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) …
It has been more than six decades since the concept of Artificial Intelligence has transformed from imagination to an academic discipline. Influencers, especially those active on social media help give direction to the policymakers and academicians. They keep common men updated on the trends and'what is what' in AI, Machine Learning and associated concepts like Big Data and BlockChain. AiThority introduces you to the 50 most popular AI-influencers of North America. A PhD in industrial-organizational psychology, his interests lies in Data Science, CX, Statistics and Machine Learning.
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.
Colin Angle is the CEO and co-founder of iRobot, a robotics company that for 29 years has been creating robots that operate successfully in the real world, not as a demo or on a scale of dozens, but on a scale of thousands and millions. As of this year, iRobot has sold more than 25 million robots to consumers, including the Roomba vacuum cleaning robot, the Braava floor mopping robot, and soon the Terra lawn mowing robot. Note: I accidentally say 29 million not 25 million in the video. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:52 - Three laws of robotics 4:50 - Robots in the home 13:32 - How to build a successful robotics company 23:21 - Autonomous vehicles and home robotics (Tesla, Lidar, Vision) 26:21 - Robot in every home 27:24 - Data, privacy, and trust 32:58 - Human-level intelligence 35:26 - Data from Star Trek CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman
Stuttgart, Germany, and Yokohama, Japan – Automated driving technology is gradually providing more and more assistance to the driver – with the future aim of the car being able to take complete control. But there is more to it than that: "We want to make cars better drivers than people, and in this way to increase road safety. In other words, technology has to work more reliably than people," says the Bosch management board member Harald Kröger. That presents a major challenge, particularly in terms of surround sensing. The sensing system needs to provide the data and information of what is going on around the vehicle to enable the automated vehicle to choose the appropriate driving decision under the circumstances from a safety standpoint.
François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:14 - Self-improving AGI 7:51 - What is intelligence?
A viral app which claims to'honestly' classify selfies using its in-built artificial intelligence has been spewing out vile and racist labels. Furious users say their pictures have been slapped with offensive and racist terms such as'negro', 'slant eye' and'rape suspect' by the app which was developed at Princeton University. Developers say causing offence was exactly the intention and it was intended to be deliberately provocative to draw attention to the in-built prejudice and discrimination in many forms of machine learning. But many users are still furious that their images have played a seemingly unwitting part in the controversial project. One MailOnline staffer who tried the app was dubbed a'rape suspect' when he uploaded his selfie.
The tool, called ImageNet Roulette, detects human faces in any uploaded photo and assign them labels using ImageNet, an academic training set with millions of pictures depicting almost anything imaginable, and WordNet, the corresponding text tags. As viral examples on Twitter have shown, the results of this process are more often than not completely useless--nonsensical at best and racist or otherwise offensive at worst. In some cases, it would label black men as "offenders" or "wrongdoers," while other times it would spit out racial slurs against Asians or outdated and offensive terms for black people. I might have a bad sense of humor but I don't think this particularly funny #imagenetroulette pic.twitter.com/RR578nhCOU The offensiveness was more or less the point, says co-creator, Kate Crawford, who is also a co-founder of New York University's AI Now Institute, which studies the social implications of artificial intelligence.