How difficult can it be to detect rectangles on an image? Well… it can be nearly impossible if there are no actual rectangles at all. Edges between a YouTube video's background and overlaid GoT scenes are visible only due to the optical illusion called illusory contours. In this task, we had to rely a little bit on the incredible vision of humans and make some annotations. Thankfully, it was enough to manually mark coordinates only on a single frame per YouTube video.

The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and feature extraction from MP3 audio files. The metric learning process considers the learning of a set of parameterized distances employing a structured prediction approach from a set of MP3 audio files containing several music genres. The main objective of this work is to make possible learning a personalized metric for each customer. To extract the acoustic information we use the Mel-Frequency Cepstral Coefficient (MFCC) and make a dimensionality reduction with the use of Principal Components Analysis. We attest the model validity performing a set of experiments and comparing the training and testing results with baseline algorithms, such as K-means and Soft Margin Linear Support Vector Machine (SVM). Experiments show promising results and encourage the future development of an online version of the learning model.

Music has the power of inducing emotions, and human beings exploit such a phenomenon in order to empower a variety of mental states and activities, both positively and negatively. The study of emotions and music has a long and still vibrant tradition. New findings and changes of perspective in the field are not uncommon. More recent is the field investigating music emotion recognition through computational means. Music emotion recognition (MER) is an emerging and cross-disciplinary field spanning information retrieval (audio, symbolic and metadata) and machine learning, on a strong backing of music cognition (semiology of music and psychology) and music theory.

A'selfie robot' that is set to replace instant cameras and photo booths at parties has made its debut at at a UK wedding. The robot, designed by Birmingham-based firm, allowed wedding goers at a local reception to send photos it to themselves by email or text. Armed with AI software that can detect faces, the robot can'roam freely' around a room and stops to ask people if they would like their photo taken. It also has an infrared sensor that prevents it smashing into people and obstacles. Eva Photography Robot has been developed by a computer programmer in Birmingham to take selfies of partygoers and made its first debut at a wedding.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. LOS ANGELES – When you pose the simplest of questions to Siri and it can't answer "what's 1% of $1 million," yet Amazon Alexa and the Google Assistant can, you know just how far behind Apple's assistant has fallen to rivals. Siri was the first voice assistant, announced in 2011 for the iPhone 4S, but over the past several years, Amazon and Google have rolled over it, by investing heavily and introducing many new features, while Siri "hasn't moved forward much," says Bret Kinsella, who runs the Voicebot.ai Apple traditionally takes center stage at its Worldwide Developer's Conference (WWDC) to unveil new software features to whet app makers' appetites, and often they involve Siri.

A two-person team from the UNICEF's Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning "Hackathon" As part of efforts to develop the technology and innovation ecosystem to support development of Sierra Leone, UNICEF is collaborating with the Directorate of Science, Technology and Innovation (DSTI) in the Office of the President, on a knowledge exchange partnership, around innovative Machine Learning techniques which, it is hoped, will add value to Government's work around data for decision making in the country. A two-person team from the UNICEF's Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning "Hackathon" to work on data from the education sector in support of the Government's Free Quality School Education initiative. Officials from different Government Ministries, Departments and Agencies joined the team to enhance their knowledge of Machine Learning and advanced data analysis techniques, for use in their own areas of government. Shane O'Connor, Technology for Development Specialist at UNICEF Sierra Leone, stated that the opportunity afforded by this collaboration is huge. "With the President's establishment of the DSTI and with UNICEF's collaboration, there really is great potential for a step change in how Technology and Innovation can be leveraged to deliver for Sierra Leone," he said.

Beauty brands are using everything from artificial intelligence (AI) to augmented reality (AR) to keep their customers engaged in a fiercely competitive market. But do such innovations actually work or are they simply marketing hype? When L'Oreal said last year it no longer wanted to be the number one beauty firm in the world, but "the number one beauty tech company", it was clear things in the industry had changed. "Women have had the same beauty concerns for 30 to 40 years, but technology has created a more demanding consumer," explains Guive Balooch, global vice president of L'Oreal's Technology Incubator. "They want more personalised and precise products, and we have to respond."

Abstract: Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise, for example, as an individual reads a textbook chapter-by-chapter. Through simulations involving sequences of ten related tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming domain experts. We observed two key phenomena. First, _forward facilitation_---the accelerated learning of task $n+1$ having learned $n$ previous tasks---grows with $n$. Second, _backward interference_---the forgetting of the $n$ previous tasks when learning task $n+1$---diminishes with $n$. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.

Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches.