of that, and that's why having humans in the loop and then processing information is going to be very helpful to make sure that your large language models aren't just simply an extension of bad biases. Concern number two is job loss. Jeffrey Hinton, who is kind of like the Godfather of AI, in 2016 said that if you work as a radiologist, you're like the coyote.

that's already over the edge of the cliff but hasn't yet looked down, so it doesn't realize there's no ground underneath them. People should stop training Radiologists now. It's just completely obvious that within 5 years, deep learning is going to do better than Radiologists. However, it's been 5 years now, and guess what? We still have Radiologists. So, Curtis Lules Langlet, professor of radiology from Stanford University, countered this by saying that AI won't replace Radiologists, but Radiologists that use AI will replace Radiologists that don't. Therefore, to survive in your industry, the best way is to learn AI. And final concern of the day, concern number three, is human extinction. There have been examples, Andre acknowledges, of harm that has been caused by AI, unjust sentencing in criminal cases. However, extinction arguments, Andrew argues, are not yet very concrete, and most arguments boil down to the fact that it could happen, but there hasn't been any very good evidence to support the fact that this is going to happen.

anytime soon. Like I said, Andrew himself is clearly an AI enthusiast, but I really do appreciate how much effort he puts into doing the research and then just conveying to us that there are harms and concerns that we should be thinking about. And he's calling people to start addressing these concerns now. He says that humanity has ample experience controlling a lot of things more powerful than a single person, like corporations and nations and states, and lots of things that we don't have full control over either, but nonetheless are considered valuable and safe. For example, airplanes. If we look at the real risk to humanity, such as climate change and pandemics, AI will be a key part of this solution. The next frontier that AI engineers and researchers are looking at is called artificial general intelligence, or AGI. So, this is a threshold in which an AI can do any intellectual task that a human can. For example, it can learn to drive a car in around 20 hours of practice. Apparently, that's all how long humans.

take it can complete a PhD thesis. You can do all the tasks of a computer programmer. Um, basically AGI is when artificial intelligence truly has a generalized intelligence to be able to do anything that a human can do. And companies like OpenAI, like Google, they're all focused on developing the first real artificial general intelligence. We don't know when it's going to happen or even if it's going to happen, but regardless, AI is clearly here to stay. And my personal opinion is that just by the progress that has been made in the past few years, or really even just the few past few months, I can't even imagine um what things will be like in the next few years. So, I think if you're going to learn any new skill set, you should be learning AI. And with that conclusion, you have also finished this speedrun cliffnote version of generative AI for Everyone by Anding. So, I hope this was a good introduction and summary of engineering's course and introduction to AI. And let me know in the comments if

You want me to do more of these kind of like speedrun cliffo version of courses. I will if you're interested in me taking these courses and talking about them, and I will see you guys in the next video or live stream.

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Hi! My name is Tina and I'm a ex-Meta data scientist. Now I create content and other internet things! 

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