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) …
Increased use of AI can drive efficiencies and reduce costs in compliance management. Here's what that means for CIOs in highly regulated industries. Complying professionals can utilize automation tools rather than investing in additional solutions to decrease capital expenditures, expedite compliance, and increase flexibility. These solutions enable businesses across various industries to automate repetitive procedures, speed up business processes to increase efficiency and production, lower costs, and eliminate errors. Enterprises can expand the possibilities of automation with cognitive capabilities by combining RPA and AI, thereby increasing business value and competitiveness.
In 2020, OpenAI's machine learning algorithm GPT-3 blew people away when, after ingesting billions of words scraped from the internet, it began spitting out well-crafted sentences. This year, DALL-E 2, a cousin of GPT-3 trained on text and images, caused a similar stir online when it began whipping up surreal images of astronauts riding horses and, more recently, crafting weird, photorealistic faces of people that don't exist. Now, the company says its latest AI has learned to play Minecraft after watching some 70,000 hours of video showing people playing the game on YouTube. Compared to numerous prior Minecraft algorithms which operate in much simpler "sandbox" versions of the game, the new AI plays in the same environment as humans, using standard keyboard-and-mouse commands. In a blog post and preprint detailing the work, the OpenAI team say that, out of the box, the algorithm learned basic skills, like chopping down trees, making planks, and building crafting tables.
Robotics is a multi-disciplinary field in computer science dedicated to the design and manufacture of robots, with applications in industries such as manufacturing, space exploration and defence. While the field has existed for over 50 years, recent advances such as the Spot and Atlas robots from Boston Dynamics are truly capturing the public's imagination as science fiction becomes reality. Traditionally, robotics has relied on machine learning/deep learning techniques such as object recognition. While this has led to huge advancements, the next frontier in robotics is to enable robots to operate in the real world autonomously, with as little human interaction as possible. Such autonomous robots differ to non-autonomous ones as they operate in an open world, with undefined rules, uncertain real-world observations, and an environment -- the real world -- which is constantly changing.
Few-shot learning plays an important role in the field of machine learning. Many existing methods based on relation network achieve satisfactory results. However, these methods assume that classes are independent of each other and ignore their relationship. In this paper, we propose a hierarchical few-shot learning model based on coarse- and fine-grained relation network (HCRN), which constructs a hierarchical structure by mining the relationship among different classes. Firstly, we extract deep and shallow features from different layers at a convolutional neural network.
Ultimately, facial authentication will be a prevalent method in anything that requires more security but eventually trickles down to every industry, including replacing keys and card readers to become the de facto access-control-authentication method. Facial authentication shouldn't be confused with facial recognition. The latter is prone to misuse, as governments, corporations, and other entities leverage public data (as well as data collected without consent) to identify or track people without their awareness. This isn't what Apple's FaceID does, and it's not what access-control solutions do either. Rather, authentication matches specifically enrolled, consenting people to their own identity without plugging into significant databases.
Babies can help unlock the next generation of artificial intelligence (AI), according to Trinity neuroscientists and colleagues who have just published new guiding principles for improving AI. The research, published today in the journal Nature Machine Intelligence, examines the neuroscience and psychology of infant learning and distills three principles to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning. Dr. Lorijn Zaadnoordijk, Marie Sklodowska-Curie Research Fellow at Trinity College explained: "Artificial intelligence (AI) has made tremendous progress in the last decade, giving us smart speakers, autopilots in cars, ever-smarter apps, and enhanced medical diagnosis. These exciting developments in AI have been achieved thanks to machine learning which uses enormous datasets to train artificial neural network models. "However, progress is stalling in many areas because the datasets that machines learn from must be painstakingly curated by humans.
A lot has changed in the world of automation since Apple first launched its virtual assistant, Siri, on October 4, 2011. It's a date that musician, actress, and public speaker, Susan Bennett, remembers well because she's the original female, American voice behind Siri. "It is still weird," she said on this episode of The Insuring Cyber Podcast. "It's like, 'How many millions of people know my voice?' …I don't really think about that aspect of it." After getting her start in entertainment as a musician working on commercial jingles and singing backup vocals on tour with Burt Bacharach and Roy Orbison, Bennett began voiceover work at Doppler Studios in Atlanta.