On March 31, 1913, in the Great Hall of the Musikverein concert house in Vienna, a riot broke out in the middle of a performance of an orchestral song by Alban Berg. Police arrested the concert's organizer for punching Oscar Straus, a little-remembered composer of operettas. Later, at the trial, Straus quipped about the audience's frustration. The punch, he insisted, was the most harmonious sound of the entire evening. History has rendered a different verdict: the concert's conductor, Arnold Schoenberg, has gone down as perhaps the most creative and influential composer of the 20th century. You may not enjoy Schoenberg's dissonant music, which rejects conventional tonality to arrange the 12 notes of the scale according to rules that don't let any predominate. But he changed what humans understand music to be. This is what makes him a genuinely creative and innovative artist.

GPT2 represents a major advancement in what's known as unsupervised learning. With most neural networks, the training consists of supervised learning. That means you have to feed in labeled data sets and evaluate the outcome to tune the various processing nodes until the network functions as intended. Unsupervised networks like GPT2 can assimilate large volumes of data without human involvement. Many researchers believe this is key to the future of AI, and OpenAI just showed that it can work and produce impressive results.

Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve state-of-the-art performance in many challenging video datasets. Because of the high level of complexity of these methods, as the convolution operations are also extended to additional dimension in order to extract features from them as well, providing a visualization for the signals that the network interpret as informative, is a challenging task. An effective notion of understanding the network's inner-workings would be to isolate the spatio-temporal regions on the video that the network finds most informative. We propose a method called Saliency Tubes which demonstrate the foremost points and regions in both frame level and over time that are found to be the main focus points of the network.

Artificial Intelligence can now be used to write fake news stories. NBC's Jacob Ward joins Ali Velshi to discuss how this works and why some brand new advances in AI are especially dangerous.Feb.

AI start-ups Crossbar, Gyrfalcon Technology, Neural Networks Corporation and Robosensing are getting together to deliver an AI platform and standard for edge computing, gateways, cloud and data centers. The group, called SCAiLE (SCalable AI for Learning at the Edge), is already working with Japanese authorities to review opportunities for the 2020 Olympics, including video-based event detection and response capability. The organization will combine advanced acceleration hardware, resistive memory (ReRAM), optimized neural networks to create ready-made, power-efficient solutions with unsupervised learning and event recognition capability. The consortium addresses the restrictions of traditional AI methodologies that depend on classification of data. The huge growth of IoT systems including thousands of remote edge devices such as sensor-equipped cameras creates a torrent of unstructured information in multiple forms that pours into cloud-located servers and that cannot be handled effectively by classification alone.

Innovation is a top priority for almost every organization. But to achieve success through innovation, companies must put as much energy and investment into marketing new offerings as they do in generating them. The role of marketing in some companies seems to have diminished in recent years, with the growth of artificial intelligence-driven algorithms and predictive analytics that offer up information, goods, and services to customers. The popularity of private label goods and products from companies like Brandless and others that seem to eschew marketing also seems to make the argument for less marketing, rather than more. But marketing is and should not be executed merely through tactical functions of acquiring and retaining customers, as many companies practice it today. The search, content, and loyalty campaigns that most managers call marketing these days are common downstream tactics for generating or maintaining awareness or repeat purchase; the full, business-growing power of the marketing function comes way upstream -- from creating markets.

You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like? You're also not reassured by the vendors the city interacts with.

Artificial intelligence (AI) is not just a technology seen in futuristic Hollywood films involving AI-powered robots and super-intelligent machines -- it's now an increasingly mainstream technology that is being used by companies you probably interact with on a daily basis. Facebook, for example, uses AI for image recognition, while Netflix uses AI to make content recommendations. So it's perhaps no surprise that AI can also be used for a wide range of other functions, including business development and strategic partnerships. My company creates AI solutions including predictive analytics, natural language processing and virtual sales assistants. Here are some of the benefits and downsides I've noticed in these technologies -- and how to tell whether they have a place in your organization as either a built or bought solution.

How do companies like Amazon and Netflix know precisely what you want? Whether it's that new set of speakers that you've been eyeballing, or the next Black Mirror episode -- their use of predictive algorithms has made the job of selling you stuff ridiculously efficient. But as much as we'd all like a juicy conspiracy theory, no, they don't employ psychics. They use something far more magical -- mathematics. Today, we'll look at an approach called collaborative filtering.

CBS's research team is getting a faster read on how viewers respond emotionally to its TV shows -- by using the dispassionate logic of machines. The broadcaster is using the data-analytics platform developed by New York startup Canvs, which uses proprietary artificial-intelligence processing to parse natural-language comments. CBS started using the Canvs Surveys tool to automate the coding of open-ended responses to surveys fielded by its research team starting in the fourth quarter of 2018. It now uses Canvs to process feedback on its entire slate of programming and tentpole events, including viewer response to this month's Super Bowl LIII and Grammy Awards broadcasts. The real power of the AI system is its ability to crunch unstructured data far more efficiently than human researchers can, said Radha Subramanyam, chief research and analytics officer at CBS.