Facebook AI Research (FAIR) scientists yesterday unveiled a neural network capable of translating music from one style, genre, and set of instruments to another. Soon, you won't have to blow your own horn; you can just whistle to an AI and it'll turn your song into the symphony or dance hit of your dreams. The AI takes one input, such as a symphony orchestra playing Bach, and translates it into something else, like the same song played on a piano in the style of Beethoven, for example. FAIR becomes the first AI research team to create an unsupervised learning method for recreating high-fidelity music with a neural network. Our results present abilities that are, as far as we know, unheard of. Asked to convert one musical instrument to another, our network is on par or slightly worse than professional musicians.

In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either 1) incorporate content information into collaborative filtering to perform hybrid recommendation, or 2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leverage both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.

Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.

We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-independent encoder allows us to translate even from musical domains that were not seen during training. The method is unsupervised and does not rely on supervision in the form of matched samples between domains or musical transcriptions. We evaluate our method on NSynth, as well as on a dataset collected from professional musicians, and achieve convincing translations, even when translating from whistling, potentially enabling the creation of instrumental music by untrained humans.

Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-the-loop settings.

I do think that beating TensorFlow on tasks like logistic regression is not particularly hard. A student asked me once to help optimize his Tf code for a large scale linear regression model on multiple GPUs. It was magnitudes slower than the single-core scikit-learn implementation. We spent hours trying to get the best performance out of it, including various experiments with the data loading directly to the GPU tensors bypassing the Python runtime. TensorFlow is just not optimized for this kind of stuff because of various overheads, I assume. People underestimate how fast scikit-learn is for generalized linear models thanks to BLAS and LIBLINEAR.

The report expects the global Artificial Intelligence (AI) in construction market to grow from USD 407.2 Million in 2018 to USD 1,831.0 The rising demand for AI-based solutions and platforms, the need for more safety measures at construction sites, and the capabilities of AI solutions and services to reduce the production costs are expected to drive the growth of the AI in construction market. The component segment has been further segmented into solutions and services. The solutions segment is expected to have the larger market size. AI in construction solutions play a vital role in the efficient and effective functioning of construction businesses using Natural Language Processing (NLP); and machine learning and deep learning technologies.

We're already used to virtual assistance like Siri and Alexa, and with more sophisticated forms of artificial intelligence these virtual assistants will be …

By Will Yang Technology in today's day and age has enabled a human to do things and accomplish far more than one could think of a few years back. Thanks to rapidly evolving and innovative technologies, personal lives have become more enriched. Meaningful collaborations between a human and machine/technology has in many ways provided a wealth of opportunities to us making our lives comfortable. One such technology buzzword in the industry today is Artificial Intelligence. Once a topic for science fiction, Artificial Intelligence technology is now being used by brands across industries and categories.

The logo of Sony Corp. is seen at the company's headquarters in Tokyo, Japan, May 22, 2018. TOKYO – Electronics and entertainment company Sony Corp. said Tuesday it plans to spend $2.3 billion acquiring an additional 60 percent stake in EMI Music Publishing, home to the Motown catalog and contemporary artists like Kanye West, Alicia Keys and Pharrell Williams. Sony already owns 30 percent of EMI so once the purchase from Mubadala Investment Co. is finalized, it will own 90 percent of the company, CEO Kenichiro Yoshida said in a news conference at Sony's headquarters. Mubadala is a government-backed investment fund controlled by the emirate of Abu Dhabi, the oil-rich capital of the United Arab Emirates, a seven-state federation that also includes the Mideast commercial hub of Dubai. Its holdings include semiconductor maker Globalfoundries, and stakes in General Electric Co., Washington-based private equity firm The Carlyle Group and numerous utility and energy companies.