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) …
Kizuna Ai, the most popular streamer in Japan, is an anatomically exaggerated, perpetually adolescent girl in frilly thigh-high socks and a pink hair ribbon. She's also an entirely virtual character, given life by the actions and voice of an invisible actress. In the home of anime and "Ghost in the Shell" futurism, millions now follow Kizuna Ai online, and that success has spawned thousands of copycat acts and a cottage industry catering to so-called virtual YouTubers, or VTubers. Defying the Western streamer blueprint of young male gamers like PewDiePie and Ninja, Japan has invented a new class of streaming star that's equal parts digital avatar and interactive anime. "What separates VTubers from regular anime characters is that you can believe they actually exist," said Takeshi Osaka, founder of Activ8 Inc., the Tokyo-based company behind Kizuna Ai. "That presence is an important part of what makes them so appealing."
This first video in the logistic regression series introduces this powerful classification algorithm. The logistic regression algorithm is used when the dependent variable or target variable is categorical. Simple Logistic Regression and Multinomial Logistic Regression are explained. This second video in the logistic regression series compares logistic regression with linear regression in terms of their purpose, use cases, equations, error minimizations, and assumptions. This third video in the logistic regression series covers the four ways of preprocessing data before performing logistic regression: missing data handling, categorical data handling, splitting into train and test set, and feature scaling.
"We are crossing over into an era where we have to be skeptical of what we see on video," says John Villasenor, a senior fellow at the Brookings Institution. Villasenor is talking about deepfakes--videos that are digitally manipulated in imperceptible ways, often using a machine-learning technique that superimposes existing images or audio onto source material. The technology's verisimilitude is alarming, Villasenor argues, because it undermines our perception of truth and could have disastrous consequences for the upcoming U.S. presidential election. "I do think deepfakes are going to be a feature of the 2020 elections in some way," Villasenor says. "And their shadow will be long."
Swiping is no longer the only way to find matches on Tinder. In a choose-your-own-adventure style series set to be rolled out next month, users will be able to match with other dating hopefuls by clicking their way through an interactive narrative. 'Swipe Night,' as Tinder is calling it, will air on October 6 and is designed to match users based on the choices they make during a short ''first-person apocalyptic adventure.' All of the episodes will be'live', so-to-speak, with each being available for viewing only between the hours of 6 pm and midnight during a respective users' local time. The series will consist of short five-minute videos during which users are periodically given seven seconds to choose what happens next.
It is an age old question – does he love me, does he not? Now you can harness the power of artificial intelligence to determine if your relationship will have a fairy tale or tragic ending. Called Mei, this'relationship assistant' studies your text conversations to determine compatibility and delivers a'crush' score, along with personalized dating advice. Mei is a'relationship assistant' that studies your text conversations to determine compatibility and delivers a'crush' score (pictured), along with personalized dating advice The Mei app had its beta release in Google Play last year and was specifically designed to help users better understand themselves and the people they communicate with. And now it has become a dating guru.
The real robotics revolution is not having robots take care of tasks but having them available to businesses as a service. And so another acronyms to represent the expanding world of as a service is added to today's business vocabulary. The business world has introduced a number of different functions as a service, including software-as a-service (SaaS), platform-as-a-service (PaaS) and Infrastructure-as-a-service (Iaas) among others. But another as a service category has come on the scene in the past couple of years: robotics-as-a-service (RaaS). RELATED: WHY ARE WE SO SCARED OF ROBOTS? 15 EXPERTS WEIGH IN ON WHAT THE REAL DANGERS ARE The video above explains RaaS "is a cloud computing unit that facilitates the seamless integration of robot and embedded devices into Web and cloud computing environment."
Pick any image or video and detect objects and background automatically - and not only for background removal, but for various other cool effects too. The app is based on semantic image segmentation, which is the concept of finding objects and boundaries in images. Google Research DeepLab is a state-of-art deep learning neural network for the semantic image segmentation - and now with AI Green Screen this awesome technology is available as an easy app for everyday use. Simply let AI detect the image objects and pick the effect to apply.
Deepfakes – or fake videos produced to look real through the use of artificial intelligence – pose a growing challenge. That's why an EPFL research group has been teaming up with the Swiss startup Quantum Integrity to develop a deepfake detection solution over the past two years. The team has been awarded an Innosuisse grant starting on 1 October, with deployment as early as next year. Barak Obama insulting Donald Trump, Trump accusing Obama of stealing, and Mark Zuckerberg making worrying claims about how Facebook uses personal data – fake videoslike these, which look disturbingly real as a result of artificial intelligence, have been spreading across social media over the past few years. Until recently there were still some tell-tale signs that could indicate videos were fabricated, like eyes that never blink.
Machine learning (ML) is hard; making it work within the resource-constrained environment of an embedded device can easily become a quagmire. In light of this harsh reality, anyone attempting to implement ML in an embedded system must consider, and frequently revisit, the design aspects crucially affected by its requirements. A bit of upfront planning makes the difference between project success and failure. For this article, our focus is on building commercial-grade applications with significant, or even dominant, ML components. We'll use a theoretical scenario in which you have a device, or better yet an idea for one, which will perform complex analytics, usually in something close to real time, and deliver results in the form of network traffic, user data displays, machine control, or all three.
Bots removed opponents' tools from the game space, and launched themselves into the air… Two teams of AI agents tasked with playing a game (or million) of hide and seek in a virtual environment developed complex strategies and counterstrategies – and exploited holes in their environment that even its creators didn't even know that it had. The game was part of an experiment by OpenAI designed to test the AI skills that emerge from multi-agent competition and standard reinforcement learning algorithms at scale. OpenAI described the outcome in a striking paper published this week. The organisation, now heavily backed by Microsoft, described the outcome as further proof that "skills, far more complex than the seed game dynamics and environment, can emerge" (from such experiments/training exercises). Some of its findings are neatly captured in the video below.