Machine Learning and Artificial Intelligence (AI)

Machine Learning at Carnegie Mellon University is the number 1 school globally for Artificial Intelligence and Machine Learning, our faculty members are world renowned due to their contributions to Machine Learning and AI, multiple awards and professorships.

What is Machine Learning

Manuela Veloso, Chair at the Machine Learning Department at Carnegie Mellon University provides us with this definition: Machine Learning (ML) is a fascinating field of Artificial Intelligence (AI) research and practice where we investigate how computer agents can improve their perception, cognition, and action with experience. Machine Learning is about machines improving from data, knowledge, experience, and interaction. Machine Learning utilizes a variety of techniques to intelligently handle large and complex amounts of information build upon foundations in many disciplines, including statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural language processing. AI agents with their core at Machine Learning aim at interacting with humans in a variety of ways, including providing estimates on phenomena, making recommendations for decisions, and being instructed and corrected. In our Machine Learning Department, we study and research the theoretical foundations of the field of Machine Learning, as well as on the contributions to the general intelligence of the field of Artificial Intelligence. In addition to their theoretical education, all of our students, advised by faculty, get hands-on experience with complex real datasets. Machine Learning can impact many applications relying on all sorts of data, basically any data that is recorded in computers, such as health data, scientific data, financial data, location data, weather data, energy data, etc.As our society increasingly relies on digital data, Machine Learning is crucial for most of our current and future applications.

Tom M. Mitchell, Former Chair at the Machine Learning Department at Carnegie Mellon University provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.

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The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. Tom M. Mitchell, Former Chair at the Machine Learning Department at Carnegie Mellon University provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed. Machine learning tasks: Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning "signal" or "feedback" available to a learning system: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback: Semi-supervised learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling. Reinforcement learning: training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Machine learning applications: Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system: In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised manner. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are "spam" and "not spam". In regression, also a supervised problem, the outputs are continuous rather than discrete. In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task. Density estimation finds the distribution of inputs in some space. Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked with finding out which documents cover similar topics. Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. History and relationships of Machine Learning to other fields: Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM. As a scientific endeavour, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. It also benefited from the increasing availability of digitized information, and the ability to distribute it via the Internet. Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.

What is Artificial Intelligence?

Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

Symbolic: When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI". During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field. Cognitive simulation: Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.Artificial Intelligence Artificial Intelligence and Machine Learning Artificial Intelligence Major Goals AI Artificial Intelligence Knowledge Reasoning Artificial Intelligence Planning Artificial Intelligence Machine Learning Artificial Intelligence Natural Language Processing Artificial Intelligence Computer Vision Artificial Intelligence Robotics Artificial General Intelligence Artificial Intelligence Approaches Artificial Intelligence Symbolic Artificial Intelligence Deep Learning Artificial Intelligence Bayesian Networks Artificial Intelligence Evolutionary Algorithms Artificial Intelligence Ethics Artificial Intelligence Existential Risk Artificial Intelligence Turing Test Artificial Intelligence Chinese Room Artificial Intelligence Friendly AI Artificial Intelligence Timeline Artificial Intelligence History Artificial Intelligence Progress Artificial Intelligence AI Winter Artificial Intelligence Applications Artificial Intelligence Technology Artificial Intelligence Philosophy Artificial Intelligence Projects Artificial Intelligence Programming Languages Artificial Intelligence Data Science Artificial Intelligence and ML Artificial Intelligence Glossary Artificial Intelligence History Artificial Intelligence Definition What is Artificial Intelligence What is Machine Learning Artificial Intelligence Basics Artificial Intelligence Problems Artificial Intelligence Reasoning and Problem Solving Artificial Intelligence Perception Artificial Intelligence Knowledge Representation Artificial Intelligence Motion Artificial Intelligence Manipulation Artificial Intelligence Social Intelligence Artificial Intelligence General Intelligence Artificial Intelligence Sub-Symbiotic Artificial Intelligence Symbiotic Artificial Intelligence Sub-Symbolic Artificial Intelligence Symbolic Artificial Intelligence Statistical Learning Artificial Intelligence Search and Optimization Artificial Intelligence Evaluating Progress Artificial Intelligence Healthcare Artificial Intelligence Automotive Artificial Intelligence Industry Artificial Intelligence Finance Artificial Intelligence Economics Artificial Intelligence Video Games Artificial Intelligence Movie Industry Artificial Intelligence Military Artificial Intelligence Audits Artificial Intelligence Government Artificial Intelligence Education Artificial Intelligence Philosophy Artificial Intelligence and Ethics Artificial Intelligence Limits Artificial Intelligence Potential Risks Artificial Intelligence Moral Reasoning Artificial Intelligence Machine Consciousness Artificial Intelligence Singularity Artificial Intelligence Sentience Artificial Intelligence Mind Artificial Intelligence Superintelligence Artificial Intelligence References Artificial Intelligence Textbooks Artificial Intelligence Sources Artificial Intelligence Further Reading Artificial Intelligence History Artificial Intelligence Logic Artificial Intelligence Linear Algebra Artificial Intelligence Mathematical Artificial Intelligence Math Artificial Intelligence Bachelors Bachelors of Artificial Intelligence Bachelors of AI Bachelor Degree in AI Bachelor Degree in Artificial Intelligence Artificial Intelligence Probabilistic Methods Artificial Intelligence Uncertain Reasoning Artificial Intelligence Classifiers Artificial Intelligence Machine Learning Methods Artificial Intelligence Methods Artificial Intelligence Learning

Advancing in the Age of AI

Advancing in the Age of AI – We defy current Machine Learning research and applications. We work on solving high-level difficulty AI problems.