Hollywood legend Robert Downey Jr. is putting down his Iron Man suit and taking up the bigger challenge of cleaning up the Earth. Iron Man's on-screen nemesis Thanos had a radical way of reducing humanity's impact on the Earth, but Downey Jr. hopes to use a more humane solution. Appearing on-stage at Amazon's new re:MARS (Machine Learning, Automation, Robotics and Space) conference in Las Vegas, Downey Jr. launched an initiative called Footprint Coalition. "I don't pretend to understand the complexities we face as a species, just because I portrayed a genius in my professional life. My scholastic achievement peaked at a correctional finishing," Downey Jr. joked, in reference to being incarcerated on drug charges and never making it past high school.

This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and are in general correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out of stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.

Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.

IBM (NYSE:IBM) has announced the launch of Watson Ads Builder, an AI-powered ad developer that creates a dialogue between the brand and consumer. The platform employs natural language processing to enable fast and accurate conversation with the consumer. "Consumer expectations are shifting, and people expect the ability to communicate with brands on demand. Watson Ads Builder can change where, when and how brands connect with consumers - helping marketers increase loyalty and purchase consideration." Accenture (NYSE:ACN) has teamed with auto company Faurecia and tech firm Affectiva to develop the Connected Car Lab - "a digital product and service innovation facility that fits into a car ... to ideate, test and develop applications and experiences for the car cabin of the future".

AI is the de facto gold rush at the moment. Venture capitalists say AI startups in Thailand are in the initial stage of the AI journey. Artificial intelligence (AI) is one of the hottest buzzwords in the startup scene, with AI companies attracting 15-50% more money in funding rounds. Even larger corporations, many not even tech companies, must still pay lip service and claim to use AI or risk looking like they are unprepared for an AI-driven future. But how much of this so-called AI technology is actually of any value and not just empty buzzwords?

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.