This thesis considers sequential decision problems, where the loss/reward incurred by selecting an action may not be inferred from observed feedback. A major part of this thesis focuses on the unsupervised sequential selection problem, where one can not infer the loss incurred for selecting an action from observed feedback. We also introduce a new setup named Censored Semi Bandits, where the loss incurred for selecting an action can be observed under certain conditions. Finally, we study the channel selection problem in the communication networks, where the reward for an action is only observed when no other player selects that action to play in the round. These problems find applications in many fields like healthcare, crowd-sourcing, security, adaptive resource allocation, among many others. This thesis aims to address the above-described sequential decision problems by exploiting specific structures these problems exhibit. We develop provably optimal algorithms for each of these setups with weak feedback and validate their empirical performance on different problem instances derived from synthetic and real datasets.

This paper presents a novel approach to the acquisition of language models from corpora. The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of probabilistic concepts that used a tabular, attribute-value encoding of training cases and concepts, making it unsuitable for sequential input like language. In response, we explore three new extensions to Cobweb -- the Word, Leaf, and Path variants. These systems encode each training case as an anchor word and surrounding context words, and they store probabilistic descriptions of concepts as distributions over anchor and context information. As in the original Cobweb, a performance element sorts a new instance downward through the hierarchy and uses the final node to predict missing features. Learning is interleaved with performance, updating concept probabilities and hierarchy structure as classification occurs. Thus, the new approaches process training cases in an incremental, online manner that it very different from most methods for statistical language learning. We examine how well the three variants place synonyms together and keep homonyms apart, their ability to recall synonyms as a function of training set size, and their training efficiency. Finally, we discuss related work on incremental learning and directions for further research.

In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics.

Is broccoli or beetroot the best option for your personal health? Which foods are likely to trigger the biggest glucose spikes or cholesterol surges in your blood? Custom diets are the next frontier in nutrition science – and artificial intelligence (AI) will play a key role in figuring out what each of us should, and shouldn't, be eating. An ambitious program called Nutrition for Precision Health (NPH) kicked off in the US in January 2022 when the National Institutes of Health (NIH) awarded $170 million to fund institutions across the country to conduct a five-year study with 10,000 participants. CNN spoke to Holly Nicastro, program director in the NIH office of nutrition research and coordinator of NPH, about the aims and scale of the project and how AI can benefit our health by helping to devise optimal diets for every individual.

Arthur C. Clarke once remarked, "Any sufficiently advanced technology is indistinguishable from magic." That ambient sense of magic has been missing from the past decade of internet history. Each new tablet and smartphone is only a modest improvement over its predecessor. The expected revolutions--the metaverse, blockchain, self-driving cars--have plodded along, always with promises that the real transformation is just a few years away. The one exception this year has been in the field of generative AI.

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available algorithms. Gain a good understanding of how to plan a Machine Learning project.

AWS has many advanced and useful ML/AI services. If you would like to get a general understanding of AWS ML/AI services, this course is for you. The course starts with a high-level understanding of ML, AI, Computer Vision, and Robotics. Then, you will get a high-level overview of many AWS ML services. You will learn about these services with the help of diagrams and key use cases.

AI has touched all aspects of human existence, be it business, travel, medical services or training. Innovation is developing rapidly, and with the increase in its speed, this direction will disturb the business more than ever. To be sure, teachers and educators cannot be replaced, however, it is also a fact that revolutionary innovations, for example, ML will, fundamentally change traditional positions and create new prescribed processes. The world of schooling is becoming more customized as it is proving to be more profitable. The powerful idea of ML leaves many potentially open doors for commitment to learning.

I came of age in the 1980s, programming personal computers such as the Commodore VIC-20 and Apple ][e at home. Going on to study computer science (CS) in college and ultimately getting a Ph.D. at Berkeley, the bulk of my professional training was rooted in what I will call "classical" CS: programming, algorithms, data structures, systems, programming languages. In Classical Computer Science, the ultimate goal is to reduce an idea to a program written by a human--source code in a language like Java or C or Python. Every idea in Classical CS--no matter how complex or sophisticated, from a database join algorithm to the mind-bogglingly obtuse Paxos consensus protocol--can be expressed as a human-readable, human-comprehendible program. When I was in college in the early 1990s, we were still in the depths of the AI Winter, and AI as a field was likewise dominated by classical algorithms.

Generations of computing professionals may remember Frederick P. Brooks, Jr., as the author of the seminal text on system engineering, The Mythical Man-Month: Essays on Software Engineeringa and his essays such as No Silver Bullet--Essence and Accident in Software Engineering.b Those who worked with Brooks, winner of the 1999 ACM A.M. Turing Award "for landmark contributions to computer architecture, operating systems, and software engineering," may also remember him as the lead designer of IBM's System/360, as an innovator in graphics and virtual reality, and as the founder of the University of North Carolina's computer science department. Brooks was born on April 19, 1931, in Greenville, North Carolina. He received his A.B. in Physics from Duke University in 1953. As a freshman, he saw an article in the January 23, 1950 issue of Time Magazine entitled "The Thinking Machine" that sparked his interest in computing.