Machine Learning and Neural Networks - page 6

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The History of Artificial Intelligence [Documentary]

The History of Artificial Intelligence [Documentary]

The History of Artificial Intelligence documentary takes us through the early days of the concept of a "thinking machine," spawned by science fiction writers and the movie industry, to the present day advances in AI and deep learning processes.The documentary shows the progress made in AI, the ability of machines to learn like humans, and the principles behind how computers work. The video explores the limitations of computers, the potential for their development, and the possible future of artificial intelligence (AI). Scientists discuss the possibility of machines being able to think and produce new ideas, and the goal is to create a more general computer system that can learn by experience, form concepts, and do logic. The first steps towards AI can be seen in a small computing machine that can learn from experience, as shown in the example of an electrically-controlled mouse solving a maze.

The second part explores the limitations and potential of computers in terms of thinking, feeling, and creativity. While computers excel at logical operations and mathematical calculations, they struggle with recognition, pattern recognition and generalization, recognizing blocks, translating languages, and performing simple tasks. Despite initial underwhelming results, expert systems and programs such as SHRDLU and TENDRIL showed how computers could use knowledge to resolve ambiguity and language learning. However, the challenge of teaching common sense knowledge, which includes both factual knowledge and experiences that people acquire over time, remains. Neural networks, while initially appealing, have limitations and are only capable of tackling small tasks. Researchers need to train computers to understand how nature builds and coordinates many micro-machines within the brain before a fully artificial version can be built.

The third part covers a wide range of topics related to the history and future of artificial intelligence. It discusses ongoing efforts to achieve general-purpose intelligence based on common sense, including the Cyc project and the potential for general natural language understanding in AI. The challenges in achieving human-like intelligence, including the need for formal models of intelligence and the role of psychology, are also explored. The interviewees discuss the impact of computers on the field of psychology, as well as the challenges posed by non-monotonic reasoning and the need for conceptual breakthroughs. Despite criticisms, the interviewees see the goal of AI as a noble project that can better help us understand ourselves.

• 00:00:00 In this section, we are transported back to the 1950s where the idea of a "thinking machine" was still a dream, held only by science fiction writers and the movie industry. The film "The Thinking Machine" followed a discussion on whether machines can indeed think, a concept that was still too far-fetched at the time, as the field of artificial intelligence was only in its early stages, and researchers had not yet figured out how to make machines produce genuinely new ideas. Today, the documentary reminds us of the progress made in AI and the deep learning processes that have contributed to advances in technology that we sometimes take for granted.
  
  
• 00:05:00 In this section of the transcript, a child is being taught the alphabet and a psychologist questions how the brain recognizes patterns. The conversation then shifts to the potential of computers to mimic the same process of learning as a child by being shown the alphabet for the first time. The computer is tested and while it is not perfect, it can accurately identify letters with increasing accuracy as it is presented with more information. The possibility of machines being able to learn like humans is explored, but the specific thought processes of machines are still unclear and the full range of the usefulness of computers is being discovered.
  
  
• 00:10:00 In this section, a group of Carnegie Tech professors named Simon and Newell are shown attempting to understand logical problems like the puzzle of missionaries and cannibals. They represent their progress by addressing the problem of getting all three missionaries and cannibals to cross the river in a boat that can only hold two people at a time without the cannibals outnumbering the missionaries. A conversation between professors reveals that one of their students named Barbara has come up with a solution to the problem which the computer has now reproduced. Additionally, professors show a demonstration of a man playing checkers against a computer which draws attention to a computer's ability to learn on the basis of probabilities or reasonableness that have been programmed into it.
  
  
• 00:15:00 In this section, the video explores the question of how computers work, explaining that they take inputs, process them using mathematical operations, and output results through programming. While there are similarities between computers and living nervous systems, neurophysiologists believe there are many more differences than similarities. The video also touches on the idea that humans are programmed both hereditarily and by experience, offering an experiment where ducklings raised in isolation failed to recognize a silhouette of a goose. Overall, the section provides a brief explanation of the principles behind how computers work and dives into some ideas about programming in both machines and living beings.
  
  
• 00:20:00 In this section, two men discuss the concept of programming versus instinct in animals. While one argues that the duck's ability to differentiate between a goose and a hawk is an example of instinct, the other suggests that some animals are born with more built-in knowledge than previously thought. They analyze research done on frogs and suggest that the fibers in a frog's eye only report specific things to the brain that are related to the frog's survival, such as movement and potential prey. This theory, while not yet widely accepted, could explain the existence of instinct.
  
