Introducing Large Language Models (LLMs)

Welcome to this Team Treehouse micro course, Introducing Large Language Models. 0:00 This micro course was written with the help of a large language model 0:05 called Chat GPT, let's get started. 0:10 Large Language Models or LLMs are a type of machine learning model, 0:16 specifically trained to understand and generate natural language text. 0:21 It's a subset of artificial intelligence, or AI, that allows computers to 0:26 automatically improve their performance in a task by learning from examples provided. 0:30 In order to understand this concept better, 0:36 let's first talk about machine learning. 0:38 Machine learning is a method of teaching computers to learn from data without 0:41 being explicitly programmed. 0:46 It's a type of AI that allows systems to automatically improve their 0:48 performance with experience. 0:52 Let me give you an example to help illustrate this idea. 0:54 Imagine you want to teach a computer to recognize pictures of cats. 0:57 You would start by providing the computer with pictures of cats along with 1:02 pictures of other animals like dogs and birds. 1:07 As the computer sees more and more pictures, it learns to recognize 1:10 the characteristics of a cat like its tail, ears, and whiskers. 1:14 This process is called training. 1:18 Once the computer has been trained and you show it a new picture, 1:20 it will be able to tell you if it's a picture of a cat or not. 1:25 Large Language Models began in the mid-20th century with the development of 1:31 artificial neural networks, 1:36 a type of machine learning model inspired by the way the human brain works. 1:37 Large Language Models are typically implemented as neural networks. 1:42 Neural networks are good at handling large amounts of data and 1:46 can be trained to perform well on a wide range of tasks, 1:50 so they're the most popular choice for building LLMs. 1:54 The parameters or values that are learned during the training process 1:58 are used to make predictions. 2:02 They are called large because they typically have a large number of 2:04 parameters which allows them to understand and 2:09 generate human language with a high degree of accuracy. 2:12 LLMs are used in natural language processing or NLP, a subfield of AI that 2:16 focuses on the interaction between human language and computers. 2:20 There are many types of machine learning, but 2:25 the two main categories are supervised and unsupervised. 2:28 In supervised learning, the computer is given a labelled data set, 2:32 which means that the correct answer is provided for each example. 2:37 In our cat recognition example, each picture is labeled as cat or not cat. 2:41 The computer learns to find the patterns in the data that 2:46 correspond to the correct labels. 2:50 In unsupervised learning, the computer is given an unlabeled data set, and 2:52 it must find the patterns and structure in the data on its own. 2:56 Unsupervised learning can be more challenging than supervised learning, 3:00 because there is no clear guidance on what the model should be learning, but 3:04 it can still be a powerful tool for uncovering patterns and insights in data. 3:09 LLMs are trained using unsupervised learning, because it allows them to learn 3:14 patterns in large amounts of text data, such as the vast amount of text data 3:19 available on the internet without the need for explicit supervision. 3:23 This makes them ideal for tasks such as natural language, understanding, and 3:28 generation. 3:33 But also raises concerns about their ability to perpetuate biases, 3:33 which we'll talk about a little later. 3:38 Here are some examples of unsupervised learning. 3:40 Clustering is a technique used to group similar data points together. 3:44 For example, a clustering algorithm can be used to group customers with similar 3:48 purchasing habits into the same cluster. 3:53 Dimensionality reduction is the process of reducing the number of features in 3:56 the data while preserving as much information as possible. 4:01 A real-world example is in the field of image compression. 4:04 Digital images are often large in size and can take up a lot of storage space. 4:08 Dimensionality reduction can be used to reduce the number of 4:13 pixels in an image while preserving its overall appearance. 4:17 Anomaly detection is a technique used to identify data points that deviate from 4:21 the normal or expected behavior. 4:26 For instance, this can be used to detect fraud. 4:28 If a credit card transaction is abnormal, it can be flagged as suspicious. 4:31 Unsupervised learning, can help to discover hidden patterns and 4:36 relationships in data even when we don't have any labeled data. 4:40 And it can be useful for many industrial applications such as quality control, 4:45 fraud detection, and more. 4:50 Next, let's take a look at the ways biases can occur in training data. 4:52 Bias in training data refers to the phenomenon where the training data 4:57 used to train a machine learning model does not accurately represent 5:02 the population of interest. 5:06 This can lead to models that perform well on the training data but 5:08 poorly on new unseen data causing inaccurate or unfair results. 5:13 Biases in training data may be introduced in a number of ways. 5:17 Sampling bias occurs when the training data is not randomly sampled from 5:21 the population, leading to over representation or 5:26 under representation of certain groups. 5:29 Measurement bias occurs when the features used to represent the data are not 5:32 relevant or are measured differently for different groups. 5:37 Demographic bias occurs when the majority of data used to train the model is 5:41 from a specific group and not representative of other minority groups. 5:46 It can lead to a model that performs well on the majority group, but 5:51 poorly on minority groups. 5:55 Temporal bias occurs when the data used to train the model is from 5:57 a specific time period and not representative of the current time. 6:01 This can lead to a model that performs well on historical data, but 6:06 poorly on current data. 6:10 It's important to be aware of potential biases in training data and 6:11 address them by collecting more representative data or 6:15 by using techniques such as resampling, data preprocessing, or reweighting. 