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) …
Word embedding is one of the most important concepts in Natural Language Processing (NLP). It is an NLP technique where words or phrases (i.e., strings) from a vocabulary are mapped to vectors of real numbers. The need to map strings into vectors of real numbers originated from computers' inability to do operations with strings. Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. Before diving into word embedding, let's compare these three options to see why Word embedding is the best.
Welcome to my first blog on topics in artificial intelligence! Here I will introduce the topic of edge computing, with context in deep learning applications. This blog is largely adapted from a survey paper written by Xiaofei Wang et al.: Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. If you're interested in learning more about any topic covered here, there are plenty of examples, figures, and explanations in the full 35 page survery: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp & arnumber 8976180 Now, before we begin, I'd like to take a moment and motivate why edge computing and deep learning can be very powerful when combined: Deep learning is becoming an increasingly-capable practice in machine learning that allows computers to detect objects, recognize speech, translate languages, and make decisions. More problems in machine learning are solved with the advanced techniques that researchers discover by the day.
The creation of poems via neural networks is relatively easy nowadays and the internet is replete with corresponding examples. However, it largely lacks interpretive concepts. What should be done with the results generated in this way? How can we draw scientific conclusions from them? This is all the more difficult to answer as it still remains unclear where to position deep‐learning approaches in the canon of digital‐humanities methods. But it is clear that humanities scholars must reckon with machines being responsible for, or at least involved in, the creation of their objects of study.
A new sensing method has made tracking movement easier and more efficient. A research group from Tohoku University has captured dexterous 3D motion data from a flexible magnetic flux sensor array, using deep learning and a structure-aware temporal bilateral filter. "We can now track complex motions with higher accuracy," said Yoshifumi Kitamura, co-author of the study. Dexterous 3D motion data can be used for multiple purposes: biologists can use the data to record detailed movements of small animals in their living environments, scientists can track the flow of fluids, and researchers can track finger movements and objects being manipulated by users in virtual reality. Currently, optical cameras are the most prominent method of tracking movements.
Recently, researchers from the Western Kentucky University proposed a multi-modal deep learning framework that has the capability to classify genres of video games based on the cover and textual description. The researchers claimed that this research is the first-ever attempt on automatic genre classification using a deep learning approach. Videos games have been one of the most widespread, profitable, and prominent forms of entertainment around the globe. Also, genre and its classification systems play a significant role in the development of video games. According to the researchers, video game covers and textual descriptions are usually the very first impression to its consumers, and they often convey important information about the video games.
If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook's AI Research Lab that is powerful, easy to learn, and very versatile. When it comes to training material, however, PyTorch lags behind TensorFlow, Google's flagship deep learning library. There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions. Deep Learning with PyTorch is split across two main sections, first teaching the basics of deep learning and then delving into an advanced, real-world application of medical imaging analysis.
In the winter of 2011, Daniel Yamins, a postdoctoral researcher in computational neuroscience at the Massachusetts Institute of Technology, would at times toil past midnight on his machine vision project. He was painstakingly designing a system that could recognize objects in pictures, regardless of variations in size, position and other properties -- something that humans do with ease. The system was a deep neural network, a type of computational device inspired by the neurological wiring of living brains. "I remember very distinctly the time when we found a neural network that actually solved the task," he said. It was 2 a.m., a tad too early to wake up his adviser, James DiCarlo, or other colleagues, so an excited Yamins took a walk in the cold Cambridge air. "I was really pumped," he said. It would have counted as a noteworthy accomplishment in artificial intelligence alone, one of many that would make neural networks the darlings of AI technology over the next few years.
Artificial something that is not natural, or, anything that is human-made. AI is a broad area of computer science that makes machines seem like they have human intelligence. It's the broader category -- all Machine Learning and Deep Learning systems count as Artificial Intelligence. The vice versa is not valid, tho. Not all AI is Machine Learning or Deep Learning.
Quantum mechanics was once a very controversial theory. Early detractors such as Albert Einstein famously said of quantum mechanics that "God does not play dice" (referring to the probabilistic nature of quantum measurements), to which Niels Bohr replied, "Einstein, stop telling God what to do". However, all agreed that, to quote John Wheeler "If you are not completely confused by quantum mechanics, you do not understand it". As our understanding of quantum mechanics has grown, not only has it led to numerous important physical discoveries but it also resulted in the field of quantum computing. Quantum computing is a different paradigm of computing from classical computing.