Machine Learning and Neural Networks - page 20

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Mega-R3. Games, Minimax, Alpha-Beta

Mega-R3. Games, Minimax, Alpha-Beta

This video covers various topics related to game theory and the minimax algorithm, including regular minimax, alpha-beta additions, alpha-beta pruning, static evaluation, progressive deepening, and node reordering. The instructor provides explanations and demonstrations of these concepts using examples and asks the audience to participate in determining the values at different nodes in a game tree. The video ends with a discussion of the potential flaws in heuristic functions and advice for the upcoming quiz.

• 00:00:00 In this section, the lecturer introduces the concept of games and mentions that the focus will be on the different components of games. They then proceed to explain the regular minimax algorithm and how to figure out the minimax value at a particular point in a game tree. Using an example game tree, the lecturer guides the audience through the algorithm, and they determine the minimax value at various nodes. The Snow White principle and the grandfather clause are also briefly mentioned.
  
  
• 00:05:00 In this section of the video, the speaker explains the alpha and beta additions to the minimax formula in game theory. He compares the addition of these numbers to the Cold War, where each side tried to find the best possible outcome while preparing for the worst. Alpha and beta represent numbers that provide a failsafe or the worst-case scenario for each side. The speaker suggests that alpha-beta search is more complicated than minimax and can be a challenge for some people. However, he also mentions that mastering alpha-beta search can help in understanding and solving minimax problems.
  
  
• 00:10:00 In this section, the lecturer explains the concept of alpha and beta as the nuclear options for the Maximizer and Minimizer, respectively. Setting alpha as negative infinity and beta as positive infinity creates a failsafe that ensures both the Maximizer and Minimizer will look at the first path they see every time. As the algorithm progresses, the value of alpha and beta change depending on the potential outcome of the game. When beta gets lower than alpha or alpha gets lower than beta, the algorithm prunes the branch, signaling that one of the players no longer wants to explore that branch. The lecturer also notes that there are different methods to draw alpha and beta numbers at different nodes of the game tree.
  
  
• 00:15:00 In this section, the speaker explains the Snow-White principle used in the alpha-beta algorithm. The principle involves inheriting the alpha and beta values from parent nodes but taking the better value for oneself when going up to a parent node. The default alpha and beta values were also discussed, with the alpha being negative infinity and beta being positive infinity. The speaker then shows an example of alpha-beta pruning and asks the audience to determine the alpha and beta values at each node in the search tree. A trick question is thrown in to emphasize that the alpha-beta algorithm can avoid searching certain nodes based on the values inherited from the parent nodes.
  
  
• 00:20:00 In this section, the speaker explains the principle of alpha-beta pruning, which involves cutting off branches of a decision tree that are not likely to lead to a better outcome. The speaker gives an example involving the options of an enemy's nuclear attack and determines which choices to prune based on the principle of alpha-beta pruning. Additionally, the speaker provides a sanity test to determine whether a branch can be pruned or not, and the Maximizer's ability to determine whether to skip a branch or not, unlike the minimizer who starts with infinity in the decision tree game.
  
  
• 00:25:00 In this section of the video, the speaker discusses the process of determining alpha and beta values in a minimax algorithm by analyzing the values at different nodes in a game tree. The speaker explains that when encountering a minimizer node, the beta value is set to positive infinity, and when encountering a maximizer node, the alpha value is set to negative infinity. The speaker then uses specific values in a game tree to demonstrate how the algorithm works and how nodes are pruned when the alpha value is greater than or equal to the beta value. Finally, the speaker discusses the order in which nodes are evaluated in a game tree using progressive deepening.
  
  
• 00:30:00 In this section, the speaker explains the concept of static evaluation, which is essentially the function responsible for assigning numeric values to leaf nodes. The static evaluator assigns these values to the bottom of the leaves, and the order of evaluation refers solely to leaves. The speaker also explains the Snow White Principle, whereby every node starts by taking the value of the same type from its grandparent (alpha or beta). The maximizer doesn't have control over which path to take; it's the minimizer who selects which path to pursue. The concept of static evaluation is crucial to the alpha-beta pruning technique since it helps in determining whether to eliminate a particular path or not. In essence, static evaluation contributes to the efficiency of the algorithm, enabling alpha-beta pruning to save more time by getting rid of a few static evaluations.
  
  
• 00:35:00 In this section, the speaker explains the concept of static evaluations, which are used to evaluate the board position in games like chess. The evaluation takes a long time and requires careful analysis of the state of the game. The leaf nodes of the search tree are called static because they are heuristic guesses of the value based on the analysis of the state of the game. The speaker also introduces the concept of progressive deepening on a tree that is only two levels deep and asks how the tree can be reordered to allow alpha-beta to prune as much as possible.
  
  
• 00:40:00 In this section, the instructor explains how to use the minimax algorithm to optimize the process of searching for the best node by reordering the branches based on the potential winner, as it is easier to reject all the wrong ones quickly when the eventual winner is chosen first. The instructor illustrates this concept by assigning a binary value to each leaf node and uses the values to calculate the ultimate winner for each sub-tree, thus finding the optimal move. Combining this approach with progressive deepening would significantly reduce the number of nodes that need to be evaluated.
  
  
• 00:45:00 In this section, the lecturer discusses progressive deepening and the possibility of reordering nodes to improve alpha-beta pruning. While progressive deepening may be a waste of time for small, non-branching trees, it is essential for larger, more complex trees. However, the concept of reordering nodes based on progressive deepening results depends on the accuracy of the heuristic function. The lecturer emphasizes that no heuristic function is perfect, and a flawed heuristic function could lead to worse outcomes when reordering nodes. Finally, the lecturer explains how caching heuristic values can be done for consistent heuristic functions, such as in cases where the same heuristic value will always be associated with a particular game state, regardless of how the state was reached.
  
  
• 00:50:00 In this section of the video, the instructor discusses the potential downside of using a heuristic that always gives the worst move instead of the best move. While it could help minimize risks, it could also result in the worst pruning possible, leading to a lack of guaranteed success. The instructor mentions that the upcoming quiz will be interesting and involve varied challenges. However, he advises students not to stress out too much about it and to enjoy their weekend.

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Mega-R4. Neural Nets

Mega-R4. Neural Nets

The video covers various aspects of neural nets, including their representations, confusion over inputs and outputs, sigmoid and performance functions, weights and biases, backpropagation, changing the sigmoid and performance functions, threshold weights, visualization, and the potential of neural nets. The instructor explains various formulas needed for the quiz and how to calculate and adjust deltas recursively. He also discusses the types of neural nets required to solve simple problems and mentions a recent real-world application of neural nets in a game-playing competition at the University of Maryland. Finally, he mentions that while neural nets have fallen out of favor due to their limitations and complexities in research, they are still useful for quizzes.

• 00:00:00 In this section, Patrick introduces a new way of drawing neural nets for the problems in 603. He shows two different representations of the same neural net and explains why the one on the right is preferable. He also discusses some problems that students commonly encounter when working with neural nets, such as confusion over inputs and outputs and the implied multiplication with weights. Patrick provides a conversion guide for students working with older quizzes and works through the formulas needed for the quiz. Finally, he mentions the possibility of the sigmoid function being changed to a different function and advises students to change it to a plus if this happens.
  
  
• 00:05:00 In this section, the instructor explains the sigmoid function, which is 1 over 1 plus e to the minus x, and its important property, where the derivative of sigmoid is itself. The performance function, which tells the neural nets how wrong their results are, is also discussed. They have chosen their preference function to be 1/2 D, which is the desired output minus the actual output squared, and the reason for this is that the derivative of performance is negative, making it easier to compute. The instructor then talks about changing the sigmoid function into some other function and analyzing what happens to the backpropagation functions, particularly the new weight calculation, which involves changing the weights incrementally towards the desired result.
  
  
• 00:10:00 In this section, the speaker explains the weights and biases used in neural nets. The weights are represented by names such as "w1I" and "w2B," where "I" and "B" are nodes in the network. Bias offsets are always attached to -1, and the value of alpha, which determines the size of hill-climbing steps, is given on quizzes. Inputs to the nodes are represented by "I," and they are multiplied by delta, which is the change in the output of the neural net due to a change in a specific weight. The deltas are calculated using partial derivatives to determine how much the weights contribute to the performance of the net.
  
  
• 00:15:00 In this section, the speaker discusses the process of using derivatives and the chain rule to obtain the final weights in the last level of neural nets. The derivative of the sigmoid function is used, and the weights in the previous layers need to be considered as well in order to calculate the deltas for the new weights. The speaker proposes a recursive solution, which involves summing over all children of a given node, which in turn affects the output. This process is carried out recursively until the deltas for the final weights are obtained.
  
  
• 00:20:00 In this section, the instructor discusses how changing the sigmoid function and performance function can impact the equations used in the neural network. He explains that if the sigmoid function is changed, the only thing that alters is the Delta F equation, which is replaced by the new derivative of the sigmoid function. Similarly, if the performance function is replaced, only the Delta F equation needs to be adjusted. The instructor then goes on to explain the difference between threshold weights and regular weights in a neural net and how they impact the overall function of the net.
  
  
• 00:25:00 In this section, the speaker talks about how to visualize neural nets and how it is important to have a representation that makes sense to you to solve the problems effectively. He explains how an adder node works and that its derivative is just one. He provides a formula for Delta F and Delta a and explains the process for Part B, which involves calculating the output for a neural net and performing one-step back propagation to find the new weights. He asks the students to ask questions to clarify their doubts as he won't be able to call on everyone to check if they are following along.
  
  
• 00:30:00 In this section, the video discusses the new weights for the neural net and what the output would be after one step of backpropagation. The new weights were calculated using the old weights, the learning rate constant, and the delta values. The output was ultimately determined to be 3. The video then raises the question of what would happen if the net was trained to learn the given data and proceeds to explain how neural nets can draw lines on graphs for each of the nodes in the net. However, it is noted that predicting what this net will draw is a bit tricky.
  
  
• 00:35:00 In this section of the transcript, the speaker discusses a neural net that boils down to just one node as it adds up every time and never takes a threshold, making it analog instead of digital. The simplified form of the neural net contains nodes represented by circles where each circle has a sigmoid. There is a problem given where ABCDEF needs to be matched with one to six using each only once. The speaker explains that each sigmoid node can draw one line into the picture, which can be diagonal if it receives both inputs or horizontal/vertical if one input is received. The secondary level nodes can do a logical boolean operation such as and/or on the first two. The speaker then proceeds to identify the easiest problem, which is problem 6 and concludes that there is a one-to-one mapping of each net to a problem, which can solve all six problems together.
  
  
• 00:40:00 In this section, the speaker discusses how challenging it is to create an X or neural net because it is hard to distinguish between the two inputs that must be high in a single node. However, there are many possibilities, and the speaker suggests using node 3 and node 4 to give values and node 5 to provide a threshold combination that results in an XOR. The speaker also explains that pairing two horizontal lines like B is impossible, but because D has to draw one horizontal line and one vertical line, they must use B to create two horizontal lines.
  
  
• 00:45:00 In this section, the speaker explains the purpose of the drawing exercise for neural nets. By drawing simple problems, people can see the types of neural nets that may be needed to solve them. This can help people avoid designing neural nets that are too simple or too complex for a given problem. The speaker also provides an example of a recent real-world application of neural nets in a game-playing competition at the University of Maryland.
  
  
• 00:50:00 In this section of the video, the speaker discusses the potential of neural nets in learning different tasks and rules. He describes an experiment where a neural net was trained to learn anything from a set of random data, and while the results of the experiment were unclear, other participants in the study attempted to find fundamental properties of the rules through experimental testing. The speaker goes on to explain that neural nets have been used in many areas of research including cognitive science and artificial intelligence, however, they have fallen out of favor due to their limitations and complexities. Despite this, the speaker mentions that they create simple nets for the purpose of quizzes, though he clarifies that any actual neural net used in research today would be too complicated for a quiz.

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Mega-R5. Support Vector Machines

Mega-R5. Support Vector Machines

The video explains Support Vector Machines (SVMs), which determine the dividing line or decision boundaries in the data by finding the support vectors that are not the same as any other data point. It also includes the use of kernel functions that enable the kernel to calculate the dot product without directly manipulating the vectors. The professor clarifies the goal of finding the Alphas that provide the best W for the widest road and how W is the decision boundary for SVM. Students inquire about the intuition behind SVM, and the optimization-based on Alphas creates the widest road for better data classification. SVM Kernel also helps optimize the process, making it more efficient.

• 00:00:00 In this section, the speaker introduces Support Vector Machines (SVMs) and states that they are one of the hardest things to learn in the course. However, he explains that there are now some shortcuts available that can help solve some problems without having to deal with vast, complex sets of equations. The problem at hand requires circling support vectors, drawing the edges of the street, illustrating the dotted line in the middle and giving both W and B. The speaker then explains the important equations in SVMs and how to find the dotted line by using two coefficients and a linear equation, where W1 and W2 are two coefficients and X1 and X2 are two components of vector X.
  
  
• 00:05:00 In this section, the video discusses the equation of a line in Cartesian coordinates and how it relates to the equation W dot X plus B equals 0 in support vector machines. The video explains that alphas are used to determine the significance of each point towards creating the boundary, and that the positive alphas are equal to the negative alphas. The video also provides equations to use when solving for W and B, and mentions that support vectors are important in determining the solution. The presenter clarifies that support vectors are vectors on the boundary lines, and the goal is to circle them.
  
  
• 00:10:00 In this section, the speaker addresses the question of what is a support vector and clarifies that in more complex problems, where there are numerous dimensions, vectors are used to represent the data points when they cannot be graphed on a two-dimensional plane. The speaker explains that support vectors are the points that bind the hyperplane and are found by attempting to have the widest possible space between the positive and negative data points. Additionally, the speaker notes that sometimes the third support vector may not exist, and they illustrate their point with an example of a pair of points on a plane.
  
  
• 00:15:00 In this section, the speaker explains how to find W and B in a support vector machine. Instead of using the old method of plugging points into an equation, the speaker introduces a cheap strategy by converting the equation into the form y = mx + b. By setting y = x - 1, the speaker shows how this can be used to find a new equation y = -w1/w2 - b/w2. Using this form, the speaker shows that there are infinitely many possible equations and that w1/w2 is some scalar multiple of -1 and B/w2 is some scalar multiple of 1.
  
  
• 00:20:00 In this section, the speaker discusses how to determine the value of K in order to calculate W1, W2, and B for a support vector machine. The magnitude of W can be calculated using the square root of the sum of the components squared, which equals root 2 over 4. Since the ratio of W1 and W2 equals negative 1, when squared, W1 squared equals W2 squared. Thus, using this formula, W1 is calculated to be negative 1/4, and since W1 is negative, W2 and B equal positive 1/4. The speaker also suggests that the alpha plus and alpha minus values are equal based on an equation.
  
  
• 00:25:00 In this section, the speaker continues to work through examples of Support Vector Machines. The speaker notes that in example number two, an extra minus sign has been added. They go on to explain how to determine the support vector given this new negative sign. The calculations for determining the distance are shown, and the magnitude of W is found to be root 2 over 3. The speaker notes that the alphas take longer to calculate in this example due to the addition of new points, but the final answer is achieved.
  
  
• 00:30:00 In this section, the focus is on using support vector machines on a one-dimensional vector, which makes a linear basis line unsuitable for the classification of the data. To solve this problem, a kernel function is used to bring the data into a new dimension. The function is typically called Phi, and when applied to vector X, it brings it into this new dimension. In this new dimension, a straight line can be drawn to classify the data. The inventor of SVMs realized that there is no need to work with the function Phi, even if it is an awful monster, since the kernel can be used to calculate the dot product between two vectors in the new dimension without explicitly calculating Phi.
  
  
• 00:35:00 In this section, the speaker explains how to use a kernel function to find the dot product of two vectors in a regular space, which eliminates the need to directly use the vectors themselves. By putting the vectors X and Z into the kernel, the resulting function will return Phi of X dotted with Phi of Z, which replaces the dot product of the two vectors. The speaker gives an example of a kernel function and challenges the audience to find the corresponding Phi function in order to solve the quiz. The speaker also notes that although calculating alphas for SVMs can be complicated, using the kernel function is a helpful shortcut in eliminating the need for direct vector manipulation.
  
  
• 00:40:00 In this section, the speaker discusses the graphing of points in a new dimension using their cosine and sine values. The pluses and minuses are shown as well as their respective cosine and sine values. There are three points in the second quadrant and three points in the third quadrant. The speaker then discusses the distinction between two negatives and how to locate the support vectors, which are found to be the negative and positive points on the perpendicular bisector. The two negative points are on the same line and are circled instead of being on opposite sides of the bisector.
  
  
• 00:45:00 In this section, the professor explains the idea behind support vectors and their usage in SVM. He clarifies that a support vector isn't the same as any other data point, and the dividing line or boundaries created by SVM are determined by these vectors. In test data, the dotted line is the decision boundary for SVM. The algorithm optimizes Alphas by mathematically checking the combination of the Alphas that give the best W for the widest road. The students query the intuition behind SVM, and the professor explains that W is the decision boundary, and the optimization-based on Alphas creates the widest road to classify data in a better way. The SVM Kerne also helps optimize the optimization process, making it easier and efficient.

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Mega-R6. Boosting

Mega-R6. Boosting

In the video "Mega-R6. Boosting", the speaker explains the concept of boosting in machine learning and demonstrates the process of selecting the correct classifiers to minimize errors. They give an example of identifying vampires based on certain qualities and discuss how to choose the most effective classifiers. The selected classifiers are used to create a final classifier that is applied to the data points to determine how many are classified correctly. The speaker also emphasizes that choosing when to stop the process is important and acknowledges that achieving complete accuracy may not always be feasible.

• 00:00:00 In this section, the speaker discusses the concept of boosting in machine learning, which involves a series of different classifiers. The problem used as an example involves identifying vampires based on various qualities such as evil, emo, sparkle, and number of romantic interests. The key to boosting is that for any possible classifier, as long as it's not a 50/50 split of the data, it can be used in some way to create a superior classifier. Furthermore, the speaker notes that there are actually more classifiers than the ones listed, as many of them have opposite versions which are ignored for this particular problem.
  
  
• 00:05:00 In this section, the speaker explains how a 50/50 split for boosting is useless since it is as good as flipping a coin. However, in some cases, a classifier that is worse than 50/50 is still better than a 50/50 classifier. Later rounds in boosting require changing the weights of each data point, and the classifier that performs the best will be the one that gets the most weight right. Although classifiers that get less than half of the weight right are usually okay, the speaker recommends using their inverses to get more than half of the weight right.
  
  
• 00:10:00 In this section, the speaker goes through each classifier and figures out which data points are misclassified. With the assumption that all the evil things are vampires and all the non-evil things are not vampires, they determine that they get angels, Edward Cullen, Saya Otonashi, and Lestat de Lioncourt wrong when the evil equals no. Similar logic is applied to emo characters and transforming characters. However, when sparkly equals yes, they get one, two, four, five, six, seven, and eight wrong, and when the number of romantic interests is greater than two, they get Searcy and Edward Cullen wrong. When it comes to the number of romantic interests being greater than four, no characters fall into that category, so none are misclassified.
  
  
• 00:15:00 In this section of the video, the speaker discusses the classification of vampires and which classifiers are likely to be incorrect. The speaker notes that there are certain positive classifiers that will inevitably lead to incorrect negative classifiers. The speaker then lists several classifiers and claims that in their wildest dreams, individuals would only ever use six of them. The speaker asks for input from viewers on which classifiers they think are useful and circles the ones that are considered worth using. The classifiers that are considered useful are ones that only get a few wrong, such as classifiers E and F.
  
  
• 00:20:00 In this section, the speaker explains the process of selecting the correct six data points for boosting in Mega-R6. One key point is that while there are many different data points to choose from, some of them are strictly better than others. For instance, the data point F is always worse than E, so it should never be chosen. The speaker also notes that when selecting the six data points, it is important to choose ones that do not have a strict subset of the same incorrect answers. The process of selecting the six data points requires careful consideration of the weight of each data point in order to minimize error.
  
  
• 00:25:00 In this section of the video, the presenter discusses the process of boosting and how to select the best classifiers for the task. He explains how to cross off any useless classifiers and how to choose ones that minimize the error. The presenter then moves on to demonstrate the boosting process, starting with weighting all ten data points equally and selecting classifier E as the best one. The error is then calculated at one-fifth, and the process continues from there.
  
  
• 00:30:00 In this section of the video, the presenter explains how to make all the decisions made by a classifier right. This process involves changing the weights of each decision to be 1/2 for those that were correct and 1/2 for those that were incorrect. The presenter outlines a method for automating this process, which involves rewriting the weights in a way that makes it easier to add them up and choose the best decision. In this example, the decision with the smallest amount of error is chosen.
  
  
• 00:35:00 In this section, the speaker discusses the process of determining the best classifier in the Mega-R6 boosting game. The transcript includes calculations involving the sum of the numbers in and outside of the circles and the process of changing the numbers in the circle to make it easier to determine the best classifier. The speaker states that it is important to ignore previous rounds and only consider the current weights when determining a classifier. The speaker also explains that classifiers cannot be used twice in a row and discusses the reason for this design feature. The best classifier is determined to be A because it had the least number of wrong answers.
  
  
• 00:40:00 In this section of the transcript, the speaker discusses how to calculate the final classifier using the boosting method. The final classifier is a combination of the weighted classifiers that were used to create it. The speaker then applies the final classifier to ten data points to determine how many are classified correctly, using a simple vote to determine the output. One data point, Edward Cullen from Twilight, is incorrect because two out of three classifiers did not classify him as a vampire.
  
  
• 00:45:00 In this section of the video, the speaker discusses various characters as being either evil, emo, or a vampire based on their characteristics and love interests, and the accuracy of a boosting algorithm in classifying them. The discussion leads up to a question about using multiple classifiers to make the classifying process quicker, which the speaker explains is correct to some extent, but requires going through a larger number of classifiers. The speaker also emphasizes that the converging process to get everything correct is not always easy and might require choosing to stop after a certain number of rounds.

[MetaQuotes]

Mega-R7. Near Misses, Arch Learning

Mega-R7. Near Misses, Arch Learning

In the video, the concept of near-miss learning is introduced, involving learning about different types of light sources and their characteristics. The Arch Learning approach uses six heuristics to refine a model, including require link, forbid link, climb-tree, extend set, closed interval, and drop link. The video discusses various techniques used in machine learning, such as extend set, climb tree, closed interval, and drop link. The speakers also talk about issues related to the Arch Learning model's fragility and vulnerability to ordering, leading to inconsistent reactions to contradictory information. The video also discusses the concept of generalization for the Mega-R7 and how it differs from previous models. Additionally, the trade-offs between Irish learning and lattice learning in terms of their ability to express subsets of information are discussed, as well as teaching the system using multiple models with different implementation details.

• 00:00:00 In this section, the concept of near-miss Learning Tree is introduced, which involves learning about different types of light sources and their characteristics. The starting model is an incandescent bulb with a flat base and a shade, powered by electricity. The arch learning approach involves using six heuristics, including require link, forbid link, climb-tree, extend set, closed interval, and drop link. Require link makes a previously irrelevant feature a requirement, and forbid link forbids a feature. These heuristics help refine the model by making certain features necessary or unnecessary, and can help identify near-miss scenarios.
  
  
• 00:05:00 In this section, the speaker discusses various techniques used in machine learning, including extend set, climb tree, closed interval, and drop link. The extend set technique involves creating a set of positive examples but forbidding certain elements to save space. The climb tree technique moves up the tree to create a more generalized model, while the closed interval covers the entire interval to make it acceptable. The drop link technique allows the system to be parsimonious by dropping a link if all elements are acceptable. The speaker then goes over the usage of each technique and highlights the importance of knowledge in machine learning to make the model more accepting of new examples and to speed up the quiz time.
  
  
• 00:10:00 In this section, the video discusses the idea of a generalizer and how it can be extended to positive examples or made larger for close intervals. However, if there is a negative example, it can complicate the system, and the implementation may have to adjust. The video then provides an example of a lamp and how the model can be adapted using the generalizer heuristic to generalize the interval when there is a positive example. If there is a negative example, the implementation may have to use the drop-link approach to make the system work effectively.
  
  
• 00:15:00 In this section of the video, the speakers discuss a few issues related to the Arch Learning model, which is a type of machine learning model that was developed in the 1960s. They describe how the system is fragile and particularly vulnerable to ordering, meaning that the order in which data is presented can greatly impact the system's ability to learn. Furthermore, they explain how the system can be inconsistent and react poorly to contradictory information. The speakers also explain an alternative type of learning called lattice learning, which stores all the examples it has seen and compares and contrasts them to new examples, allowing it to identify patterns and refine its understanding of a topic.
  
  
• 00:20:00 In this section, the video discusses the concept of arch learning, a system that intentionally does not remember things to seek elegance and simplicity. This section compares the idea to a baby who cannot tell you about a block they played with previously as they do not store and remember everything they have experienced. However, humans are good teachers and offer appropriate examples that a machine can learn from. The video also talks about how to generalize for the hit by climbing the tree instead of setting the extent to be more parsimonious, elegant, and simple. Finally, a fluorescent lamp example is discussed, and the heuristic used for generalization is to climb the tree from a flat base to a base support itself.
  
  
• 00:25:00 In this section, the speaker discusses a new model for the Mega-R7 and how it differs from the previous ones. They go over some examples of near-misses, which are instances where the system encounters inputs that are similar but not quite the same as what it has seen before. The speaker explains that these near-misses do not require any changes to the model and that it is acceptable to leave them as is. Furthermore, the speaker addresses a question about whether a negative example, such as fluorescent, would be considered a near-miss, to which they answer that it would not because the system is memoryless and does not know that fluorescent used to be a positive example.
  
  
• 00:30:00 In this section, the speaker discusses the trade-offs in Irish learning and lattice learning in terms of their ability to express subsets of information. Irish learning, while being memoryless, cannot express a subset as acceptable without seeing a positive example of it, which can lead to losing some expressiveness. However, this issue is fixed in lattice learning, but it has its own set of problems. The speaker also highlights how to teach the system, such as presenting multiple models that fulfill the requirement of having base support while using different light bulbs and electricity sources. The implementation details need to be asked and clarified, as choosing one over the other could lead to different outcomes.

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AlphaGo - The Movie | Full award-winning documentary

AlphaGo - The Movie | Full award-winning documentary

A documentary about the development of the AlphaGo computer program, which is designed to beat human players at the game of Go. The film follows the program's victory over a world champion human player in a five-game match. Some viewers feel that AlphaGo's victory may herald the end of the human race as we know it, as machines become increasingly better at performing cognitive tasks.

• 00:00:00 This video is about AlphaGo, a computer program that beat a world champion human player at the game of Go. The video describes the significance of AlphaGo's victory, and shows footage of the computer playing against a human player. The company behind AlphaGo, DeepMind, wants to invite the world's strongest Go player, Demyster Harbis, to visit their offices in London to see the project in action. If you're interested in attending, they would be very grateful!
  
  
• 00:05:00 AlphaGo, a computer program developed by DeepMind, defeats professional Go player Lee Sedol in a five-game match. The documentary follows the team's efforts to develop and train the program, and the match itself.
  
  
• 00:10:00 AlphaGo, a computer program developed by Google, defeats European champion Go player Lee Sedol in a five-game match. The documentary follows AlphaGo's development and the preparations for the match. Despite initial skepticism, the public is largely impressed by AlphaGo's performance, with some even heralding it as a sign of the end of human dominance in the field of artificial intelligence.
  
  
• 00:15:00 AlphaGo, a computer program designed to beat human champions at the game of Go, was publicly defeated by a human player, Lee Sedol, in a match held last week. The video discusses the significance of the loss, as well as the ongoing efforts of the AlphaGo team to improve their system.
  
  
• 00:20:00 AlphaGo, a computer program that is said to be "the best Go player in the world," is pitted against a professional human player in a five-game match. Fanway is an advisor to the team and helps to improve their strategy.
  
  
• 00:25:00 AlphaGo is set to face off against professional South Korean go player, Lee Sedol, tomorrow in a historic match. The documentary follows the team as they prepare for the game and discusses their expectations.
  
  
• 00:30:00 AlphaGo, a computer program that defeated a human champion in a board game, is the subject of a full award-winning documentary. The documentary follows the development of the program and its successful matchup against a human opponent.
  
  
• 00:35:00 AlphaGo, a computer program developed by Google, defeats a world champion human player in a five-game match. The program's success is a surprise to many, as was its ability to learn from its experience.
  
  
• 00:40:00 AlphaGo, a computer program developed by DeepMind, trounced a professional Go player in a five-game match. The computer program was developed by humans and is considered a breakthrough in artificial intelligence, research.
  
  
• 00:45:00 AlphaGo, a computer program designed to defeat a human professional player in a game of Go, stunned observers with its performance in game two of the Google DeepMind Challenge. The AI's policy network, value net, and tree search were all highly effective in predicting the best move for the game situation at hand, leading to a victory for AlphaGo.
  
  
• 00:50:00 AlphaGo, a computer program developed by Google, won a championship match against a world-renowned human player. The documentary examines the match and the significance of AlphaGo's victory.
  
  
• 00:55:00 AlphaGo won two out of three games against a world champion human player, but the sadness and sense of loss among the audience is palpable. AlphaGo is just a computer program, but commentators refer to it as if it is a conscious being, and worry about the implications of its increasing power.

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Deepmind AlphaZero - Mastering Games Without Human Knowledge

Deepmind AlphaZero - Mastering Games Without Human Knowledge

The video explores the development of DeepMind's deep reinforcement learning architecture, AlphaZero, which utilizes a unified policy and value network to succeed in games with enormous state spaces without any prior human data. AlphaZero's algorithm involves training a neural network to predict the action chosen by an entire Monte Carlo tree search, iteratively distilling knowledge to generate stronger players over time. The algorithm showed impressive learning curves, outperforming previous versions in just a few hours of training and displaying remarkable scalability despite evaluating fewer positions than previous search engines. The video also discusses AlphaZero's ability to combine the best of human and machine approaches while showing potential for general-purpose reinforcement learning.

• 00:00:00 In this section of the video, David discusses AlphaGo, the original version of DeepMind's deep reinforcement learning architecture that was able to defeat a human professional player and world champion. AlphaGo utilizes two convolutional neural networks: a policy network, which recommends moves to play based on a distribution of probabilities, and a value network, which predicts the winner of the game. The networks are trained through supervised learning and reinforcement learning on a human dataset and games played against itself. The success of AlphaGo in the game of Go demonstrates the potential for machine learning and artificial intelligence-based approaches to succeed in games with enormous state spaces.
  
  
• 00:05:00 In this section, the speaker discusses the training pipeline of AlphaGo and how it uses the policy network and value network to make search more tractable given the vastness of the search basin in the game of Go. The policy network suggests moves to reduce the breadth of the search tree, while the value network predicts the winner of the game from any position to reduce the depth of the search. This allows the algorithm to efficiently search through important parts of the tree using Monte Carlo tree search, which effectively expands a large search tree selectively by considering only the most relevant parts. This led to the development of AlphaGo Master, which was trained with deeper networks and more iterations of reinforcement learning, winning 60 games to zero against the top-ranked human players in the world.
  
  
• 00:10:00 In this section, the speaker describes the development of AlphaGo Zero, which learns how to play the game of Go without any prior human data, instead starting from completely random games and using only the rules of the game. AlphaGo Zero differs from the original AlphaGo in that it uses no handcrafted features, unifies the policy network and value network, uses simpler search without randomized Monte Carlo rollouts, and has a simpler approach to reduce complexity, which leads to greater generality, potentially applicable in any domain. The algorithm for AlphaGo Zero involves executing a Monte Carlo tree search using the current neural network for each position and playing the move suggested, then training a new neural network from those positions reached in the completed game.
  
  
• 00:15:00 In this section, the speaker explains the process of the AlphaGo Zero algorithm, which involves training a neural network to directly predict the action that was chosen by an entire Monte Carlo Tree Search (MCTS) to distill all of the knowledge into its direct behavior, and training a new value network to predict the winner of the game. The procedure is iterated to generate a stronger player each time and generate higher quality data, leading to stronger and stronger play. AlphaGo Zero uses search-based policy improvement by incorporating its search into the policy evaluation, which enables high-quality outcomes and precise training signals for neural networks. The learning curve shows that AlphaGo Zero surpassed previous versions in just 72 hours and defeated human players by 60 after 21 days.
  
  
• 00:20:00 In this section, the speaker discusses the various versions of AlphaGo that were developed, starting from the original version that defeated the European champion by five games to nil to the AlphaGo Zero, which was trained completely from random weights and was around 5,000 Elo, making it the strongest version of AlphaGo. The new version, AlphaZero, applies the same algorithm to three different games: chess, shogi, and go. The game of chess, in particular, has been a highly studied domain in AI, with computer chess being the most studied domain in artificial intelligence history, culminating in highly specialized systems that are currently indisputably better than humans.
  
  
• 00:25:00 In this section, the speaker discusses the complexity of the game of shogi, which is harder to compute and has a larger and more interesting action space than chess. He explains that the strongest computer programs for shogi have only recently achieved human world champion level, making it an interesting case study for DeepMind to pursue. The state-of-the-art engines for both chess and shogi are based on alpha-beta search, which has been augmented by a handcrafted evaluation function that's been tuned by human grandmasters over many years, as well as a huge number of highly optimized search extensions. The speaker then compares the components of the top chess program Stockfish to AlphaZero, which literally has none of the same components, replacing them with principled ideas based on self-play, reinforcement learning, and Monte Carlo search. The speaker notes that chess is unlike Go in that it has perfect translational invariance, lacks symmetry, has a more interesting action space with compound actions, and contains draws.
  
  
• 00:30:00 In this section, the speaker discusses the learning curves for the three games: chess, shogi, and go. AlphaZero outperformed the world champion Stockfish in the game of chess within just four hours of training from scratch, using its same network architecture and settings for all games. AlphaZero defeated previous versions of AlphaGo Zero and current world champion Shogi with ease after only a few hundred thousand steps, or 8 hours of training. The scalability of AlphaZero's Monte Carlo tree search was compared to the alpha-beta search engines used in previous programs, including Stockfish, which evaluates around 70 million positions per second, whereas AlphaZero evaluates only around 80 thousand positions per second. The speaker theorizes that the reason MCTS is so effective, despite evaluating orders of magnitude fewer positions, when combined with deep function approximators like neural networks is that it helps to cancel out approximation errors that are present in the search, thereby resulting in better performance and scalability. Finally, AlphaZero also discovered human chess knowledge for itself by picking out the 12 most common human openings in the game of chess.
  
  
• 00:35:00 In this section, the speaker discusses AlphaZero's use of specific chess openings and how it played them during self-play. AlphaZero spent a significant amount of time playing these variations but eventually began to prefer different openings, dismissing some that were played more often. The speaker also mentions the progress being made in using AlphaZero's methods for general-purpose deep reinforcement learning, which can transfer to other domains. The more specialized an algorithm, the less it can adapt to other domains. While the use of human and machine together is an interesting prospect, the speaker emphasizes that AlphaZero plays in a more human way than previous chess programs, indicating its ability to combine the best of both worlds.
  
  
• 00:40:00 In this section, the speaker explains that although they only embedded the rules of the game as human knowledge into AlphaGo Zero, this includes basic encoding and decoding of actions. For example, in chess, they used spatial representation to encode the piece being picked up and the plane that was being used to put it down. They do exclude illegal moves from the action space. The speaker further explains that they did not include error bars in their experiments because they only conducted one run per game. However, they have run multiple experiments and the results are very reproducible.

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AlphaGo - How AI mastered the hardest boardgame in history

AlphaGo - How AI mastered the hardest boardgame in history

The video explores the technical details of AlphaGo Zero, an AI system that was trained entirely through self-play and without using human datasets. The system used a residual network architecture and a two-research approach to predict value and strong moves. The video highlights the improvements made, including the ability to predict game outcomes and the system's discovery and movement away from well-known moves in Go. However, the system's real-world application is limited by the need for a perfect simulator, making it difficult to apply the approach to other fields.

• 00:00:00 In this section, the technical details of AlphaGo Zero's improvements over previous versions are discussed. The first major change was that AlphaGo Zero trains entirely from self-play and does not use datasets from human professional Go players. It also uses none of the previously handcrafted features for the game and instead learns entirely by observing the board state. The network architecture was changed to a completely residual architecture, and instead of having a separate policy and evaluation network, they are now combined into a single large network that does both. The Montecarlo rollouts were replaced with a simpler two-research approach that uses the single network to do value prediction and come up with strong moves. Overall, this resulted in a board representation of 19 by 19 by 16 binary numbers, a residual network, and a value representation and policy vector that are generated from the feature vector.
  
  
• 00:05:00 In this section, the video explains how AlphaGo was trained to play good moves by using a network architecture that leads to high probabilities of good moves and low probabilities of bad ones. The first version of AlphaGo was trained using supervised learning on a dataset of professional Go moves followed by a fine-tune stage using self-play. However, the new version, AlphaGo Zero, does not use any data set and learns entirely through self-play using a Monte Carlo tree search the stabilizes the self-weight training process. By exploding the search tree and using Monte Carlo tree search, the system can estimate which moves are strong and which are not. Finally, the video highlights that the process is specific to games like Go, where you have a perfect simulator, making the real-world applications of this approach challenging.
  
  
• 00:10:00 In this section, the speaker discusses various graphs depicting the improvements made in AlphaGo's network architecture. One graph shows the AlphaGo Zero network's ability to predict the outcome of a game based on the current board position, with a significant improvement over previous versions. The speaker also notes that transitioning from a normal convolutional architecture to a residual network resulted in a major improvement. Additionally, a graph shows how AlphaGo Zero discovered and then moved on from well-known moves in the game of Go. Overall, the speaker is impressed with the results of the Google DeepMind team and encourages viewers to ask questions in the comments section.

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AlphaZero from Scratch – Machine Learning Tutorial

AlphaZero from Scratch – Machine Learning Tutorial

00:00:00 - 01:00:00 The "AlphaZero from Scratch – Machine Learning Tutorial" video teaches users how to build and train the AlphaZero algorithm using Python and PyTorch to play complex board games at superhuman levels, with examples given for Tic-tac-toe and Connect 4. One of the key components of the algorithm is the Monte Carlo tree search, which involves selecting the most promising action, expanding the tree, and simulating the game, with the results backpropagated for training. The tutorial demonstrates the expansion of nodes during the Monte Carlo research algorithm, the process of self-play, and how to train the model using loss functions that minimize the difference between the policy and the MCTS distribution, and value and final reward. The video finishes by creating a Tic-tac-toe game and testing it through a while loop.

01:00:00 - 02:00:00 In this section of the tutorial on building AlphaZero from scratch, the instructor demonstrates the implementation of the Monte Carlo Tree Search (MCTS) algorithm for the game Tic-tac-toe. The algorithm is implemented through a new class for MCTS which includes a search method defining a loop of repeated iterations for selection, expansion, simulation, and backpropagation phases. The video also covers the implementation of the architecture of the AlphaZero neural network, which includes two heads, one for policy and one for value, and uses a residual network with skip connections. The policy head uses a softmax function to indicate the most promising action, while the value head gives an estimation of how good the current state is. The speaker also discusses the implementation of the start block and backbone for the ResNet class and explains how to use the AlphaZero model to get a policy and a value for a given state in Tic-Tac-Toe.

02:00:00 - 03:00:00 The "AlphaZero from Scratch" tutorial demonstrates building the AlphaZero algorithm through machine learning. The presenter covers a wide range of topics from updating the MCTS algorithm, self-play and training methods, to improvements such as adding temperature to the probability distribution, weight decay and GPU support in the model, and adding noise to the root node. The tutorial takes the viewer step-by-step through the implementation of these features by showing how to encode the node state, obtain policy and value outputs, and tweak the policy using softmax, valid moves, and Dirichlet random noise to add exploration while ensuring promising actions are not missed.

03:00:00 - 04:05:00 In this YouTube tutorial on creating AlphaZero from scratch using machine learning, the instructor covers various topics such as adding exploration to the policy with a noise factor, incorporating CPU and GPU support for training models on more complex games, updating the source code to create a Connect Four game, increasing the efficiency of the AlphaZero implementation through parallelization, creating two new classes in Python for self-play games, encoding states to increase efficiency, implementing the Monte Carlo Tree Search algorithm for AlphaZero, and training a model for Connect Four using parallelized fs0. The tutorial provides step-by-step guidance on each topic with a focus on creating an efficient and effective AlphaZero implementation. The presenter demonstrates how to create a Connect Four environment using the Kegel environments package, then runs and visualizes the game with two agents who use the MCTS search algorithm based on a trained AlphaZero model. The presenter also makes minor corrections in the code and defines player one as the agent using the MCTS algorithm for predictions based on the trained model. The tutorial ends with the presenter providing a GitHub repository with jupyter notebooks for each checkpoint and a weights folder with the last model for Tic-tac-toe and Connect Four, expressing interest in making a follow-up video on Mu Zero if there is any interest in it.

Part 1

• 00:00:00 In this section, the tutorial introduces the concept of AlphaZero, an AI algorithm that uses machine learning techniques to learn to play complex board games at superhuman levels. The algorithm was initially developed by DeepMind and can achieve impressive results in games like Go and even invent novel algorithms in mathematics. The tutorial will teach users how to build AlphaZero from scratch using Python and PyTorch and train and evaluate it on games like Tic-tac-toe and Connect 4. The algorithm has two components, self-play and training, and uses a neural network to produce a policy and a value based on the input state. By repeating this cycle, the algorithm can optimize itself to play the game better than humans.
  
  
• 00:05:00 In this section, the video explains the Monte Carlo tree search, a search algorithm used for self-play and the general algorithm. It takes in a state, in this case, a block position, and finds the most promising action by building a tree into the future. Each node stores a state and a total count of wins achieved when playing in that direction into the future, as well as the total visit count. The winning ratio of each node's children is used to determine the most promising action, and this information can be used in an actual game like tic-tac-toe. The data for the nodes is generated by walking down the tree in the selection phase until reaching a leaf node that can be expanded further.
  
