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) …
This is the second story in our continuing series covering the basics of artificial intelligence. While it isn't necessary to read the first article, which covers neural networks, doing so may add to your understanding of the topics covered in this one. Teaching a computer how to'see' is no small feat. You can slap a camera on a PC, but that won't give it sight. In order for a machine to actually view the world like people or animals do, it relies on computer vision and image recognition.
Should I learn now… or later? Learning is a universal skill/trait that is acquired by any living organism on this planet. Learning is defined by: the acquisition of knowledge or skills through experience, study, or by being taught. Whether that be a plant learning how to respond to light and temperature, a monkey learning how to peel a banana, or us humans learning how to ride a bike. This commonality is what makes us unique and evolve over time.
One of the more complex and misunderstood topics making headlines lately is artificial intelligence. People like Elon Musk warn that robots could one day destroy us all, while other experts claim that we're on the brink of an AI winter and the technology is going nowhere. Making heads or tails of it all is difficult, but the best place to start is with deep learning. Here's what you need to know. Artificial intelligence has become a focal point for the global tech community thanks to the rise of deep learning.
Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning. In fact, Google's Python-based Deep Learning framework Tensorflow has helped many a developer get up to speed with the technical concepts. Besides videos and free online courses, you must also have a reading list that helps you cover the math and statistics behind the algorithms. While YouTube videos remain the main learning source and a key starting point for beginners, there is a slew of resources, especially books that can help cement fundamental concepts.
So, you want to learn deep learning? Whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills – picking the right deep learning framework to learn is the essential first step towards reaching your goal. We strongly recommend that you pick either Keras or PyTorch. These are powerful tools that are enjoyable to learn and experiment with. We know them both from the teacher's and the student's perspective.
For the last few years the talk has been on Big Data. The world has taken a step forward again – and now the buzz is on AI – artificial intelligence. The very phrase conjures images of Hollywood Robocops, Hals and Hers. But what is the reality? The reality is that AI is undoubtedly coming to a business near you very soon and it will have significant impact on how your and other businesses do business.
Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users' interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In a simple case, you could have two sets of neurons: ones that receive an input signal and ones that send an output signal.
Clustering is one of the most widely used techniques for exploratory data analysis. Its goal is to divide the data points into several groups such that points in the same group are similar and points in different groups are dissimilar to each other. Spectral clustering has become increasingly popular due to its simple implementation and promising performance in many graph-based clustering. It can be solved efficiently by standard linear algebra software, and very often outperforms traditional algorithms such as the k-means algorithm. Here, we will try to explain very briefly how it works!