Why IoT Needs Machine Learning to Thrive IoT For All

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Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights? The simple answer is yes, and it lies in machine learning (ML). The scope of ML is to mimic the way the human brain processes inputs to generate logical responses.


A technique to improve machine learning inspired by the behavior of human infants

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From their first years of life, human beings have the innate ability to learn continuously and build mental models of the world, simply by observing and interacting with things or people in their surroundings. Cognitive psychology studies suggest that humans make extensive use of this previously acquired knowledge, particularly when they encounter new situations or when making decisions. Despite the significant recent advances in the field of artificial intelligence (AI), most virtual agents still require hundreds of hours of training to achieve human-level performance in several tasks, while humans can learn how to complete these tasks in a few hours or less. Recent studies have highlighted two key contributors to humans' ability to acquire knowledge so quickly--namely, intuitive physics and intuitive psychology. These intuition models, which have been observed in humans from early stages of development, might be the core facilitators of future learning.


3 Levels of Deep Learning Competence

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Deep learning is not a magic bullet, but the techniques have shown to be highly effective in a large number of very challenging problem domains. This means that there is a ton of demand by businesses for effective deep learning practitioners. The problem is, how can the average business differentiate between good and bad practitioners? As a deep learning practitioner, how can you best demonstrate that you can deliver skillful deep learning models? In this post, you will discover the three levels of deep learning competence, and as a practitioner, what you must demonstrate at each level.


How to get started with Machine Learning in about 10 minutes

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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.


Build a simple Neural Network with TensorFlow.js - Towards Data Science

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TL;DR Build a simple Neural Network model in TensorFlow.js to make a laptop buying decision. Learn why Neural Networks need activation functions and how should you initialize their weights. It is in the middle night, and you're dreaming some rather alarming dreams with a smile on your face. Suddenly, your phone starts ringing, rather internationally. You pick up, half-asleep, and listen to something bizarre.


Current status of use of big data and artificial intelligence in RMDs

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Objective To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).


AI Gaining Foothold in University Advancement

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"Artificial intelligence should improve all that it touches and now that we're seeing the impact of AI on advancement, it's critical that we apply this …


Column: Can AI solve renewable energy's problems? India may show the way - Reuters

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LAUNCESTON, Australia (Reuters) - One of humankind's most enduring weaknesses is to assume that the way things are presently will somehow persist into the future, and that current trends are inexorable. This thinking is behind the often repeated view that renewable energy sources such as wind and solar cannot replace thermal electricity generation such as coal and natural gas. Presently, it is correct that the most significant weakness of these renewables is that they are intermittent, meaning they don't generate close to their installed capacity and cause instability in electricity grids. While storage through batteries or pumped hydro is often touted as a solution to the drawbacks of wind and solar, there are other emerging technologies that may well make renewables more effective. One of those is harnessing artificial intelligence (AI) to improve the efficiency of wind and solar by using machine learning programmes to enhance predictability of generation and grid stability.


IoT's Role in Revolutionizing Agriculture

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It's rare to see tech headlines about agriculture, but the field (pardon the pun) is often at the forefront of implementing new technology Perhaps no recent tech development has had a greater impact on the industry than smart technology, and this IoT data is being used to improve operations across nearly all modern farming operations around the globe. Here are a few examples. Farmers were among the first to adopt GPS technology; John Deere was the first tractor manufacturer to implement GPS technologies in the early 1990s, and farmers quickly began using GPS assistance and even automated steering to reduce user errors. GPS technology can be combined with sensor data to create ultra-precise maps of varying factors. Knowing how soil quality varies across large plots of land, for example, can help farmers know which areas need which type of fertilizers.


AI software market posts huge growth amid digital transformation boom - CityAM

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Revenue from AI software soared more than 63 per cent to $846m (£664m) last year amid a surge in firms investing in the new technology. Robotic process automation (RPA), which provides AI tools for businesses, is now the fastest-growing segment of the enterprise software market, with revenue set to reach $1.3bn in 2019, according to research firm Gartner. "The RPA market has grown since our last forecast, driven by digital business demands as organisations look for'straight-through' processing," said Fabrizio Biscotti, research vice president at Gartner. "Competition is intense, with nine of the top 10 vendors changing market share position in 2018." The top five RPA vendors controlled 47 per cent of the market in 2018, though the vendors ranked sixth and seventh posted trip-digit revenue growth in a sign of heightening competition.