This document provides an overview of an introduction to artificial intelligence course. It discusses course administration details like the instructor, TAs, meeting times, grading, and textbook. It then covers topics that will be discussed in the course like what AI is, the ingredients of intelligence, history of AI, applications of AI, and goals of AI. Key problems in AI like representation, search, inference, learning, and planning are also summarized. Different design methodologies like thinking rationally to formalize inference and thinking like humans from a cognitive science perspective are contrasted.
Artificial Intelligent introduction or historyArslan Sattar
- Begging for small things can become a habit over time as one comes to rely on getting others to provide even minor things.
- It is better to be self-sufficient as much as possible and only ask for help when truly needed, to avoid forming a dependent mindset.
- Developing independence over small matters builds confidence and strength of character.
The document provides an introduction to artificial intelligence, including definitions of AI, a brief history of the field, and the current state of the art. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that perceive and act to maximize goals. The document also outlines some of the key topics that will be covered in the course, including search, logic, planning, and learning.
The document discusses the history and scope of artificial intelligence, including definitions of AI as designing intelligent systems, different perspectives on what constitutes intelligence, and approaches to AI such as strong AI, weak AI, applied AI, and cognitive AI. It also examines what current AI systems can and cannot do and provides examples of tasks that have been achieved as well as limitations.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system checked.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery problems, like the crankshaft position sensor
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
computer science engineering spe ialized in artificial IntelligenceKhanKhaja1
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
Introduction to Artificial Intelligence.pdfgqgy2nsf5x
Artificial intelligence (AI) is the study of intelligent agents that act rationally to maximize their chances of success. The document discusses several key aspects of AI including definitions, goals, foundations, topics, history and applications. Some of the major topics in AI are search, knowledge representation and reasoning, planning, learning, natural language processing, expert systems, and interacting with the environment through vision, speech recognition and robotics.
The document discusses various topics related to artificial intelligence including definitions of AI, goals of AI, whether machines can think, the Turing test, types of AI tasks including mundane, formal and expert tasks, technologies based on AI such as machine learning, natural language processing, computer vision, and applications of AI such as in healthcare, gaming, finance, data security, social media, travel and more.
Artificial intelligence- The science of intelligent programsDerak Davis
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
This document provides an overview of an introductory lecture on artificial intelligence (AI) given by Subash Chandra Pakhrin. The lecture introduces AI, discussing its definition, components of intelligent behavior, different approaches to AI, example AI systems, and a brief history highlighting Alan Turing's contributions. The goal is for students to understand what AI is, its scope, and currently solved and unsolved problems.
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
-------------------------
This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
Uncharted Together- Navigating AI's New Frontiers in LibrariesBrian Pichman
Journey into the heart of innovation where the collaborative spirit between information professionals, technologists, and researchers illuminates the path forward through AI's uncharted territories. This opening keynote celebrates the unique potential of special libraries to spearhead AI-driven transformations. Join Brian Pichman as we saddle up to ride into the history of Artificial Intelligence, how its evolved over the years, and how its transforming today's frontiers. We will explore a variety of tools and strategies that leverage AI including some new ideas that may enhance cataloging, unlock personalized user experiences, or pioneer new ways to access specialized research. As with any frontier exploration, we will confront shared ethical challenges and explore how joint efforts can not only navigate but also shape AI's impact on equitable access and information integrity in special libraries. For the remainder of the conference, we will equip you with a "digital compass" where you can submit ideas and thoughts of what you've learned in sessions for a final reveal in the closing keynote.
Intel Unveils Core Ultra 200V Lunar chip .pdfTech Guru
Intel has made a significant breakthrough in the world of processors with the introduction of its Core Ultra 200V mobile processor series, codenamed Lunar Lake. This innovative processor marks a fundamental shift in the way Intel creates processors, with a high degree of aggregation, including memory-on-package (MoP). The Core Ultra 300 MX series is designed to power thin-and-light devices that are capable of handling the latest AI applications, including Microsoft's Copilot+ experiences.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
Improving Learning Content Efficiency with Reusable Learning ContentEnterprise Knowledge
Enterprise Knowledge’s Emily Crockett, Content Engineering Consultant, presented “Improve Learning Content Efficiency with Reusable Learning Content” at the Learning Ideas conference on June 13th, 2024.
This presentation explored the basics of reusable learning content, including the types of reuse and the key benefits of reuse such as improved content maintenance efficiency, reduced organizational risk, and scalable differentiated instruction & personalization. After this primer on reuse, Crockett laid out the basic steps to start building reusable learning content alongside a real-life example and the technology stack needed to support dynamic content. Key objectives included:
- Be able to explain the difference between reusable learning content and duplicate content
- Explore how a well-designed learning content model can reduce duplicate content and improve your team’s efficiency
- Identify key tasks and steps in creating a learning content model
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
2. Today’s topics
• Course administration.
• What is AI?
• AI and …
– Cognitive science, philosophy, psychology,
economics, computer science, control theory, …
• History of AI.
• Applications of AI.