  
• 00:25:00 In this section, we see a researcher conducting an experiment with a five-year-old child to prove that humans are born with certain innate abilities. The child is asked to fill his glass with milk to the same level as the researcher's glass, but he fills it up to the brim, thinking it's the same amount. This suggests that some notions about the world around us are preconceived in our minds, and we rely on our eyes to form our concepts of the world around us. The video goes on to prove that what we see with our eyes is not always accurate, and illusions can play tricks on our brains.
  
  
• 00:30:00 In this section, a professor speaks to another man about how humans are programmed to react based on preconceived beliefs and rules. The man questions whether a computer can do anything original, and the professor shows him a play that was written by a computer with the assistance of a programmer named Harrison Morse. The audience is amazed by the printout of the play and the professor explains that it is not magic, but rather the result of a well-designed program.
  
  
• 00:35:00 In this section, the MIT staff member, Doug Ross, explains how they used a program to write a playlet that illustrates the rules that constitute intelligent behavior. They highlight that intelligent behavior is rule-obeying behavior and showcase how a computer can be made to do creative work. They mention the rules that the computer uses to determine reasonable behavior, and they have even programmed an inebriation factor that affects the robber's behavior. They emphasize that there is no black magic about doing these things on machines, and they show that the computer writes a different play each time, demonstrating its creativity.
  
  
• 00:40:00 In this section, the video explores the limitations of computers and the potential for their development. The animation highlights the trial and error process involved in programming a computer and the possibility for errors. The video then shows an experiment in which a computer is used to study signals in the human brain, highlighting the potential for computers to improve our understanding of learning processes. The video then shows viewers the Lincoln Laboratory and its TX2 computer, which is one of the world's largest and most versatile computers. The video suggests that computers such as the TX2 are being used to study learning processes and that computers are being developed globally for scientific purposes.
  
  
• 00:45:00 In this section, scientists discuss the possibility of machines being able to think and produce new ideas. While some believe that machines and computer programs will be able to behave intelligently and aid humans in relieving the burden of intellectual work, others doubt that machines will ever be capable of true creative thinking. The future of computers is predicted to have both direct and indirect effects, such as putting machines to work in various ways and learning new things as humans work with computers. The second industrial revolution is expected to be the age of assistance of the human mind by the computer, and the possibilities of what machines can do with human aid are hard to imagine.
  
  
• 00:50:00 In this section, the focus is on the potential of artificial intelligence (AI) and its possible development in the future. The goal is to create a more general computer system that can learn by experience, form concepts, and do logic. It will consist of sense organs, a large general-purpose flexible computer program, and output devices. While progress is being made, there is concern about managing the technology's impact. However, one scientist believes that if we handle it correctly, we can make a much better world. The first steps towards AI can be seen in a small computing machine that can learn from experience, as shown in the example of an electrically-controlled mouse solving a maze.
  
  
• 00:55:00 In this section, we see a demonstration of a mouse navigating a maze, controlled by a system of telephone relays and Reed switches. The mouse is capable of adding new information and adapting to changes. The demonstration showcases the mouse replacing old, obsolete information with what it is learning about the new maze configuration. While it is the machine underneath the maze floor that actually moves the mouse, the demonstration offers a glimpse into the type of intelligent behavior that can be achieved.
  
  
• 01:00:00 In this section, the video explores the definition of "thinking" and how it relates to computers. While computers excel at storing and recalling information, that alone does not encompass true thinking. However, computers can perform logical operations, such as playing chess, where they analyze data and determine the best move. This display of basic logic functions has awarded some computers first place in amateur chess tournaments.
  
  
• 01:05:00 In this section, the video explores how computers are capable of performing logical operations, even making millions of logical decisions each day, but are limited in terms of visualization and recognition abilities. While computers can produce pictures and simulate designs, they struggle with recognizing patterns and generalizing. The video also notes the difficulty in teaching a computer to translate languages due to the lack of one-to-one correspondence between words of different languages. Ultimately, computers lack the ability to think, feel, or have consideration for anything.
  
  
• 01:10:00 In this section, the video discusses the capabilities of computers in terms of emotions and creativity. While computers cannot actually feel emotions, they can be programmed to simulate them. Similarly, while creativity is often thought to be a uniquely human capability, computers are capable of producing animated films and even music. The usefulness and efficiency of computers, including the billions of mathematical operations they can perform without making a mistake, is undeniable, but the question of whether they have the ability to truly "think" is still up for debate.
  
  
• 01:15:00 that computers could play games like checkers and solve complex problems led to the birth of artificial intelligence (AI). This became a frontier for exploration by a group of mathematicians, led by Marvin Minsky and John McCarthy, who set up a department at MIT to explore the possibilities of AI. Students like Jim Slagle developed programs to solve problems in calculus, and in 1960, a computer was able to get an A on an MIT exam, performing as well as an average student. This showed that computers could have intelligence and raised hopes for a future where machines could think.
  
