Week 1 - Lecture 2.pptx

Introduction to Data Science and AI – Part II Week 1 - Lecture 2 COSC 202 Data Science and AI Fall 2024 The Role of Data Scientists The role of a data scientist is to extract knowledge and meaningful insights from large amounts of data by combining principles and practice...

Introduction to Data Science and AI – Part II Week 1 - Lecture 2 COSC 202 Data Science and AI Fall 2024 The Role of Data Scientists The role of a data scientist is to extract knowledge and meaningful insights from large amounts of data by combining principles and practices from the fields of mathematics, statistics, and computer science, often in a multidisciplinary context. 2 The Role of Data Scientists H T SI G G N NI N +I E A + M T E XT O N WISDOM +C KNOWLEDGE How does a data scientist INFORMATIO Make informed transforms raw N decisions. data into DATA Reveal patterns. actionable Exercise more to wisdom? Organize and Increased step improve your Raw data structure the count leads to health and data. improved sleep fitness A smartwatch quality collects number Daily step count, of steps taken, average heart heart rate, and rate, and hours 3 sleep duration. The Role of Data Scientists What should be done? Work from What will home happen? Why did it Expecting heavy What are the happen? rain tomorrow different types of analytics a data What scientist uses the happened More cloud data for? ? caused sudden drop in temperature Description of the weather over the past month 4 Life Cycle of Data Science Problem Statement Data Collection Data Cleaning Exploratory Data Analysis Data Transformation Modeling Validation Decision Making & Deployment 5 Life Cycle of Data Science - Example 1. Problem: Classify different breeds of dogs, given their pictures. 2. Data collection: Collect pictures of each breed in different lighting, from different angles, correctly labeled. 3. Data Cleaning: Remove duplicates and poor-quality pictures. 4. EDA & Transformation: Distribution counts, heatmaps of the densest points. Resizing images and normalize. 5. Modeling & Validation: Start with some basic baseline model and validate on pictures we haven't trained our model on. 6. Decision Making and Deployment: Good accuracy? Communicate with stakeholders about how to put this in production. 6 What is Computer Science? 7 What is Computer Science? “Computer science is the the study of computers and computing, including their theoretical and algorithmic foundations, hardware and software, and their uses for processing information.” - Britannica It encompasses areas such as: Algorithms Programming languages Software engineering Computer architecture Artificial intelligence, etc. It drives technological innovation and impacts various aspects of everyday life. 8 What is Artificial Intelligence? A term coined by Stanford Professor John McCarthy in 1955. “A branch of computer science, dealing with the simulation of intelligent behaviors in computers” – Merriam-Webster It allows computer systems to perform complex tasks that traditionally required human intervention, such as reasoning, decision-making, or problem- solving. 9 What is Artificial Intelligence? Is a calculator considered AI? What about SIRI? What happens when you ask SIRI for help in a mathematical calculation? 10 What is Machine Learning? “The study and construction of programs that are not explicitly programmed but learn patterns as they are exposed to more data over time.” The more the data, the more the algorithm can learn the underlying patterns Machine Spam learning program Not spam Emails labelled as The more emails the …the better it gets at spam or not spam program sees… classification 11  Rule-based systems! Like rule-based expert systems. They use programmed rules and patterns to simulate What is AI the judgment and behavior but not ML? of a human or an organization that has expertise and experience in a particular field. 12 What is AI but not ML? Examples of Rule-Based Expert Systems: Medical Diagnosis: Based on a patient's symptoms, medical history, and test findings, a rule-based system in AI can make a diagnosis. Fraud Detection: Based on transaction's value, location, and time of day, a rule-based system in AI can be used to spot fraudulent transactions. When the rules are unknown, or too complex to write down, this is when machine learning comes in 13 What is Deep Learning? “Machine learning that involves using very complicated models called “deep neural networks”. Neural networks mimics the way human brains work The models themselves determine the best representation of the original data. In classical machine learning, humans must do this. Requires huge amount of data 14 Deep Learning Breakthroughs Natural Language Computer Vision Processing Example: Image classification Example: Sentiment Analysis 15  Some History AI has gone through several hype cycles where there have been both significant amounts of investments and excitement, as 1950s- well as disappointments. 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 16 1950s- 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 17 Birth of AI Alan Turing published “Computer Machinery and Intelligence” which proposed a test of machine intelligence called The Imitation Game in 1950. Arthur Samuel developed a program to play checkers, the first to ever learn the game independently. John McCarthy coined the word AI and created LISP (List Processing), the first programming language for AI research, which is still in popular use to this day. 18 1950s- 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 19 Growth and Decline This period saw significant interest in AI, but also a decline due to the lack of tangible progress in some applications. The first autonomous vehicle was built by a student at Stanford University. The U.S. government showed little interest in continuing to fund AI research. 20 1950s- 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 21 AI Boom Rapid growth and increased interest due to advanced discoveries in research and increased government funding to support researchers. Deep learning techniques and expert systems became more popular, as both allowed computers to learn from their mistakes and make independent decisions. 22 1950s- 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 23 AI Winter Private investment and the government lost interest in AI and stopped their funding due to the high costs compared to the apparent low returns. The market for specialized hardware based on LISP collapsed due to the emergence of cheaper competitors that were more capable, such as IBM and Apple. This led to the failure of many specialized LISP companies, as the technology became more readily available. 24 1950s- 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 25 Return of Interest This period saw the introduction of the first AI system, Deep Blue, which was able to defeat a world champion in chess. It also brought AI into everyday life with innovations like the first Roomba and the first commercially available speech recognition software on Windows computers. 26 1950s- 1980s 1960s Expert Early Systems Algorithm Neural 1990s-2000s s Networks ML Success 1970s 1980s- Present AI winter 1990s Breakthroughs AI Winter in DL 27 General AI We have witnessed the widespread adoption of common AI tools such as virtual assistants, search engines, and more. This period has also seen the rise of deep learning and big data. 28 Some History 29 Limitations of AI as of Today Limited understanding of context Limited common sense Not yet! Bias Is AI set Limited emotion to take Limited robustness over? 31 Recommended Reading Artificial Intelligence with Python, by Alberto Artasanchez and Prateek Joshi. Publisher: Packt Publishing Ltd, 2nd Edition, 2020. ISBN-10: 183921953X. ISBN-13: 978- 1839219535. – Pages 2-11 32