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) …
With the rise of Machine Learning inside industries, the need for a tool that can help you iterate through the process quickly has become vital. Python, a rising star in Machine Learning technology, is often the first choice to bring you success. So, a guide to Machine Learning with Python is really necessary. In my experience, Python is one of the easiest programming languages to learn. There is a need to iterate the process quickly, and the data scientist does not need to have a deep knowledge of the language, as they can get the hang of it real quick.
Machine Learning Opens Pathway For Digital Transformation Dave Fellers As companies face exponentially growing amounts of data that can overwhelm individuals' decision-making ability, machine learning provides a powerful method for helping people improve decision-making bandwidth, responsiveness, accuracy, and consistency of results. But what is machine learning? It is the ability of software systems to learn by studying data to detect patterns and/or by applying known rules to the data for processing. Some of the key areas where machine learning can help are: Categorizing and cataloging information like transactions, accounts, companies, people, etc. Predicting likely outcomes and/or deciding on actions by analyzing identified patterns Identifying previously unknown patterns and relationships within the data Detecting new, anomalous, or unexpected behaviors and events from data Machine learning software systems use specialized algorithms to understand the data and actions being handled by relevant processes and to learn how to improve those processes. As new observations of data, events, responses, and changes in the data environment are analyzed by the algorithms, the machine's performance is improved and refined.
Given ideal circumstances, deep learning models can churn out what appear to be extraordinary feats of near-human or even super-human intelligence. The problem is that in the real world this correlative house of cards comes rapidly crashing down. Rather than "learn" about the world, today's algorithms merely encode databases of simplistic statistical correlations that yield results that are entirely dependent on how similar the inputs are to their training data. Neural translation algorithms hyped as replacing humans in reality oscillate wildly between human fluency and indecipherable gibberish with the change of a single word. Driverless cars can slam on the brakes or accelerate towards an obstacle with the slightest deviation from their training examples, while image understanding algorithms are rendered helpless by a few spurious pixels.
Since the first use of advanced software in asset-intensive industries more than four decades ago, manufacturers have been on a journey to transform their businesses and create added value for stakeholders. Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behavior by recognizing complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences.
Not yours or your friend's or one you saw in a home makeover show, but one purely from your imagination--perhaps your ideal living room. You should have no trouble doing it: We take this kind of imagination for granted. Rarely do we find ourselves wondering how the mind chooses what objects to put into these novel scenes and which ones to exclude. But it's worth reflecting on, perhaps especially for creative types, because our visual imagination appears to be constrained by regularities in visual memories. Diversifying what you see may mean enriching what you can imagine.
Artificial intelligence might be coming for your next job, just not in the way you feared. The past few years have seen any number of articles that warn about a future where AI and automation drive humans into mass unemployment. To a considerable extent, those threats are overblown and distant. But a more imminent threat to jobs is that of algorithmic bias, the effect of machine learning models making decisions based on the wrong patterns in their training examples. A online game developed by computer science students at New York University aims to educate the public about the effects of AI bias in hiring.
To succeed in machine learning, we must do a decent amount of prep work. Just adding data, data, data can lead to false signals and invalid correlations. We can end up missing the signal in all the noise. In "Why Machine Learning Works," computer scientist George Montañez walks the reader through the prerequisites for successful machine learning. He notes that, at its core, machine learning is a form of search algorithm.
Plate from Muybridge's Animal Locomotion series published in 1887. Deep learning has become the dominate lens through which machines understand video. Yet video files consume huge amounts of storage space and are extremely computationally demanding to analyze using deep learning. Certain use cases can benefit from converting videos to sequences of still images for analysis, enabling full data parallelism and vast reductions in data storage and computation. Representing video as still imagery also presents unique opportunities for non-consumptive analysis similar to the use of ngrams for text.
We propose simple solutions to important problems that all data scientists face almost every day. Many statistics, such as correlations or R-squared, depend on the sample size, making it difficult to compare values computed on two data sets of different sizes. Based on re-sampling techniques, use this easy trick, to compare apples with other apples, not with oranges. We propose a generic methodology, also based on re-sampling techniques, to compute any confidence interval and for testing hypotheses, without using any statistical theory. Also, it is easy to implement, even in Excel.
Artificial intelligence is often hailed as a great catalyst of medical innovation, a way to find cures to diseases that have confounded doctors and make health care more efficient, personalized, and accessible. But what if it turns out to be poison? Jonathan Zittrain, a Harvard Law School professor, posed that question during a conference in Boston Tuesday that examined the use of AI to accelerate the delivery of precision medicine to the masses. "I think of machine learning kind of as asbestos," he said. "It turns out that it's all over the place, even though at no point did you explicitly install it, and it has possibly some latent bad effects that you might regret later, after it's already too hard to get it all out."