If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
ODSC East 2018 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC East 2018 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field. See schedule for many more.. The largest applied data science conference is now 4 days including 2 full training days for even more talks, trainings, and workshops vested in 8 focused courses.
Google's AI chief isn't fretting about super-intelligent killer robots. Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. "The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased," Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see "Biased Algorithms Are Everywhere, and No One Seems to Care").
One of the most amazing things about Python's scikit-learn library is that is has a 4-step modeling pattern that makes it easy to code a machine learning classifier. While this tutorial uses a classifier called Logistic Regression, the coding process in this tutorial applies to other classifiers in sklearn (Decision Tree, K-Nearest Neighbors etc). In this tutorial, we use Logistic Regression to predict digit labels based on images. The image above shows a bunch of training digits (observations) from the MNIST dataset whose category membership is known (labels 0–9). After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image.
AAI's Nineteenth National Conference on Artificial Intelligence (AAAI-04) filled the top floor of the San Jose Convention Center from July 25-29, 2004. The week's program was full of recent advances in many different AI research areas, as well as emerging applications for AI. Within the various topics discussed at the conference, a number of strategic domains emerged where AI is being harnessed, including counterterrorism, space exploration, robotics, the Web, health care, scientific research, education, and manufacturing. Counter-Terrorism / Crisis Management / Defense--For decades, the Department of Defense has been a major funding source for AI research. Since the tragedies of September 11, there has been a new urgency to develop and field AIbased systems to aid the intelligence, defense, and emergency response communities.
Parametric tests are only valid if the data satisfy certain assumptions. If these assumptions hold, they will, however, typically give more accurate results. The analysis of statistical learning theory has very much the flavor of a nonparametric statistical test. The weakness of pac, therefore, is that its results must hold true even in worst-case distributions. There is, however, a new twist to this story in that the more recent pacstyle results are able to take account of observed attributes of the function that has been chosen by the learner, for example, its margin on the training set.
The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this article, we review our work over the last 10 years in the area of supervised learning, focusing on three interlinked directions of research--(1) theory, (2) engineering applications (making intelligent software), and (3) neuroscience (understanding the brain's mechanisms of learnings)--that contribute to and complement each other. Because seeing is intelligence, learning is also becoming a key to the study of artificial and biological vision. In the last few years, both computer vision--which attempts to build machines that see--and visual neuroscience--which aims to understand how our visual system works--are undergoing a fundamental change in their approaches. Visual neuroscience is beginning to focus on the mechanisms that allow the cortex to adapt its circuitry and learn a new task.
Automatic speech recognition is one of the fastest growing and commercially most promising applications of natural language technology. The technology has achieved a point where carefully designed systems for suitably constrained applications are a reality. Commercial systems are available today for such tasks as large-vocabulary dictation and voice control of medical equipment. This article reviews how state-of-the-art speech-recognition systems combine statistical modeling, linguistic knowledge, and machine learning to achieve their performance and points out some of the research issues in the field. Speech is the most natural communicative medium for humans in many situations, including applications such as giving dictation; querying database or information-retrieval systems; or generally giving commands to a computer or other device, especially in environments where keyboard input is awkward or impossible (for example, because one's hands are required for other tasks).
Machine-learning research has been making great progress in many directions. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models. This explosion has many causes: First, separate research communities in symbolic machine learning, computational learning theory, neural networks, statistics, and pattern recognition have discovered one another and begun to work together. Second, machine-learning techniques are being applied to new kinds of problem, including knowledge discovery in databases, language processing, robot control, and combinatorial optimization, as well as to more traditional problems such as speech recognition, face recognition, handwriting recognition, medical data analysis, and game playing. In this article, I selected four topics within machine learning where there has been a lot of recent activity.
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized.
I review current statistical work on syntactic parsing and then consider part-of-speech tagging, which was the first syntactic problem to successfully be attacked by statistical techniques and also serves as a good warm-up for the main topic--statistical parsing. Here, I consider both the simplified case in which the input string is viewed as a string of parts of speech and the more interesting case in which the parser is guided by statistical information about the particular words in the sentence. Finally, I anticipate future research directions. In this example, I adopt the standard abbreviations: s for sentence, np for noun phrase, vp for verb phrase, and det for determiner. It is generally accepted that finding the sort of structure shown in figure 1 is useful in determining the meaning of a sentence.