  
• 01:20:00 In this section, the documentary explores the early days of artificial intelligence and how the pioneers in the field were not concerned with the physical construction of the brain. They viewed the mind as a symbolic processing entity, while the brain was simply the hardware on which the mind runs. The documentary argues that blindly copying nature's way of doing things isn't always a good idea, and that attempts at artificial flight based on the way birds fly had been a disaster. The documentary highlights the difficulties that arose when MIT scientists attempted to build a computer mind that could interact with the world and stack blocks. It's said that although it may seem like a simple task, recognizing blocks is actually very complicated, and the program had some strange ideas about what happens to blocks when you let them go.
  
  
• 01:25:00 In this section, the documentary explores the challenges of teaching computers to see and move like humans. Researchers found that the computational problems of vision were so immense that many decided to focus on a disembodied form of intelligence, known as the Turing test, which measures a machine's ability to use language intelligently. One of the first computer programs created for this purpose was the "Eliza" program, which used a series of tricks to simulate conversation but couldn't possibly pass the Turing test. The documentary highlights how the complexity of human language comprehension made it difficult to develop AI language models that could understand meaning and context like humans.
  
  
• 01:30:00 In this section, the video discusses the early attempts to use computers to translate languages, which were met with deep trouble due to issues of ambiguity and context. Despite claims that computers could replace human translators, the complexity of language and the need for common human knowledge and understanding made this task much harder than anticipated. The inability for computers to recognize faces, learn language, and perform simple tasks such as putting on clothes shows that the things people think are easy are actually very hard for AI to accomplish. The failures of AI led to a decline in funding and a bleak outlook for the field.
  
  
• 01:35:00 In this section, we see that despite initial underwhelming results, Terry Winograd's program called SHRDLU showed that computers could use knowledge to resolve ambiguity and language learning. However, it was restricted to a simulated micro-world of blocks. Edward Feigenbaum and his colleagues then developed a system called TENDRIL which captured the rules that experts in narrow fields use to make decisions. They found that expert behavior in narrow areas only requires a few hundred pieces of knowledge. This led to the development of expert systems that proved to be brittle and lacked the flexibility to operate outside their fields of knowledge.
  
  
• 01:40:00 In this section, the documentary covers the challenges faced by language researchers in the 1970s who were trying to get computers to follow simple stories as children do. They discovered the problem wasn't what the story said, but the huge number of things that were left unsaid because they were too obvious to be worth stating. The researchers developed the idea of building frames or scripts for different situations the computer might encounter, such as a birthday party, which would contain all the things that usually happened at birthday parties. However, the challenge was how to include general background knowledge, which wasn't specific to the situation or context. This general knowledge created a common sense knowledge problem, making it challenging to teach computers to interpret simple stories.
  
  
• 01:45:00 In this section, the excerpt discusses common sense knowledge and the difficulty of teaching it to machines. Common sense knowledge is the intuitive knowledge that everyone shares, such as knowing that objects fall when released. However, it is not just factual knowledge, it is also skills and experiences that people acquire over time. Scientists have long been interested in teaching machines how to learn and acquire knowledge like humans, but computers have started out with such low levels of learning that machine learning was not effective until they were given vast amounts of common sense knowledge. The PYSCH project was created in Texas in 1984 to input common sense knowledge and was the ultimate test of AI. However, critics argued that real common sense depended on having a human body, and that common sense knowledge isn't just made of facts, but also experiences and skills that children acquire over time.
  
  
• 01:50:00 In this section, the video explores the idea of common sense knowledge and how it is acquired through experiences of the world, but also presents the case of a patient with no physical experiences who still acquired common sense through language. The video then delves into the argument that to build an artificial mind, one must first build an artificial brain. The complexity of the human brain, made up of billions of neurons connected in thousands of ways, inspired scientists in the 1950s to pursue the idea of building an artificial brain, leading to the development of perceptrons which later evolved into neural networks. The modern perception model of neural networks is a growing movement called connectionists and focuses on machine learning through the brain rather than the mind.
  
  
• 01:55:00 trial and error, in this section the documentary focuses on neural networks and their limitations. While neural networks were initially appealing, they are only capable of conquering tasks that are small in scope, and researchers don't yet fully understand how they learn. The example of a neural net learning to distinguish between pictures with tanks and without them highlights the potential for nets to come to incorrect conclusions. While the possibility of tiny neural networks capturing something as elaborate as common sense is intriguing, researchers admit that this long-term goal is still far from being achievable with today's technology. Additionally, attempts to create neural networks larger than a few hundred neurons often backfire due to the long training time required. Therefore, researchers must understand how nature builds and coordinates many micro-machines within the brain before a fully artificial version can be built.
  
