Now, it is not just traditional robotics but robotics that is powered by technologies like artificial intelligence and machine learning.

We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input parameters to the corresponding waveform. We propose two datasets to evaluate our approach on both a restrictive context, and in one covering a broader spectrum of sounds. The timbral features used as parameters are taken from recent literature in signal processing. We also use these features for evaluation and validation of the presented model, to ensure that changing the input parameters produces a congruent waveform with the desired characteristics. Finally, we evaluate the quality of the output sound using a subjective listening test. We provide sound examples and the system's source code for reproducibility.

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates. 1 Introduction Starting with the seminal work of Mochales Palau and Moens (2009), argument mining has mainly focused on the following tasks - identifying argumentative text segments within a given document; labeling these text segments according to the type of argument and its stance; and elucidating the discourse relations among the detected arguments. Typically, the considered documents were argumentative in nature, taken from a well defined domain, such as legal documents or student essays. More recently, some attention had been given to the corresponding retrieval task - given a controversial topic, retrieve arguments with a clear stance towards this topic. This is usually done by first retrieving - manually or automatically - documents relevant to the topic, and then using argument mining techniques to identify relevant argumentative segments therein. This documents-based approach was originally explored over Wikipedia (Levy et al. 2014; Rinott et al. 2015), and more recently over the entire Web (Stab et al. 2018). For an argument retrieval system to be of practical use requires: (1) high precision, and (2) wide coverage.

ABOUT THE TALK: For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why this is a good title for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. Our image personalization engine is driven by online learning and contextual bandits.

I want to build a model for Chess/Go/Shogi that is trained and tested on real players, and I want it to pass the Turing test. I don't want my model to play the best move in a position, I want it to play the move that a person would play (of a certain strength, time control, etc..). It's easy to make this a classification problem and train a CNN on a one-hot encoded policy of actual moves played. The only problem is, without some kind of look-ahead algorithm (MCTS for example) the model fails to learn sequences that require multiple moves, such as tactics. However, current MCTS/alpha-beta/minimax models require evaluation of leaf nodes. I don't have a way to shape the reward to an evaluation of a leaf node.

Wangchuk, who inspired Aamir Khan's character Phunsukh Wangdu in the film 3 Idiots, is the founding director of the Students' Educational and Cultural Movement of Ladakh (SECMOL). Wangchuk said that SEMCOL was founded in 1988 by students who were, "victims of an alien education system foisted on Ladakh." He has set up the campus in Ladakh that runs on solar energy and uses no fossil fuels for lighting, cooking, or heating. Talking about his courtship with innovation, he said, "I grew up in a place where innovation was all around and without it, life wouldn't have been possible." "I saw how in the trans-Himalayan cold desert of Ladakh where temperatures reach minus 30 degrees, our ancestors not only survived but also contributed to the thriving inter-mingled civilisations. So, the freezing winters were not something to fear but something to look forward to – the hard work of ploughing, farming, winnowing, and thrashing, became in the carrying out of these processes, a community festival," he added.

At Oracle, customer service chatbots use conversational AI to respond to consumers with more speed and complexity. Suhas Uliyar, vice president of bots, AI and mobile product management at Oracle, stopped by to talk to AI Podcast host Noah Kravitz about how the newest wave of conversational AI can keep up with the nuances of human conversation. Many chatbots frustrate consumers because of their static nature. Asking a question or using the wrong keyword confuses the bot and prompts it to start over or make the wrong selection. Uliyar says that Oracle's digital assistant uses a sequence-to-sequence algorithm to understand the intricacies of human speech, and react to unexpected responses.

Headlines from the likes of The New York Times ("Deepfakes Are Coming. We Can No Longer Believe What We See"), The Wall Street Journal ("Deepfake Videos Are Getting Real and That's a Problem"), and The Washington Post ("Top AI researchers race to detect'deepfake' videos: 'We are outgunned'") would have us believe that clever fakes may soon make it impossible to distinguish truth from falsehood. Deepfakes -- pieces of AI-synthesized image and video content persuasively depicting things that never happened -- are now a constant presence in conversations about the future of disinformation. These concerns have been kicked into even higher gear by the swiftly approaching 2020 U.S. election. A video essay from The Atlantic admonishes us: "Ahead of 2020, Beware the Deepfake."