6:19 Next, let's look at some LLMs in use today and the types of products that use them. 6:24 Some examples of Large Language Models in use today 6:31 include GPT-3, BERT, T5, and XLNet. 6:36 Which have been trained on massive amounts of text data and 6:40 can perform a variety of language tasks such as translation, 6:44 summarization, and question answering. 6:48 In fact, the majority of this micro course was written with ChatGPT, 6:51 a variation of GPT-3. 6:56 ChatGPT is trained on an immense dataset of conversational text, 6:58 which allows the model to understand and generate human like text that is 7:02 appropriate for a wide range of conversational contexts. 7:07 It can be used in many natural language processing tasks, 7:10 such as creating learning resources like the one you're watching now. 7:14 Large Language Models are used in a variety of products, 7:19 including language translation services. 7:23 A great example of a language translation service is Google Translate, 7:26 a free online language translation service developed by Google that can 7:31 translate text, speech, images, and web pages in over 100 languages. 7:36 LLMs are also used to power the conversational abilities of chatbots and 7:41 virtual assistants. 7:46 They can understand and respond to a wide range of inputs, 7:48 from simple questions to complex conversations. 7:52 Siri and Alexa are a few well known examples. 7:55 Content generation for social media and other forms of digital media. 7:59 LLMs can generate human like text, which can be used to generate articles, 8:04 product descriptions and instructional videos. 8:08 Automated writing and content creation tools, 8:13 ChatGPT is a recent high profile example of a product in this category. 8:16 Sentiment analysis, LLMs can be used to understand the emotions and 8:21 opinions expressed in text and social media content. 8:26 Text summarization and analysis tools, GitHub Copilot is one example. 8:29 Copilot is a code completion and code assistance tool developed by GitHub 8:34 designed to help developers write code more efficiently and accurately by 8:39 providing suggestions and predictions for code snippets as they type. 8:44 GitHub Copilot uses machine learning models to understand the context 8:48 of the code and predict what the developer is trying to write. 8:53 Demand for professionals in these areas has grown rapidly in recent years, 8:58 as more and more companies adopt these technologies. 9:03 So, what does the future hold for Large Language Models? 9:07 The world is likely to see continued advancements and 9:12 increased adoption in a wide range of industries as the technology behind 9:15 language models improves, and becomes more sophisticated. 9:19 The outlook for career opportunities in Large Language Models is positive. 9:23 With more and more companies using language models for 9:29 tasks like language translation and chatbots, there are many opportunities for 9:32 individuals with the right skills and experience to work on these applications, 9:36 and to work within the broader field of data science. 9:41 LLMs are only a subset within the vast field known as data science. 9:45 Data science is a rapidly growing field, 9:51 encompassing a wide range of techniques, tools, and applications. 9:53 With technologies advancing every day, the demand for 9:59 data scientists will continue to advance as well. 10:02 In fact, faster than the average for most other occupations. 10:05 As new discoveries are made in the field, so too will new professions emerge. 10:10 So, if you're asking yourself, how can I get started in the field of LLMs? 10:16 Here are a few next steps to consider. 10:22 Study the fundamentals of machine learning and natural language processing, 10:26 and experiment with pretrained LLMs like GPT-3, BERT, or T5. 10:30 This will help you understand how they work and 10:36 how to use them in real world applications. 10:38 Join a community or forum where people discuss and 10:42 share their experiences with LLMs. 10:45 Develop your own LLMs. 10:49 You can use open source libraries such as TensorFlow or 10:51 PyTorch to build your own models and gain hands on experience. 10:54 It's worth noting that the field of data science is rapidly evolving. 10:59 The skills required to work with LLMs are constantly changing. 11:04 So, staying up to date with the latest developments is essential for 11:08 professionals in this field. 11:11 Finally, consider taking a course or earning a degree in a related field such 11:14 as computer science, data science, or artificial intelligence. 11:18 To continue your learning journey with Treehouse, 11:24 check out these Treehouse courses. 11:26 In the course Machine learning basics, 11:30 dive deeper into machine learning frameworks. 11:33 You'll learn to use a Python library called scikit-learn, 11:37 which includes well designed tools for performing common machine learning tasks. 11:40 As well as Anaconda, 11:44 a Python-based platform focused on data science and machine learning. 11:46 In introduction to algorithms, take your first steps toward understanding 11:54 the world of algorithms, time complexity, and data structures. 11:58 In this course, our teaching team will examine algorithmic thinking, and 12:03 you will learn how to implement algorithms in code. 12:07 And data analysis basics provides a comprehensive overview 12:12 of charting, visualizing, and analyzing data. 12:17 We hope you found this micro course helpful in introducing you to 12:24 Large Language Models. 12:28 As we've seen, machine learning and AI are positioned as the future of tech. 12:30 We at Treehouse hope this step we've taken into AI generated 12:35 content has been a positive experience for you. 12:39 We want to know what you thought of this video, 12:43 please email your honest feedback to feedback@teamtreehouse.com. 12:47 Thanks for watching. 12:52

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