• Reading:
– This week: AIMA, Ch. 1
– Next week: AIMA, Ch. 2 & 3
3. Course administration
• Instructor
Vladimir Pavlovic
Office: 312 CoRE
Email: vladimir@cs.rutgers.edu
Web: www.cs.rutgers.edu/~vladimir
Phone: 732-445-2654
Office hours: Mon, 3:00-4:00
• TA
Zhi Wei
Office: 416 Hill
Email: zhwei@paul.rutgers.edu
Phone: 732-445-6996
Office hours: Thu, 2:00-4:00 PM
• Web site
http://www.cs.rutgers.edu/~vladimir/class/cs440
• Mailing list
cs440-fall03@rams.rutgers.edu
4. Course administration (cont’d)
• Lectures: Mon & Wed, 4:30 – 5:50
• Discussion: Wed, 6:35 – 7:30
• Classroom: Arc-105
• Textbook:
Russell & Norvig, "Artificial Intelligence: A Modern Approach", 2nd
Edition, Prentice Hall, 2003. Also referred to as AIMA
• Prerequisites:
CS314 (Principles of Programming Languages). You also need a solid
knowledge of calculus. Some knowledge of probability and linear
algebra will be beneficial.
5. Course administration (cont’d)
• Grading
Homework 30%
Midterm 30%
Final 40%
• Homework assignments
– Weekly, will include programming problems (mini projects).
– Programming in Java / Matlab (Lush? Lisp?)
– Assignments are due in class, on due date.
– No late homeworks accepted!
• Tests
– Midterm, around Oct. 20
– Final
– Closed book, closed notes
6. What is AI?
• What is intelligence?
– “The capacity to learn and solve problems” [Webster
dictionary]
– “The computational part of the ability to achieve goals in the
world. Varying kinds and degrees of intelligence occur in
people, many animals and some machines.” [McCarthy] &
Alice Bot (http://www.alicebot.org/)
– Ability to think and act rationally.
• What are “ingredients” of intelligence?
7. “Ingredients” of intelligence
• Ability to interact with real world
– Perceive, understand, act.
– Language understanding and formation.
– Visual perception.
• Reasoning and planning
– Modeling external world
– Problem solving, planning, decision making
– Ability to deal with unexpected problems, dealing
with uncertainty
8. “Ingredients” of intelligence (cont’d)
• Learning and adaptation
– Continuous update of our model of the world and
adaptation to it
9. What is AI?
• A field that focuses on developing techniques to enable computer
systems to perform activities that are considered intelligent (in humans
and other animals). [Dyer]
• The science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of using
computers to understand human intelligence, but AI does not have to
confine itself to methods that are biologically observable. [McCarthy]
• The study of how to make computer do things which, at the moment,
people do better. [Rich&Knight]
• The design and study of computer programs that behave intelligently.
[Dean, Allen, & Aloimonos]
• The study of [rational] agents that exist in an environment and perceive
and act. [Russell&Norvig]
10. Goals of AI
• Scientific and engineering
– Understanding of computational mechanisms
needed for intelligent behavior
– Intelligent connection of perception and action
– Replicate human intelligence
– Solve knowledge-intensive tasks
– Enhance human-human, human-computer and
computer-computer interaction/communication
11. Some applications of AI
• Game Playing
Deep Blue Chess program beat world champion Gary Kasparov
• Speech Recognition
PEGASUS spoken language interface to American Airlines' EAASY SABRE reseration system, which
allows users to obtain flight information and make reservations over the telephone. The 1990s has seen
significant advances in speech recognition so that limited systems are now successful.
• Computer Vision
Face recognition programs in use by banks, government, etc. The ALVINN system from CMU
autonomously drove a van from Washington, D.C. to San Diego (all but 52 of 2,849 miles), averaging 63
mph day and night, and in all weather conditions. Handwriting recognition, electronics and manufacturing
inspection, photointerpretation, baggage inspection, reverse engineering to automatically construct a 3D
geometric model.
• Expert Systems
Application-specific systems that rely on obtaining the knowledge of human experts in an area and
programming that knowledge into a system.
• Diagnostic Systems
Microsoft Office Assistant in Office 97 provides customized help by decision-theoretic reasoning about an
individual user. MYCIN system for diagnosing bacterial infections of the blood and suggesting treatments.
Intellipath pathology diagnosis system (AMA approved). Pathfinder medical diagnosis system, which
suggests tests and makes diagnoses. Whirlpool customer assistance center.
12. Some applications of AI (cont’d)
• Financial Decision Making
Credit card companies, mortgage companies, banks, and the U.S. government employ AI systems to detect
fraud and expedite financial transactions. For example, AMEX credit check. Systems often use learning
algorithms to construct profiles of customer usage patterns, and then use these profiles to detect unusual
patterns and take appropriate action.
• Classification Systems
Put information into one of a fixed set of categories using several sources of information. E.g., financial
decision making systems. NASA developed a system for classifying very faint areas in astronomical images
into either stars or galaxies with very high accuracy by learning from human experts' classifications.