  
• 02:00:00 In this section, the transcript discusses how practical applications of artificial intelligence have appropriated the term, but they are far from the original quest of achieving general-purpose intelligence based on common sense. However, the quest for AI has not been abandoned, and the Cyc project, started by Doug Leonard in 1984, is still ongoing. The project aims to build a mind that knows enough to understand language and learn everything that humans know. Despite being software with no body or direct experience of the world, Psyche, the AI entity in the Cyc project, analyzes inconsistencies in its database and makes interesting new discoveries, showing that it sees the world in a unique way.
  
  
• 02:05:00 In this section, the discussion focuses on the potential for general natural language understanding in artificial intelligence and the need to achieve this to prevent the demise of symbolic AI. The Psyche project is mentioned as a high-risk project with a high payoff potential if successful in passing the Turing test of general natural language understanding. Such success could lead to the development of machine learning programs to learn unknown things, thereby amplifying intelligence in ways that are currently unimaginable. Dr. John McCarthy, one of the founders of artificial intelligence, reflects on the history of the discipline and predicted the impact it would have on society.
  
  
• 02:10:00 In this section, the video discusses the difficulties in achieving computer programs that are as intelligent as humans. Despite some early progress with difficult problems such as solving mathematical theorems, common sense tasks like recognizing speech have proven to be difficult for computer intelligence. The speaker and his colleagues have been working to develop formal models of intelligence that are equivalent to human intelligence, but there are different approaches to achieving this goal. The field of psychology also had a role to play in this, with computer science helping them to move away from behaviorism and gain insights on cognition.
  
  
• 02:15:00 In this section, experts discuss the impact of computers on the field of psychology, and how the concept of consciousness has been approached in both fields. While computers have offered great insights into the workings of the mind, the question of whether computers can ever truly be self-conscious remains a subject of philosophical debate. Furthermore, the notion that consciousness is merely the sum of its parts, like a machine, is not entirely accurate, as the mind is a complex system of specialized parts that interact in specific ways.
  
  
• 02:20:00 In this section, the interviewee discusses the retreat of the view that human beings have something that transcends the mechanistic aspects of our being, as more has been discovered about human physiology and psychology. Despite this, there are still aspects of human consciousness that have not been realized in machines in computer programs. The interviewee, who is optimistic about AI, talks about the collection of problems on which computer brute force can be applied, which is rather limited, and that the central problem of artificial intelligence involves expressing the knowledge about the world necessary for intelligent behavior. Mathematical logic has been pursued as the tool for this, and in the late 1970s, several people discovered ways of formalizing what they call non-monotonic reasoning, greatly extending the power of mathematical logic in the common sense area.
  
  
• 02:25:00 In this section, the interviewee discusses non-monotonic reasoning and how it poses a challenge to human-like thinking in computers. Ordinary logic works by adding more premises to draw more conclusions, whereas human reasoning doesn’t always have that property. For example, the term “bird” has the built-in assumption that it can fly, and additional context can change conclusions drawn from that. Non-monotonic reasoning can be used as a mathematical tool to formalize this type of thinking and introduce awareness of context in computers. However, the challenge with context is that there are always exceptions that cannot be accounted for, so a system is needed where an assumption is made unless there is evidence to the contrary.
  
  
• 02:30:00 In this section, John McCarthy, a pioneer of AI, discusses the history of AI and why it took so long for humans to develop artificial intelligence. He explains that our limited ability to observe our own mental processes hindered our progress, as seen with Leibniz's failure to invent propositional calculus which was instead invented by Boule 150 years later. He also acknowledges that conceptual breakthroughs are needed for the future of AI and that it may take anywhere from a few decades to several hundred years to achieve true human-like intelligence in machines. Despite criticisms about the impossibility of replicating human intelligence, McCarthy sees the goal of AI as a noble project to better understand ourselves.

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The Birth of Artificial Intelligence

The Birth of Artificial Intelligence

The video discusses the birth of modern artificial intelligence (AI) and the optimism that came with it during the 'golden years' of AI in the 60s and early 70s. However, the field faced significant challenges, including the first AI winter in the mid-70s due to the difficulty of the problems they faced and limited computational performance.

Expert systems marked a turning point in the field, shifting the focus from developing general intelligence to narrow domain-specific AI, and helped increase business efficiency. However, the hype surrounding expert systems led to a decrease in funding, particularly after the 1987 market crash. The video acknowledges the challenges of understanding and defining AI, recommending Brilliant as a resource for people to learn about AI from foundational building blocks to more advanced architectures.

