deep learning AI Artificial Intelligence

The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

A survey on deep learning : one small step toward AI
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Deep learning is a recently-developed field belonging to Artificial Intelligence. It tries to mimic the human brain, which is capable of processing the complex input data, learning different knowledges intellectually and fast, and solving different kinds of complicated tasks

Nuts and bolts of building AI applications using Deep Learning
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Given the safety-critical requirement of autonomous driving and thus the need for extremely high levels of accuracy, a pure end-to-end approach is still challenging to get to work. End- toend works only when you have enough(x, y) data to learn function of needed level of

Chitty-Chitty-Chat Bot: Deep Learning for Conversational AI .
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Conversational AI is of growing importance since it enables easy interaction interface between humans and computers. Due to its promising potential and alluring commercial values to serve as virtual assistants and/or social chatbots, major AI , NLP, and Search

Corrosion detection using AI : a comparison of standard computer vision techniques and deep learning model
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In this paper we present a comparison between standard computer vision techniques and Deep Learning approach for automatic metal corrosion (rust) detection. For the classic approach, a classification based on the number of pixels containing specific red components

Deep learning for conversational AI
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Abstract Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this

Arithmetical Logic for AI Deep Learning
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A logico-mathematical foundation for deep learning in artificial intelligence is proposed using Kroneckers theory of homogeneous polynomials and Hensels lifting lemma in modular arithmetic. It is suggested that such a foundation is appropriate for the multileveled

Online Courses on Machine Learning and Deep Learning : Five Issues Found in a Fully Automated Learning Environment for the Purpose of Scalable AI
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Information technology is becoming increasingly sophisticated and rapidly developing. Although the demand for highly skilled technical human resources is rising, the supply is insufficient. Since this is a global problem, not only industries but also governments have

AI ML NIT Patna at HASOC 2019: Deep Learning Approach for Identification of Abusive Content⋆
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Social media is a globally open place for online users to express their thoughts and opinions. There are numerous advantages of social media but some severe challenges are also associated with it. Antisocial and abusive conduct has become more common due to

A Brief Survey of Visualization Methods for Deep Learning Models from the Perspective of Explainable AI
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The fairly recent success of neural networks in large scale tasks ranging from speech recognition to localization and mapping of autonomous systems, has made them prevalent in state-of-the-art AI applications. Although the success is in part due to algorithmic

Research Project Proposal: Deep Learning AI for Racing Games
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This is especially true in racing games, where AI is typically provided with a simplified physics and vehicle model with respect to the ones the players is subject to. This leads to noticeable incoherencies, such as opponents overcoming physical limitations under the

Spyware Detection and Prevention using Deep Learning AI for user applications
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A user application (Smartphone or personal computers) plays an essential role in our daily life. As usage of smartphones and PCs keeps on increases, every day in one life, each and everyone uses to do every task in their daily life using smartphone or PCs to access

Efficient Deep Learning for Ubiquitous AI
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Together Page 22. Knowledge Distillation Page 23. Model Distillation Student model has much smaller model size! Page 24. Page 25. Page 26. Hardware/Software codesign Energy will soon be one of the determining factors in AI . Either companies will find it too expensive to run energy

Deep learning for the AI industry
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Deep learning is powering a transformation in computational sciences and its industry. The classic paradigm of humans programming the computational operations is challenged today by neural networks capable of self-tunning based on large amounts of sensor data or

State of the Art on: Deep Learning for Video Games AI Development
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With the recent advancements in machine learning and, specifically, deep learning techniques, video games applications for AI research are becoming more and more popular, as they prove to be very useful testbeds for general AI algorithms evaluation . At the same

3.14 From Turing to Deep Learning : Explaining AI through neurons and symbols
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Learning and reasoning have been the subject of great research interest since the dawn of Artificial Intelligence . In the 1950s, Turing already described principles for neural computation, machine learning and formal computational reasoning. Over the last decades

Spatial Data Science in the Context of Deep Learning and AI
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The Convolutional Neural Network (CNN), first conceived in 198 famously came to the fore with the creation of AlexNet, which won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) contest in 2012 (Krizhevsky et. Al.). Refinements have followed

Explainable AI : An Intuitive Analysis of Deep Learning in Medical Imaging and Biosignals.
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The introduction of state-of-the-art Deep Learning (DL) techniques in medicine provided outstanding performance in many cases better than expert clinicians. This initiated the debate on the explainability of these remarkable predictions of DL models. In particular

Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge
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Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long

AI Encountering Interactive Systems: A Deep Learning Reinforced Musical Composition Model
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In this paper, we present an artificial intelligence-based musical composition algorithm for generating harmonic and various arpeggios based on given chords in real-time, which incorporates a recurrent neural network (RNN) with the gated recurrent unit (GRU) and a

AI deep learning tumour detection directly on ER, PR and KI-67 IHC slides yields a single slide automated workflow with high concordance to manual scoring
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Assessment of ER, PR and Ki-67 provides essential prognostic information in the classification of breast carcinomas1. Conventional manual assessment of these biomarkers has shown inter-and intra-observer variation and are both tedious and time demanding

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    Three Main Stages of Artificial Intelligence

    AI is rapidly evolving, which is one reason why a career in AI offers so much potential. As technology evolves, learning improves. Van Loon described the three stages of AI and machine learning development as follow:

    Stage one is machine learning – Machine learning consists of intelligent systems using algorithms to learn from experience.
    Stage two is machine intelligence – Which is where our current AI technology resides now. In this stage, machines learn from experience based on false algorithms. It is a more evolved form of machine learning, with improved cognitive abilities.
    Stage three is machine consciousness – This is when systems can do self-learning from experience without any external data.