You may not realize this, but all of us in some way have used or have associated with Artificial Intelligence. Sometimes it can be hard to tell such as; a phone call which sounds like a real person who is actually responding to what you say or even an online customer service portal that answers your questions. We may not be living in a world like the films iRobot or Ex Machina right now, but we are constantly moving forward with AI to a point where that could be a reality. AI technology comes in handy for many different businesses, especially ones with a website. There are many ways and reasons to use AI to help better your online business no matter how big or small.

Artificial intelligence (AI) and its impact on the accounts payable function is an issue causing concern among the accounting and accounts payable community. In this episode of the AP Now podcast, he Mary Schaeffer to clarify this point of view. He begins by explaining exactly what he means by artificial intelligence and machine learning … that is the machine doing the learning … not online learning facilities for professionals (which is what I thought was meant by that term the first few times I heard it). As you can probably guess, the applicability of these concepts to the accounts payable function, and more specifically the invoice handling processes is huge. Eyal explains how these applications work.

Just like the way human beings can draw, paint, sing, dance, recite poems and do other creative work, there is an understanding that machines powered by some of the latest technologies could possibly do the same perfectly. Artificial Intelligence (AI) is uniquely billed as one of those emerging technologies that will power machines to just do that. But answers to questions of how truly creative these machines can be are still varied and at some extent not sufficient. There are already concerns around the integrity of tech machines; how empathetic they can be, how emotional they can get along with existing humans without offending them, and so on. There is a reality already.

An actor's life is often portrayed as a struggle to get to the top, but now scientists claim to have created an AI that can predict success in show business. Mathematicians at Queen Mary University, London, say they can accurately predict whether an actor's career has peaked - or if their most successful days still lie ahead. They discovered that an actors' most productive year - defined as the year with the largest number of credited jobs - is towards the beginning of their career. Clear signals preceding and following the'annus mirabilis' (AM) enable them to predict with around 85 per cent accuracy if it has passed or not. An actor's life is often portrayed as a struggle to get to the top, but now scientists claim to have worked our a formula to predict success in show business.

Detecting scene types in a movie can be very useful for application such as video editing, ratings assignment, and personalization. We propose a system for detecting kissing scenes in a movie. This system consists of two components. The first component is a binary classifier that predicts a binary label (i.e. kissing or not) given a features exctracted from both the still frames and audio waves of a one-second segment. The second component aggregates the binary labels for contiguous non-overlapping segments into a set of kissing scenes. We experimented with a variety of 2D and 3D convolutional architectures such as ResNet, DesnseNet, and VGGish and developed a highly accurate kissing detector that achieves a validation F1 score of 0.95 on a diverse database of Hollywood films ranging many genres and spanning multiple decades. The code for this project is available at http://github.com/amirziai/kissing-detector.

The creation and performance of music has inspired AI researchers since the very early times of artificial intelligence [8, 13, 10], and there is today a rich literature of computational approaches to music [11], including AI systems for music composition [3] and improvisation [2]. As pointed out by Thom [15], however, these systems rarely focus on the spontanous interaction between the human and the artificial musicians. We claim that such interaction demands a combination of reactivity and anticipation, that is, the ability to act now based on a predictive model of the companion player [12]. This paper reports our initial steps in the generation of collaborative human-machine music performance, as a special case of the more general problem of anticipation and creative processes in mixed human-robot, or anthrobotic systems [4]. We consider a simple case study of a duo consisting of a human pianist accompained by an off-the-shelf virtual drummer, and we design an AI system to control the key perfomance parameters of the virtual drummer (patterns, intensity, complexity, fills, and so on) as a function of what the human pianist is playing. The AI system is knowledge-based: it relies on an internal model represented by means of fuzzy logic.

If there is anything every business leader in the world can agree on – let's face it, there's not a lot – it is that Artificial Intelligence (AI) is about to have an indescribable impact that will change the way business looks. Among the areas, it is set to revolutionize marketing entirely. In 2018, we saw the marketing landscape change as AI adoption was fueled by growing customer expectations. The demand for one-to-one personalized interactions with brands has influenced the way marketers communicate with their customers. Most notably, it has forced the marketers to turn to AI to not only supplement but also to enhance their marketing strategies and beat out the competition.

When you think "contact center," you probably think of static scripts. But all that is about to change. "One of the top things customers tell us is, 'Know me. Know who I am when you engage with me.'" What we're all after here is the ultimate customer experience.

Let's say that I find it curious how Spotify recommended a Justin Bieber song to me, a 40-something non-Belieber. That doesn't necessarily mean that Spotify's engineers must ensure that their algorithms are transparent and comprehensible to me; I might find the recommendation a tad off-target, but the consequences are decidedly minimal. This is a fundamental litmus test for explainable AI – that is, machine learning algorithms and other artificial intelligence systems that produce outcomes that humans can readily understand and track backwards to the origins. Conversely, relatively low-stakes AI systems might be just fine with the black box model, where we don't understand (and can't readily figure out) the results. "If algorithm results are low-impact enough, like the songs recommended by a music service, society probably doesn't need regulators plumbing the depths of how those recommendations are made," says Dave Costenaro, head of artificial intelligence R&D at Jane.ai. I can live with an app's misunderstanding of my musical tastes.

"AI art", or more precisely art created with neural networks, has recently started to receive broad media coverage in newspapers (New York Times), magazines (The Atlantic), and countless blogs. Combined with the ongoing general "AI hype" and multiple recent museum and gallery exhibitions, this coverage has produced the impression of a new star rising in the art world: that of machine-generated art. It has also led to the popularization of an ever-growing list of philosophical questions surrounding the use of computers for the creation of art. This brief article provides a pragmatic evaluation of the new genre of AI art from the perspective of art history. It attempts to show that most of the philosophical questions commonly cited as unique issues of AI art have been addressed before with respect to previous iterations of generative art starting in the late 1950s. In other words: while AI art has certainly produced novel and interesting works, from an art historical perspective it is not the revolution as which it is portrayed.