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Driving The Next Generation of AI

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This article is a response to an article arguing that an AI Winter maybe inevitable. However, I believe that there are fundamental differences between what happened in the 1970s (the fist AI winter) and late 1980s (the second AI winter with the fall of Expert Systems) with the arrival and growth of the internet, smart mobiles and social media resulting in the volume and velocity of data being generated constantly increasing and requiring Machine Learning and Deep Learning to make sense of the Big Data that we generate. For those wishing to see a details about what AI is then I suggest reading an Intro to AI, and for the purposes of this article I will assume Machine Learning and Deep Learning to be a subset of Artificial Intelligence (AI). AI deals with the area of developing computing systems that are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. The rapid growth in Big Data has driven much of the growth in AI alongside reduced cost of data storage (Cloud Servers) and Graphical Processing Units (GPUs) making Deep Learning more scalable.


Exoplanet Detection using Machine Learning

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We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of these methods to improve the conventional algorithm based approach used in astrophysics today to detect exoplanets. We used the popular time-series analysis library'TSFresh' to extract features from lightcurves. For each lightcurve, we extracted 789 features.


Convolutional Layer

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The Conv layer is the core building block of a Convolutional Network that does most of the computational heavy lifting. Overview and intuition without brain stuff. Let’s first discuss what the CONV…


The unreasonable effectiveness of deep learning in artificial intelligence

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Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and nonconvex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures, and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals. In 1884, Edwin Abbott wrote Flatland: A Romance of Many Dimensions (1) (Figure 1). This book was written as a satire on Victorian society, but it has endured because of its exploration of how dimensionality can change our intuitions about space. Flatland was a 2-dimensional (2D) world inhabited by geometrical creatures.


Artificial Intelligence Explained in Simple Terms

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There are many great articles about Artificial Intelligence (AI) and its benefits for business and society. However, many of these articles are too technical for the average reader. I love reading about AI, but I sometimes think to myself, 'Gee, I wish the author had explained this in simple English.' I will try and explain AI and its related technologies in simple terms, using real-life examples, as though I were talking to someone at a party. Your colleagues or your (close) friends may tolerate your endless and complex ramblings, but I guarantee you that people at parties are far less forgiving.


Driving The Next Generation of AI

#artificialintelligence

This article is a response to an article arguing that an AI Winter maybe inevitable. However, I believe that there are fundamental differences between what happened in the 1970s (the fist AI winter) and late 1980s (the second AI winter with the fall of Expert Systems) with the arrival and growth of the internet, smart mobiles and social media resulting in the volume and velocity of data being generated constantly increasing and requiring Machine Learning and Deep Learning to make sense of the Big Data that we generate. For those wishing to see a details about what AI is then I suggest reading an Intro to AI, and for the purposes of this article I will assume Machine Learning and Deep Learning to be a subset of Artificial Intelligence (AI). AI deals with the area of developing computing systems that are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. The rapid growth in Big Data has driven much of the growth in AI alongside reduced cost of data storage (Cloud Servers) and Graphical Processing Units (GPUs) making Deep Learning more scalable.


DeepMind co-founder: Gaming inspired AI breakthrough

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And there's been a long-standing more than 50-year-old grand challenge in science, which is can you go from the amino acid sequence - which is like a genetic sequence of letters that describes a protein - can you just from that one-dimensional letter sequence come up with a 3D structure?


Search and Rescue Drones Use AI to Find People Lost in Woods

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New drones equipped with a deep learning application that improves the images they collect during search and rescue missions can better distinguish people from their surroundings. Researchers from Austria's Johannes Kepler University have developed drones equipped with a deep learning application that improves the images they collect during search and rescue missions to better distinguish people from their surroundings. The team noted, "automated person detection under occlusion conditions can be notably improved by combining multi-perspective images before classification." The researchers achieved 96% precision and 93% recall rates with image integration using airborne optical sectioning, a synthetic aperture imaging technique that captures unstructured thermal light fields using camera drones, compared to 25% achieved by traditional thermal imaging. The researchers say the drones are ready for use.


Deep Learning with TensorFlow 2.0 [2020]

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Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding


Convolutional Neural Networks (CNNs) Tutorial with Python

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A CNN is a particular kind of multi-layer neural network [2] to process data with an apparent, grid-like topology. The base of its network bases on a mathematical operation called convolution. Fundamentally, machine learning algorithms use matrix multiplication, but in contrast, CNNs use convolutions in place of matrix multiplications at least in one layer -- a convolution is a specialized kind of linear operation. Convolutional neural networks (CNNs) are undoubtedly the most popular deep learning architecture. Their applications are everywhere, including image and video recognition, image analysis, recommendation systems, natural language processing, computing interfaces, financial time-series, and several others [3].