pattern recognition


A Deepfake Putin and the Future of AI Take Center Stage at Emtech

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Singer talked about how AI now has four big "superpowers." Pattern recognition is the most common, and this is used in many domains, including image recognition, speech recognition, and fraud detection. It can be a universal approximator, as it learns the correlation between input and output, and is able to make predictions about results, which allows it to be used for simulations for things like particle movements at CERN or flight routes, using much less power and much less time than conventional simulations even if it's not quite as accurate. It is good at sequence mapping, used in things like cleaning DNA sequences or language translation. And it works for similarity-based generation--creating the next examples of something, such as creating voices, photos, or video.


Pattern Recognition and Machine Learning (Bishop) - How is this log-evidence function maximized with respect to $\alpha$?

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So it is not obvious that the additional $\alpha$ dependence of $E (\textbf{m}_N)$ that you point out has vanishing derivative, but there it is, it does. I too was puzzled when I saw no mention of it in the text, or in the solution posted for exercise 3.20 asking to deriver the result, which is therefore rather incomplete. A similar thing happens when maximizing the evidence wrt to $\beta$.


The Seven Patterns Of AI

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From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns. The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules.


What You Absolutely Need to Know about CNNs

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CNN stands for Convolutional Neural Networks. It's a class of neural networks that is usually used for image recognition and is based on the idea of … well, convolution. Essentially, convolution here is the way the information is processed by artificial neurons: they take advantage of the hierarchical pattern in images and assemble more complex patterns using smaller and simpler patterns. The neurons are grouped into layers where each layer tries to recognize certain level of detail in small rectangular areas of a picture: neurons in the first layer strive to find lines and dots, then they hand over their findings to the next level, whose task is to analyze the lines and dots and see if they can form a nose, an eye or an ear. The last layer will convolve the found parts into a human face or … not.


See how an AI system classifies you based on your selfie

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Modern artificial intelligence is often lauded for its growing sophistication, but mostly in doomer terms. If you're on the apocalyptic end of the spectrum, the AI revolution will automate millions of jobs, eliminate the barrier between reality and artifice, and, eventually, force humanity to the brink of extinction. Along the way, maybe we get robot butlers, maybe we're stuffed into embryonic pods and harvested for energy. But it's easy to forget that most AI right now is terribly stupid and only useful in narrow, niche domains for which its underlying software has been specifically trained, like playing an ancient Chinese board game or translating text in one language into another. Ask your standard recognition bot to do something novel, like analyze and label a photograph using only its acquired knowledge, and you'll get some comically nonsensical results.


Unsupervised Learning with Clustering Techniques w/Srini Anand

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As humans we are able to discern differences among different groups within a collection. We might group a collection by broad groups such as birds versus plants versus animals or detect subtle features to identify different makes and models of cars. Clustering techniques allow us to automate the process and apply them to data where groupings are not immediately obvious. These techniques are used for different purposes such as detecting market segments, identifying properties of online communities, fraud detection, and cybersecurity. Srini Anand is a Data Scientist at Ameritas Life Insurance Company and holds a Masters degree in Data Science from Indiana University.


Open source and open data

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There's currently an ongoing debate about the value of data and whether internet companies should do more to share their data with others. At Google we've long believed that open data and open source are good not only for us and our industry, but also benefit the world at large. Our commitment to open source and open data has led us to share datasets, services and software with everyone. For example, Google released the Open Images dataset of 36.5 million images containing nearly 20,000 categories of human-labeled objects. With this data, computer vision researchers can train image recognition systems.


r/deeplearning - What creates bias in AI?

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It has nothing to do with any of the things you listed. Machine learning and pattern recognition basically come down to learning a model of the dataset and then predicting something based on that model. If the model is "biased" then it's because the dataset was "biased". I don't understand what you are getting at when you talk about the black/white/male/female stuff. Black/white/male/female are just arbitrary labels defined by you.


A Gentle Introduction to Uncertainty in Machine Learning

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Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any models developed from such data. Managing the uncertainty that is inherent in machine learning for predictive modeling can be achieved via the tools and techniques from probability, a field specifically designed to handle uncertainty. In this post, you will discover the challenge of uncertainty in machine learning. A Gentle Introduction to Uncertainty in Machine Learning Photo by Anastasiy Safari, some rights reserved.


WHAT IS ARTIFICIAL INTELLIGENCE AND HOW DOES IT WORK?

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What are the differences between artificial intelligence and ordinary software? How do intelligent robots work and how exceeds human intelligence? Humans are the smartest creatures we know and artificial intelligence imitates human intelligence. However, in turn, artificial intelligence (AI) is a large area of research within computer science. The goal of the AI area is to create intelligent systems that operate independently of human beings.