Machine Learning and Neural Networks - page 19

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Lecture 15. Learning: Near Misses, Felicity Conditions

15. Learning: Near Misses, Felicity Conditions

In this video, Professor Patrick Winston discusses the concept of learning from near misses and felicity conditions. He uses different examples, including building an arch and identifying the specific constraints necessary for it to be considered an arch. He also explains how a computer program could identify key features of a train using heuristic learning. The speaker emphasizes the importance of self-explanation and storytelling, especially how incorporating both into presentations can make an idea stick out and become famous. Ultimately, he believes that packaging ideas is not just about AI, but also about doing good science, making oneself smarter, and becoming more famous.

• 00:00:00 In this section, Professor Patrick Winston explains a new way to learn from a single example in one shot. The classroom example of an arch is used to demonstrate how it is possible to learn something definite from every example by using a model and what he calls a "near miss". This process involves abstracting away from all the details that do not matter, such as height and material, to suppress information about blemishes on the surface and to make the structure explicit. This approach ultimately leads to more efficient learning and has implications for human learning and becoming smarter.
  
  
• 00:05:00 In this section, the concept of learning from near misses and felicity conditions is discussed. The speaker uses the example of building an arch to illustrate the point. As they go through different examples of arches and near misses, they begin to identify the specific constraints necessary for something to truly be considered an arch. From the presence of supportive relations to the prohibition of touch relations, the speaker outlines the key elements of arch-building. Furthermore, the color of the top of the arch is identified as an imperative. Through this process of identifying what is necessary and what isn't, the speaker highlights how the constraints can be learned in a matter of steps, rather than through countless trials.
  
  
• 00:10:00 In this section, the speaker explains how to make a new model by considering the nature of the world that one is working in. For instance, in a flag world where only three colors are available, if all colors have been seen, the evolving model is adjusted accordingly. The speaker presents examples of child's blocks and explains how the hierarchy of parts can be represented to make a conservative generalization. The speaker then contrasts this type of learning with neural nets and presents an example task for humans to perform, which involves giving a description of the top trains that distinguishes and separates them from the trains on the bottom.
  
  
• 00:15:00 In this section, the speaker explains how a computer program could identify the key features of a train with a closed top through a process of heuristic learning. The program is given sets of positive and negative examples and a "seed" example is chosen to begin constructing a description that covers as many positive examples as possible while excluding negative ones. The heuristics, or rules, applied to the seed can be combined in different ways to form a large tree of possible solutions, which must be kept under control using techniques like beam search. The speaker also introduces a vocabulary for the heuristics developed by his friend, including the "require link" heuristic that helps identify essential features of a model.
  
  
• 00:20:00 In this section, Professor Patrick Winston explains how the different heuristics, such as "forbid link," "extend set," "drop link," and "climb tree," can be used to specialize or generalize in learning. He also touches on the idea of near misses and examples, and how they are connected to generalization and specialization. The use of these heuristics can help in matching fewer or more things, and depending on the problem, can be better suited for humans or for computers with larger memories. The way to determine which method is better would depend on the specific problem one is trying to solve.
  
  
• 00:25:00 importance of near misses and felicity conditions in the process of learning. In this section, Professor Patrick Winston explains how the teacher and student must establish covenants that hold between them in order to transform the student's initial state of knowledge into a new state of knowledge. With the use of a network model that represents the student's state of knowledge, the teacher can identify the types of mistakes made by the student, and provide feedback accordingly. By doing so, the teacher can effectively push the wavefront of the student's knowledge out, and enhance the student's ability to learn and apply new information.
  
  
• 00:30:00 In this section, the speaker discusses how understanding the computational capacity of the student is important when teaching them. This includes taking into account the limited ability of a third-grader to store information compared to a computer. They also talk about how covenants, such as trust and understanding of the teacher's style, are necessary for a student to learn effectively. The speaker further explains how talking to oneself, or building descriptions, is crucial for learning. An experiment conducted by Michelene Chi showed the advantages of talking to oneself when it comes to learning about elementary physics.
  
  
• 00:35:00 In this section, the focus is on how self-explanation can affect problem-solving ability. The smartest individuals, who scored twice as high as the less intelligent ones, talked to themselves three times as much as the participants in the lower-scoring group. Self-explanation can break down into two categories, those related to physics and others related to monitoring instead of physics. The more someone speaks to themselves, the better they seem to score with problem-solving. While there is no clear indication that speaking to yourself to encourage better scores works, anecdotal evidence suggests that speaking to oneself more might help. Finally, the discussion moves to packaging ideas, particularly useful if you want your idea to be well-known, and five qualities that aid the process, starting with the need for a symbol or visual handle associated with your work.
  
  
• 00:40:00 In this section, Professor Patrick Winston discusses the importance of a surprise and a salient point in making an idea well-known. He explains that a good idea must have something that sticks out in order to become famous, and it’s essential to incorporate a story in presentations that can appeal to the audience. Moreover, he clarifies the term “salient” by stating that although it indicates importance, it explicitly means "stick out." He suggests that education is essentially about storytelling and urges individuals to consider incorporating these qualities into their presentations to make them more effective. Ultimately, he believes that being famous isn't immoral, as long as the ideas are packaged well to have the best chance of success.
  
  
• 00:45:00 In this section, the speaker tells a story about sitting next to Julia Child and asking her about being famous. Child replied that one gets used to it, which made the speaker think about the opposite experience of being ignored. He emphasizes the importance of packaging ideas and how it is not just about AI but also about doing good science, making oneself smarter, and more famous.

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Lecture 16. Learning: Support Vector Machines

16. Learning: Support Vector Machines

In the video, Patrick Winston discusses how support vector machines (SVM) work and how they can be used to optimize a decision rule. He explains that the SVM algorithm uses a transformation, Phi, to move a input vector, x, into a new space where it is easier to separate two similar vectors. The kernel function, k, provides the dot product of x sub i and x sub j. All that is needed is the function, k, which is a kernel function. Vapnik, a Soviet immigrant who worked on SVM in the early 1990s, is credited with reviving the kernel idea and making it an essential part of the SVM approach.

• 00:00:00 Support vector machines are a sophisticated way of dividing up a space to determine decision boundaries. They were developed by Vladimir Vapnik and are a big deal because they allow for more accurate decision making.
  
  
• 00:05:00 The video discusses how support vector machines work, and provides a decision rule for when a sample is positive or negative.
  