  
• 00:10:00 In this section, the speaker discusses the different phases involved in Monte Carlo Tree Search (MCTS) for game playing. The selection phase involves choosing a child node that has a higher UCB formula and has been visited relatively few times. The expansion phase involves creating a new node and adding it to the tree, while the simulation phase involves playing the game randomly until a terminal node is reached. In the backpropagation phase, the results obtained from the simulation phase are propagated back up the tree, updating the winning and visit count for each node.
  
  
• 00:15:00 In this section of the video, the instructor goes through an example of the Monte Carlo Tree Search (MCTS) process, starting with the selection phase where the algorithm walks down the tree to choose the next node. They then proceed to the expansion phase where a new node is created, followed by the simulation phase where random actions are taken to reach a terminal node. The algorithm then checks if the game has been won, lost, or if a rule has been violated. Since the game was won, back propagation is carried out, updating the win and visit count for the nodes traversed during the MCTS process. The process is reiterated with a new selection phase, and expansion phase, with a new node created and the simulation phase carried out once again.
  
  
• 00:20:00 In this section, the tutorial goes through the process of simulating and back propagating in the MCTS algorithm using AlphaZero. The example presented in this tutorial shows a loss during the simulation stage. When back propagating, only the visit count is increased, and the total number of wins remains the same since the AI lost the game during simulation. The tutorial then moves on to explain the selection and expansion processes of MCTS using AlphaZero. The tutorial demonstrates how to calculate the UCB score for each child, and how to select the child with the highest score. The process then repeats with the AI calculating the UCB formula for each node until reaching a leaf node, where expansion takes place.
  
  
• 00:25:00 In this section of the tutorial, the focus is on how the Monte Carlo research technique changes when it is adapted to the General AlphaZero algorithm. There are two critical changes that are made to the algorithm. Firstly, the policy that was obtained from the model is incorporated into the selection phase by updating the UCB formula with the policy information. Secondly, the simulation phase is eliminated, and the value obtained from the neural network is used for back propagation, along with the policy for the selection phase. With these changes, the Monte Carlo research technique can significantly improve when there is a model that understands how to play the game.
  
  
• 00:30:00 In this section of the video "AlphaZero from Scratch – Machine Learning Tutorial", the presenter makes a minor change to expand the oil possible directions during the expansion phase, so all possible nodes can be created rather than only one new node. They then walk through iterations on a whiteboard to show how the multicolored research is adapted. During the expansion phase, new nodes are created by calling the neural network to get policy and value, and adding the number of wins, visit count, and policy information to the nodes. Then, in the backpropagation step, the value is back propagated. The presenter mentions the UCB formula and notes that the winning probability cannot be calculated for nodes with a visit count of zero, which needs to be addressed to avoid a division by zero error.
  
  
• 00:35:00 In this section of the tutorial, the speaker explains the process of expanding nodes during the Monte Carlo research algorithm. The algorithm is used to determine the best move for a given state in a game. The speaker walks through an example of how nodes are expanded and how the policy and value are calculated for each child node. The process of backpropagating the value of the new child node to the root node is also explained. The tutorial then proceeds to explain the process of self-play, where a game is played by an algorithm against itself, beginning with a blank state, and using Monte Carlo research to determine the best move based on the visit count distribution of the children of the root node.
  
  
• 00:40:00 In this section, we see how to train the model using Monte Carlo Tree Search (MCTS). The goal is to store all the information gained while playing, including the MCTS distribution and the reward for each state. The reward depends on the final outcome of the game for the player in that state. Once we have collected the data, we use it to train the model using a loss function that minimizes the difference between the policy and the MCTS distribution and the value V and the final reward Z. We do this using backpropagation to update the weights of the model Theta. Overall, this process helps the model better understand how to play the game and become optimized.
  
  
• 00:45:00 In this section, the video tutorial on building AlphaZero from scratch using machine learning begins by creating a Jupyter Notebook and building a simple game of tic-tac-toe with a row and column count, as well as an action size. The tutorial then writes methods for getting the initial state, the next state after an action has been taken, and legal moves. The action input is encoded into a row and column format to be used in a NumPy array. The code is written to be flexible to solve different environments or board games, with plans to expand to Connect Four.
  
  
• 00:50:00 In this section, the YouTuber is writing a method that will check if a player has won or not after their move. They start by getting the row and column of the move and then determining the player who made that move. Then, they check for all the possible ways to win the game of tic-tac-toe, which are three in a row, three in a column, and two diagonals, by using the np.sum and np.diac methods. In addition, they check for a draw by calculating the sum of valid moves and checking if it's zero. Lastly, they create a new method called get value and terminate it that returns the value and true if the game has ended.
  
  
• 00:55:00 In this section, the author writes a Tic-tac-toe game and an opponent method to change the player. They test the game by executing it and use a while loop. In the loop, the state and valid moves are printed, and the user is prompted to input their move. The game checks if the action is valid and whether the game has been terminated. If the game continues, the player is flipped. If the value equates to one, the player wins, and if there has been a draw, it will be printed.

Part 2

• 01:00:00 In this section of the tutorial on building AlphaZero from scratch using machine learning, the instructor begins by picking tic-tac-toe as the game to be used for demonstration purposes. A new class for multi-colored research (MCTS) is created, which initializes with the game and hyperparameters as arguments. Within this class, a search method is defined with a loop for repeated iterations of selection, expansion, simulation, and backpropagation phases, ultimately returning the visit count distribution of the root node's children. Then, a class for a node is defined with attributes such as game state, parent, action taken, children, and visit count. The root node is also defined with the game, hyperparameters, initial state, and None for parent and action taken placeholders.
  
  
• 01:05:00 In this section, the video walks through the process of selecting nodes in the tree during gameplay in the AlphaZero algorithm. The method for determining if a node is fully expanded is defined, using information on the number of expandable moves and if the node is not terminated. During the selection phase, the algorithm selects downwards while the node is fully expanded. For selection, the algorithm loops over all children of the node, calculates the UCB score for each child, and then chooses the child with the highest UCB score. The UCB score is calculated using the Q value, a constant C for exploration or exploitation, and a logarithmic function. The Q value is defined as the visit sum of the child divided by its visit count, with adjustments made to ensure the Q value falls between a range of 0 and 1.
  
  
• 01:10:00 In this section, the video tutorial covers the selection process of AlphaZero. The code selects a child that has a very negative or low value, as choosing that will put the opponent in a bad situation. The Q value of the child is then inverted to give the parent a Q value that is almost 1. This way, the tree is worked on in such a way that the child is in a bad position. The video goes over the steps to implement these changes in the code and explains the importance of checking whether the node finally selected is a terminal one or not. Additionally, a new method is added to account for the game's perspective in the get value attribute.
  
  
• 01:15:00 In this section of the tutorial on building AlphaZero from scratch using machine learning, the instructor explains how to check if a node is terminal, back propagate, and perform expansion and simulation. By sampling one expandable move out of the defined ones, a new state for a child is created and a new node is appended to the list of children for later reference inside of the select method. The instructor also discusses the idea of flipping state to change players rather than explicitly defining players, making the logic simpler for one player games and ensuring the code is valid.
  
  
• 01:20:00 In this section, the speaker is creating a child node for the Tic-Tac-Toe game and explaining the change perspective method. They set the player as -1 for the opponent and use multiplication to flip the perspective. After creating the child node, they append it to the children list and return it. Then, the speaker moves on to discussing the simulation process, where they use rollouts to perform random actions until a terminal node is reached and the value is obtained. They use the obtained value to backpropagate the nodes where the player of the node won to be more promising.
  
  
• 01:25:00 In this section of the video, the presenter continues building the Monte Carlo Tree Search (MCTS) algorithm for the AlphaZero game-playing program. They show how to use the current raw state to choose an action, get the next state and check if that state is terminal. The presenter writes an if statement to differentiate between player one or player two receiving a positive value when the game is won by them, and then proceeds to write the back propagation method to update the value and visit count for each node. Finally, the presenter creates a variable that calculates the probability of the most promising actions, action_props.
  
  
• 01:30:00 In this section, the video tutorial shows how to implement a standalone Monte Carlo Tree Search (MCTS) algorithm for the game Tic-tac-toe. The tutorial demonstrates how to loop over all the children and write action props for each child. The visit count of each child is used to turn them into probabilities. The sum of the probabilities is then divided by 1 to convert them into probabilities. The MCTS object is then created, and the square root of 2 is used for the C value in the UCB formula. The script is tested against the game, and the algorithm is tested with the neutral state. The MCTS tree is then used, along with the best child function, to return the child that has been visited the most times.
  
