"AI systems–like people–must often act despite partial and uncertain information. First, the information received may be unreliable (e.g., a patient may mis-remember when a disease started, or may not have noticed a symptom that is important to a diagnosis). In addition, rules connecting real-world events can never include all the factors that might determine whether their conclusions really apply (e.g., the correctness of basing a diagnosis on a lab test depends whether there were conditions that might have caused a false positive, on the test being done correctly, on the results being associated with the right patient, etc.) Thus in order to draw useful conclusions, AI systems must be able to reason about the probability of events, given their current knowledge."
– from David Leake, Reasoning Under Uncertainty
A probability on its own is often an uninteresting thing. But when we can compare probabilities, that is when their full splendour is revealed. By comparing probabilities we are able form judgements; by comparing probabilities we can exploit the elements of our world that are probable; by comparing probabilities we can see the value of objects that are rare. In their own ways, all machine learning tricks help us make better probabilistic comparisons. Comparison is the theme of this post--not discussed in this series before--and the right start to this second sprint of machine learning tricks.
If you'd go by the marketing newsletters of leading IT solutions vendors of the world, it would appear that artificial intelligence and machine learning are ideas that have come into being, almost magically, in the past two to three years. Artificial intelligence, in fact, is a term that was coined way back in the 1950s by computer programmers and researchers to describe machines that could respond with appropriate behaviors to abstract problems without human input. Machine learning is one of the more prominent approaches to making artificial intelligence a reality. It is centered on the idea of creating algorithms that are inherently capable of identifying patterns in data and improving their outcomes based on the large datasets. This guide is dedicated to helping you understand and identify the fundamental skills you need to master machine learning technologies and find fulfilling employment in this hot and growing field.
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.
About this course: Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it.
In this post I'll explain what the maximum likelihood method for parameter estimation is and go through a simple example to demonstrate the method. Some of the content requires knowledge of fundamental probability concepts such as the definition of joint probability and independence of events. I've written a blog post with these prerequisites so feel free to read this if you think you need a refresher. Often in machine learning we use a model to describe the process that results in the data that are observed. For example, we may use a random forest model to classify whether customers may cancel a subscription from a service (known as churn modelling) or we may use a linear model to predict the revenue that will be generated for a company depending on how much they may spend on advertising (this would be an example of linear regression).
This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific. It would be quite a daunting task to manually monitor prices and make bidding decisions at all web sites currently offering the camera--especially if accessories such as a flash and a tripod are sometimes bundled with the camera and sometimes auctioned separately. However, for the next generation of trading agents, autonomous bidding in simultaneous auctions will be a routine task.
The Fifth Annual AAAI Mobile Robot Competition and Exhibition was held in Portland, Oregon, in conjunction with the Thirteenth National Conference on Artificial Intelligence. The competition consisted of two events: (1) Office Navigation and (2) Clean Up the Tennis Court. The first event stressed navigation and planning. The second event stressed vision sensing and manipulation. In addition to the competition, there was a mobile robot exhibition in which teams demonstrated robot behaviors that did not fit into the competition tasks.
In this respect, what Pearl seems to have accomplished sometimes looks like a formalism in search of an interpretation without which the truth or the falsity of his claims is often impossible to assess. If the conceptions upon which his view is based do indeed conform to one or another of the traditional Bayesian models, moreover, then the very idea of a probability-based heuristic confronts a number of difficult problems of its own with respect to the distribution of probabilities to sets of alternative hypotheses, paths, or solutions, relative to the proposed refinements of those alternative hypotheses, paths, or solutions.6 These considerations suggest that traditional conceptions should not be taken for granted, especially if we assume that this is what Pearl intends by his observation that "Probability theory is today our primary (if not the only) language for formalizing concepts such as "average" and "likely," and therefore it is the most natural language for describing those aspects of (heuristic) performance that we seek to improve" (p. On general theoretical grounds, I think, there are excellent reasons to suppose that (a)-(f) are fundamental problems in AI science and that an extensional probabilistic analysis of this sort simply cannot lead to their effective solutions. In order to understand the traditional approach, however, this book is recommended with the reservations implied above, namely, that the author has omitted basic definitions that might not be familiar to some readers, and that serious difficulties seem to confront the theoretical framework he apparently endorses, where these difficulties are especially severe from an epistemological perspective.
The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be tradeoffs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area. In problems where actions can lead to a number of different possible outcomes, or where the benefits of executing a plan must be weighed against the costs, the framework of decision theory can be used to compare alternative plans.
This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using in-depth examples to illustrate some of the nuts and bolts of the basic approach.