  
• 00:10:00 In this video, Patrick Winston introduces the concept of a support vector machine (SVM), which is a machine learning algorithm that assists in finding a optimal solution to a problem. The first equation in an SVM is a cost function, which is a function that takes in a vector of variables and outputs a number. The cost function is multiplied by a weight vector, which is a vector that corresponds to the importance of each variable in the cost function. The second equation in an SVM is the optimization problem, which is a function that takes in the cost function and a weight vector and tries to find the best solution. The optimization problem is solved by minimizing the cost function. The final equation in an SVM is the output vector, which is the output of the SVM.
  
  
• 00:15:00 The video discusses the use of support vector machines (SVM) to solve problems, and demonstrates how to calculate the width of a street using this technique.
  
  
• 00:20:00 In this video, Patrick Winston discusses how Lagrange multipliers work to optimize a function with constraints. The video also covers how Lagrange multipliers are used to find the extremum of a function with constraints.
  
  
• 00:25:00 In this video, a linear sum of samples is discovered to be equal to a linear sum of the components of the samples. In addition, the derivatives of the Lagrangian with respect to different variables are differentiated, and it is shown that the sum of the alpha i times y sub i is equal to 0, which implies that the vector w is equal to the sum of some alpha i, some scalars, times this minus 1 or plus 1 variable times x sub i over i.
  
  
• 00:30:00 In this video, he explains how to solve a quadratic optimization problem using support vector machines. It is explained that the decision vector is a linear sum of the samples, and that the algebra is easy. The student explains that for every term in the problem, the algebra is simplified by taking the sum of the alpha i times y sub i times x sub i.
  
  
• 00:35:00 In this video, a mathematician explains how the optimization of a decision rule depends only on the dot product of pairs of samples. This demonstrates that the mathematical analysis is feasible and that the optimization algorithm will find a straight line separating the two optimal solutions.
  
  
• 00:40:00 In support vector machines, a transformation, Phi, is used to move a input vector, x, into a new space where it is easier to separate two similar vectors. The kernel function, k, provides the dot product of x sub i and x sub j. All that is needed is the function, k, which is a kernel function.
  
  
• 00:45:00 The video discusses how support vector machines (SVM) work, and how a kernel can be used to improve the performance of SVM. Vapnik, a Soviet immigrant who worked on SVM in the early 1990s, is credited with reviving the kernel idea and making it an essential part of the SVM approach.

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Lecture 17. Learning: Boosting

17. Learning: Boosting

The video discusses the idea of boosting, which is combining several weak classifiers to create a strong classifier. The idea is that the weak classifiers vote, and the strong classifier is the one with the most votes. The video explains how to use a boosting algorithm to improve the performance of individual classifiers.

• 00:00:00 The video discusses the idea of boosting, which is combining several weak classifiers to create a strong classifier. The idea is that the weak classifiers vote, and the strong classifier is the one with the most votes.
  
  
• 00:05:00 The YouTube video explains how to use a boosting algorithm to improve the performance of individual classifiers. The algorithm involves training each classifier on a different dataset, and then combining the results. The video also explains how to avoid overfitting when using this algorithm.
  
  
• 00:10:00 In the video, the speaker talks about how to improve the accuracy of a machine learning algorithm by "boosting" it. Boosting involves looking at a distorted set of samples, where the ones the algorithm gets wrong have an exaggerated effect on the result. This allows the algorithm to learn from its mistakes and improve its accuracy.
  
  
• 00:15:00 In the YouTube video, the speaker explains how boosting can be used to create a batch of tests. He also explains how the error rate is calculated and how weights can be used to exaggerate the effect of some errors.
  
  
• 00:20:00 The speaker explains how to build a classifier by combining multiple classifiers, each with its own weight. He explains that this is state of the art for classifiers, and that it is more effective than just adding classifiers together.
  
  
• 00:25:00 The video discusses the various steps involved in the boosting learning algorithm. These steps include picking a classifier that minimizes the error rate, calculating the alpha value, and using the classifier to produce revised weights. The overall goal of the algorithm is to produce a classifier that produces a perfect set of conclusions on all the sample data.
  
  
• 00:30:00 The video discusses how a machine can be taught to boost its performance by minimizing error rates. It demonstrates this through a series of examples, showing how the error rate can be exponentially decreased.
  
  
• 00:35:00 In this video, the speaker explains how to use the alpha value to compute new weights. He talks about how the program works and how it is necessary to know how to do the math in order to find better ways of doing this sort of thing. He also explains how the square root of the error rate divided by 1 minus the error rate is the multiplier for the weight if the answer is correct, and the square root of 1 minus the error rate divided by the error rate is the multiplier for the weight if the answer is incorrect.
  
  
• 00:40:00 The sum of the weights for the samples that are classified correctly is 1/2, and the sum of the weights for the samples that are classified incorrectly is 1/2.
  
  
• 00:45:00 Boosting is a method used to improve the performance of machine learning models. It works by combining multiple weak models to create a stronger model. Boosting is effective in reducing overfitting, and is often used in fields such as handwriting recognition and speech understanding.
  
  
• 00:50:00 This video discusses the concept of "boosting" which is a method of improving the performance of machine learning algorithms. Boosting involves training a series of weak classifiers and then combining their predictions. This typically results in a significant performance improvement over using a single strong classifier.

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Lecture 18. Representations: Classes, Trajectories, Transitions

18. Representations: Classes, Trajectories, Transitions

In this video, Professor Patrick Winston discusses the concept of human intelligence, the ability to form symbolic representations and its relation to language, and the use of semantic nets to represent inner language and thoughts. Winston emphasizes the importance of understanding fundamental patterns and developing a vocabulary of change to help understand different objects and their behavior. Additionally, he discusses the use of trajectory frames to describe actions involving motion from a source to a destination and the importance of multiple representations for better understanding a sentence. Finally, Winston offers tips on how to improve technical writing, particularly for non-native English speakers, by avoiding ambiguous language, confusing pronouns, and switching words.

• 00:00:00 In this section, Patrick Winston begins by reflecting on the nature of human intelligence in comparison to machine intelligence. He explains that while machines can perform smart tasks through methods such as support vector machines and boosting, they lack an understanding of what they are doing and do not offer insight into human intelligence. Winston then discusses the evolutionary perspective of human intelligence, highlighting the increasing brain size in our family tree. However, he notes that brain size isn't enough to explain human intelligence as the Neanderthals, who had bigger brains than modern humans, did not have much influence. Instead, it was a group of Homo Sapiens in Southern Africa that developed something that nobody else had and quickly took over, as evidenced by tools and artwork.
  
