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) …
Image reproduced under a CC BY-NC 4.0 licence. Researchers have designed a machine learning method that can predict the structure of new materials. The researchers, from Cambridge and Linköping Universities, have designed a way to predict the structure of materials given its constitutive elements. The results are reported in the journal Science Advances. The arrangement of atoms in a material determines its properties.
Imagine this: You've been heads down at work for several weeks when suddenly you hear a ping. You've received an email suggesting you're overdue for a vacation and are presented a list of resorts in the very cities you want to visit. When you arrive at your hotel, the staff knows your name. They know that you want an ocean view and extra towels, and that you'll likely need a 9 a.m. They are aware that you need a gluten-free menu for room service and that you'll want a late checkout.
Are you tired of switching between Windows and Linux environments to perform machine learning (ML) tasks? Do you want to accelerate inference of your ML applications in an effective way? This blog post is intended to serve as a guide to configure your Windows based system to get the most out of your Intel Integrated Graphics Processing Unit (iGPU). Now let's see how Intel's iGPU works with a Linux distribution (such as Ubuntu, openSUSE, Kali, Debian, Arch Linux, and more) on WSL to see the performance benefits of OpenVINO . I gave this combination of tools a try, with the demo shown below, and was really amazed by how seamlessly it works -- not to mention, with added acceleration!
Scientists from the University of Bristol have developed a small robot to teach ants and in turn, the ants were able to teach others in a unique experiment with the potential to replace one day the way humans are taught. The team built the robot to emulate the behavior of rock ants that use one-to-one tuition. Whenever an ant finds a much better nest, it teaches the route to another and the transfer of knowledge goes on to the entire ant community. Here researchers replaced the teacher ant with a small robot and taught an ant in tandem running along to a new nest. The pupil ant was not only able to learn the route but also found its way back home and then led a tandem run with another ant to the new nest.
Michael Stefferson received his PhD in Physics from the University of Colorado before deciding to make the jump into machine learning (ML). He spent the last several years as a Machine Learning Engineer at Manifold, where he first started working on projects in the healthcare industry. Recently, Stefferson joined the team at Cerebral as a Staff Machine Learning Engineer and hopes to leverage data to make clinical improvements for patients that will improve their lives in meaningful ways. Here, he talks about use cases, best practices, and what he has learned along his journey into the field of ML. What is your background and how did you first get into machine learning?
A degree in artificial intelligence will soon be relevant to just about any field. As the problems humans try to solve become bigger, some of the best solutions may be achieved with AI. AI involves subdisciplines like machine learning and deep learning, both of which are means by which computers can be trained to tackle specific issues. As AI systems become more sophisticated and ubiquitous, it will also be important to consider the ethics of their deployment. How did Gizmodo determine this year's honorees?
For the past few years, the scientific community worldwide has been advocating the accessibility of science. 'Open Science', as they call it, is an ongoing movement to make research papers accessible to all. Open information is vital for research, even in space tech. Not many know that three years ago, when scientists created the first-ever black hole image, it was made possible only because of an open-source software, Matplotlib. The research papers that often claim to have their dataset/code open, are often found to be making false proclamations.
In the world of analytics,modeling is a general term used to refer to the use of data mining (machine learning) methods to develop predictions. If you want to know what ad a particular user is more likely to click on, or which customers are likely to leave you for a competitor, you develop a predictive model. There are a lot of models to choose from: Regression, Decision Trees, K Nearest Neighbor, Neural Nets, etc. They all will provide you with a prediction, but some will do better than others depending on the data you are working with. While there are certain tricks and tweaks one can do to improve the accuracy of these models, it never hurts to remember the fact that there is wisdom to be found in the masses.
The news: Researchers have used deep learning to model more precisely than ever before how ice crystals form in the atmosphere. Their paper, published this week in PNAS, hints at the potential to significantly increase the accuracy of weather and climate forecasting. How they did it: The researchers used deep learning to predict how atoms and molecules behave. First, models were trained on small-scale simulations of water molecules to help them predict how electrons in atoms interact. The models then replicated those interactions on a larger scale, with more atoms and molecules.
I have loved science fiction ever since I was a kid and read all my Dad's ancient issues of Analog Science Fiction and Fact from the 1940s. The first book I can remember reading was The Green Hills of Earth anthology by Robert Heinlein. Fast forward to the 1990s, when, as a new professor of computer science, I began adding sci-fi short stories and movies as extra credit for my AI and robotics courses. Later as a Faculty Fellow for Innovation in High-Impact Learning Experiences at Texas A&M, I created the Robotics Through Science Fiction book series as a companion to my textbook, Introduction to AI Robotics. A Firby-like robot pet becomes an international fad, where a "keeper" buys a little wheeled robot and is randomly paired with a "dweller" who teleoperates the robot.