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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.