  
• 00:05:00 In this section, the speaker discusses the idea that the ability to form symbolic representations enabled humans to tell and understand stories. This ability, which was related to the development of language, allowed our species to become special, as we could take two concepts and put them together to form a third, limitlessly. He also discusses the concept of an "inner language" - the language with which we think, which may not be the same as the language with which we communicate. The speaker proposes the use of semantic nets, which are networks of nodes and links that convey meaning, to represent inner language and thoughts. He provides examples of semantic nets, such as one that notes support relations and another that tracks the events in Macbeth.
  
  
• 00:10:00 In this section, the speaker discusses the concept of semantic nets, their elements, and their application in artificial intelligence. Semantic nets are a way to represent information using nodes and links, with links connecting the nodes. They allow the connections between links to be treated as objects that can be the subject or object of other links. Another concept is "reification," which is the process of treating links as objects. The speaker emphasizes the importance of putting a localization layer on top of the concept of combinator networks. The use of classification is one of the most useful elements in the inner language of semantic nets, applying to things like pianos, tools, and maps. There is also a risk of parasitic semantics, where we project our understanding onto the machine, which is not grounded in any contact with the physical world.
  
  
• 00:15:00 In this section, Professor Patrick Winston discusses the concept of levels in our understanding of objects. He emphasizes that we know about different things on different levels, and some objects are easier to visualize than others based on the specificity of their categorization. For example, it is difficult to form a picture of a tool, but a ball-peen hammer is more specific and, therefore, easier to visualize. Winston also notes that we use elements in a hierarchy to hang knowledge about objects, and the basic level in a hierarchy is where we hang most of our knowledge, like the word "piano." Additionally, Winston discusses how we talk about objects on different levels in a hierarchy, using the example of a car crashing into a wall, which involves thinking about various things like the speed of the car, distance to the wall, and the condition of the car.
  
  
• 00:20:00 In this section, the speaker discusses how a vocabulary of change can be used to understand objects in different time periods, such as before, during, and after an event like a car crash. The vocabulary includes elements such as decrease, increase, change, appear, and disappear, all of which are heavily connected with vision. Analogies are also used to help understand different concepts such as how a camera works. The speaker also introduces trajectory as the third element of representation, which involves objects moving along trajectories. Overall, the speaker highlights the importance of understanding fundamental patterns and developing a language that can help us understand different objects and their behavior.
  
  
• 00:25:00 In this section, the speaker discusses the use of trajectory frames to describe actions involving motion from a source to a destination. These frames are made up of various elements including the object, agent, and instrument, among others. The speaker notes that prepositions are often used to decorate these elements in languages such as English. Additionally, the speaker discusses role frames, which lack a trajectory but still contain elements such as instruments and beneficiaries. The speaker explains that these frames are commonly found in the Wall Street Journal Corpus and can be used to analyze the density of transitions and trajectories in a given text. Finally, the speaker introduces the concept of story sequences and provides an example of a gender-neutral name chosen to avoid trouble.
  
  
• 00:30:00 In this section, the video discusses the importance of multiple representations and how they can lead to a better understanding of a sentence. The example given is of Pat comforting Chris, which can be broken down into a role frame and a transition frame that involves an object (Chris) whose mood is presumably improved. The video also explores how changing the action to something negative (like terrorizing) would affect the frames. Additionally, the video introduces the idea of a trajectory frame as a type of mental image that can be formed from a sentence like "Pat kissed Chris."
  
  
• 00:35:00 In this section, Professor Jordan Peterson discusses how humans use sequences of events to create a representation of a story. He explains how this representation can range from a simple act like kissing or stabbing to complex stories, and how it varies depending on the context in which an event occurs. He also talks about the importance of the sequence in storytelling and how our memory is rooted in the idea of sequences. Finally, he discusses how libraries of stories can help humans understand more about the stories they encounter based on the super class they belong to, such as event frames, disaster frames, and party frames.
  
  
• 00:40:00 In this section, the speaker discusses how events can be grouped into types of frames, such as parties and disasters. Each frame has specific slots to be filled with types of information, such as fatalities or the bride and groom's names. However, understanding stories can be difficult due to syntactic challenges in pronoun antecedents. The speaker emphasizes the importance of not adding unnecessary syntactic difficulty to storytelling, as it can hinder understanding. Newspaper journalists would write stories in a clear and concise way to ensure readers can easily understand the information.
  
  
• 00:45:00 In this section, Patrick Winston offers tips on how to improve technical writing, particularly for Russian and German writers looking to write clearly in English. He suggests avoiding pronouns to reduce ambiguity and confusion for readers, using clear nouns instead. He also emphasizes the importance of avoiding words like "former" and "latter" that require readers to refer back to identify what they mean and avoiding switching words like "shovel" and "spade." According to Winston, by following these simple rules, technical writers can make their writing clearer and easier for readers to understand.

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Lecture 19. Architectures: GPS, SOAR, Subsumption, Society of Mind

19. Architectures: GPS, SOAR, Subsumption, Society of Mind

This video discusses various architectures for creating intelligent systems, including the general problem solver and the SOAR architecture, which heavily incorporates cognitive psychology experiments and is focused on problem-solving. The speaker also discusses Marvin Minsky's "Emotion Machine," which considers thinking on many layers, including emotions, and the common sense hypothesis that argues for equipping computers with common sense like humans. The subsumption architecture, inspired by the human brain's structure, is also discussed, with the Roomba being a successful example. The ability to imagine and perceive things is connected to the ability to describe events and understand culture, and language plays a crucial role in building descriptions and combiners. The importance of engaging in activities such as looking, listening, drawing, and talking to exercise the language processing areas of the brain is highlighted, and the speaker warns against fast talkers who can jam the language processor and lead to impulsive decisions.

• 00:00:00 In this section, the professor discusses various alternative architectures for creating an intelligent system. He starts off by talking about the Estonian cyber attack in 2007 and how no computer can understand the story behind it, except for one which he will demonstrate later. He then goes on to talk about the general problem solver developed by Newell and Simon at Carnegie Mellon, in which an intelligent system operates by measuring the symbolic difference between the current state and the goal state and selecting operators to move from the intermediate state to a better state, repeating the process until the goal is achieved. The section ends with the explanation of the idea that will be covered in the next lecture, which will focus on how to avoid going broke when starting a company in the AI business.
  