  
• 01:35:00 In this section, the architecture of the neural network for the AlphaZero algorithm is discussed. The state given as input to the neural network is a board position encoded into three different planes for player positive one, player negative one, and empty fields. This encoding allows for recognizing patterns and understanding how to play the game. The neural network architecture used is a residual network with skip connections to store the initial X value and give the output as the sum of the output from the convolutional blocks and the initial X value. The model is split into two parts, the policy head, and the value head, and for the case of tic-tac-toe, there are nine neurons in the policy head, one for each potential action.
  
  
• 01:40:00 In this section, the speaker explains the architecture of the AlphaZero neural network from scratch. The network has two "heads," one for policy and one for value. The policy head has nine neurons, and the output is applied with a soft-max function to turn it into a distribution of probabilities indicating how promising a certain action is. The value head has only one neuron and uses the 10h activation function to squish all potential values into the range of negative one to positive one, giving an estimation of how good the current state is. The code is built inside a Jupyter notebook using the PyTorch deep learning framework. The model includes a start block and a backbone of convolutional residual blocks, and each block contains a convolutional layer followed by two layers of batch normalization and a ReLU activation function.
  
  
• 01:45:00 In this section, the speaker discusses the creation of the start block for the AlphaZero model, which involves creating a conf 2D block, a batch norm block, and a value block. They also set up a backbone for the model using an array of rest blocks and create a class for rest blocks. The rest blocks consist of a conf block, a batch norm block, and another conf block, which are used to update the input using skipped connections. The forward method is defined to feed the input through the conf blocks and add the resulting output to the residual.
  
  
• 01:50:00 In this section, the speaker goes through the code for creating a residual network (ResNet) for the AlphaZero algorithm from scratch. They show how to create the backbone of the ResNet by looping over the residual blocks for the specified number of hidden layers. They then create the policy head and value head by using nn.sequential and defining the layers in the sequence. Finally, the speaker shows how to define the forward method for the ResNet class by passing the input through the start block, looping over the residual blocks, and returning the sum at the end.
  
  
• 01:55:00 In this section, the speaker explains how to use the AlphaZero model to get a policy and a value for a given state in Tic-Tac-Toe. He writes code to get the policy and value by passing a tensor state through the model, and then flattens the policy and gets the value item. He also explains the importance of encoding the state in the correct format and adding a batch dimension to the tensor.

Part 3

• 02:00:00 In this section, the speaker shows how to convert a policy tensor to a float using the dot item method and then applies the softmax function to choose actions with the highest probability. The speaker then visualizes the policy distribution using Matplotlib to show where to play. Next, the speaker sets a seed for Torch to ensure reproducibility and updates the MCTS algorithm by giving a ResNet model input for predicting a value and a policy based on the state of the leaf node. The simulation part is removed, and the value obtained from the model is used for backpropagation.
  
  
• 02:05:00 In this section of the video, the presenter demonstrates how to encode the node state of a tic-tac-toe game and turn it into a tensor using torch.tensor in order to give it as input to the model. The policy, which consists of logits, needs to be turned into a distribution of likelihoods using torch.softmax. The presenter also explains how to mask out illegal moves using the policy and valid moves, and how to rescale the policies so that they represent percentages. The value is extracted from the value head by calling value.item(). Furthermore, the presenter shows how to use the policy for expanding and the value for backpropagation in case the node is a leaf node.
  
  
• 02:10:00 In this section of the video tutorial on building AlphaZero from scratch using machine learning, the speaker explains how to update the expand and UCB formula methods. The expand method is updated to immediately expand in all possible directions and to store the probability inside of the node object for later use in the UCB formula during selection. The new UCB formula uses a different formula than standard multi-colored research and the speaker demonstrates how to remove the math.log and add a one to the visit count of the child. These updates allow for the use of the UCB method on a child that hasn't been visited before.
  
  
• 02:15:00 In this section, the instructor updates the MCTS with a child-right policy for selecting moves and tests it by running a game. They then move on to building the main AlphaZero algorithm by defining an AlphaZero class that takes in a model, optimizer, game, and other arguments. They also define the self-play and training methods and create a loop that iterates through multiple cycles of playing, collecting data, training the model, and testing it again. The instructor also creates a memory class for storing training data and loops over each self-play game in the training cycle.
  
  
• 02:20:00 In this section of the video tutorial, the presenter goes through the code for the self-play and training loop for AlphaZero. They cover how to extend the new data obtained from the self-play method into the memory list and how to change the mode of the model to evalu mode to avoid batch knobs during play. The training loop is also detailed with how to call the train method and store the weights of the model. Finally, the self-play method is explained, including defining a new memory, creating an initial state, and looping through the gameplay while also checking for terminal states and returning data to the memory in Tuple format.
  
  
• 02:25:00 In this section, the video tutorial walks through how to store the neutral state, action props, and player information in memory in order to use it later to gather training data. The tutorial demonstrates how to sample an action from the action props using NumPy's random.choice function and then play based on this action. The video also goes over how to check if the state is terminated or not, and if it is, how to return the final outcome for each instance in which a player has played. Finally, the tutorial shows how to append the neutral state, action props, and outcome to the memory variable and how to retrieve this data later for training.
  
  
• 02:30:00 In this section of the "AlphaZero from Scratch" tutorial, the code is updated to make it more general by changing negative values to values as perceived by the opponent for different games. The visualization of loops is improved by using the tqdm package and progress paths. The implementation of AlphaZero is tested by creating an instance of the class with a resnet model, an Adam optimizer, and specific arguments. The Tic-Tac-Toe game is used as an example with 4 rest blocks and a hidden dimension of 64. The exploration constant, number of searches, iterations, self-play games, and epochs are set and the model is saved for future use.
  
  
• 02:35:00 In this section of the tutorial, the Training Method is implemented inside the AlphaZero implementation by shuffling the training data and looping over all the memory in batches to sample a batch of different samples for training. The states, MCTS props, and final rewards are obtained from the sample by calling the zip method to transpose the list of tuples to lists of MP arrays. These are then changed to NP arrays, and the value targets are reshaped so that each value is in its own sub-array for better comparison to the output of the model.
  
  
• 02:40:00 In this section of the tutorial, the video creator discusses how to turn the state, policy targets, and value targets into tensors, using torch.float32, in order to get the out policy and out value from the model by allowing it to predict the state. They go on to define the policy loss and value loss, which they use to calculate the sum of both losses in order to minimize the overall loss through back propagation. They then demonstrate the training process using a default batch size of 64, with progress bars that show the iterations of the training process. After training the model for 3 iterations, they load the static model to test what the neural network has learned about the game.
  
  
• 02:45:00 In this section of the video tutorial, the presenter demonstrates how to use the neural network model to play a game and test its ability to predict where to make moves. By running a simulation in the MCTS search, the model is able to provide a distribution of where to play and a value prediction for the given state. The presenter also adds GPU support to the algorithm to make it faster during training and testing. The presenter shows how to declare the device and pass it to the model as an argument in order to use a Nvidia GPU if available. Additionally, the model is moved to the device during self-play and training for optimizing speed.
  
  
• 02:50:00 In this section, the speaker discusses several tweaks that can be added to AlphaZero to improve its performance. First, they add weight decay and GPU support to the model. Next, they introduce the concept of temperature, which allows for a more flexible distribution of probabilities when sampling actions. A higher temperature leads to more exploration, while a lower temperature leads to more exploitation. Finally, the speaker suggests adding noise to the initial policy given to the root node during Monte Carlo research. These tweaks can significantly enhance the results of the AlphaZero algorithm.
  
  
• 02:55:00 In this section of the tutorial on building AlphaZero from scratch through machine learning, the focus is on adding noise to the root node to incorporate randomness and explore more while also ensuring that no promising action is missed. This is accomplished by first obtaining a policy and value by calling save.model and using torch.tensor and the device of the model for the state. The policy is then tweaked using softmax and multiplied with valid moves to mask out illegal moves. Dirichlet random noise is added to the policy by multiplying the old policy with a coefficient smaller than one and adding this coefficient with another coefficient multiplied by the random noise. This way, the policy is changed to allow for more exploration, especially at the beginning when the model does not know much about the game.

Part 4

• 03:00:00 In this section, the video tutorial focuses on adding exploration to the policy by using a noise factor. By modifying the policy, the bot can prioritize actions that have not been selected often by increasing exploration. The video outlines how to adjust the equation for policy and use the Alpha value as input to the NP dot random dot dirichlet function, which changes the way the random distribution looks like based on the number of different actions in the game, so the Alpha might change depending on the environment. The root node expansion policy is outlined too, ensuring that the node is backpropagated (visit count set to one) upon expansion so that the prior agreed with the selection of the child at the beginning of Monte Carlo research.
  
