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 third instalment of Data Science Crash Course and today we're going to review mathematics needed for Data Science. Linear algebra is all about manipulations with vectors and matrices. It's both notation and useful way of manipulating object. You can perform operations on vectors like adding by adding each respective term -- they need to have the same length. You can multiply a vector by a scalar, that is a real number, by multiplying each of the entries by this real number.
Apple reportedly spent around $200 million to purchase US artificial intelligence startup Xnor.ai, according to GeekWire. The startup's low-power, edge-based AI tools will allow Apple to add AI features to power-constrained devices, like smart cameras or phones. For instance, Xnor.ai's most notable technology is an AI-based image recognition tool that enables on-device human detection for smart home cameras. Apple will also have access to a platform created by Xnor.ai that allows software developers who aren't well-versed in AI to implement AI-related code and data libraries in their apps. Apple's Xnor.ai acquisition is just one of many it has recently made, as it aims to create more powerful and personalized AI features.
We partnered with Driven Data and the World Bank to develop the Open Cities AI Challenge. This competition asks contestants to build semantic segmentation models that identify buildings in aerial imagery from several African cities. In other words, the goal is to automatically extract building footprints from each image. Contestants will be judged on the quality of their predictions and will be competing for a share of a combined $15,000 cash prize. Disaster relief efforts rely on accurate and up-to-date infrastructure maps.
Newark Venture Partners hosted a full house at its biannual Demo Day, for its 7th NVP Labs class, at the Audible Innovation Cathedral. The event featured presentations from founders of the graduating companies, Botmock, Brahmin Solutions, Galaxy.AI, MindRight Health, omniX, Speak2 Software and SpeechKit. Other featured speakers included Don Katz, Founder and Executive Chairman of Audible, Newark Assemblywoman Eliana Pintor Marin, and Wole Coaxum, Founder and CEO of MoCaFi (an NVP portfolio company) who paid tribute to Dr. Martin Luther King, Jr.'s birthday. Don Katz, Founder and Executive Chairman of Audible said, "Everyone loves a comeback story and Newark has a great one, including Newark Venture Partners, which is an internationally acknowledged phenomenon that has exceeded all of my founder expectations. When I recently visited NVP labs I was dazzled by one impassioned founder, team, and company after another. Now it is time to double down on NVP's measurable success."
Researchers of the ICAI Group–Computational Intelligence and Image Analysis–of the University of Malaga (UMA) have designed an unprecedented method that is capable of improving brain images obtained through magnetic resonance imaging using artificial intelligence. This new model manages to increase image quality from low resolution to high resolution without distorting the patients' brain structures, using a deep learning artificial neural network –a model that is based on the functioning of the human brain–that "learns" this process. "Deep learning is based on very large neural networks, and so is its capacity to learn, reaching the complexity and abstraction of a brain," explains researcher Karl Thurnhofer, main author of this study, who adds that, thanks to this technique, the activity of identification can be performed alone, without supervision; an identification effort that the human eye would not be capable of doing. Published in the scientific journal "Neurocomputing," this study represents a scientific breakthrough, since the algorithm developed by the UMA yields more accurate results in less time, with clear benefits for patients. "So far, the acquisition of quality brain images has depended on the time the patient remained immobilized in the scanner; with our method, image processing is carried out later on the computer," explains Thurnhofer.
Sony CSL Paris develops technology for AI-assisted music production. The goal is not to replace musicians, but to provide them with better tools to be more efficient in realizing their creative ideas. DrumNet is based on an artificial neural network which learns rhythmic relationships between different instruments and encodes these relationships in a 16-dimensional style space. A similar example is the Logic Pro X Drummer, allowing the user to specify the playing style by navigating a two-dimensional space. The difference of DrumNet to the Logic Pro X Drummer, however, is that it dynamically adapts to the existing music.
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works. In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning.
The'mobile first' movement has resulted in most UX investments being focused on smartphones, tablets, smart home devices, etc. However, the faithful computer and laptop continues to be the workhorse of the masses and is where the most demanding and high-security tasks are performed. So why is it that there are no user-friendly and secure solutions for authenticating into computers and laptops? The insecurity of passwords is a UX problem, and shortcuts to make them easier lead to security risks, which lead to breaches. Most of the much-publicized mega-data breaches the past few years have been because of compromised or stolen passwords.
Robotic Process Automation (RPA) is kind of a confusing term. It's not about physical robots--instead, it refers to software bots that streamline repetitive and tedious processes in the workplace. Something else to note: RPA is the fastest growing segment in enterprise software, according to Gartner. As a result, the mega tech operators are looking at the market. SAP, for example, has acquired Contextor to bolster its efforts.