  
• 00:05:00 In this section, we learn about the concept of means-ends analysis, which involves identifying the difference between the current state and a desired end state and selecting the appropriate operator to minimize the difference. The example of using means-ends analysis to solve the problem of getting home from MIT is presented, illustrating the recursive process of identifying differences and selecting operators until the desired end state is achieved. While the general problem solver concept was an exciting idea at the time, it did not turn out as expected due to the difficulty of building the table that relates differences to operators. This led to the development of the newer SOAR architecture, which stands for "State Operator And Result," although the proponents of the architecture assert that it is merely a label and not an acronym.
  
  
• 00:10:00 In this section, the focus is on the SOAR architecture and its different components. SOAR consists of long-term and short-term memory, a vision system, an action system, and a preference system. The architecture heavily incorporates cognitive psychology experiments, and its primary focus is on problem-solving. Additionally, SOAR has an elaborate subsystem for breaking ties in rule-based systems, and it is centered on the idea that people are symbol manipulators. The system is designed to solve problems systematically, and it has an elaborate preference system for breaking ties in rule-based systems.
  
  
• 00:15:00 In this section, the speaker discusses various architectures that are heavily biased towards problem-solving, including SOAR and Newell's architecture. However, the most important architecture, according to the speaker, is Marvin Minsky's "The Emotion Machine," which highlights how problem-solving may come in layers. The speaker provides an example of Marvin's architecture through a short vignette, where a woman is crossing a road. Marvin's architecture highlights the various levels of thinking that the woman experiences, starting from an instinctual reaction upon hearing a sound to reflective thinking in a social context.
  
  
• 00:20:00 In this section, the SOAR architecture focuses on problem-solving while Minsky's "Emotion Machine" considers thinking on many layers, including emotions. However, the development of common sense poses as a blocker to achieving such thinking, as computers have never had much of it. Thus, the common sense hypothesis argues that in order for computers to have such intelligent thought processes, they must be equipped with common sense like humans. This spawned the open mind project and the gathering of common sense from the world wide web as a means of achieving layered thinking. In contrast, Rod Brooks and his subsumption architecture believe that robots cannot do much because people are thinking about building robots in the wrong way, with an encapsulated vision system, reasoning system, and action system. Instead, Brooks suggests having layers of abstraction focused on dealing with the world, such as avoiding objects, wandering, exploring, and seeking.
  
  
• 00:25:00 In this section, the speaker discusses the architecture proposed by Rodney Brooks which was inspired by how the human brain is built, with the old parts down in deep and the neocortex layered over it. Brooks hypothesized that one could get a machine to act as smart as an insect without necessarily needing representation in the way that we focused on representation in the course. His idea was to use the world instead of a model, so everything one does is reactive rather than having a map of the room in one's head. The mechanisms in their purest form are just finite-state machines. Brooks named this idea subsumption architecture, which was used in the Roomba robot that has been highly successful. The Roomba uses infrared proximity sensors for navigation, which helps avoid centralized controllers and the need for a world model.
  
  
• 00:30:00 In this section of the video, the speaker discusses the subsumption architecture, which is exemplified in a robot that is capable of finding a can and picking it up. The robot uses a laser light striper to locate the can and has sensors in its arm to grab the can in a specific way. The robot also uses a magnetic compass to navigate back to its starting point. The speaker also mentions other architectures like SOAR and GPS and introduces the genesis architecture, which centers around language and guides the perceptual systems.
  
  
• 00:35:00 In this section, the speaker discusses how the ability to imagine and perceive things is connected to the ability to describe events, tell and understand stories, and ultimately understand culture. He gives examples of how people know things that are not explicitly taught to them, such as the danger of wearing gloves while operating a table saw. He proposes the "strong story hypothesis" as a possible explanation for the flowering of our species 50,000 years ago, which he believes provided us with the ability to tell stories and understand them.
  
  
• 00:40:00 In this section, we learn about an experiment that is considered the most important series of experiments ever done in cognitive and developmental psychology. The experiment involves placing food in baskets at two opposite corners of a rectangular room and spinning a rat, a small child, and an adult to see where they go. They all tend to go to the two corners with the food, except when one wall is painted blue. The rat and the child still go to the two diagonal corners with equal probability, while the adult goes only to the corner with the food. The child becomes an adult when they start using the words left and right to describe the world.
  
  
• 00:45:00 In this section, the speaker conducts an experiment with a volunteer that demonstrates how language plays a crucial role in building descriptions and combiners. The experiment involves reading a passage from a book while the volunteer repeats it back simultaneously, jamming their language processor, which results in their inability to connect certain shapes and colors. The speaker advises that engaging in activities such as looking, listening, drawing, and talking can exercise the same areas of the brain responsible for language processing and make you smarter. Additionally, the speaker warns against fast talkers and how they can jam your language processor, leading you to make decisions impulsively.

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Lecture 21. Probabilistic Inference I

21. Probabilistic Inference I

In this video on probabilistic inference, Professor Patrick Winston explains how probability can be used in artificial intelligence to make inferences and calculate probabilities based on various scenarios. He uses examples such as the appearance of a statue, a dog barking at a raccoon or a burglar, and the founding of MIT in 1861 BC to demonstrate the use of a joint probability table, how to calculate probabilities using axioms and the chain rule, and the concepts of independence and conditional independence. The speaker emphasizes the need to correctly state variable independence and proposes the use of belief nets as a way to represent causality between variables while simplifying the probability calculations.

• 00:00:00 In this section of the video, Professor Patrick Winston discusses the use of probability in artificial intelligence, specifically as it pertains to the observation of random events. He uses the example of observing the appearance of a statue on campus and constructs a table to keep track of possible combinations of events that could lead to the statue's appearance. He notes that the number of rows in the table is 2 to the number of variables, and that long periods of observation could be used to determine the probability of each of these events occurring. Ultimately, the probability of any given event is simply the frequency of its occurrence divided by the total number of observations.
  
  
• 00:05:00 In this section, the presenter demonstrates how to use a joint probability table to calculate various probabilities. The example used involves knowing the probability of a statue appearing, given certain conditions are met, such as the presence of an art show and a hack. The presenter also performs similar calculations for the probability of a raccoon showing up based on a barking dog, and the probability of the dog barking given the presence of a raccoon. The demonstration shows how a joint probability table can be used to make inferences and calculate probabilities based on different scenarios.
  
  
• 00:10:00 In this section, the speaker discusses the use of a joint probability table to calculate probabilistic inferences. Despite the usefulness of this tool, the high number of rows required for more complex situations can be challenging to manage, making it necessary to consider other methods in addition to probabilistic inference. The speaker also presents a hypothetical scenario in which MIT was founded in 1861 BC and discusses the experimental methods that might have been used to determine which objects float.
  
