The generalisation of overparameterised neural networks has long piqued the curiosity of the machine learning community.

OTT platforms are now the primary entertainment source for a lot of people. You want to watch a movie, catch up on your favorite TV show, or just kick back and support your sports team -- you can access any content more conveniently via streaming services. There is a ton of effort that goes into keeping all of those users engaged with the OTT platform. Apart from delivering the best content, the platforms have to think about ways to make the viewing experience the most convenient. So, let's talk about just that -- you can enhance user engagement by providing a better viewing experience with cognitive computing. Ever find yourself looking at the credits when the movie or a TV series episode is over?

Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning applications recommend entertainment we might enjoy through streaming services such as Netflix and Spotify. In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand a lot of energy. Currently, the need for computing power related to these technologies is doubling roughly every three to four months.

This thesis investigates Bach's composition style using deep sequence learning. We develop BachBot: an automatic stylistic composition system for composing polyphonic music in the style of Bach's chorales. We find a 3-layer stacked LSTM performs best and conduct analyses and evaluations to understand its success and failure modes. Unlike many previous works, we avoid allowing prior assumptions about music impact model design, opting instead to build systems that learn rather than ones which encode prior hypotheses. While this is not the first application of deep LSTM to Bach chorales, our work consists of the following novel contributions.

Touch a penny to a live electrical plug: that's what Alexa told a 10-year-old child who asked it for a challenge to do. Amazon's virtual assistant made the headlines in a bad way last month after reportedly scraping this'challenge' off TikTok and regurgitating it wholesale. Alexa's gaffe recalls Microsoft's Tay chatbot, which had to be taken down in 2016 after learning to swear and spew extremist ideology from Twitter. Almost six years separate the two incidents, and machine learning should have long since advanced beyond returning such wildly inappropriate responses. It's an important consideration now that AI has become the foundation of multiple workplace tools, especially recruitment and retention.

Over the last few years, Text classification is one of the fundamental tasks in natural language processing (NLP) in which the objective is to categorize text documents into one of the predefined classes. The news is full of our life. Therefore, news headlines classification is a crucial task to connect users with the right news. The news headline classification is a kind of text classification, which can be generally divided into three mainly parts: feature extraction, classifier selection, and evaluations. In this article, we use the dataset, containing news over a period of eighteen years provided by Kaggle platform to classify news headlines. We choose TF-IDF to extract features and neural network as the classifier, while the evaluation metrics is accuracy. From the experiment result, it is obvious that our NN model has the best performance among these models in the metrics of accuracy. The higher the accuracy is, the better performance the model will gain. Our NN model owns the accuracy 0.8622, which is highest accuracy among these four models. And it is 0.0134, 0.033, 0.080 higher than its of other models.

Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods. We propose a novel unsupervised model-based deep learning approach to musical source separation. Each source is modelled with a differentiable parametric source-filter model. A neural network is trained to reconstruct the observed mixture as a sum of the sources by estimating the source models' parameters given their fundamental frequencies. At test time, soft masks are obtained from the synthesized source signals. The experimental evaluation on a vocal ensemble separation task shows that the proposed method outperforms learning-free methods based on nonnegative matrix factorization and a supervised deep learning baseline. Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio. This work makes powerful deep learning based separation usable in scenarios where training data with ground truth is expensive or nonexistent.

The article explores new ways of written language aided by AI technologies, like GPT-2 and GPT-3. The question that is stated in the paper is not about whether these novel technologies will eventually replace authored books, but how to relate to and contextualize such publications and what kind of new tools, processes, and ideas are behind them. For that purpose, a new concept of synthetic books is introduced in the article. It stands for the publications created by deploying AI technology, more precisely autoregressive language models that are able to generate human-like text. Supported by the case studies, the value and reasoning of the synthetic books are discussed. The paper emphasizes that artistic quality is an issue when it comes to AI-generated content. The article introduces projects that demonstrate an interactive input by an artist and/or audience combined with the deep-learning-based language models. In the end, the paper focuses on understanding the neural aesthetics of written language in the art context.

Advanced technologies like artificial intelligence (AI), machine learning (ML), cloud computing, and 5G networking are all within the scope of …

We have all seen movies depicting AI as a threat. In Avengers 2, Ironman created Ultron so that it can help avengers to defend people from enemies. But Ultron went out of control and wanted to dominate people, becoming an enemy. But as a professional machine learning engineer, I do not think AI will dominate us. There are a lot of pessimistic opinions making you think that AI and machine learning are turning modern society into a dystopia.