A deep-learning algorithm has been developed which can solve the Rubik's cube faster than any human can. It never fails to complete the puzzle, with a 100 per cent success rate and managing it in around 20 moves. Humans can beat the AI's mark of 18 seconds, the world record is around four seconds, but it is far more inefficient and people often require around 50 moves. It was created by University of California Irvine and can be tried out here. Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration.

An AI has been studying the cookbooks and has taught itself how to make intriguing new pie recipes -- including Scotch egg pies and one with a salad filling. Working with a Sussex-based pie makers, the algorithm has produced thousands of recipes, five of which have been selected for production and will be going on sale. The AI works by looking for patterns in existing recipes and then trying to make its own based on what it learnt. While some of the early recipes it proposed were perhaps less-than-mouth-watering, with a little guidance it soon got the hang of cooking up new pie concepts. The experiment illustrates how artificial intelligence can provide new insights for small businesses and help dream up novel products to take to market.

A store themed around the work of "Astro Boy" manga artist Osamu Tezuka opened earlier this month in Tokyo's Asakusa district, putting an array of available products on display, from traditional Japanese crafts to artificial intelligence robots. The Tezuka Osamu Shop & Cafe is currently the only store, apart from the artist's memorial museum in western Hyogo Prefecture where he grew up, that sells character goods featuring his manga and anime, according to the shop's operator. With theme songs from his animation work playing in the background, the first floor displays approximately 300 types of merchandise, including wooden kokeshi (Japanese dolls) in the shape of characters including Astro Boy and his father figure Professor Ochanomizu, as well as ties featuring another masterpiece, "Phoenix," made in traditional Nishijin textiles. "Astro Boy" tells the stories of the adventures of a boy android with human emotions. The sci-fi manga series, serialized from 1952 to 1968 and also adapted into an animation series, has many fans in Asia and beyond.

Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist's prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results.

The construction industry is massive. People all around the world need buildings to live in, work in and relax in. As more people join the population, more buildings will be needed. With 8.6 billion people estimated to inhabit this planet by 2030, we'll need to build an average of 13,000 buildings every single day to accommodate everybody. Last week, Autodesk held its annual Connect and Construct Summit in London to discuss how the industry can tackle the growing demand.

This will sound familiar to anyone who has owned a smartphone in the last decade. I simply ask the question – and Google lays out the entire weather pattern for me. It saves me a ton of time and I can quickly glance at my screen and get back to work. But how does Google understand what I'm saying? And how does Google's system convert my query into text on my phone's screen?

This article aims to explain how the activation functions work in a neural network. Activation function is nothing but a mathematical function that takes in an input and produces an output. The function is activated when the computed result reaches the specified threshold. Finally, the computed value is fed into the activation function, which then prepares an output. Think of the activation function as a mathematical operation that normalises the input and produces an output.

Over the past few years, the delivery services in India has witnessed a massive growth. Today, we have a delivery service for every task -- whether you want to order food, get something delivered to one point to another in a city etc. However, there was a small void in this sector as well (for every task there is a different app) until Dunzo came into the scenario. Founded in 2014 by Mukund Jha, Kabeer Biswas, Dalvir Suri, and Ankur Agarwal, Dunzo in recent times has gained a significant amount of traction in the industry. Dunzo is an on-demand delivery service platform has changed the way people move things, shop and commute.

Fifty years is a long time by human standards, and an eon by technology standards. In 1969, not many organizations even knew what a computer was, let alone used one. Though it's trivial, revisiting and comparing the compute power of then to what we have now can help us realize the effort it took to realize the achievement that the moon landing was. The scale of our compute and storage capabilities has changed dramatically as Moore's law has been in full effect. Like many "laws," Moore's law is more like a rule of thumb, stating that the number of transistors in dense integrated circuit doubles about every two years.

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