  
• 00:15:00 In this section, the speaker discusses the basics of probability and the axioms that underpin it. They explain that probabilities must be greater than 0 and less than 1, and that in a binary world, the probability of true is 1 and false is 0. The speaker also introduces the third axiom, which states that the probability of A plus the probability of B minus the probability of A and B is equal to the probability of A or B. They note that this basic understanding of probability serves as the foundation for more complex calculations used in probabilistic inference.
  
  
• 00:20:00 In this section, the speaker explains the formal approach to dealing with probability using axioms, and how it can be mirrored by intuitions that involve discussions of spaces. The probability of a is associated with the size of the circle relative to the total area in the rectangle, and axioms one to three make sense in terms of that picture. The speaker then explains conditional probability and how it is defined as the probability of a given b, which is equal to the probability of a and b divided by the probability of B. This definition makes sense as it restricts the universe of consideration to just that part of the original universe.
  
  
• 00:25:00 In this section, the speaker introduces the idea of breaking up the probability space into three parts and explains how the probability of a, b, and c can be determined. By expanding the formula, the probability of all things being so is broken up into a product of three conditional probabilities. The speaker then generalizes this idea into the chain rule, which states that the probability of a group of things can be written as a product of conditional probabilities. Even though the speaker is only halfway through their diagram, they show that they are making good progress. The next concept they discuss is the idea of conditional probability.
  
  
• 00:30:00 In this section, the professor explains the definition of independence and conditional independence. Independence is when the probability of a doesn't depend on what's going on with b. For instance, if a and b are independent, then a given b equals a. Conditional independence means that if the world is restricted to being in z, then the probability of a doesn't depend on the value of b. The professor illustrates these concepts using intuitionist diagrams, using the areas of diagrams to denote probabilities.
  
  
• 00:35:00 In this section, the lecturer discusses conditional independence in probabilistic inference and how it leads to inferring the joint probabilities of variables. He explains the concept using the example of a dog that barks at a raccoon or a burglar, and how adding two more variables leads to the need for a large joint probability table. He then introduces the idea of belief nets as a way to represent causality between variables, and emphasizes the need to correctly state that every node is independent of its non-descendant variables.
  
  
• 00:40:00 In this section, the speaker discusses the concept of independence given the parents of non-descendants and the importance of understanding this language in probabilistic inference. The speaker then creates a model to determine the probabilities of various events such as a burglar appearing or the dog barking based on the presence of other factors such as a raccoon. The speaker notes that only 10 numbers are required to specify the model, which saves considerable effort compared to attempting to build a joint probability table straightaway.
  
  
• 00:45:00 In this section, the speaker discusses the use of the chain rule in calculating the full joint probability table. They explain how using conditional independence knowledge, they are able to scratch certain probabilities from the formula since they do not depend on a descendant. By arranging the formula in a specific way, the speaker is able to calculate the full joint probability table without making up numbers or taking a lot of measurements. The speaker notes that in this particular case, they only had to devise 10 numbers out of 32 and questions how much saving would be achieved if there were more properties.

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Lecture 22. Probabilistic Inference II

22. Probabilistic Inference II

In this video, Professor Patrick Winston explains how to use inference nets, also known as "Bayes Nets," to make probabilistic inferences. He discusses how to order variables in a Bayesian network using the chain rule to calculate the joint probability of all variables. The speaker demonstrates how to accumulate probabilities by running simulations and how to generate probabilities using a model. He also discusses the Bayes rule and how it can be used to solve classification problems, select models, and discover structures. The video emphasizes the usefulness of probabilistic inference in various fields such as medical diagnosis, lie detection, and equipment troubleshooting.

• 00:00:00 In this section, Professor Patrick Winston discusses the use of inference nets, also known as "Bayes Nets," which are used to make a probabilistic inference. He starts by reviewing the joint probability table, which can be used to decide a probability by clicking the appropriate boxes, but the issue is that it becomes difficult and time-consuming to make up or collect the numbers when lots of variables are involved. He moves on to use the inference nets to perform computations to obtain the likelihood of the events happening together. The chain rule is used here, and this section ends by giving an explanation of this rule.
  
  
• 00:05:00 In this section, the speaker talks about the process of ordering variables in a Bayesian network and how that can be used with the chain rule to calculate the joint probability of all variables. By arranging the variables in such a way that none of its descendants appear to its left in a linear order and using the chain rule, he is able to calculate the probability of any particular combination of those variables. He asserts that all conditional probabilities in this scenario are non-descendants and scratching out variables based on dependencies can help calculate any entry in the table.
  
  
• 00:10:00 In this section, the speaker explains how to use a small network to do anything that can be done with a table, and the probabilities required to achieve this. He discusses how he extends the tables to keep track of the tallies required to calculate the probability of the dog barking or B happening, and uses experimental results to give tick marks or tallies in the relevant sections of the table, eventually leading to a demonstration of the process.
  
  
• 00:15:00 In this section of the video, the professor begins by demonstrating how to accumulate the probabilities of a network by running simulations. He explains how to interpret the table and keep track of what the data elements are telling you about how often a particular combination appears. He runs multiple simulations to obtain more accurate probabilities. He then demonstrates how to simulate the system generating a combination of values for all the variables by going back and forth from the top probability tables and flipping a coin.
  
  
• 00:20:00 In this section, the speaker discusses the process of generating probabilities for a scenario by selecting the appropriate row in a table of probabilities. The speaker then goes on to explain how these probabilities can be generated using a model on the left, which can be used to produce data to compute the probabilities on the right. However, the speaker acknowledges that there can be multiple correct models for a given scenario, making it difficult to determine which one is correct. To address this issue, the speaker introduces the concept of naive Bayesian inference, which involves rewriting conditional probabilities in a way that allows for their calculation using Bayes' theorem.
  
  
• 00:25:00 In this section, the video explains how Bayes' rule can be used to solve a classification problem. For example, in diagnosing a disease, the probability of the disease given the evidence can be calculated by dividing the evidence's probability given the disease by the evidence's overall probability, and then multiplying this by the prior probability of the given disease. If multiple pieces of independent evidence are present, the joint probability of evidence for the given disease divided by the overall probability of evidence can be calculated, and then the probabilities of all relevant classes can be compared.
  