• 00:00:00 In this section, we learn about the official birth of modern artificial intelligence at the Dartmouth summer research project in 1956, where the term "artificial intelligence" was first coined. The conference aimed to simulate human learning by describing every feature of intelligence that machines could simulate. The seven aspects included programming computers to use language, neural networks, abstraction, self-improvement, and randomness and creativity. The period after the conference was known as the "golden years" of AI where computing and AI theories and algorithms began to be implemented, including reasoning as search, semantic nets, and micro-worlds. These algorithms were groundbreaking and infused the field with optimism, causing individuals like Marvin Minsky to believe that creating artificial intelligence could be solved substantially within a generation.
  
  
• 00:05:00 In this section, the video explores the birth of artificial intelligence and how it generated a lot of optimism and hype during the 60s and early 70s. This led to massive amounts of funding primarily from government for AI research and implementation, resulting in many research institutions at the forefront of AI research today. However, in the mid-70s, the first AI winter began due to the failure to appreciate the difficulty of the problems they faced, coupled with the fact that the field of computer science was still being defined during this period. Five problems were listed, including the fact that breakthroughs were made with limited computational performance, and Moravec's paradox, a theory postulated by AI and robotics researcher Hans Moravec at Carnegie Mellon.
  
  
• 00:10:00 In this section, we learn about how expert systems marked an important turning point in the field of AI by shifting the focus from developing general intelligence to narrow domain-specific AI. Expert systems, based on the knowledge of experts in a specific domain, had tangible, real-world impacts and helped businesses increase their efficiency, as seen with XCON logistics expert system, which saved digital equipment corporation almost 40 million dollars per year. Moreover, the rise of expert systems helped to revive connectionism, which emerged as a viable way to learn and process information. The Hopfield net and backpropagation, methods for training AI, were popularised and refined during this time period, paving the way for deep learning. However, as expectations for expert systems spiralled out of control, and cracks in their brittle, conditional logic-based systems began appearing, funding for AI decreased again due, in part, to the 1987 crash in world markets.
  
  
• 00:15:00 In this section, the transcript discusses the challenges of defining and understanding artificial intelligence (AI), particularly due to the hype cycles that have come and gone over the past century. The video acknowledges the confusion that has arisen with the rise and fall of AI buzzwords, from deep learning to artificial human intelligence. The hope is to separate the hype from the practical present applications of AI, such as domain-specific expertise in deep learning systems. The video recommends Brilliant as a resource for individuals to keep their brain sharp and learn about AI from its foundational building blocks to more advanced architectures.

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Supervised Machine Learning Explained

Supervised Machine Learning Explained

The video explains that supervised learning involves a labeled dataset, with the goal of learning a mapping function from input variables to output variables. The labeled dataset is divided into a training set and a testing set, with the model being trained on the training set and evaluated on the testing set to measure its accuracy.
The video notes that overfitting can occur if the model is too complex and fit too closely to the training set, resulting in poor performance on new data, while underfitting occurs if the model is too simple and unable to capture the complexity of the data. The video provides the example of the iris dataset and walks through the process of training a model to predict the species of a new iris flower based on its measurements, using the decision tree algorithm.

• 00:00:00 In this section, the video explains the definition and purposes of machine learning, which can be used to make predictions based on past data. The video provides the example of regression, which measures the relationships between variables, creates a line of best fit, and uses that line to predict new data. The video then expands on this idea to explain classification problems, which involves adding label data and creating decision boundaries to classify the output label of new data. The video examines the accuracy of this model and explains that machine learning algorithms seek to maximize model accuracy. The video notes that decision trees are a type of machine learning algorithm that uses a conditional statement-based approach, similar to expert systems.
  
  
• 00:05:00 In this section, the video dives into the different types of algorithms that can be used for machine learning, including support vector machines and how additional variables can be added for higher dimensional spaces. The video also touches on the intersection of artificial intelligence, big data and data science, with data science and statistics being considered one and the same for simplicity. The video then goes on to explain supervised learning, which is comprised of two primary modes of learning models, regression and classification, and how it's essentially statistical mathematics for pattern recognition problems, rebranded as machine learning. The video concludes with a mention of unsupervised learning and deep learning, which will be covered in future videos, and a recommendation of Brilliant.org for those interested in learning more about the mathematics and science behind these concepts.

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Unsupervised Machine Learning Explained

Unsupervised Machine Learning Explained

The video explains unsupervised machine learning, which deals with unlabeled and unstructured data, and is mainly used for deriving structure from unstructured data. It is divided into two types: association and clustering, where clustering involves using algorithms like K-means clustering to divide decision space into discrete categories or clusters.

Association problems identify correlations between data set features, and to extract meaningful associations, the complexity of the columns must be reduced through dimensionality reduction. This process involves minimizing the number of features needed to represent a data point and achieve meaningful results and associations while preventing underfitting or overfitting. The final segment of the video introduced the concept of learning mathematics and science on Brilliant, a platform that offers enjoyable and interconnected math and science learning and provides a 20% discount on premium subscriptions for viewing futurology content. The video also solicited support for the channel on Patreon or YouTube membership and welcomed suggestions for future topics in the comments.

• 00:00:00 In this section, we learn about unsupervised machine learning, which is for data that is unlabeled and unstructured. It is representative of most real world problems and takes place in the crossover between big data and the field of artificial intelligence. Unsupervised learning is primarily used for deriving structure from unstructured data. This type of learning is subdivided into two primary types: association and clustering. Clustering involves the use of algorithms like K-means clustering, where the goal is to divide a decision space with a number of data points into a specified number of discrete categories or clusters. This is done by first adding centroids and then iteratively reassigning data points to their new clusters while updating the centroids.
  
  
• 00:05:00 In this section, the focus shifts from clustering to association in unsupervised learning. Association problems identify correlations between features of a data set, unlike clustering, which groups similar data points together. To extract meaningful associations, the complexity of the columns in the data set must be reduced through dimensionality reduction, where the number of features to uniquely represent a data point is minimized. Feature extraction can be done by selecting an optimal number of features to avoid the underfitting or overfitting of the data set. Dimensionality reduction is achieved through manifold learning, where high-dimensional data can be represented by low-dimensional manifolds. The low-dimensional representation of the data set contains the reduced feature set needed to represent the problem and still produce meaningful results and associations. Feature engineering is a subfield in machine learning that includes dimensionality reduction, feature selection, and extraction.
  
  
• 00:10:00 This final segment of the video introduces the concept of learning mathematics and science to gain a deeper understanding of the concepts discussed in the channel. Brilliant, a platform that makes math and science learning exciting, interconnected and offers offline learning. Additionally, users can learn about futurology and get a 20% discount on premium subscriptions by visiting the link provided. Finally, viewers are encouraged to support the channel on Patreon or YouTube membership and leave suggestions for future topics in the comments.

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What Is Machine Learning (Machine Learning Explained)

What Is Machine Learning (Machine Learning Explained)

Machine learning is a field of study that enables computers to learn without being explicitly programmed. It involves using algorithms to form decision boundaries over a dataset's decision space. This understanding of machine learning is second most widely-used and established by Dr. Tom Mitchell.

Machine learning can be attributed to the increase in computing power and storage that allowed for bigger and better data, and the rise of deep learning. While it's classified as weak artificial intelligence since the tasks it performs are often isolated and domain-specific. Machine learning encompasses many different approaches and models, and while they can never be 100% accurate at predicting outputs in real-world problems due to abstractions and simplifications, they can still be useful in a broad array of applications. Brilliant is mentioned as one of the resources for learning about machine learning and other STEM topics.

• 00:00:00 In this section, the focus is on the meaning and definition of machine learning and how it relates to artificial intelligence. Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. It involves the use of algorithms to form decision boundaries over a dataset's decision space. The process of forming the model is known as training, and once a trained model exhibits good accuracy on training data, it can be used for inference to predict new data outputs. This process defines the second most widely-used definition of machine learning established by Dr. Tom Mitchell of Carnegie Mellon University.
  
  
• 00:05:00 In this section, the video explores the rise of machine learning and artificial intelligence by highlighting the five primary tribes of machine learning: symbolists, connectionists, evolutionaries, Bayesians, and analogizers. It goes on to explain how the development of AI moved from trying to create a more general, strong AI in the early days of AI, to focusing on acquiring domain-specific expertise in various fields. The rise of machine learning can be attributed to the increase in computing power and storage that allowed for bigger and better data and the rise of deep learning. Additionally, the video touches on how many AI breakthroughs were made possible due to data being a huge bottleneck in the industry.
  
  
• 00:10:00 This section explains that while machine learning is a form of artificial intelligence, it is classified as weak AI because the tasks it performs are often isolated and domain-specific. Machine learning encompasses many different approaches, from complex rules and decision trees to evolution-based approaches and more, all with the goal of modeling the complexities of life much like how our brains try to do. While it is acknowledged that models can never be 100% accurate at predicting outputs in real-world problems due to abstractions and simplifications, machine learning models can still be useful in a broad array of applications. The video encourages viewers to seek additional resources to learn more, including Brilliant, a platform that offers courses and daily challenges covering a variety of STEM topics.

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Deep Learning Explained (& Why Deep Learning Is So Popular)

Deep Learning Explained (& Why Deep Learning Is So Popular)

The video explains that deep learning's popularity stems from the fact that it can learn features directly from data and uses neural networks to learn underlying features in a data set. The rise of deep learning can be attributed to big data, increased processing power, and streamlined software interfaces.

