There's another Kubrick science–fiction project that received a similarly puzzled critical reception to 2001, and which is overdue for reappraisal. AI: Artificial Intelligence, which came out 13 years ago, was developed by Kubrick and latterly with Steven Spielberg, a perfect fear–and–wonder pairing. The film is an Oedipal fairy tale about a robot boy called David driven by his programming to seek a mother's love. It's equal parts Pinocchio and Frankenstein and, like those stories, is fascinated less by the creator than their creation. David, perfectly played by Haley Joel Osment, is a new brand of "mecha", or humanoid robot, invented by a Dr Hobby (William Hurt) as a kind of child–surrogate.

With the new year approaching and a new decade on the horizon (yes, technically it starts in 2021), we wanted to look at transformations in media that are likely to define the 2020s. Especially in the media and computing fields, the decade proves to be a useful marker. Consider that at the end of 2009, Digital Cinema and 3D film was just being introduced with the release of Avatar, GPU rendering was in its infancy, YouTube was less than 1/20th the size it is today, and Netflix video streaming had yet to be launched internationally. Meanwhile, cloud computing had just started its adoption curve, the first augmented reality print campaign was launched, and blockchain mining was in its first year. It's fair to expect that when we look back a decade later, the end of 2019 will similarly look distant - if not more remote with the accelerating rate of change in media and technology.

Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practice areas. The system will be useful in a lot of areas. It will be mostly used in news feed categorization. News websites can automatically categorize news into classes such as sports, business, education, science, etc. The system is also used in sentiment analysis for product reviews. For example, the company shares a photo of a new product on Facebook and the company receives a thousand comments for new products. The systems classify the comments into categories like positive or negative. The system can also be applied in recommended systems, spam filtering, etc. Various machine learning techniques such as Naive Bayes, SVM, Decision Trees have been devised to solve the text classification problem in Azerbaijani language.

Recently OpenAI announced the launch of its 1.5 billion parameter language model GPT-2. GPT-2 has been in the news as the scary AI text generator with potential threats regarding fake news stories, and so on. But now we have'GPTrue or False' browser extension that displays the GPT-2 Log Probability of selected portions of text. This browser extension allows you to select text on a website and finds out what you selected is written using OpenAI's GPT-2 A.I. model. GPTrue or False is available both for Chrome and Firefox.

A simple device, just a heat and movement sensor attached to digital camera, has revolutionised the way that conservationists learn about animals in the wild. Camera traps are a very simple solution to the task of working out when, where and how wildlife interacts with its environment. Monitoring populations without damaging habitats, these relatively simple devices have provided some astonishing finds including revealing species previously hidden in the untouched depths of the forest. Elusive new creatures aren't their only speciality, however, as in 2015, similar devices helped reveal that the critically endangered Javan rhinoceros was breeding and significantly adding to its tiny population. After identifying a likely area for a sighting, usually with the help of local guides, traps are placed at animal height on trees and posts and left to wait until wildlife walks by.

Eventually, content focused on any area plateaus and becomes repetitive, unimpressive and ineffective. The initial freshness of a topic wears off, and marketers are forced to deliver commonplace posts that reiterate widely-known information and don't really solve readers' problems. Few content creators truly address this problem by creating fresh and beneficial content based on new data, while most others simply recycle and rehash existing information to keep up with content demand. With AI, marketers can satisfy the content demand, create hyper-personalized content and ensure targeted delivery. Here's a summary of 5 such ways in which AI will redefine content creation and delivery.

The manufacturing industry exhibits some contradictions when it comes to automation and technology. On the one hand, manufacturing was among the first industries to integrate any type of technology more than a century ago, as companies incorporated tools to aid in the production process. On the other hand, manufacturing companies are risk-averse when it comes to implementing new technology quickly, mainly due to the large amount of capital and time at stake. However, according to a new report from Tractica, manufacturing companies are now incorporating artificial intelligence (AI) technology within their environments at a modest, yet steady, pace. The market intelligence firm forecasts that annual worldwide manufacturing sector investment in AI software, hardware, and services will increase from $2.9 billion in 2018 to $13.2 billion by 2025.

Multiple Pretext-T ask for Self-Supervised Learning via Mixing Multiple Image Transformations Shin'ya Y amaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda NTT Tokyo, Japan {shinya.yamaguchi.mw,sekitoshi.kanai.fu,tetsuya.shioda.yf,shoichiro.takeda.us}@hco.ntt.co.jp Abstract Self-supervised learning is one of the most promising approaches to learn representations capturing semantic features in images without any manual annotation cost. T o learn useful representations, a self-supervised model solves a pretext-task, which is defined by data itself. Among a number of pretext-tasks, the rotation prediction task (Rotation) achieves better representations for solving various target tasks despite its simplicity of the implementation. However, we found that Rotation can fail to capture semantic features related to image textures and colors. T o tackle this problem, we introduce a learning technique called multiple pretext-task for self-supervised learning (MP-SSL), which solves multiple pretext-task in addition to Rotation simultaneously. In order to capture features of textures and colors, we employ the transformations of image enhancements (e.g., sharpening and solarizing) as the additional pretext-tasks. MP-SSL efficiently trains a model by leveraging a Frank-W olfe based multi-task training algorithm. Our experimental results show MP-SSL models outperform Rotation on multiple standard benchmarks and achieve state-of- the-art performance on Places-205. 1. Introduction Convolutional neural networks (CNNs) [27, 16, 44] are widely adopted to solve many target tasks in applications of computer vision such as object recognition [30], semantic segmentation [4], and object detection [42]. However, these successes depend on supervised training of CNNs with the vast amount of labeled data [43], which is expensive and impractical because of the manual annotation cost. Since the cost of labeled data limits the practical applications of CNNs, a number of researches focus on the training techniques to alleviate the requirement of many labeled data; the techniques include transfer learning, semi-supervised learning, and self-supervised learning . A demonstration describing our motivation to modify self-supervised learning by predicting rotations of images (Rotation).

Star Wars-style lasers could help future space missions to accurately pinpoint the location of space junk in Earth's orbit and avoid deadly collisions. Millions of pieces of space junk are currently whizzing around the planet at around 20,000 miles per hour -- providing a threat to future rockets and space craft. However a new system based on neural networks has provided a better way to keep track of these hazardous objects with telescopes. The approach can detect debris as small as 3 feet wide and allow courses to be plotted to avoid them. Star Wars-style lasers could help future space missions to accurately pinpoint the location of space junk in Earth's orbit and avoid deadly collisions.