If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The web has transformed from the world's most widely used document platform to its most widely used application platform. In the past few years, we have seen tremendous growth in the field of AI. Web as a platform is making great progress, allowing developers to ship some excellent experiences leveraging AI advancements. Today, we have devices with great processing power and browsers capable of leveraging them to the full extent. Tech giants have invested heavily in making it easier for developers to ship AI features with their web apps.
Today, Neural Networks have made the headlines in many fields, such as image classification of cancer tissues, text generation, or even credit scoring. Nevertheless, one big issue that is rarely tackled with these models, is the uncertainty of the prediction. When we humans learn, one of our greatest strengths is knowing our weaknesses, and not acting when there is too much uncertainty. However, the same is not true for most machine learning models, where decisions are taken without taking into account the uncertainty. For instance, if you train a classifier on cats and dogs, it will only be able to output cat or dog.
The impact of AI on human lives can be felt the most in the healthcare industry. AI-powered computer vision technology can help bring affordable healthcare to millions of people. Computer vision practices are already in place for sorting and finding images in blogs and retail websites. It also has applications in medicine. Medical diagnosis depends on medical images such as CAT scans, MRI images, X-rays, sonograms, and other images.
The open source AI projects particularly pay attention to deep learning, machine learning, neural network and other applications that are extending the use of AI. Those involved in deep researches have always had the goal of building machines capable of thinking like human beings. For the last few years, computer scientists have made unbelievable progress in Artificial Intelligence (AI) to this extent that the interest in AI project ideas keeps increasing among technology enthusiasts. As per Gartner's prediction, Artificial Intelligence technologies going to be virtually prevalent in nearly all new software products and services. The contribution of open source software development to the rise of Artificial Intelligence is immeasurable.
Face Recognition, Image Classification, Image Enhancement... Is your smartphone capable of running the latest Deep Neural Networks to perform these AI-based tasks? Does it have a dedicated AI Chip? Run AI Benchmark to comprehensively evaluate it's AI Performance! AI Benchmark measures the speed, accuracy and memory requirements for several key AI and Computer Vision algorithms. Among the tested solutions are Image Classification and Face Recognition methods, Neural Networks used for Image Super-Resolution and Photo Enhancement, AI models playing Atari Games and performing Bokeh Simulation, as well as algorithms used in autonomous driving systems.
It's not who has the best algorithm that wins; It's who has the most data -- Andrew Ng. Image classification is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. In this blog I will be demonstrating how deep learning can be applied even if we don't have enough data. I have created my own custom car vs bus classifier with 100 images of each category.
Understanding deep learning technology Understand correlation between deep learning, machine learning and artificial intelligence History of deep learning Deep learning networks Intuition behind deep learning and artificial neural network A Powerful Skill at Your Fingertips Learning the fundamentals of deep learning puts a powerful and very useful tool at your fingertips. Jobs in deep learning area are plentiful, and being able to learn deep learning will give you a strong edge. Deep learning is becoming very popular. Tesla self-driving cars, Alexa, Siri, IBM Deep Blue and Watson are some famous example of deep learning application. Understanding deep learning is vital in information retrieval, image classification and autonomous car driving.
Convolutional Neural Network (CNN) is a special type of deep neural network that performs impressively in computer vision problems such as image classification, object detection, etc. In this article, we are going to create an image classifier with Tensorflow by implementing a CNN to classify cats & dogs. With traditional programming is it not possible to build scalable solutions for problems like computer vision since it is not feasible to write an algorithm that is generalized enough to identify the nature of images. With machine learning, we can build an approximation that is sufficient enough for use-cases by training a model for given examples and predict for unseen data. CNN is constructed with multiple convolution layers, pooling layers, and dense layers.