• 00:00:00 In this section, the video explains that deep learning is a subfield of artificial intelligence that has become popular due to the success of the connectionist tribe of machine learning. Deep learning systems can learn features directly from data and use neural networks to learn underlying features in a data set. The key element of deep learning is that layers of features are learned from data using a general-purpose learning procedure, as opposed to being hand-engineered. The video also offers an example of a neural network detecting a face in an input image, starting with low-level features, discerning mid-level features, and finally discovering high-level features to identify various facial structures. The video ultimately notes that the true birth of deep learning came in 2012 with the ImageNet competition, where the winning algorithm had an error rate of 16%, almost 10% better than its closest competitor.
  
  
• 00:05:00 In this section, the video discusses how the rise of deep learning can be attributed to factors such as the pervasiveness of big data, increases in computing power, streamlined software interfaces like TensorFlow, and the ability of deep learning to process unstructured data. The video also touches on the historical development of neural nets, from single-layer perceptron nets of the '60s to modern deep networks with tens to hundreds of layers. Furthermore, the video recommends Brilliant.org as a great learning resource for those interested in diving deeper into the field of deep learning.
  

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From The Brain To AI (What Are Neural Networks)

From The Brain To AI (What Are Neural Networks)

The video discusses the components of an artificial neuron, which is the major element of an artificial neural network, and how it is based on the structure of a biological neuron.

It also explains how neural networks derive representation from large amounts of data in a layer-by-layer process that can apply to any type of input. The video recommends going to brilliant.org to learn more about the foundational building blocks of deep learning algorithms.

• 00:00:00 In this section, the video explains the basics of an artificial neuron, the primary component of an artificial neural network. The structure of an artificial neuron is similar to a biological neuron, with three primary components: the soma or the nucleus, the dendrites or the arms that connect to other neurons, and the axon or the long tail that transmits information to and from the cell body. The video shows how the basic structure of a deep learning neural network architecture was derived from Santiago Ramon y Cajal's first drawing of a neuron, representing dendrites as the inputs, the soma as the processing center and the axon as the output. Furthermore, the connections or synapses between neurons were modeled, and the strength of the connection was linked to the thickness of the line.
  
  
• 00:05:00 In this section, the video discusses how neural networks work in deriving representation from extensive amounts of data. It goes on to explain how this happens in a layer-by-layer process which can translate to any type of input, from the pixel values of an image recognition to the audio frequencies of speech for speech recognition or a patient’s medical history to predict the likelihood of cancer. The video also mentions that to learn more about the field, one should consider visiting brilliant.org, a platform for keeping one's brain sharp and creative, and understanding the foundational building blocks of deep learning algorithms.

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How To Make A Neural Network | Neural Networks Explained

How To Make A Neural Network | Neural Networks Explained

The video explains how neural networks form pattern recognition capabilities by discussing the structure and mathematics involved. It uses an image as an example and discusses the input layer, output layer nodes, and introduces the idea of hidden layers.

The video then delves into activation functions and how they convert input signals into output signals. The hyperbolic tangent function and the rectified linear unit layer are discussed, and it is revealed that the neural network built requires significant human engineering to ensure non-ambiguous values. The video recommends Brilliant.org to learn more.

• 00:00:00 In this section, the video continues where the last one left off by further discussing the structure and mathematics of neural networks to see how they form pattern recognition capabilities. To better understand this complex topic, an image will be used as an intuitive example. The input layer is defined as the pixels that comprise the image, and the output layer nodes are set arbitrarily for four different types of structures. The video then introduces the idea of hidden layers, which can have activation functions applied to remap the input value and add boundaries to the raw node value. Weights are also incorporated to show how the input to our hidden layer node gets affected by random input images.
  
  
• 00:05:00 In this section, the video discusses how activation functions work to convert input signals into an output signal that can be understood by the following layers in a neural network. The hyperbolic tangent function is used as an example, which maps all values on the X-axis to a Y value between minus one and one. By adding more nodes, the receptive fields become more complicated, and for example, in the third hidden layer, the network starts to recognize patterns like an inverted cross. Finally, the rectified linear unit layer is introduced, which rectifies negative values and keeps the positive ones the same, leading to a completed neural network ready for testing.
  
  
• 00:10:00 In this section, the neural network built in the previous section is analyzed in depth to understand how it identifies patterns in an input image. It is revealed that the network built is not perfect and requires significant human engineering to ensure non-ambiguous values. The next video in the series will cover gradient descent and backpropagation, the methods that put the learning in deep learning, and how they allow the network to build its own representation.

