Artificial Intelligence: Foundations Of Computational Agents 3rd Edition David L. Poole , Alan K. Mackworth - Solutions

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A. What is the role of human feedback in Reinforcement Learning from Human Feedback (RLHF)?1. Human feedback is used to specify the reward function for the AI model.2. Human feedback is used to train the AI model to improve its decisions.3. Human feedback isn’t required in RLHF.4. Human feedback
D. Why is the reward function crucial in reinforcement learning?1. It provides the rules the agent needs to follow 2. It provides feedback based on the agent's actions and encourages the agent to take actions that lead to better outcomes 3. It guides the agent's exploration of the environment 4. It
C. In the context of reinforcement learning, what does the explorationexploitation tradeoff refer to?1. The balance between trying new actions and sticking with known effective ones 2. The tradeoff between maximizing rewards and minimizing penalties 3. The balance between the speed of learning and
B. What is the goal of an agent in reinforcement learning?1. To maximize negative feedback 2. To maximize positive rewards 3. To minimize positive rewards 4. To minimize negative feedback
A. Reinforcement learning is akin to which type of human learning process?1. Memorization 2. Trial and error 3. Observation 4. Theory formation
D. In the context of DALL-E, the model generates a new image from a description. How does this demonstrate self-supervised learning?1. It doesn't - this is an example of supervised learning.2. DALL-E learns to understand the inherent relationship between different concepts and generates a coherent
C. How does the predictive text feature on a smartphone relate to selfsupervised learning?1. It's not related since it's a supervised learning task.2. The feature uses self-supervised learning to predict the next word based on the context of the preceding words.3. The feature uses self-supervised
B. Consider the analogy of solving a puzzle for self-supervised learning.What does each piece of the puzzle represent?1. Each piece represents a label provided by a human annotator.2. Each piece represents a separate model trained on a specific task.3. Each piece represents an individual data point
A. Which of the following best describes self-supervised learning?1. It is purely supervised learning with human-provided labels.2. It's a type of unsupervised learning that doesn't require labels.3. It is a method where labels are automatically generated from the input data itself.4. It is a
D. What is one limitation of unsupervised learning?1. It cannot find patterns in unlabeled data.2. It always requires a large amount of labeled data.3. The results may not always be interpretable or easily understood.4. It cannot handle large and complex datasets.
C. What is dimensionality reduction?1. A technique that involves increasing the number of variables in a dataset.2. A technique that involves reducing the number of features or variables in a dataset while retaining as much information as possible.3. A technique used to detect anomalies in a
B. Which unsupervised learning technique is commonly used in customer segmentation in marketing?1. Classification 2. Clustering 3. Regression 4. Reinforcement learning
A. What is unsupervised learning?1. A type of machine learning where the goal is to find patterns in labeled data.2. A type of machine learning where the goal is to find patterns or structure in unlabeled data without any specific outcome in mind.3. A type of machine learning where the goal is to
D. What is the main difference between Classification and Regression tasks in Supervised Learning?1. Classification predicts a numerical value, while Regression predicts a category 2. Classification is used for large data sets, while Regression is used for smaller ones 3. Classification predicts a
C. What are the two main types of tasks in Supervised Learning?1. Regression and Association 2. Classification and Clustering 3. Regression and Classification 4. Association and Clustering
B. Which of the following is an example of a Supervised Learning task?1. Clustering customers into different groups based on their purchasing behavior 2. Predicting housing prices based on factors like location and number of bedrooms 3. Finding hidden patterns in user interaction data on a website
A. What is Supervised Learning in Machine Learning?1. An algorithm learning from unlabeled data 2. An algorithm learning from labeled data 3. An algorithm learning from semi-labeled data 4. An algorithm learning from the environment
C. Which type of machine learning is well-suited to problems where the target variable is known?1. Supervised Learning 2. Unsupervised Learning 3. Reinforcement Learning 4. All of the above
B. What does it mean to "train" a machine learning model?1. It means programming the model with specific instructions for every possible scenario 2. It refers to the process where the model learns patterns from large amounts of data to improve accuracy over time 3. It involves tuning the hardware
A. What is the key difference between expert systems and machine learning?1. Expert systems are manual while machine learning is automated 2. Expert systems require human experts for rules and knowledge encoding, while machine learning uses algorithms to learn patterns in data 3. Expert systems are
B. What are the advantages of expert systems?1. They can provide consistent and reliable decision-making in a specific domain 2. They can easily adapt to new or unexpected situations 3. They can capture and preserve the knowledge of human experts 4. They require minimal resources to build and
A. What are expert systems, and what type of artificial intelligence do they represent?1. Systems that employ human experts to make decisions 2. Rule-based systems that simulate human expert decision-making in a specific domain 3. Machine learning algorithms designed to learn from experience 4.
