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) …
In 2020, one message in the artificial intelligence (AI) market came through loud and clear: AI's got some explaining to do! Explainable AI (XAI) has long been a fringe discipline in the broader world of AI and machine learning. It exists because many machine-learning models are either opaque or so convoluted that they defy human understanding. But why is it such a hot topic today? AI systems making inexplicable decisions are your governance, regulatory, and compliance colleagues' worst nightmare. But aside from this, there are other compelling reasons for shining a light into the inner workings of AI.
For many surgeons, the possibility of going back into the operating room to review the actions they carried out on a patient could provide invaluable medical insights. Using a mix of Facebook's PyTorch framework and machine-learning platform Allegro Trains, med-tech company theator is now providing surgeons with a tool that lets them watch over and analyze in detail the past operations they have performed, and access video footage of procedures carried out by colleagues around the world. Dubbed the "surgical intelligence platform", theator's platform uses computer vision technology to extract key information from videos taken during surgical operations. The data is annotated, compiled and organized to let doctors review specific content by simply typing in key words through the platform. Surgeons can use the tool to jump to a specific step, re-watch critical moments, or access analysis about the procedure, such as time taken to perform a given action.
Saiph Savage, director of the human-computer interaction lab at West Virginia University, advocates for the workers who put in the time to develop training data for artificial intelligence. Many of the most successful and widely used machine-learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three-quarters of their income this way.
It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if it had been done by a human, would have to apply intelligence in order to accomplish it. That's obviously a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. Modern definitions of what it means to create intelligence are slightly more specific. Francois Chollet, AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios. "Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for," he said. "Intelligence is not skill itself, it's not what you can do, it's how well and how efficiently you can learn new things." It's a definition under which modern AI-powered systems, such as virtual assistants, would be characterised as having demonstrated'narrow AI'; the ability to generalise their training when carrying out a limited set of tasks, such as speech recognition or computer vision. Typically, AI systems demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. AI is ubiquitous today, used to recommend what you should buy next online, to understanding what you say to virtual assistants, such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud.
It's very important to know where our model works well and where it fails. If there is a low latency requirement, definitely KNN will be a worse choice. Similarly, if data is non-linear, then choosing logistic regression is not good so let's dive deep into the discussion and find the pros and cons of models.
Becoming a physicist was not Maria Schuld's life goal. As an undergrad, she started out studying political science, taking physics in parallel. Her plan was to work for a nonprofit organization in a capacity that had a very clear benefit to society. But then, she says, "life happened"--jobs fell through and other opportunities opened up--and she found herself with a career in quantum machine learning. Today Schuld, who works for the Canadian quantum computing company Xanadu from her home in South Africa, says that she has matured in what she thinks it means for a person to benefit society.
Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena. Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning. But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills.
It's no secret that machine-learning models tuned and tweaked to near-perfect performance in the lab often fail in real settings. This is typically put down to a mismatch between the data the AI was trained and tested on and the data it encounters in the world, a problem known as data shift. For example, an AI trained to spot signs of disease in high-quality medical images will struggle with blurry or cropped images captured by a cheap camera in a busy clinic. Now a group of 40 researchers across seven different teams at Google have identified another major cause for the common failure of machine-learning models. Called "underspecification," it could be an even bigger problem than data shift.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves, i know that sounds a little bit confuse but will be clear at the end. At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.
"The Uncanny Valley"is Flash Art's new digital column offering a window on the developing field of artificial intelligence and its relationship to contemporary art. The last decade has seen exponential growth in the aesthetic application of AI and machine learning: from DeepDream's convolutional neural networks that detect and intensify patterns within individual images; to NST (neural style transfer) techniques that manipulate one image into the style of another; to GANs (generative adversarial networks) that digest large datasets of images in order to generate new visions without human intervention. Although the community of computational artists and creative AI hackers still exists largely outside of the contemporary art scene, a growing body of artists has sought to traverse both territories, in the process foregrounding the cultural, ethical, and social problems that underpin our new digital architecture. In recent years, Jake Elwes has distilled the full range of AI-informed strategies into a diverse series of outputs: transcriptions of tech leaders' numerical babblings (dada da ta, 2016); video installations projecting conversations between two neural networks (Closed Loop, 2017); and 2016's Auto-Encoded Buddha -- a tribute to Nam June Paik's TV Buddha (1974) -- in which a computer struggles to depict the Buddha's true essence. Through these works and others, Elwes has actively positioned himself within the long histories of video and computer art, and against the notion that AI is capable of expressing intentionality.