While going through machine learning topics, I got the point about classification and clustering.

The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.

Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. Neural architectures have been employed to generate text excerpts to various degrees of success, in a multitude of contexts and tasks that fulfil various user needs. Notably, high capacity deep learning models trained on large scale datasets demonstrate unparalleled abilities to learn patterns in the data even in the lack of explicit supervision signals, opening up a plethora of new possibilities regarding producing realistic and coherent texts. While the field of natural language generation is evolving rapidly, there are still many open challenges to address. In this survey we formally define and categorize the problem of natural language generation. We review particular application tasks that are instantiations of these general formulations, in which generating natural language is of practical importance. Next we include a comprehensive outline of methods and neural architectures employed for generating diverse texts. Nevertheless, there is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field. To this end, we also review current approaches to evaluating natural language generation systems. We hope this survey will provide an informative overview of formulations, methods, and assessments of neural natural language generation.

Just in time for its 25th anniversary, Ghost in the Shell is getting a 4K Ultra HD rerelease. On September 8th, you'll be able to buy Mamoru Oshii's landmark film in a beautiful collection that will let you watch the 1995 feature across multiple formats. Lionsgate has remastered both the original Japanese and English audio tracks for Dolby Atmos. Included in the release is a digital copy of the movie. For good measure, you also get the film on Blu-ray.

Artificial intelligence has much to offer in improving the way we speak to one another in the social distance era, and has already seen wide adoption in …

These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!

After years of being seen as an emerging tactic, automated journalism has finally made it to the mainstream. Each day, more robots and smart computers are in to give more and more impact on the news media industry. From Facebook using artificial intelligence (Al) to detect the pattern of the words that indicate fake news to Trint using voice recognition to transcribe interviews in multiple languages in no time, journalists around the world embrace AI to speed-up the research and cross-referencing data and beyond. Fake news has now become a part of our everyday life. What's more, that such news tends to go viral because people can spread lies faster than the actual fact itself.

Layered on top of NVIDIA CUDA, RAPIDS is a suite of open-source software libraries and APIs that provide GPU parallelism and high-bandwidth memory speed through DataFrame and graph operations, achieving speedup factors of 50x or more on typical end-to-end data science workflows. For Spark 3.0, new RAPIDS APIs are used by Spark SQL and DataFrames for GPU accelerated memory efficient columnar data processing and query plans. With Spark 3.0 the Catalyst query optimizer has been modified to identify operators within a query plan that can be accelerated with the RAPIDS API, and to schedule those operators on GPUs within the Spark cluster, when executing the query plan. A new Spark shuffle implementation, built upon GPU accelerated communication libraries including Remote direct memory access (RDMA), dramatically reduces the data transfer among Spark processes. RDMA allows GPUs to communicate directly with each other, across nodes, at up to 100Gb/s, operating as if on one massive server.

Back on October 12, 2019, the world witnessed a previously unimaginable accomplishment- the first sub-two-hour marathon was run in an incredible time of 1:59:40 by Kenyan native Eliud Kipchoge. He would later say in regards to the amazing achievement that he "expected more people all over the world to run under 2 hours after today" [1]. While Kipchoge set new records in long distance running, across the world a team of natural language processing (NLP) experts at OpenAI, the Elon Musk-backed AI firm, published a new transformer-based language model with 1.5 billion parameters that achieved previously unthinkable performance in nearly every language task it faced [2]. The main takeaway from the paper by many experts was that bigger is better-the intelligence of transformer models can dramatically increase with the scale of parameters. In March of 2020, this theory gained support with OpenAI's release of version three of the model or GPT-3 which encapsulates a staggering 175 billion parameters and achieved even more remarkable performance than version 2, despite sharing, quite literally, the same architecture [3].

What if an Artificial Intelligence-enabled combat system simply goes too far by expediting and completing a decision-making cycle beyond what was …