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) …
Microsoft's war chest is a dynamo. With revenues that rival the GDP of a small nation, it's got enough cash on hand to buy whatever it wants. When it does, it just acquires another money-making machine. Video game company Activision Blizzard, which Microsoft announced yesterday it was buying for a staggering $68.7 billion--more than the $26.2 billion it paid for LinkedIn in 2016, almost 10 times the $7.5 billion it paid for Bethesda's parent ZeniMax Media last year. Microsoft now owns Call of Duty and Halo; it owns The Elder Scrolls and World of Warcraft.
In 2014, Microsoft bought Minecraft's developer Mojang for what seemed, at the time, an eye-popping figure: $2.5bn (£1.8bn). It was the first in a series of bullish video-game studio acquisitions by the tech giant, whose games division has been led by executive Phil Spencer, a long-time advocate for video games within Microsoft and the wider business world, for the past eight years. More studios followed, for undisclosed amounts: beloved Californian comedy-game artists Double Fine, UK studio Ninja Theory, RPG specialists Obsidian Entertainment. It seemed that under Spencer's leadership, Microsoft was cementing its commitment to the Xbox console and the video-games business by investing in what makes games great: the people who make them. Then came 2020's deal to acquire Zenimax (and with it Bethesda), for a properly astonishing $7.5bn.
Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks.
One of the most surprising and fascinating applications of Artificial Intelligence is for sure recommender systems. In a nutshell, a recommender system is a tool that suggests you the next content given what you have already seen and liked. Companies like Spotify, Netflix or Youtube use recommender systems to suggest you the next video or song to watch given what you have already seen or listened to. The idea of build recommender system has surely not been developed yesterday. In 2006 Netflix announced a 1 million dollar reward to the research team able to build the best recommender system possible given some test data.
You may have heard the term artificial intelligence many times before thinking that you didn't have anything to do with it. It is possible that you searched for artificial intelligence and ended up here. Artificial intelligence can be used in many areas of our daily lives. This is sometimes called the "robots taking over the world in an evil genius manner" scenario. However, artificial intelligence has made our lives easier by reducing our time, energy, and money.
Netflix launched in 1997 as a mail-based DVD rental business. Alongside the growing US DVD market in the late 1990s and early 2000s, Netflix's business grew and the company went public in 2002. Netflix posted its first profit a year later. By 2007, Netflix introduced its streaming service, and by 2013, the company began producing original content. Today, Netflix is one of the world's largest entertainment services with over 200 million paid memberships spanning 190 countries, according to the company's 2020 Annual Report.
In an increasingly digitized world, the artificial intelligence (AI) boom is only getting started. But could the risks of artificial intelligence outweigh the potential benefits these technologies might lend to society in the years ahead? In this segment of Backstage Pass, recorded on Dec. 14, Fool contributors Asit Sharma, Rachel Warren, and Demitri Kalogeropoulos discuss. Asit Sharma: We had two questions that we were going to debate. Well, I'll have to choose one.
Lilly and Lana Wachowski thrive on being complicated. As directors, their calling cards are mixing disparate genres and styles--American action, Chinese martial arts, Dicksian sci-fi, Hollywood romance, German experimental thriller; you name it, they do it. And they create these mash-ups sometimes coherently (at best), other times unintelligible (at worst), like they ripped what they enjoyed from each category and squashed it altogether willy-nilly. Take The Matrix franchise, undoubtedly the Wachowskis' best work: They're high-concept sci-fi movies, punctuated by bullets and kung-fu. The pair added a sheen of cyberpunk fiction over a first-year philosophy class conundrum, but they also personified hard rock music as men in slick duster coats and women in vinyl catsuits who shoot faceless goons in slo-mo as they strut.
Artificial intelligence in streaming solutions has made significant steps in the technology industry in many ways. From content material enhancement and discovery to video indexing, it can improve the workflow to deliver a highly satisfactory service to end-users. AI can make extraordinary engagements inside the streaming media industry and promote online content material. An incredible amount of tech advisors suggest investing in artificial intelligence to boost your business or video service reach. With AI technology, computers or machines can analyze, learn, and argue results to make better predictions for future engagements.
Confirmation bias is "the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one's prior beliefs or values", says Wikipedia. Social media has brought the mechanics of confirmation bias into the public consciousness and discussion of filter bubbles and echo chambers – indeed, a 2018 study showed that 90% of Wikipedia contributors were male. However, confirmation bias has been a force throughout history. Technology and algorithms have merely accelerated and automated the ways we can avoid the hard work of critical thinking. Confirmation bias exists because the human brain exists in a strange state of being simultaneously overworked, enraptured, and lazy.