Most of our decisions on social media are impacted by ML. From feeds that we see on the timeline to notifications that we receive from the social media apps, everything is curated by ML. While we travel, work, live life our decisions are examined by machine learning to provide us with a better experience. ML takes all the past behavior, web searches, interaction and everything else that we do when we are on these websites and tailors the experience for us. It helps enhance our web surfing into a personalized one.

As the world becomes more deeply connected through IoT devices and networks, consumer and business needs and expectations will soon only be sustainable through automation. Recognizing this, artificial intelligence and machine learning are being rapidly adopted by critical industries such as finance, retail, healthcare, transportation and manufacturing to help them compete in an always-on and on-demand global culture. However, even as AI and ML provide endless benefits -- such as increasing productivity while decreasing costs, reducing waste, improving efficiency and fostering innovation in outdated business models -- there is tremendous potential for errors that result in unintended, biased results and, worse, abuse by bad actors. The market for advanced technologies including AI and ML will continue its exponential growth, with market research firm IDC projecting that spending on AI systems will reach $98 billion in 2023, more than two and one-half times the $37.5 billion that was projected to be spent in 2019. Additionally, IDC foresees that retail and banking will drive much of this spending, as the industries invested more than $5 billion in 2019.

We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. A new large-scale dataset, named Violin (VIdeO-and-Language INference), is introduced for this task, which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels. In order to address our new multimodal inference task, a model is required to possess sophisticated reasoning skills, from surface-level grounding (e.g., identifying objects and characters in the video) to in-depth commonsense reasoning (e.g., inferring causal relations of events in the video). We present a detailed analysis of the dataset and an extensive evaluation over many strong baselines, providing valuable insights on the challenges of this new task.

We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering. The problem is formulated as a token classification task similar to named entity extraction. With the help of a domain expert and text processing methods, we construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts. We use a pre-trained language model and fine-tune it with the labeled dataset of concepts. In addition, we also create some essential datasets for information such as abbreviations and definitions from the systems engineering domain. Finally, we construct a simple knowledge graph using these extracted concepts along with some hyponym relations.

Most research in Bayesian optimization (BO) has focused on direct feedback scenarios, where one has access to exact, or perturbed, values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A/B tests or recommender systems, there is a need of methods that are able to replace direct feedback with preferential feedback, obtained via rankings or pairwise comparisons. In this work, we present Preferential Batch Bayesian Optimization (PBBO), a new framework that allows to find the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets.

This is a list of films about computers, featuring fictional films in which activities involving computers play a central role in the development of the plot. Not Rated 95 min Biography, Drama, History. Jun 6, 2015 -- Benjamin, a young German computer whiz, is invited to join a subversive hacker group that wants to be noticed on the world's stage. Many Hollywood films played on the big screen are devoted to computer science, whether fiction or not. Computer-related movies have been produced since the--...

Introducing one of the most advanced music AI engines ever created. CREATE BEATS Every MixPack features 4 tracks, 12 loops, and 100 combinations. ADD VIDEOS Shoot live or add clips from your library. The KR38R camera lets you combine up to 3 clips - perfect for rappers to multi-track vocals or dancers to shoot routines from multiple angles. COMING 2020 Coming soon to the KR38R app are features and functionality guaranteed to change the game, including: AI-powered tools for producers, video and audio filters, audio-reactive video fx, and more.

Deep learning techniques are proving to be extremely useful for analyzing all kinds of data, ranging from images to text, online posts and audio recordings. These techniques are designed to identify patterns in large datasets, separate items in different categories and make predictions far quicker than humans. In a recent study, researchers at Simon Fraser University, Academia Sinica and Dartmouth College have applied deep learning techniques to identify similarities and differences between Chinese and Western classical music. Their paper, pre-published on arXiv, presents a comparative analysis of music recordings using sound event detection (SED) and soundscape emotion recognition (SER) models. "We have listened to both Chinese and Western classical music," Jianyu Fan, one of the researchers who carried out the study, told TechXplore.

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.