D. As machine learning and AI technologies become increasingly prevalent across industries, why is it necessary for business leaders to understand these technologies?1. To stay competitive.2. To increase the company's stock price.3. To ensure their company is always in the news.4. None of the above.
C. How can understanding machine learning algorithms help in resource allocation?1. It can help identify which projects are likely to yield the most significant returns.2. It helps business leaders to identify the need for more staff.3. It aids in the purchase of better machine learning software.4.
B. Understanding machine learning algorithms isn’t necessary for business leaders because they can always rely on their data science teams to make the right 1. True 2. False
A. Why is it essential for business leaders to understand the basics of machine learning algorithms?1. To communicate more effectively with their data science teams.2. To allocate resources more effectively.3. To address ethical considerations related to machine learning.4. All of the above.
D. What should a governance framework for AI oversee?1. AI strategy, policies, and procedures 2. Only AI strategy 3. Only AI policies 4. Only AI procedures
C. Who is more likely to be liable for errors resulting from tasks delegated to AI?1. The AI model 2. The company using the AI tool 3. The AI vendor 4. The individual who interacts with the AI tool
B. What solution is suggested to mitigate data privacy concerns with pre-trained AI tools?1. Segregating general training data and proprietary data 2. Using encrypted data only 3. Not using proprietary data at all 4. Sharing all data openly for transparency
A. Why is data governance in AI systems important?1. It makes AI models work faster 2. It can prevent the use of unverified or biased data 3. It is a legal requirement 4. It makes AI systems cheaper
D. Which of the following strategies is recommended for AI developers in regard to data sourcing and managing?1. Leveraging only proprietary datasets for AI development to avoid copyright issues.2. Ignoring the provenance of AI-generated content as it is not significant in the development
C. How can companies strike a balance between protecting their competitive advantages and fostering innovation through data sharing?(Choose three)1. Identifying core proprietary data 2. Ignoring open data initiatives 3. Contributing to open data initiatives 4. Keeping all data private 5.
B. Which of the following are benefits of open data initiatives? (Choose three)1. Democratizing AI 2. Reducing storage costs 3. Fostering collaboration 4. Addressing societal challenges 5. Limiting access to data
A. What is the role of open data and data sharing initiatives in AI research and development?1. To provide companies with a competitive advantage 2. To limit access to high-quality data sets 3. To train AI models, test performance, and iterate on algorithms more effectively 4. To discourage
C. Why is it important for companies relying on customer data to continuously adapt and evolve their data acquisition strategies?1. To navigate changing regulations and external factors 2. To reduce the overall cost of data storage 3. To make their AI models look more appealing 4. To eliminate the
B. What are the benefits of having unique proprietary data sets for businesses in the AI era? (Choose three)1. Enhanced performance 2. Reduction in data storage costs 3. Differentiation 4. Barriers to entry 5. Increased simplicity
A. Why is data considered the lifeblood of AI development?1. Data helps in marketing AI products 2. Data is used to make AI models look appealing 3. Data is used to train and refine AI models effectively 4. Data is only necessary for deep learning models
D. What are feedback loops in machine learning systems, and why are they important?1. They are an ongoing source of user inputs.2. They help to mine value from archival and historical data.3. They allow user interactions to inform and refine future outputs.4. They solve the 'cold start' problem in
C. How does synthetic data generation assist in situations where real data is limited?1. By creating artificial information that closely mimics real data.2. By ensuring the data is always 100% accurate.3. By increasing the quantity of real data available.4. By replacing the need for real data
B. How do data augmentation techniques improve the quality of training data?1. By replacing the original data with new data.2. By transforming the data in a way that does not change its underlying meaning.3. By generating synthetic data that closely mimics real data.4. By collecting more user
A. What is one of the main challenges when using user-generated data?1. It is typically outdated.2. It often contains biases.3. It involves substantial responsibility for user privacy.4. It does not provide real-time insights into user behavior.
