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) …
The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning. This blog is however not addressing the absolute beginner.
Threat data is no exception: Cybercriminals add to its abundance as they continuously up their game by tweaking old and creating new threats to evade detection. To address the vast amounts of threat data, security providers turn to machine learning to automate processes and improve security solutions. With the great diversity and volume of threat data available, machine learning is necessary to efficiently go through a dataset, learn from it, and help reinforce defenses against cyberthreats. The importance of the quantity of threat data is evident. But is data quantity the end all and be all of effective machine learning?
Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.
As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated--at least in the short term. The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM's Watson, Rethink Robotics' Baxter, DeepMind, or Google's driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines' ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.
AI's rapid evolution is producing an explosion in new types of hardware accelerators for machine learning and deep learning. Some people refer to this as a "Cambrian explosion," which is an apt metaphor for the current period of fervent innovation. From that point onward, these creatures--ourselves included--fanned out to occupy, exploit, and thoroughly transform every ecological niche on the planet. The range of innovative AI hardware-accelerator architectures continues to expand. Although you may think that graphic processing units (GPUs) are the dominant AI hardware architecture, that is far from the truth.
Duchenne Muscular Dystrophy (DMD) is a rare muscle disorder affecting children that results in loss of the ability to walk. It's rapidly progressive -- average life expectancy is about 20 years -- and caused by genetic mutations on the X chromosome that regulates the production of dystrophin, a protein thought to play a role in maintaining muscle cell membranes. For DMD sufferers and other patients with rare degenerative diseases, there's now hope on the horizon. Insilico Medicine and A2A Pharmaceuticals today launched Consortium.AI, a new venture founded with the goal of applying advances in artificial intelligence (AI) to cutting-edge drug discovery. Through Consortium.AI, the two companies will collaboratively develop therapeutic treatments for DMD and other severe genetic disorders and use machine learning to validate the most promising candidates.
Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology -- or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large. What do we really want when we say we want software smart enough to write applications for us?
"Companies want to build great teams by hiring the best talent. But the best people don't apply on job sites typically. So then how do you hire those people?" Founded in 2014 by Exotel co-founder Vijay Sharma, Sudheendra Chilappagari, Saiteja Veera, and Rishabh Kaul, the idea was simple- instead of the inbound approach to hiring which companies follow, Belong bet on outbound hiring to disrupt the hiring space. The predictive hiring startup aims to give companies the tools to help them reach out to the best people, making it a very personalized engagement.
Hello, with this article I'm starting series of articles about full featured C Machine Learning frameworks . This articles covers how to use Shogun library for solving classification problem. Shogun is an open-source machine learning library that offers a wide range of machine learning algorithms. From my point of view it's not very popular among professionals, but it have a lot of fans among enthusiasts and students. Library offers unified API for algorithms, so they can be easily managed, it somehow looks like to scikit-learn approach.
Artificial intelligence has been the stuff of mad dreams, and sometimes nightmares, throughout our collective history. We've come a long way from a 15th-century automaton knight crafted by Leonardo da Vinci. Within the past century, artificial intelligence has inched itself further into our realities and day to day lives and there is now no doubt we're entering into a new age of intelligence. Early computing technology ushered in a new branch of computer science dealing with the simulated intelligence of machines. In recent history, we've used A.I. for common tasks, such as playing against the computer in chess matches and other gameplay behaviors.