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) …
Automated emotion recognition has been with us for some time already. Ever since it entered the market, it has never stopped getting more accurate. Even tech giants joined the race and released their software for emotion recognition, after smaller startups had successfully done the same. We set out to compare the most known algorithms. Emotions are subjective and variable, so when it comes to accuracy in emotion recognition, the matters are not that self-evident.
Few biometric technologies are sparking the imagination quite like facial recognition. Equally, its arrival has prompted profound concerns and reactions. With artificial intelligence and the blockchain, face recognition certainly represents a significant digital challenge for all companies and organizations - and especially governments. In this dossier, you'll discover the 7 face recognition facts and trends that are set to shape the landscape in 2019. Let's jump right in .
Artificial Intelligence (AI) has the capability to provide radiologists with tools to improve their productivity, decision making and effectiveness and will lead to quicker diagnosis and improved patient outcomes. It will initially deploy as a diverse collection of assistive tools to augment, quantify and stratify the information available to the diagnostician, and offer a major opportunity to enhance and augment the radiology reading. It will improve access to medical record information and give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular will form the foundation for decision and workflow support tools and diagnostic capabilities. Algorithms will provide software the ability to "learn" by example on how to execute a task, then automatically execute those tasks as well as interpret new data.
One of the landmark events in the course of evolution of technology has been the advent of Artificial Intelligence, which has subsequently impacted different sectors of the society profoundly. Its multifaceted benefits have successfully initiated a complete paradigm shift even in our education sector. Education is one of the primary tools which is inextricably linked with the growth of human resources in the country. Artificial intelligence immensely helps to accentuate the growth and development index. It utilizes data models (as part of primary and secondary data sources) and makes decisions based on the input data whose success rate improves with further iterations.
There's a limit to the volume of death metal humans can reproduce -- their fingers and vocal chords can only handle so much. Thanks to technology, however, you'll never have to go short. CJ Carr and Zack Zukowski recently launched a YouTube channel that streams a never-ending barrage of death metal generated by AI. Their Dadabots project uses a recurrent neural network to identify patterns in the music, predict the most common elements and reproduce them. The result isn't entirely natural, if simply because it's not limited by the constraints of the human body.
In this tutorial, you will learn how to perform liveness detection with OpenCV. You will create a liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems. How do I spot real versus fake faces? Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system. Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smartphone that they could hold up to the camera responsible for performing face recognition (such as in the image at the top of this post).
At the end of the concrete plaza that forms the courtyard of the Salk Institute in La Jolla, California, there is a three-hundred-fifty-foot drop to the Pacific Ocean. Sometimes people explore that drop from high up in a paraglider. If they're less adventuresome, they can walk down a meandering trail that hugs the cliff all the way to the bottom. It's a good spot from which to reflect on the mathematical tool called "stochastic gradient descent," a technique that is at the heart of today's machine learning form of artificial intelligence. Terry Sejnowski has been exploring gradient descent for decades.
Facebook's AI Research team has created an AI called Vid2Play that can extract playable characters from videos of real people, creating a much higher-tech version of '80s full-motion video (FMV) games like Night Trap. The neural networks can analyze random videos of people doing specific actions, then recreate that character and action in any environment and allow you to control them with a joystick. The team used two neural networks called Pose2Pose and Pose2Frame. First, a video is fed into a Pose2Pose neural network designed for specific types of actions like dancing, tennis or fencing. The system then figures out where the person is compared to the background, and isolates them and their poses.
When Katie O'Nell's high school biology teacher showed a NOVA video on epigenetics after the AP exam, he was mostly trying to fill time. But for O'Nell, the video sparked a whole new area of curiosity. She was fascinated by the idea that certain genes could be turned on and off, controlling what traits or processes were expressed without actually editing the genetic code itself. She was further excited about what this process could mean for the human mind. But upon starting at MIT, she realized that she was less interested in the cellular level of neuroscience and more fascinated by bigger questions, such as, what makes certain people generous toward certain others?
For the majority of newcomers, machine learning algorithms may seem too boring and complicated subject to be mastered. Well, to some extent, this is true. In most cases, you stumble upon a few-page description for each algorithm and yes, it's hard to find time and energy to deal with each and every detail. However, if you truly, madly, deeply want to be an ML-expert, you have to brush up your knowledge regarding it and there is no other way to be. But relax, today I will try to simplify this task and explain core principles of 10 most common algorithms in simple words (each includes a brief description, guides, and useful links).