D. What is Data Science?1. The ability of machines to learn without being explicitly programmed 2. An interdisciplinary field that uses scientific methods to extract knowledge from data 3. A field limited to only statistical methods 4. The process of communicating results and insights to
C. Which of these tasks is more likely to be performed by a machine learning engineer than a data scientist?1. Understanding the business problem 2. Designing and developing machine learning algorithms 3. Exploring and visualizing data 4. Communicating results and insights to stakeholders
B. Which field involves formulating the right questions and identifying relevant problems that can be solved using data?1. Machine Learning 2. Data Science 3. Both 4. Neither
A. Which of the following is NOT a role of a data scientist?1. Data cleaning 2. Designing, developing, and deploying machine learning algorithms 3. Exploratory data analysis 4. Statistical inference
D. In the context of big data, what does feature engineering involve?1. Selecting the best machine learning model 2. Selecting and transforming raw data features 3. Cleaning data from errors and inconsistencies 4. Visualizing data to identify patterns
C. Which of the following types of data is NOT considered structured data?1. Spreadsheet 2. Social media post 3. Relational database 4. CSV file
B. Which of the following tools is commonly used to process and analyze large amounts of data quickly and efficiently?1. Microsoft Excel 2. SQL 3. Hadoop 4. Adobe Photoshop
A. What are the four V's of big data?1. Volume, Variety, Velocity, Veracity 2. Volume, Versatility, Velocity, Veracity 3. Volume, Variety, Validation, Veracity 4. Volume, Variety, Velocity, Verification
A. What role have Graphics Processing Units (GPUs) played in the deep learning boom, and how has their architecture contributed to the acceleration of deep learning model training and inference?1. GPUs have not contributed significantly to the deep learning boom.2. GPUs have been vital in the deep
D. Speech recognition technology is crucial for:1. Automatic speech recognition (ASR)2. Text-to-speech (TTS)3. Speaker identification and verification 4. All of the above
C. Computer vision is a field of AI that enables machines to:1. Understand and interpret visual data from the real world 2. Understand, interpret, and generate human language 3. Predict and tailor content to user preferences 4. Identify rare events or observations which raise suspicions by
B. Which of the following is NOT an application of Natural Language Processing?1. Text classification 2. Image generation 3. Machine translation 4. Sentiment analysis
A. What is Natural Language Processing (NLP)?1. A subfield of AI dealing with image recognition 2. The part of AI that helps machines understand, interpret, and generate human language 3. The technology that translates spoken language into written text 4. A subfield of AI dealing with robotics
D. What philosophical and ethical questions arise from the increasing role of AI in creative processes?1. What will be the price of AI in the future, and who will have access to it?2. How can we improve the efficiency and speed of AI in creative outputs?3. At what point does the creative output
C. How does the phenomenon of anthropomorphism manifest in our interactions with AI, especially in the context of creative endeavors?1. We view AI merely as a tool and do not attribute any human-like characteristics to it.2. We ascribe human-like attributes to AI, often viewing it as a collaborator
B. What is the difference between explicit and implicit context?1. Explicit context refers to unspoken assumptions, while implicit context refers to observable facts.2. Explicit context is easy to communicate and understand, while implicit context relies on intuition and personal history.3.
A. Which of the following is a key difference between human intelligence and machine intelligence?1. Human intelligence lacks common sense reasoning.2. Machine intelligence can understand implicit context better than humans.3. Human intelligence requires extensive data to learn effectively.4.
D. Why is data critical for machine learning?1. Data isn’t important for machine learning algorithms.2. Machine learning algorithms rely on large amounts of data to identify patterns and make predictions.3. Machine learning algorithms perform better with smaller amounts of data.4. Data is only
C. Which of the following is a challenge associated with implementing AI effectively?1. AI has limited applications in various industries.2. AI doesn’t require any investment in technology and talent.3. AI can lead to privacy, ethics, and bias concerns.4. AI has no impact on workforce transition.
B. What is the primary difference between Artificial Intelligence, Machine Learning, and Deep Learning?1. AI is a subfield of ML, and DL is a subfield of AI.2. AI, ML, and DL are interchangeable terms.3. AI is a broad field, ML is a subfield of AI, and DL is a subfield of ML.4. ML and DL are
A. Which of the following statements about Artificial Intelligence (AI)is true?1. AI only focuses on creating algorithms for specific tasks.2. AI involves creating machines that can perform tasks typically requiring human intelligence.3. AI is limited to natural language processing and computer
B. What does a balanced AI implementation strategy entail?1. Only focusing on immediate applications for quick wins.2. Only focusing on long-term strategies and ignoring immediate opportunities.3. Balancing quick wins with long-term strategic work on foundational elements.4. Avoiding AI adoption
A. What is required for a successful transformation towards an AIcentric business model?1. Access to relevant data, a supportive technological infrastructure, and a skilled workforce.2. Just a strong AI software.3. Only a team of AI programmers.4. No change in the organization's current systems.