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How Computers Learn | Neural Networks Explained (Gradient Descent & Backpropagation)

How Computers Learn | Neural Networks Explained (Gradient Descent & Backpropagation)

This video explains how neural networks learn through changing the weights in the hidden layers to allow the network to determine them. The concept of cost function is introduced to minimize the error rate of the neural network, and backpropagation is explained as the essential process in tuning the network's parameters.

The three primary components of machine learning, including representation, evaluation, and optimization, are covered in the tribe of connectionism. The video also notes that the network does not always arrange itself perfectly in layers of abstraction. The goal of deep learning is for the network to learn and tune the weights on its own.

• 00:00:00 In this section, the focus is on how neural networks actually learn. The first step is to tweak the network by changing the weights in the hidden layers, which we previously set by hand, to allow the network itself to determine them. With 181 potential weights, it becomes an arduous task to visualize the effect of each on the output decision space. In order to simplify things, a simple number is used with 12 weights, and the output node equations are plotted out with decision boundaries, where P is greater than Q in red and less than Q in blue. Changing the weighted values in the network alters the slope of the decision boundary. It is observed that all changes resulting from weight tweaking result in linear outputs with straight lines until an activation function like the sigmoid function is applied to add non-linearity. To achieve deep learning, the goal is for the learning process and weight tuning to be done by the network itself.
  
  
• 00:05:00 In this section, the video explains the concept of cost function and how it helps in minimizing the error rate of the neural network. The video also explains the process of backpropagation that is essential in tuning the values of the neural network parameters. Gradient descent is the method for determining which direction to move in, and backpropagation is actually tuning the parameters to that value, enabling the network to produce the desired results. The goal is to get the weighted values close to the ground truth to minimize the cost function value. The process repeats while training the network until the weights are at a point where they are producing the results that we expect to see.
  
  
• 00:10:00 In this section, we learn about the three primary components of machine learning in the tribe of connectionism, which include representation, evaluation, and optimization. The representation is achieved by using a neural net function, which defines a representation space, and the evaluation is done by calculating the squared error of the nodes at the output, which is used to obtain a cost or utility function. Finally, optimization is achieved by searching the space of representation modules, and this is accomplished through gradient descent and backpropagation. Although we have made many generalizations about how artificial neural networks should work, there are still many things we did not cover. One such thing is that the network does not always arrange itself in perfect layers of abstraction.

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How Neural Networks Work | Neural Networks Explained

How Neural Networks Work | Neural Networks Explained

The video explains the bias parameter in neural networks, which jump-starts nodes to activate when a certain threshold is met, as well as the difference between parameters and hyperparameters, with hyperparameters needing fine-tuning through optimization techniques.

The learning rate is also discussed, and the challenges of finding the optimal rate while avoiding overfitting or underfitting are highlighted. Feature engineering is another subfield found in neural networks, where analysts must determine input features that accurately describe a problem. The video notes that while theoretical artificial neural networks involve perfect layers of abstraction, it is much more random in reality due to the type of network used, which is chosen through selecting the most important hyperparameters.

• 00:00:00 In this section, the video discusses some of the concepts that were not covered in past videos on deep learning. The bias parameter in neural networks is explained, which is another parameter that has to be tweaked to learn representation. The purpose of the bias parameter is to jump-start the nodes to activate strongly when a certain threshold is met. The video explains that the bias is a Y intercept to a linear equation, where the weight being the slope. The concept of parameters versus hyperparameters is also discussed, where hyperparameters are configurations that are external to the model and whose value cannot be estimated from data. The discussion highlights that hyperparameters tuning and optimization is a whole sub-field of deep learning, and different techniques are needed to find the best values for different parameters. The learning rate, which is a hyperparameter, is also explained, and the value of the learning rate has huge implications for the representation that a neural net will build.
  
  
• 00:05:00 In this section, the video explains the challenges of finding the ideal learning rate and feature engineering in neural networks. To find the optimal learning rate, it takes a lot of work to ensure that the neural network functions properly. An inappropriate learning rate can lead to overfitting or underfitting that could cause increased computational power and time consumption. Feature engineering, on the other hand, is the subfield in which an analyst must determine the input features that accurately describe the problem he or she is trying to solve. It is essential to capture the features that strengthen the signal and remove the noise, as underfitting may happen when there are few features, while overfitting is when the model is too specialized and brittle to respond to new data.
  
  
• 00:10:00 In this section, the video explains that while the theoretical concept of artificial neural networks involves perfect layers of abstraction, in reality, it is much more random. The type of network used for a particular problem, which is chosen through selecting the most important hyperparameters, is a big reason why this is. A feed-forward neural network is typically chosen for learning about deep learning because it is easy to understand. However, there are now many types of neural networks that have since come into existence that would be much better suited for various problems, including convolutional networks and recurrent networks. The video concludes by urging individuals to keep their brains sharp and think of creative solutions to multidisciplinary problems.