  
• 03:05:00 In this section of the tutorial, the instructor adds CPU and GPU support for training models on more complex games like Connect Four. They define a device using torch.device() and check if torch.cuda.is_available() to decide whether to use a CPU or CUDA device. They also add the device to the tensor state stack and to the loading of the static file. The instructor trains and tests the model on Tic-Tac-Toe and shows that the model has learned to recognize illegal moves. They then define the game of Connect Four with row count, column count, and action size.
  
  
• 03:10:00 In this section, the video tutorial walks through updating the source code to create a Connect Four game. The game is initialized with a blank array and a saved dot in a row variable of four for the number of stones needed to win. The get next state method is updated to retrieve a row by looking at a given column and then finding the deepest empty field in that column to place a stone. The get valid moves method is updated to check the top row for available moves. The check for a win method is copied from the Tic Tac Toe game with tweaks to check both diagonals, and the get next state method is updated to use the action variable instead of the column's variable. The updated code is tested to make sure it works.
  
  
• 03:15:00 In this section, the speaker replaces Tic-tac-toe with the game Connect Four and sets the number of searches to 20 in order to validate. The size of the model is also changed to 9 for the number of rest blocks and 128 for the hidden dim to let the model learn better. The efficiency of the training is then increased so that it takes less time for complex environments. The model is then trained for one iteration, which takes several hours. The evaluation set is then used to test whether the model has learned something or not.
  
  
• 03:20:00 In this section of the tutorial, the focus is on increasing the efficiency of the AlphaZero implementation through parallelization. The plan is to parallelize as much of the implementation as possible by batching up the states to get parallel predictions for the policy and value. This way, the number of times the model is called is drastically reduced, thereby utilizing GPU capacities fully and increasing speed. The tutorial explains how to implement the parallelized version using Python without using packages like Ray, and a new class called "AlphaZeroParallel" and "MCTSParallel" is created by copying over the original classes.
  
  
• 03:25:00 In this section, the speaker discusses the creation of two new classes in Python: `SPG` to store information about self-play games, and `ParallelMCD`, which implements the `save_play` and `search` methods using the new `SPG` class. The `SPG` class stores the initial state of the game, an empty memory list, and `root` and `note` variables set to `None`. The `ParallelMCD` class also updates the `set_play` method to create a list of `SPG` instances using the `game` and the number of parallel games as inputs. The `while` loop then runs the `set_play` method until all self-play games have finished, which allows for efficient parallelization.
  
  
• 03:30:00 In this section, the speaker explains how to get a list of all the states and turn them into a number array to increase efficiency. They also show how to change the perspective for all states using one function call to multiply the values with negative one for player set to negative one. Next, the speaker demonstrates how to pass the neutral states to the Monte Carlo research, update the Monte Carlo research search method, and get policies on the video using all batch states. Finally, they explain how to swap the order of the encoded state to work with several states and not just one when calling the get encoded state method, and how to copy this process over to the game of tic-tac-toe.
  
  
• 03:35:00 this loop into a numpy array and using numpy's vectorization instead. Then we can apply the model to all of the states in the numpy array without having to loop through each one, which saves a lot of time. We can then reshape the output back into its original form and proceed with the rest of the MCTS search as usual. Finally, we update the statistics for each self-play game and return the root node for the chosen action. This completes the implementation of the MCTS search with policy and value network using AlphaZero algorithm.
  
  
• 03:40:00 In this section of the video, the instructor is making some changes to the code to store all expandable nodes instead of SPG classes. Then, the focus shifts to finding out which safe play games are expandable or not by creating a list to store them and getting the mapping index for each safe play game. The instructor checks if there are any expandable games and if there are, the states are stacked up and encoded so that the policy and value can be obtained later on.
  
  
• 03:45:00 In this section of the tutorial, the instructor explains the code implementation for the Monte Carlo Tree Search algorithm for AlphaZero. They show how to use the expandable states and do not need to unsqueeze, squeeze or add noise, as well as creating an index to get the policy and mapping indexes for allocating policies in the self-play game index. The nodes are expanded using the SPG policy, backpropagated using SPG dot value and then action props are obtained instead of using the OSF Planet method. The instructor copies over the parallelization code and makes changes to work with the action props instead of the OSF Planet method as part of implementing the Monte Carlo Tree Search algorithm for AlphaZero.
  
  
• 03:50:00 In this section, the video tutorial focuses on updating the code for the parallel implementation of MCTS search. The instructor emphasizes the importance of removing self-play games from the list if they are terminal and updating the state by calling `spg.state` instead of `SPG class`. The code is also changed to append the memory to the general regional memory and flip the player after the loop over all self-play games is completed. The goal is to create a smooth loop that works efficiently and removes the circle from the list of self-play games at the appropriate time.
  
  
• 03:55:00 In this section, the speaker discusses training a model for Connect Four using the parallelized fs0 implementation. The model is trained for eight iterations and evaluates the results using a Connect Four board. The speaker notes that the number of searches is quite small when compared to other search algorithms used in practice, but the results are satisfactory. They play against the model and make some moves, and the model responds accordingly. Overall, the training took a few hours, but the final model has a good understanding of how to play the game.

• 04:00:00 In this section of the tutorial, the presenter demonstrates how to create a Connect Four environment using the Kegel environments package and play the game with two agents. The agents use the MCTS search algorithm to make predictions based on a trained AlphaZero model. The presenter also makes some minor corrections to the code, such as incorporating the temperature action props in fs03 and using the save.optimizer instead of the ordinary optimizer. Additionally, the presenter sets the temperature to zero to always obtain the arc Max of the policy and sets the reflect epsilon to one to add some randomness to the game. Finally, the presenter defines player one as a calculated agent that uses the MCTS algorithm to make predictions based on the trained model.
  
  
• 04:05:00 In this section of "AlphaZero from Scratch - Machine Learning Tutorial", the speaker models the game and the arguments by writing codes for player 1 and player 2, which provides more flexibility to try different players. They then run the cell and get visualizations of the models playing against each other, which resulted in a draw as the model can defend all attacks. They also demonstrated how to modify the code for Tic-tac-toe by changing the game and arguments and updating the path, which led to the models playing against each other to a draw again. The tutorial was completed, and the speaker provided a GitHub repository with jupyter notebooks for each checkpoint and a weights folder with the last model for Tic-tac-toe and Connect Four. The speaker also expressed their interest in doing a follow-up video on Mu Zero if there is any interest in it.

[MetaQuotes]

Google Panics Over ChatGPT [The AI Wars Have Begun]

Google Panics Over ChatGPT [The AI Wars Have Begun]

The video discusses how Google is preparing for the potential of chatbots becoming more powerful, and how this could impact their business model. Microsoft is reported to be working on a chatbot that would allow users to communicate with Bing in a more human-like way, and this feature will be beneficial for searches where images don't currently exist. Microsoft has said that they're working closely with open AI so this feature doesn't generate explicit or inappropriate visuals. So, it looks like Bing is getting a major overhaul with chat GPT and Dali 2 features integrated.

• 00:00:00 Google, in 1998, was renting a house next to another house. The ping pong table was in the other house.
  
  
• 00:05:00 The video discusses how Google is concerned about the potential of chatbots becoming more powerful, and how this could damage their business model. Google has reportedly been working on a plan to combat this, and their co-founders, Larry Page and Sergey Brin, have been invited to a meeting to discuss the issue.
  
  
• 00:10:00 In this video, Google is seen as being in a competition with Microsoft, as the latter is investing an extra 10 billion dollars into open AI. However, this may not be in the best interest of the open AI movement, as it may lead to the death of being AI before it really gets a chance to start. Google is also reported to be working on 20 AI projects, some of which are similar to chat GPT, which has led to Microsoft investing 300 million into the company. It is unclear how this will play out, but it seems that Google will be forced to put the safety issues on the back seat and unleash their AI products.
  
  
• 00:15:00 The video talks about the rumors that Microsoft is working on a chatbot that would allow users to communicate with Bing in a more human-like way. It also mentions that this feature will be beneficial for searches where images don't currently exist. Finally, the video talks about how this integration will allow users to type in text and generate images, which will be especially beneficial for searches where images don't currently exist. Microsoft has said that they're working closely with open AI so this feature doesn't generate explicit or inappropriate visuals. So, it looks like Bing is getting a major overhaul with chat GPT and Dali 2 features integrated. It sure is going to grab everyone's attention when it's launched.