D. Why is the AI era considered potentially more disruptive than the internet era?1. Because AI cannot improve efficiency or effectiveness 2. Because AI is less integrated into daily life than the internet 3. Because AI has the potential to reshape industries at an even faster pace 4. Because AI
C. In the context of AI disruption, why is it important for companies to foster a culture that embraces change and continuous learning?1. To compete with companies from the internet era 2. To retain traditional business models 3. To resist the adoption of AI technologies 4. To adapt to the rapidly
B. What type of technology is replacing traditional customer service representatives in some industries?1. Blockchain technology 2. Virtual reality 3. Chatbots and AI-powered virtual assistants 4. Augmented reality
A. What characterizes the AI era, as compared to the internet era?1. Decreased levels of connectivity and information exchange 2. Increased focus on automation, data analysis, and machine learning 3. Diminished role of technology in business strategy 4. Reduced importance of data security and cloud
Give an argument for and against each of these propositions, about the possibility of a singularity, when computers will be more intelligent than people.(a) The singularity will never occur as people have common sense that can never be matched by a computer.(b) The singularity has already occurred;
Find some current AI applications and classify the state-of-the-art for that application in terms of the dimensions. Does the application automate what Kahneman [2011] calls System 1 or System 2 (page 58) or neither or both?
Consider one of the following scenarios.(i) You are working for a company and have been asked about the feasibility of building a tool for predicting the results of an upcoming election so they can plan appropriately. You will have access to data about the outcome of previous elections, demographic
For each leaf in the decision tree of Figure 19.1 (page 786) (starting with “use” or “do”), give an example of an application that has the characteristics that would end at that leaf. For example, for the first leaf, give an application where the stakes are low, there is abundant
Consider the need for government legislation, regulation, and enforcement of restrictions on the use of AI. Give three reasons in favor of restrictions and three reasons against. Which, on balance, do you support?
Consider the proposal to require strong professional codes of ethics for AI designers and engineers. Give three reasons in favor of requiring such codes and three reasons against. Which, on balance, do you support?
Discuss how one of the normative ethical systems discussed (virtue, consequentialism, and deontological) could be used in design guidelines for an autonomous car.
Estimate and compare the energy use of deep learning systems and Bitcoin miners, globally. Compare your results to the energy use of a typical city.
Human rights and animal rights are well recognized. Outline a case for or against robot rights. Be specific about the robot rights you are discussing.
Consider the use of robots and companions for the elderly, the disabled, or infants. Investigate and describe briefly the current state of the art for these companions for one of those populations. Present three reasons in favor of their use and three reasons against.
There have been proposals made for a global ban on the use of lethal autonomous weapon systems (LAWS). Investigate and describe the current status of action on a ban on the use of LAWS. Present a brief argument in favor or against such a ban.
Represent the electrical domain of previous chapters as a probabilistic logic program, so that it will run in AIPython (aipython.org) or Problog.The representation should include the probabilistic dependencies of Example 9.15(page 391) and the relations of Example 15.11 (page 656).
Consider diagnosing the errors school students make when adding multi-digit numbers. Consider adding two multi-digit numbers to form a third multi-digit number, as in A1 A0+ B1 B0 C2 C1 C0 where Ai, Bi, and Ci are all digits.(a) Suppose you want to model whether students know the skills of
Suppose you have a relational probabilistic model for movie prediction, which represents P(likes(P, M) | age(P), genre(M))where age(P) and genre(M) are a priori independent(a) What is the treewidth (page 417) of the ground belief network (after pruning irrelevant variables) for querying age(Sam)
Consider Example 17.8 (page 748). Suppose that the motive for X to shoot Y is that they are being paid by someone else, Z, who needs to have motive to kill Y. Draw the corresponding plate model. (If the plates become complicated to draw, just use the arguments of the parametrized random variables).