  
• 00:30:00 In this section, the lecturer tells a story about selecting two coins, one biased with a 0.8 probability of heads and one fair with a 0.5 probability of heads. After flipping the coin, the lecturer uses Bayesian probability to figure out which coin was selected based on the prior probabilities and the evidence from the flips. The lecture demonstrates how evidence can be used to determine the probability of different hypotheses in probabilistic inference.
  
  
• 00:35:00 In this section, the professor demonstrates how the probabilities of different coins vary with a series of flips, and how the preponderance of evidence can change the probability of getting heads. The Law of Large Numbers sets in, and the probability of the chosen coin being in play becomes increasingly close to 1. The professor then uses this concept to create a parent party classifier by looking at the political party of a child and making inferences about the party the parent belongs to. Overall, the concept of probabilistic inference can be applied in various scenarios to make predictions and draw conclusions.
  
  
• 00:40:00 In this section of the video, the speaker discusses using the Bayesian hack to compare two models and select the best one based on data. The process involves simulating draws from a model and calculating the probability of each model given the data. The speaker then moves onto structure discovery, where they start with no linked variables and use a random search to modify and compare models until they find one that is preferred. This process requires using the sum of the logarithms of the probabilities instead of the product to avoid losing information on a 32-bit machine. However, the search for the optimal structure can be challenging due to the large space and local maxima.
  
  
• 00:45:00 In this section, the speaker discusses the usefulness of probabilistic inference and structure discovery in various fields such as medical diagnosis, lie detection, and equipment troubleshooting. He explains how probabilistic calculations are the right approach to use when information is limited, and how this method can be used to determine the most probable cause of a problem based on the observed symptoms. The speaker also hints at future discussions on how this method can be used to discover patterns and stories.

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Lecture 23. Model Merging, Cross-Modal Coupling, Course Summary

23. Model Merging, Cross-Modal Coupling, Course Summary

In this video, Professor Patrick Winston talks about model merging, cross-modal coupling and reflects on the course's material. He discusses the importance of discovering regularity without being overly fixated on Bayesian probability and the potential benefits of cross-modal coupling for understanding the world around us. He also offers suggestions for future courses and emphasizes the importance of focusing on making new revenue and capabilities with people and computers working together, rather than solely aiming to replace people. Additionally, he emphasizes the importance of identifying the problem first and selecting the appropriate methodology for addressing it. Lastly, the professor reflects on the limitations of reducing intelligence to a replicable, artificial model and highlights the exceptional work of his team.

• 00:00:00 In this section, Patrick Winston talks about model merging and cross-modal coupling. He demonstrates the idea of Bayesian story merging by showing how to discover structure in situations where you might not otherwise find it, like discovering events in two stories and assembling them into two story graphs. He also talks about the capacity to discover concepts through several levels that uses machine learning and cloud computing for efficiency. Lastly, he showcases Michael Coen's program that uses multiple modalities and correspondences between them to sort out both contributing modalities in zebra finch songs.
  
  
• 00:05:00 In this section, the concept of cross-modal coupling is explained through the example of associating gestures that produce vowel sounds with the sounds themselves. The Fourier transform of a vowel produces formants, and an ellipse around the mouth forms the second modality. With cross-modal coupling data, it is possible to cluster sounds and associate lip forms with sounds without any marked up data. A demonstration by Coen's work shows how clusters can be formed by using projections and vectors as the components of a metric.
  
  
• 00:10:00 In this section, the speaker discusses the concept of cross-modal coupling and how it can aid in understanding the world presented to us. He suggests that it is possible to discover regularity without being obsessively concerned with Bayesian probability and that this kind of coupling idea is likely bound up in our understanding of the world around us. The speaker also summarizes the course's material, emphasizing the importance of both the engineering and scientific perspectives in creating sophisticated applications for artificial intelligence. He also points out the need to focus on making new revenue and capabilities with people and computers working in tandem, rather than solely aiming to replace people.
  
  
• 00:15:00 In this section, the speaker discusses the unique advantages that programming offers for creating models and conducting experiments. Specifically, programming provides metaphors and the ability to create models that allow for experimentation to test the implications of these models. The speaker also emphasizes the importance of identifying the problem first and then selecting the appropriate methodology or machinery to use, rather than falling into mechanism envy and focusing on specific methods. Finally, the speaker briefly reviews the exam format and offers a few reminders for students, such as bringing a timepiece and calculator, and the flexibility to wear costumes during the exam.
  
  
• 00:20:00 In this section, the professor gives some suggestions for what to do next semester, including taking Marvin Minsky's subject, Society of Mind, or Bob Berwick's subjects on Language Understanding and Evolution, or Gerry Sussman's Large Scale Symbolic System subject. He also promotes his own spring course, the Human Intelligence Enterprise. The professor describes his course as a humanities course and does not have lectures, but rather is a conversation with him. He discusses some of the topics covered in the course, such as packaging and the common elements found in various intelligence systems.
  
  
• 00:25:00 In this section, the speaker discusses the importance of packaging and how it can make a difference in one's success, regardless of their career path. The speaker mentions an event, called the "How to Speak" lecture, which is a one-hour nonlinear lecture that can significantly impact someone's ability to give presentations, lectures, and job talks by offering tips such as when to tell a joke or how to open a presentation. Additionally, the speaker talks about their group's Genesis system, which is about to move into areas that can detect the onset of a possible disease.
  
  
• 00:30:00 In this section, a live demonstration shows how a system can read and understand a story from multiple perspectives, which allows for the detection of potential issues and the intervention to prevent disasters. Two personas with different educational backgrounds identify what is explicitly in the story and infer other concepts in gray. Because of their unique backgrounds, they have different perspectives on the story and can even negotiate with each other, teach other domains, and avert disasters before they occur. The system also detects potential revenge operations and Pyrrhic victories, illustrating its ability to anticipate potential issues and intervene.
  
  
• 00:35:00 In this section, we learn about using vectors of concepts instead of keyword counts for information retrieval by understanding stories on multiple levels. Propagator architecture is used to prevent individuals from going overboard with their work, and the student involvement in the MIT group is praised. As for further graduate school programs, one should think about who they want to apprentice under and find a program with a different focus, such as AI, to broaden their horizons in the field.
  
  
• 00:40:00 In this section, Professor Winston gives advice for students applying to graduate school in theoretical physics and artificial intelligence, emphasizing the importance of site visits for the former and of being focused on a specific area for the latter. He also shares an anecdote about an extreme case of the defect theory of AI career selection, in which a computer vision researcher is unable to recognize his wife due to his specialization in object recognition. Lastly, Professor Winston reflects on the usefulness and simplicity of powerful ideas in computer science and addresses the argument that understanding language may not necessarily require true intelligence.
  
