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) …
On Tinder, an opening line can go south pretty quickly. And while there are plenty of Instagram accounts dedicated to exposing these "Tinder nightmares," when the company looked at its numbers, it found that users reported only a fraction of behavior that violated its community standards. Now, Tinder is turning to artificial intelligence to help people dealing with grossness in the DMs. The popular online dating app will use machine learning to automatically screen for potentially offensive messages. If a message gets flagged in the system, Tinder will ask its recipient: "Does this bother you?"
In the ever-changing artificial intelligence landscape, developers need to embrace every possible way of keeping oneself updated with the latest developments. And one of the easiest ways to stay abreast is to be associated with AI communities on various platforms like Facebook, LinkedIn, Reddit, among others. While such groups help in keeping you informed, one should not limit to just reading about the latest developments. Instead, developers should engage in sharing ideas and contributing to the community. AIM brings you a curated list of a popular and most active Facebook group related to Artificial Intelligence that will bring value in your professional journey.
Mediation Perspectives is a periodic blog entry that's provided by the CSS' Mediation Support Team and occasional guest authors. How is artificial intelligence (AI) affecting conflict and its resolution? Peace practitioners and scholars cannot afford to disregard ongoing developments related to AI-based technologies – both from an ethical and a pragmatic perspective. In this blog, I explore AI as an evolving field of information management technologies that is changing both the nature of armed conflict and the way we can respond to it. AI encompasses the use of computer programmes to analyse big amounts of data (such as online communication and transactions) in order to learn from patterns and predict human behaviour on a massive scale.
Facebook CEO Mark Zuckerberg speaks about "News Tab" at the Paley Center, Friday, Oct. 25, 2019 in ... [ ] New York. The new feature in the Facebook mobile app will display headlines -- and nothing else -- from the Wall Street Journal, the Washington Post, BuzzFeed News, Business Insider, NBC, USA Today and the Los Angeles Times, among others.(AP Facebook has been ramping its acquisitions for AI (Artificial Intelligence) startups. While the deals appear to be relatively small, they still are likely to be critical for the company's future. The latest purchase was for Scape Technologies, which is focused on building computer vision applications that help with AR (Augmented Reality).
As the field of big data, machine learning and artificial intelligence keep growing and revolutionizing the current world as we know it and playing a big role in determining the future, it is without doubt that certain questions are beginning to get raised in terms ethics, governance, regulations, and privacy issues surrounding the big data revolution. At a first glance, these topics can all be classified commonly as thorns in the advancement of AI and machine learning especially since most businesses are largely more curious about the business benefits of the domain and not necessarily the disadvantages as well. Recent activities and global trends are however beginning to show the negative impact that can be caused by ignoring some of these seemingly looking thorns in companies trying to make money out of data. The European Union has been an example of how governments are beginning to prioritize certain regulations that most tech companies were not paying attention to before and hence affecting their business models. Facebook's dating app which was supposed to be released today, a day before valentine, has been banned by the European Union as Facebook has failed to provide adequate and required documentation to the regulatory boards.
This paper tackles the complex problem of visually matching people in similar pose but with different clothes, background, and other appearance changes. We achieve this with a novel method for learning a nonlinear embedding based on several extensions to the Neighborhood Component Analysis (NCA) framework. Our method is convolutional, enabling it to scale to realistically-sized images. By cheaply labeling the head and hands in large video databases through Amazon Mechanical Turk (a crowd-sourcing service), we can use the task of localizing the head and hands as a proxy for determining body pose. We apply our method to challenging real-world data and show that it can generalize beyond hand localization to infer a more general notion of body pose.
We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well with non-uniform sampling. We show that the weighted trace-norm regularization indeed yields significant gains on the highly non-uniformly sampled Netflix dataset. Papers published at the Neural Information Processing Systems Conference.
In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data.
Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these compe- titions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively "learn" a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and par- ticipants can modify this hypothesis by wagering on an update.
Crowdsourcing systems, in which tasks are electronically distributed to numerous information piece-workers'', have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all crowdsourcers must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in some way such as majority voting. In this paper, we consider a general model of such rowdsourcing tasks, and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give new algorithms for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm significantly outperforms majority voting and, in fact, are asymptotically optimal through comparison to an oracle that knows the reliability of every worker.