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) …
Whenever we start to talk about artificial intelligence, machine learning, or deep learning, the cautionary tales from science fiction cinema arise: HAL 9000 from 2001: A Space Odyssey, the T-series robots from Terminator, replicants from Blade Runner, there are hundreds of stories about computers learning too much and becoming a threat. The crux of these movies always has one thing in common: there are things that computers do well and things that humans can do well, and they don't necessarily intersect. Computers are really good at crunching numbers and statistical analysis (deductive reasoning) and humans are really good at recognizing patterns and making inductive decisions using deductive data. Both have their strengths and their role. With the massive proliferation of data across platforms, types, and collection schedules, how are geospatial specialists supposed to address this apparently insurmountable task?
It is very crucial for the machine learning enthusiasts to know and understands the basic and important machine learning algorithms in order to keep themselves up with the current trend. In this article, we list down 10 basic algorithms which play very important roles in the machine learning era. Logistic regression, also known as the logit classifier is a popular mathematical modelling procedure used in the analysis of data. Regression Analysis is used to conduct when the dependent variable is binary i.e. 0 and 1. In Logistic Regression, logistic function is used to describe the mathematical form on which the logistic model is based.
If you've heard of quantum computing, you might be excited about the possibility of applying it to machine learning applications. I work at Springboard, and we recently launched a machine learning bootcamp that includes a job guarantee. We want to make sure our graduates are exposed to cutting-edge machine learning applications -- so we put together this article as part of our research into the intersection of quantum computing and machine learning. Let's start by examining the difference between quantum computing and classical computing. In classical computing, your data is stored in physical bits and it is binary and mutually exhaustive: a bit is either in a 0 state or in a 1 state and it cannot be both at the same time.
When Google DeepMind's AlphaGo shockingly defeated legendary Go player Lee Sedol in 2016, the terms artificial intelligence (AI), machine learning and deep learning were propelled into the technological mainstream. AI is generally defined as the capacity for a computer or machine to exhibit or simulate intelligent behaviour such as Tesla's self-driving car and Apple's digital assistant Siri. It is a thriving field and the focus of much research and investment. Machine learning is the ability of an AI system to extract information from raw data and learn to make predictions from new data. Deep learning combines artificial intelligence with machine learning.
Chinese technology giant Tencent has open-sourced its face detection algorithm DSFD (Dual Shot Face Detector). The related paper DSFD: Dual Shot Face Detector achieves state-of-the-art performance on WIDER FACE and FDDB dataset benchmarks, and has been accepted by top computer vision conference CVPR 2019. Face detection is a fundamental step for facial alignment, parsing, recognition, and verification. Researchers from Tencent's AI-focused Youtu Lab propose three DSFD face detector techniques: The DSFD framework shows outstanding performance in experiments. Observing the following images, DSFD demonstrated high effectiveness in detecting faces with variations on scale, pose, occlusion, blurriness, makeup, illumination, modality, and reflection.
A collaboration between researchers from China's Beihang University and Microsoft Research Asia has produced TableBank, a new image-based dataset for table detection and recognition built with novel weak supervision from Word and Latex documents on the Internet. Researchers built several strong baselines using SOTA models with deep neural networks, which will enable deployment of more deep learning methods to table detection and recognition tasks. TableBank has been open-sourced on Github. "Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousands human labeled examples, which is difficult to generalize on real world applications. With TableBank that contains 417K high-quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks."
If you have observed, conventional audio and speech analysis systems are typically built using a pipeline structure, where the first step is to extract various low dimensional hand-crafted acoustic features (e.g., MFCC, pitch, RMSE, Chroma, and whatnot). Although hand-crafted acoustic features are typically well designed, is still not possible to retain all useful information due to the human knowledge bias and the high compression ratio. And of course, the feature engineering you will have to perform will depend on the type of audio problem that you are working on. But, how about learning directly from raw waveforms (i.e., raw audio files are directly fed into the deep neural network)? In this post, let's take learnings from this paper and try to apply it to the following Kaggle dataset.
It can be convenient to use a standard computer vision dataset when getting started with deep learning methods for computer vision. Standard datasets are often well understood, small, and easy to load. They can provide the basis for testing techniques and reproducing results in order to build confidence with libraries and methods. In this tutorial, you will discover the standard computer vision datasets provided with the Keras deep learning library. How to Load and Visualize Standard Computer Vision Datasets With Keras Photo by Marina del Castell, some rights reserved.
The US Food and Drug Administration has proposed a framework on how it might regulate medical devices that rely on AI and machine learning algorithms. The report published this week outlines two types of algorithms for the purposes of regulation: "locked algorithms" and "adaptive algorithms." Locked algorithms provide the same result each time they're fed the same input. The answers are normally based on things like look-up tables, decision trees, or classifiers. An adaptive algorithm, however, will "change its behavior using a defined learning process."
More than 540m Facebook records were left exposed on public internet servers, cybersecurity researchers said on Wednesday, in just the latest security black eye for the company. Researchers for the firm UpGuard discovered two separate sets of Facebook user data on public Amazon cloud servers, the company detailed in a blogpost. One dataset, linked to the Mexican media company Cultura Colectiva, contained more than 540m records, including comments, likes, reactions, account names, Facebook IDs and more. The other set, linked to a defunct Facebook app called At the Pool, was significantly smaller, but contained plaintext passwords for 22,000 users. The large dataset was secured on Wednesday after Bloomberg, which first reported the leak, contacted Facebook.