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50 Machine Learning and Data Science Companies That Are Revolutionizing Industries

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Nowadays it's hard to find a single industry where machine learning and data science aren't being used to improve productivity and deliver results. Indeed that is why people are so excited about the promise of artificial intelligence: it can be applied to so many diverse problem spaces effectively and it works! This list has been aggregated after analyzing over 200 company descriptions, and we've broadly organized them by the problem domain being tackled and have included a brief description of their mission. TLDR: A framework for providing data integrations and web interfaces for trained machine learning models. TLDR: Develops medical imaging tools powered by AI to help improve the efficacy of radiologists in detecting illnesses.


Augmented Workforce: The Emerging Trend towards the Future of Work

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The development and advancements of technology are rapidly changing the nature of work and the workforce. The relentless growing connectivity and cognitive technologies are making it possible where humans and machines can work side-by-side at a shared workplace, enhancing the abilities of the human workforce. Today, as the workplaces are evolving to a flexible workforce driven by technology advances, software, automation, IoT, robotics, and artificial intelligence, among others, almost every job in every discipline is being revived. With the introduction of new technology, companies now have opportunities to power the workplace and augment their workforce to perform tasks effortlessly. The increasing proliferation of intelligent automation into the workplace is taking away a lot of tedious, repetitive works that used to overwhelm workflow, freeing up employees to focus on more valuable tasks.


Deep Learning for Object Detection: A Comprehensive Review

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With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification.


Best Books to Expand Your NLP Knowledge

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The abundance of knowledge and resources can be at times overwhelming specifically when you are talking about new age technologies like Natural Language Processing or what we popularly call it as NLP. When trying to educate yourself, you should always choose resources with solid base and fresh books to impart unprecedented package of learnings. Here is the list of top books that can help you expand your NLP knowledge. One of the most widely referenced and recommended NLP books, written by Stanford University professor Dan Jurafsky and University of Colorado professor James Martin, provides a deep-dive guide on the subject of language processing. It's intended to accompany undergraduate or advanced graduate courses in Natural Language Processing or Computational Linguistics. However, it's a must-read for anyone diving into the theory and application of language processing as they grow and strengthen their analytics capabilities.


Ethically accelerating businesses with the use of Artificial Intelligence

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Businesses have an opportunity to discover the opportunities and challenges as well as benefits of Artificial Intelligence in a free Immersive Tech Session on Monday 6th of July from 10am to 12pm. The session will be hosted by Wo King from Hi9 and Tariq Rashid who leads the Data Science Cornwall community. Like any powerful technology, Artificial Intelligence (AI) has ethical considerations as well as benefits. Citizens, businesses and public bodies are increasingly exploring how to understand and address these risks and this session will explore a series of relatable scenarios to provoke a discussion of what can make AI-based services unsafe, unfair and unethical. Issues that will emerge include poor and biased data quality, algorithmic transparency, tech culture and engineering discipline, explainability, and the more challenging question of corporate ethics.


Best of arXiv.org for AI, Machine Learning, and Deep Learning – May 2020 - insideBIGDATA

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Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.


IoT Anomaly detection - algorithms, techniques and open source implementation

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Learning classifiers for misuse and anomaly detection using a bag of system calls representation. Anomaly detection in health data based on deep learning. Abnormal human activity recognition using SVM based approach. Anomaly detection of gas turbines based on normal pattern extraction. Contextual anomaly detection for a critical industrial system based on logs and metrics.


Machine Learning Simplified

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As we discussed previously, Machine Learning refers to algorithms that are used to identify patterns within data. But what exactly do we mean by "patterns", what all can we do with ML, and what is all this jargon about "models" and "training" them. In this article, I'll try to explain all this without getting too technical, and what you, as a business-user, should know about Machine Learning. Supervised Learning implies use-cases where we have a target we're trying to predict given the data. Supervised algorithms enable us to predict the target (for example the estimated credit limit, tractor sales, if the customer will churn, or the mail category) using the input data (customer's credit history, weather and macroeconomic conditions, customer's activity on the platform, mail specifications). There are models both for Regression and Classification problems, i.e. algorithms which can solve these types of problems.


Top Artificial Intelligence Books to Read in 2020

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A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking. Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades.


Health Checks for Machine Learning - A Guide to Model Retraining and Evaluation

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In 2013, IBM and University of Texas Anderson Cancer Center developed an AI based Oncology Expert Advisor. According to IBM Watson, it analyzes patients medical records, summarizes and extracts information from vast medical literature, research to provide an assistive solution to Oncologists, thereby helping them make better decisions. According to an article on The Verge, the product demonstrated a series of poor recommendations. Like recommending a drug to a lady suffering from bleeding that would increase the bleeding. "A parrot with an internet connection" - were the words used to describe a modern AI based chat bot built by engineers at Microsoft in March 2016. 'Tay', a conversational twitter bot was designed to have'playful' conversations with users. It was supposed to learn from the conversations. It took literally 24 hours for twitter users to corrupt it.