• Mathematical Theorem Proving
Use inference methods to prove new theorems.
• Natural Language Understanding
Google's translation of web pages. Translation of Catepillar Truck manuals into 20 languages. (Note: One
early system translated the English sentence "The spirit is willing but the flesh is weak" into the Russian
equivalent of "The vodka is good but the meat is rotten.")
• Scheduling and Planning
Automatic scheduling for manufacturing. DARPA's DART system used in Desert Storm and Desert Shield
operations to plan logistics of people and supplies. American Airlines rerouting contingency planner.
European space agency planning and scheduling of spacecraft assembly, integration and verification.
• Robotics and Path planning
NASA’s Rover mission.
• Biology and medicine
Modeling of cellular functions, analysis of DNA and proteins.
• and…
14. Turing test
(A. Turing, “Computing machinery and intelligence”, 1950)
• Interrogator asks questions of two “people” who are out of sight and
hearing. One is a human, the other one a machine.
• 30mins to ask whatever she/he wants.
• To determine only through questions and answers which is which.
• If it cannot distinguish between human and computer, the machine has
passed the test!
• Predicted that in 2000 a machine would have 30% chance of fooling a
lay person for 5min.
• Suggested major components of AI (knowledge, reasoning, language
understanding, learning)
• Anticipated arguments against AI in 50 years to follow
15. Problems with Turing test
• Newel and Simon
– As much a test of the judge as of the machine.
– Promotes artificial con-artists, not intelligence
(Loebner prize, http://www.loebner.net/Prizef/loebner-prize.html)
16. Fundamental Issues for most AI
problems
• Representation
Facts about the world have to be represented in some way, e.g., mathematical logic is one language that is
used in AI. Deals with the questions of what to represent and how to represent it. How to structure
knowledge? What is explicit, and what must be inferred? How to encode "rules" for inferencing so as to find
information that is only implicitly known? How to deal with incomplete, inconsistent, and probabilistic
knowledge? Epistemology issues (what kinds of knowledge are required to solve problems).
• Search
Many tasks can be viewed as searching a very large problem space for a solution. For example, Checkers
has about 1040 states, and Chess has about 10120 states in a typical games. Use of heuristics (meaning
"serving to aid discovery") and constraints.
• Inference
From some facts others can be inferred. Related to search. For example, knowing "All elephants have
trunks" and "Clyde is an elephant," can we answer the question "Does Clyde hae a trunk?" What about
"Peanuts has a trunk, is it an elephant?" Or "Peanuts lives in a tree and has a trunk, is it an elephant?"
Deduction, abduction, non-monotonic reasoning, reasoning under uncertainty.
• Learning
Inductive inference, neural networks, genetic algorithms, artificial life, evolutionary approaches.
• Planning
Starting with general facts about the world, facts about the effects of basic actions, facts about a particular
situation, and a statement of a goal, generate a strategy for achieving that goals in terms of a sequence of
primitive steps or actions.
17. Design methodology and goals
• Focus not just on behavior and I/O, look at reasoning process. Computational model should reflect "how"
results were obtained. GPS (General Problem Solver): Goal not just to produce humanlike behavior (like
ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed
by a person in solving the same task.
Think like humans
"cognitive science"
Ex. GPS
Think rationally =>
formalize inference
process
"laws of thought"
Act like humans
Ex. ELIZA
Turing Test
Act rationally
"satisficing" methods
Human Rational
Act
• Formalize the reasoning process, producing a system that contains logical inference mechanisms that are
provably correct, and guarantee finding an optimal solution. This brings up the question: How do we
represent information that will allow us to do inferences like the following one? "Socrates is a man. All men
are mortal. Therefore Socrates is mortal." -- Aristotle
• Behaviorist approach. Not interested in how you get results, just the similarity to what human results are.
ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the
Turing Test.
• For a given set of inputs, tries to generate an appropriate output that is not necessarily correct but gets the
job done. Rational and sufficient ("satisficing" methods, not "optimal").
18. Brief history of AI
• 1943 McCulloch & Pitts: Boolean circuit model of brain
• 1950 Turing's ``Computing Machinery and Intelligence''
• 1952-69 Look, Ma, no hands!
• 1950s Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
• 1956 Dartmouth meeting: ``Artificial Intelligence'' adopted
• 1965 Robinson's complete algorithm for logical reasoning
• 1966-74 AI discovers computational complexity and Neural network
research almost disappears
• 1969-79 Early development of knowledge-based systems
• 1980-88 Expert systems industry booms
• 1988-93 Expert systems industry busts: ``AI Winter''
• 1985-95 Neural networks return to popularity
• 1988 Resurgence of probability; general increase in technical depth
and ``Nouvelle AI'': ALife, GAs, soft computing
• 1995- Agents agents everywhere…
19. This course
• Search,
• Knowledge representation,
• Planning,
• Uncertainty,
• Learning, and
• Examples and applications in speech and
language modeling, visual perception,
medical informatics, and robotics.