In this special guest feature, Robert Roe from Scientific Computing World looks at the development of deep learning and its impact on scientific applications. Deep learning has seen a huge rise in popularity over the last five years in both enterprise and scientific applications. While the first algorithms were created almost 20 years ago with the development of artificial neural networks in 2000, the technology has come of age due to the massive increases in compute power, development of GPU technologies, and the availability of data to train these systems. Today the use of this technology is widespread across many scientific disciplines, from earthquake prediction, high-energy particle physics and weather and climate modeling, precision medicine and even the development of clean fusion energy. With so many possible applications, it can be difficult for scientists to figure out if artificial intelligence (AI) or deep learning (DL) can fit into workflow.

As Moore's law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat.

In "Anomaly Detection with Autoencoders Made Easy" I mentioned that the Autoencoders have been widely applied in dimension reduction and image noise reduction. Since then many readers have asked if I can cover the topic of image noise reduction using autoencoders. That is the motivation of this post. Modeling image data requires a special approach in the neural network world. The best known neural network for modeling image data is the Convolutional Neural Network (CNN, or ConvNet) or called Convolutional Autoencoder.

Artificial intelligence refers to a variety of software and hardware technologies that can be applied in numerous ways for different applications. The terms'machine learning' and'deep learning' are often used interchangeably in the media, but they are not the same thing. In machine learning, the machine builds up the knowledge to complete specific actions based on training data covering multiple datasets. There are many examples of machine learning in our daily lives. The performance of machine learning algorithms is directly related to the available information, which is referred to as'representation'.

Machine learning, deep learning and artificial intelligence are quite the buzzwords these days, aren't they? Wherever you go, we are bombarded with these topics. Even mass media these topics and gave them their own "doomsday" scenarios. In fact, a number of people think that this pursuit of artificial intelligence will have rater hellish outcomes, but we – the people in the field – beg to differ. We are here today to break the taboos and stigmas that cast a shadow over this field.

Welcome to the October edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI that can solve physics problems, paint portraits, judge criminals, play video games and even recognize smells! Let's start, as usual, with the comic of the month: The DeepMind's bot AlphaStar managed to enter the Grandmaster league in Starcraft II. This league is the highest of the seven ranked leagues of the game.

That is, the network has learned the generic shape of a feature, such as a mouth or a nose, and can detect this feature in the input data despite variations it might have. In the second row of the preceding image, we can see how the deeper layers of the network combine these features into even more complex ones, such as faces and whole cars. A strength of deep neural networks is that they can learn these high-level abstract representations themselves by deducing them from the training data. We could define deep learning as a class of machine learning techniques where information is processed in hierarchical layers to understand representations and features from data in increasing levels of complexity. In practice, all deep learning algorithms are neural networks, which share some common basic properties.

An object in motion looks fundamentally different from an object at rest -- especially to a computer. To get a better idea of this concept, let's imagine a film strip of a sprinter running: The person and pose in one frame look drastically different from the next frame, right? Making sense of dynamic objects is taking on new importance as cities begin incorporating IoT devices like smart cameras to streamline municipal life. The town of Yuma, Arizona, is a great example of this. The city recently installed cameras on streetlights that can detect when cars, bicycles, and pedestrians travel through intersections, and it uses that data to optimise signal switching.

When it comes to data, AI is like Pac-Man. Hard disk drives, NAS, conventional data center and cloud-based storage schemes can't sate AI's voracious appetite for speed and capacity, especially for real time. Playing the game today requires a fundamental rethinking of storage as a foundation of machine learning, deep learning, image processing, and neural network success. "AI and Big Data are dominating every aspect of decision-making and operations," says Jeff Denworth, vice president of products and co-founder at Vast Data, a provider of all-flash storage and services. "The need for vast amounts of fast data are rendering the traditional storage pyramid obsolete. Applying new thinking to many of the toughest problems helps simplify the storage and access of huge reserves of data, in real time, leading to insights that were not possible before."

The doctor is the boy's mother. My answer… After puzzling over this for a minute, I concluded that the boy had two fathers. Though I don't entirely dislike my answer (we have a bias towards heteronormative relationships) I only came to this conclusion because my brain couldn't compute the idea of the doctor being a woman. To make this worse, I work on algorithmic bias… and the question was proposed at a'Women Like Me' event. Bias is all around us in society and in each and every one of us.