The promise of machine learning has a science fiction flavor to it: computer programs that learn from their experiences and get better and better at what they do. So is machine learning fact or fiction? The global marketplace answers this question emphatically: Machine learning is not just real; it is a booming field of technology that is being applied in countless artificial intelligence (AI) applications, ranging from crop monitoring and drug development to fraud detection and autonomous vehicles. Collectively, the global AI market is expected to be worth more than $16 billion by 2022, according to the research firm MarketsandMarkets.[1] In the life sciences arena, researchers are leveraging machine learning in their work to drive groundbreaking discoveries that may help improve the health and wellbeing of people.

The same can be said about the world of science, which witnessed some of the biggest breakthroughs in decades, even as it provided several grim reminders about the impact of climate change on planet Earth. One hundred years ago, Albert Einstein predicted that the collision of massive objects such as black holes and neutron stars can create "ripples" in the curvature of space-time. Earlier this year, scientists associated with the Laser Interferometer Gravitational-Wave Observatory (LIGO) discovered these distortions. "The achievement fulfilled a 100-year-old prediction, opened up a potential new branch of astronomy, and was a stunning technological accomplishment," the journal Science, which was one of the many publications that termed the discovery of gravitational waves "Breakthrough of the Year," said in a recent statement. Currently, all we know about the cosmos is what we have gathered from electromagnetic radiation such as radio waves, visible light, infrared light, X-rays and gamma rays.

Or carmakers could utilise the technology to make autonomous cars safer and more reactive to their immediate environment. Cremers' trailblazing research into mathematical image pro-cessing and pattern recognition earned him the 2016 Gottfried Wilhelm Leibniz Prize – Germany's most esteemed award in the sciences. His question: How can we use a camera to capture and "recover" the 3D world and reconstruct it in real time? It might lie in something called "Direct Image Alignment," which is a core component of his current research into realising the 3D world in images – faster, with greater accuracy and with more robustness.

IBM and BMW announced Thursday that they have teamed up to figure out how Watson, the A.I. most famous for winning Jeopardy! in 2011, could be used to make driving better. Could this finally make KITT from Knight Rider a reality? The companies said they want to explore the potential of Watson "personalizing the driving experience and creating more intuitive driver support systems for cars of the future." That sounds an awful lot like the talking car that helped David Hasselhoff get out of all sorts of hairy situations in the '80s. BMW will have some researchers work out of IBM's $200 million Watson Internet of Things headquarters in Munich as part of the agreement. The hope is that pairing their resources will allow the two companies to make driving more fun for human operators until autonomous vehicles take over for their human drivers.

Ambar presses her hand to her forehead, nose crinkled in concentration as she considers the question on her screen: how many sevens in 91? The ten-year-old has been grappling with it for about a minute when she smiles: "13!". Her tutor responds by posting a large smiley cat picture on her screen – the virtual equivalent of a pat on the back. He is sitting on the other side of the world in an online tutoring centre in India. Ambar, who attends Pakeman primary school in north London, is one of nearly 4,000 primary school children in Britain signed up for weekly one-to-one maths sessions with tutors based in India and Sri Lanka.

Google wants to apply its machine learning technology to NHS patient data within the next five years, TechCrunch reports. The search giant's London-based artificial intelligence research lab, DeepMind, announced a partnership with the Royal Free NHS Trust in London in February but the full extent of the arrangement is only just becoming clear. A Memorandum of Understanding (MoU) between DeepMind and the Royal Free shows that the pair envisage a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximizing the potential to work on genuinely innovative and transformational projects." The MoU -- obtained via a Freedom of Information (FoI) request from New Scientist -- states that DeepMind hopes to gain access to "data for machine learning research under appropriate regulatory and ethical approvals" within the next five years. Machine learning -- a subfield of computer science that gives computers the ability to learn without being explicitly programmed -- has the potential to speed up patient diagnosis and optimise their treatments.

So you've bought a shiny new games console, or a ridiculously powerful PC, or the latest smartphone iteration, and now you want to play games on it. Well, if you've been doing the whole gaming thing for years, you'll know which review sites to go to, what developers and publishers produce the best stuff and what everyone is looking forward to playing. But if you're just starting out, it can all be a bit … overwhelming. Every year around 1,000 new titles are released on consoles and PC, and there are more than 300,000 games available on the Apple App Store. So how are you supposed to work out what to play?

The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music. GAN is a fresh direction in deep learning invented by Ian Goodfellow in 2014.

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest.

It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks. We show that steerable CNNs achieve state of the art results on the CIFAR image classification benchmark. The mathematical theory of steerable representations reveals a type system in which any steerable representation is a composition of elementary feature types, each one associated with a particular kind of symmetry. We show how the parameter cost of a steerable filter bank depends on the types of the input and output features, and show how to use this knowledge to construct CNNs that utilize parameters effectively.