Suppose Boolean parameterized random variables young(Person)and cool(Item) are parents of Boolean buys(Person, Item). Suppose there are 3000 people and 200 items.(a) Draw this in plate notation.(b) How many random variables are in the grounding of this model?(c) How many numbers need to be
Change the stochastic gradient descent algorithm of Figure 17.2(page 739) that minimizes formula (17.1) so it adjusts the parameters, including regularization, after a batch of examples. How does the complexity of this algorithm differ from the algorithm of Figure 17.2? Which one works better in
An alternative regularization for collaborative filtering is to minimize∑u,i,r ∈D(r(u, i) − r)2 + λ(ib[i]2 + ub[u]2 + ∑p(ip[i, p]2 + up[u, p]2)).(a) How doe this differ from the regularization of formula (17.1) (page 738)?[Hint: Compare the regularization for the items or users with
A variant of the gradient descent algorithm for collaborative filtering (Figure 17.2) can be used to predict P(rating > threshold) for various values of threshold in {1, 2, 3, 4}. Modify the code so that it learns such a probability.[Hint: Make the prediction the sigmoid of the linear function as
For the following, explain how each is categorized by the top-level ontology of Section 16.3.2 (page 723):(a) your skin(b) the period at the end of the first sentence of this chapter (c) the excitement a child has before a vacation (d) the trip home from a vacation (e) a computer program (f) summer
A luxury hotel has multiple rooms to rent, each of which is comfortable and has a view. The hotel must also have more than one restaurant. There must be menu items for vegetarians and for meat eaters to eat in the restaurants.(a) Define a luxury hotel in OWL, based on this description. Make
Suppose a “beach resort” is a resort near a beach that the resort guests can use. The beach has to be near the sea or a lake, where swimming is permitted. A resort must have places to sleep and places to eat. Write a definition of beach resort in OWL.
Write an ontology for the objects that often appear on your desk that may be useful for a robot that is meant to tidy your desk. Think of the categories that (a) the robot can perceive and (b) should be distinguished for the task.
Sam has proposed that any n-ary relation P(X1, X2, X3,..., Xn) can be re-expressed as n − 1 binary relations, namely P1(X1, X2), P2(X2, X3), P3(X3, X4),..., Pn−1(Xn−1, Xn).Explain to Sam why this may not be such a good idea. What problems would arise if Sam tried to do this? Use an example to
Give 10 tuples that are related to the first goal scored in the 2010 FIFA World Cup Final (Q208401 in Wikidata), scored by Andres Iniesta (Q43729) ´at 116 minutes. Either draw the relationships as in Figure 16.1 (page 706) or write the triples using the Wikidata names, as well as a translation
Christine Sinclair (Q262802) was born in Burnaby (Q244025), British Columbia. Give the triples in Wikidata that relate her place of birth to the name of the province she was born in. The result should be a sequence of triples starting with one with Q262802 as the subject, and ending with “British
A travel site has a database that represents information about hotels and feedback from users that uses the relations hotel(Hotel Id, Name, City, Province or state, Country, Address)reported clean(Hotel Id, RoomNumber,Cleanliness, day(Year, Month, Day)).Show how the following facts can be
There are many possible kinship relationships you could imagine, like mother, father, great-aunt, second-cousin-twice-removed, and naturalpaternal-uncle. Some of these can be defined in terms of the others, for example:brother(X, Y) ← father(X, Z) ∧ natural paternal uncle(Y, Z).sister(X, Y) ←
Consider what would happen in Example 15.44 (page 693) if empty course had been defined as empty course(C) ← course(C) ∧ ∼enrolled(S,C).Suppose the rest of the knowledge base is course(cs422). course(cs486). course(cs987).enrolled(huan, cs422). enrolled(sally, cs486).(a) What is Clark’s
Construct a knowledge base and a dictionary based on Figure 15.12 (page 686) to answer geographical questions such as that given in Figure 1.3(page 13). For each query, either show how it can be answered or explain why it is difficult to answer given the tools presented in this chapter.
The extra arguments in a definite-clause grammar makes it strictly more powerful than a context-free grammar. The language {anbncn | n ≥ 0}, which consists of sentences that are made up of a number of as, followed by the same number of bs followed by the same number of cs cannot be defined with a
In this question, you are to write a definite-clause knowledge base for the design of custom video presentations.Assume that the video is annotated using the relation segment(SegId, Duration,Covers)where SegId is an identifier for the segment. (In a real application this will be enough information

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Artificial Intelligence: Foundations Of Computational Agents 3rd Edition David L. Poole , Alan K. Mackworth - Solutions

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