Imagine being able to generate high-quality photos just be describing them to a computer. This sci-fi scenario is now a reality, thanks to Microsoft's new AI tool. Drawing Bot created the above image simply from the description of "a bird with a yellow body, black wings and a short beak," using a new technique where the AI pays close attention to individual words when generating images from caption-like text descriptions, resulting in a 3-fold boost in image quality compared to other text-to-image generation techniques. The bot can do more than just birds, being able to draw everything from ordinary pastoral scenes, such as grazing livestock, to the absurd, such as a floating double-decker bus. "If you go to Bing and you search for a bird, you get a bird picture. But here, the pictures are created by the computer, pixel by pixel, from scratch," said Xiaodong He, a principal researcher and research manager in the Deep Learning Technology Center at Microsoft's research lab in Redmond, Washington.

Think about the smartest person you know. What about this person leads you to describe him or her as incredibly intelligent? Is she a quick thinker, able to internalize and reuse new concepts immediately? Is he highly creative, able to endlessly generate new ideas you'd never think of? Perhaps she's highly perceptive and can pick out the tiniest details of the world around her. Or maybe he's deeply empathetic and understands what you're feeling even before you do.

Artificial intelligence (AI) is one of those technologies that excites the public and business imagination alike. Long since a favourite theme in science-fiction, it is now gaining traction in everyday practical scenarios. In 2017, we saw a considerable rise in the adoption of AI around the world and across industries, with businesses using it to improve operations, generate new innovations and boost customer experience. See also: Will 2018 be the year blockchain and AI meet? With financial services, telecoms and high tech leading the way in bringing AI into the mainstream, and other areas such as automotive, healthcare, energy and retail also embracing it, we expect the rapid growth of AI to continue in 2018 as companies strive to get the most value and competitive advantage from the data they capture.

IT operations analytics scan all the data that has been gathered using different tools and provides resolutions to problems and does root-cause analysis. But modern machine learning & Deep Learning methods can learn the IT landscape's behavior and raise proactive alarms when anomalies are deducted. It allows the system to follow, learn and unlearn patterns and can further use resources to avoid application downtime, performance degradations, and capacity bottlenecks. The modern digital systems are largely dependent on multiple sources of technology services. They use a combination of on premise, private, public data centers and cloud resources. There is a constant need for enterprises to revive their business models which allows them to adjust their processes to handle multiple situations.

These Indian subsistence farmers know just what to do: Pull out their smartphones and take their picture. The farmers then upload the images with GPS locations to a cloud-based artificial intelligence (AI) app named Plantix. The app identifies the crop type in the image and spits out a diagnosis of a disease, pest or nutrient deficiency. Plantix also aids farmers by recommending targeted biological or chemical treatments for ailing plants, reducing the volume of agrochemicals in groundwater and waterways that can result from overuse or incorrect application of herbicides and pesticides. "Nearly every household in India has a smartphone, and many want to see how Plantix works," says Srikanth Rupavatharam, a digital agriculture scientist with the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Hyderabad, India, which is collaborating with Plantix's developer to adapt the tool for Telugu and Hindi languages.

Deep learning and deep reinforcement learning have recently been successfully applied in a wide range of real-world problems. Here are 15 online courses and tutorials in deep learning and deep reinforcement learning, and applications in natural language processing (NLP), computer vision, and control systems. The courses cover the fundamentals of neural networks, convolutional neural networks, recurrent networks and variants, difficulties in training deep networks, unsupervised learning of representations, deep belief networks, deep Boltzmann machines, deep Q-learning, value function estimation and optimization, and Monte Carlo tree search. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville is a great open access textbook used by many of the courses, and Daivd Silver provides a good series of 10 video lectures in reinfrocement learning. For machine learning reviews, here are 15 online courses and tutorials for machine learning.

Microsoft today is unveiling new artificial intelligence technology that's something of an artist – a "drawing bot." The bot is capable of creating images from text descriptions of an object, but it also adds details to those images that weren't included the text, indicating that the AI has a little imagination of its own, says Microsoft. "If you go to Bing and you search for a bird, you get a bird picture. But here, the pictures are created by the computer, pixel by pixel, from scratch," explained Xiaodong He, a principal researcher and research manager in the Deep Learning Technology Center at Microsoft's research lab in Redmond, Washington, in Microsoft's announcement. "These birds may not exist in the real world -- they are just an aspect of our computer's imagination of birds."

Thanks to the rise of artificial intelligence (AI), the nature of DevOps is about to change as developers spend less time writing code in favor of training models based on various types of algorithms to accomplish a task. Dan Scholnick, general partner with Trinity Ventures, an early investor in companies such as New Relic and Docker Inc., says developers in 2018 will absolutely be required to gain mastery over how to implement machine and deep learning algorithms to stay relevant. Once a model based on those algorithms is incorporated into an application, the challenge for the DevOps team will be implementing the processes needed to make sure the AI created based on those models remains tuned and optimized to the business process it's supposed to drive. Now that the price of cloud services based on graphical processor units (GPUs) has dropped and open source tools for building AI models such as TensorFlow are readily available, Scholnick says the cost of infusing AI models into applications has dropped considerably. Before the end the end of the year, most users of business-to-business (B2B) applications will expect that application to exhibit many of same natural language and speech recognition capabilities that already are becoming commonplace in many consumer applications, he says.

Here at Vilynx we're always looking to empower our customers by providing them tools that will help them make a big impact on their KPIs in the most efficient way. We're happy to announce that we just launched a brand new feature called Trending Stories. Trending Stories accompanies, and expands on, our existing Trending Topics feature. It uses Machine Learning to help streamline the editorial process by providing access to a real time stream of breaking news. As we crawl numerous news sites and social media, our deep learning algorithms are able to break through the chaos and identify topics which are being talked about and are gaining momentum.

Google just made it a lot easier to build your very own custom AI system. A new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.