  
• 00:45:00 In this section, the speaker talks about the limitations in reducing intelligence to something that can be artificially replicated. He uses his pet raccoon as an example of a highly intelligent animal that he had no expectation of being able to build an equally intelligent machine. The idea that artificial intelligence is impossible is often based on reductionist arguments that fail to take into account the knowledge and magic that come from a running program executing over time. The speaker also takes a moment to acknowledge the exceptional work of his team and wishes the students well on their final exam.

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Mega-R1. Rule-Based Systems

Mega-R1. Rule-Based Systems

This video focuses on Mega-Recitation, which is a tutorial-style lecture to help students work with the material covered in lectures and recitations. The video covers several topics related to rule-based systems, including backward chaining, forward chaining, tiebreak order for rules, and the matching process. The backward chaining process involves looking at the consequent of a rule and adding the antecedents as needed to reach the top goal, and tiebreaking and disambiguation are crucial to the goal tree. The video also discusses forward chaining and matching rules to assertions using a series of assertions. The speaker emphasizes the importance of checking assertions before using a rule and avoiding impotent rules that do nothing. The matching process involves using backward chaining to determine which rules match the given assertions, and the system will prioritize lower-numbered rules, regardless of whether they are new or not.

• 00:00:00 In this section, Mark Seifter introduces the concept of Mega-Recitation, which is a tutorial-style lecture designed to help students work with the material covered in lectures and recitations. The goal is to help students understand and work with the algorithms that are crucial to the class and to demonstrate their understanding on quizzes. The focus is on a quiz problem from last year that tripped up many students, and Marx goes over the tricks that caught them out in the hope of preventing those mistakes from being made again. Finally, he explains the difference between two notations, in-fix and prefix, for writing rules, and why students need to be aware of them.
  
  
• 00:05:00 In this section, we learn about the six rules labeled with P's, each with a corresponding if-then statement. The first rule states that if X is ambitious and X is a squib, then X has a bad term. The question mark in the X or Y indicate a variable waiting to be bound. Backward and forward chaining will be used to determine the binding of these variables. Four assertions are also given to us to work with, including Millicent living in Slytherin dungeon and Seamus being in Gryffindor Tower and tagging Millicent. The importance of checking the assertions before using a rule is emphasized since it was a mistake that tripped some people up last year.
  
  
• 00:10:00 In this section, the presenter explains the concept of backward chaining and highlights its differences with forward chaining. Working on the hypothesis, the backward chainer tries to find a matching assertion in the list of assertions, and if there is no match, it will try to find a rule with a matching consequent. The presenter goes on to provide examples of easy problems and then tackles a real-life problem, where Millicent becomes Hermione's friend. Throughout the example, the presenter emphasizes the importance of tiebreaking and disambiguation in the goal tree.
  
  
• 00:15:00 In this section, the video discusses the process of backwards chaining in rule-based systems. Backwards chaining involves looking at the consequent of a rule and adding the antecedents as needed to reach the top goal. The video emphasizes the importance of looking for something that has the current goal in its consequent and searching for it in the assertions before checking other rules. The process involves a depth search, starting from the left node and moving down if there are any children, and looking for a rule that matches the current goal. The video also explains how to correctly add nodes to the goal tree, such as an end node with an or node at the bottom.
  
  
• 00:20:00 In this section, the speaker discusses a depth-first search while using a tree diagram to identify whether Millicent is a protagonist or villain, ultimately trying to prove that she is a villain. They follow the left branch first and try to find a rule as to whether Millicent is a protagonist. Since there isn't any rule matching their criterion, they move back up to the "or" node and backtrack to Millicent being a villain. Even though it's not in the assertions, they follow the branch to see if there is a rule with that as its consequent. Eventually, they find a rule stating Millicent is a villain but must keep going to find the ultimate answer.
  
  
• 00:25:00 In this section, the speaker explains the single-minded focus of the backward chainer and its lack of concern for the other assertions or antecedents. The backward chainer only aims to prove the possibility that Millicent might be a villain, and it does not care about the other consequences, such as Millicent being ambitious. It is noted that this can result in unnecessary computations, but it is a simple and efficient way to code the system. The potential use of a hash table is discussed, but it is concluded that it may not be worth the extra effort.
  
  
• 00:30:00 In this section, the class discusses implementing a hash table to increase the running speed of the rule-based system. However, there are some potential issues with this approach, as it loses the order in which the assertions in the table fire, and some rules depend on the order of these assertions. The lecture also addresses a question from the crowd about rule resolution when there is an assertion that states the opposite of what was previously asserted, and how to resolve this issue. The class concludes that this is why they do not have delete statements on quizzes and that they do not add assertions but instead check all the things in the goal tree until either proven or disproven.
  
  
• 00:35:00 In this section, the speaker quickly goes through the remaining parts of the example of Millicent, the protagonist, and how to use rule-based systems to determine whether she becomes Hermione's friend or not. This includes answering a few questions, such as determining the minimum number of additional assertions needed for Millicent to become Hermione's friend without adding an assertion that matches a consequent of a rule. The section also covers an uncommon situation that arises due to adding an assertion and the need to fix it by removing a contradictory assertion. Lastly, backward chaining is briefly mentioned, and the speaker asks the audience to solve a problem related to variable binding, where the goal is to determine if Millicent has a bad term.
  
  
• 00:40:00 In this section, the narrator discusses forward chaining, which involves adding new assertions as they come, and the tiebreak order for rules. The tiebreak order for rules is from 0 to 5, and if the same rule can trigger with multiple different assertions, the rules are used in numerical order. The narrator demonstrates how to match rules to assertions by using a series of assertions, and how one would fire off a rule. The narrator also tells us that impotent rules, or rules that do nothing, should not be fired, but instead, one should go to the next rule in the order. Finally, the narrator explains how they matched rules and assertions, and how they added new assertions.
  
  
• 00:45:00 In this section of the video, the speaker discusses the matching process for rule-based systems. The example given is that of a quiz question, with numbered rules and assertions. The system uses backward chaining to determine which rules match the given assertions, and in this case, only rules 1, 2, 3, and 5 match. The speaker also answers a question about whether new assertions with a lower rule number should be processed first, explaining that the system will prioritize lower-numbered rules regardless of whether they are new or not.

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Mega-R2. Basic Search, Optimal Search

Mega-R2. Basic Search, Optimal Search

This YouTube video covers various search algorithms and techniques, including depth-first search, breadth-first search, optimal search, and A* algorithm. The video uses an entertaining example of an Evil Overlord Mark Vader searching for a new stronghold to illustrate these concepts. The presenter emphasizes the importance of admissibility and consistency in graph searching and explains the usage of extended lists to prevent re-evaluation of nodes. The video addresses common mistakes and questions from the audience and encourages viewers to ask more. Overall, the video provides a thorough introduction to these search algorithms and techniques.

• 00:00:00 In this section, the video introduces the problem of Evil Overlord Mark Vader searching for a new stronghold, utilizing the start search techniques he learned in class. Vader starts at his current stronghold, Depth-first search star, and wants to reach the 6:03 fortress, which has no weaknesses and has all desirable features such as enslaved minions, sharks with laser beams, and a great escape route. The video presents a graph of the exploration choices, where edges join strongholds that differ by just one feature, and viewers are offered several methods to do search, including the reliable but slower approach and the quick but more prone to errors approach.
  
  
• 00:05:00 In this section, the video presenter discusses different approaches to solving depth-first search. While there is a very fast approach, it is more prone to mistakes and is not typically used. Instead, the presenter recommends using the goal tree and starting from the start node and ending at the goal node, which is a bit faster than drawing out the entire agenda. The presenter also explains the concept of lexicography and how it is used to break ties in alphabetical order during a search. Additionally, the video warns against biting your own tail, which is a common mistake when implementing rules in a system. Finally, the presenter emphasizes the importance of not having the same node appear twice within the same path, as this can lead to errors.
  
  
• 00:10:00 In this section, the speaker explains how to solve a problem with depth-first search using a goal tree instead of a queue. They start at node s and ask the audience for help to figure out the choices at that node. The speaker emphasizes the importance of checking connectivity and reading instructions. They use lexicographic tiebreak to decide which node to go to next and backtrack when they hit a dead-end. They also caution against the mistake of double counting backtracks and remind the audience to pay attention to how many times they backtrack.
  
  
• 00:15:00 In this section, the speaker explains the importance of the algorithm when conducting a search, as it can affect the number of steps required to find the solution. They also discuss the technique of backtracking and advise on how to keep track of it during the search. The speaker then moves on to demonstrate how to perform a depth-first search and suggests a fast way to solve the breadth-first search question. They highlight that the path found during a breadth-first search is guaranteed to have the least number of jumps, and they instruct to expand the graph level-by-level left-to-right. Finally, the speaker clarifies the usage of the type-rank order in a breadth-first search.
  
  
• 00:20:00 In this section, the speaker emphasizes the importance of not sorting the paths on the queue for the search algorithm used in the video. They explain that the best-first search will only break ties when it reaches a node and that they always add everything to the end of the queue, which means they do not have to backtrack. They also mention that while the graphical order does play a role in the search, it only does so subtly and in a sneaky way. Lastly, they discuss the possibility of breadth-first search with an extended list, which can be used to prevent the program from re-evaluating nodes it has already visited.
  
  
• 00:25:00 In this section of the video, the speaker discusses optimal search using an example of Mark trying to find the shortest path from his current universe to his goal universe with varying energy costs between universes. The graph includes distances and heuristic values given to each node, and the speaker explains that the algorithm will use the heuristic values to guide the search towards the goal node while also considering the actual cost of reaching each node. The algorithm used is the A-star algorithm which expands nodes with the lowest combined actual and heuristic cost. The speaker also explains the importance of using an extended list to prevent repetition of search and addresses a question about the order in which nodes get added to the search.
  
  
• 00:30:00 In this section, Mark introduces the concept of programming the shortest number of universe jumps that will get him to the goal without using too much energy. He explains his simple branch-and-bound search which is just like a cheese pizza, whilst an A-star search is like a meat lover's pizza with extra toppings. However, they can affect each other, so it is crucial to choose the currently shortest path. In the example, the computer adds the node C to the extended list, marking it as the only path with a length of 0. The length of SB is 3, and it has a path cost of 103, while f is 4 with a cost of 14. Despite ignoring tie-breakers in lexicographic order, the shortest path is chosen, and once B is expended, it goes to D with a length of 4, and hence the updated path length to G is 7.
  
  
• 00:35:00 In this section, the speaker continues with the optimal search algorithm, expanding the paths S, B, F, and D. The path E is then extended to H and A, and the shortest path is found to be SFHIG. The speaker also mentions using A-star as a more efficient search algorithm, and addresses questions from the audience about expanding nodes that are already on the extended list. The correct answer is ultimately achieved, despite some initial confusion about whether the path connects to C and D.
  
  
• 00:40:00 In this section, the speaker discusses some errors made in the previous section that caused some nodes to be excluded from the final tree created. He clarifies that the node should go to "e" as well, and that it would have made a difference if they asked how many times a node was executed due to not going on the extended list. They then move on to discuss the A-star algorithm and the calculation of heuristic values. It's emphasized that it's important to not add heuristic values for every node in the list but instead add the path so far to the final heuristic value. They also clarify that the decision to extend node "G" is a matter of taste and an implementation detail that won't lose points on the problem set. Finally, they resolve the A-star search and the final winner is determined to be node "D" with a value of 57.
  
  
• 00:45:00 In this section, the video summarizes a search algorithm called A* and shows how to use it optimally to find the shortest path in a graph. The video discusses the importance of having admissible heuristics at every point in the graph. Admissible means that the estimate of how much work is left is always an underestimate or an accurate prediction. A heuristic that is an overestimate will cause the algorithm to think it needs to do more work than necessary and might not explore important nodes. The video also talks about consistency, which means that the distance between adjacent nodes in a graph is less than the difference in the heuristics between those nodes. The video stresses the importance of understanding these concepts as they will likely be on the quiz.
  
  
• 00:50:00 In this section, the speaker explains the concepts of admissibility and consistency in graph searching. Admissibility is like consistency, but it requires consistency between every node and the goal node. Any graph that's consistent is always admissible, but not every admissible graph is consistent. An extended list will work on admissible graphs because it checks estimates for every node to the goal node. However, if estimates within nodes are incorrect, going through them out of order violates the assumption made when deciding to use the extended list. The graph presented in the video is expertly crafted to be a bottleneck goal node and contains inconsistencies between nodes, including I and H, which turn out to be the only inconsistencies that matter. Finally, the speaker encourages viewers to ask any questions they may have about this topic.