StarCraft players are safe – but not for long. The machines that made short work of chess, Scrabble and Go are beginning to set their sights on the venerable video game. And while the inherent complexity of most video games makes them a much harder target for AI than board games, two new projects aim to show they are far from invulnerable. One is a training ground for artificial intelligences targeting StarCraft, opened today by the game's creator, Blizzard Entertainment, in collaboration with Google's AI company DeepMind. The other is an AI being developed by researchers in Denmark whose approach stands the first good chance of beating a human at the game.

FaceApp uses neural networks for realistic-looking changes to your selfie photos. Originally, it had filters to add smiles, change your age, change gender or "beautify" your face. Unlike Snapchat's overlays, FaceApp uses deep learning technologies to change the photo itself. Now, a new update adds race to the mix, with an update to enable users to make themselves look Asian, Black, Caucasian or Indian. There has been some outrage over the new filters, with some Twitter users calling it "digital blackface."

Deep learning systems continue to gain widespread adoption in the enterprise, tackling photo and voice recognition, customer service interactions, and even spotting abnormalities in medical records. But while the artificial intelligence (AI) models, which rely on massive data sets to "train" themselves on recognizing patterns and making predictions--throughout multiple iterations--timing is still an obstacle. Developing an accurate deep learning model can take up to days, or even weeks. On Tuesday, a new deep learning model developed by IBM Research--Distributed Deep Learning--made big strides in the field: It achieved a record for image recognition accuracy of 33.8%. The model, which used a massive data set of 7.5 million images, achieved "record communication overhead and 95% scaling efficiency on the Caffe deep learning framework over 256 GPUs in 64 IBM Power systems," according to IBM--all in just seven hours.

Tic tac toe, checkers, chess, go, poker. Artificial intelligence rolled over each of these games like a relentless tide. No one expects the robot to win anytime soon. But when it does, it will be a far greater achievement than DeepMind's conquest of Go--and not just because StarCraft is a professional e-sport watched by fans for millions of hours each month. DeepMind and Blizzard Entertainment, the company behind StarCraft, just released the tools to let AI researchers create bots capable of competing in a galactic war against humans.

Today's artificial intelligence (AI) technologies are usually run using machine learning algorithms. These operate on what's called a neural network -- systems designed to mimic the human brain inner workings -- as part of what is called deep learning. Currently, most AI advances are largely due to deep learning, with developments like AlphaGo, the Go-playing AI created by Google's DeepMind. Now, IBM has announced that they have developed an AI that makes the entire machine learning process faster. Instead of running complex deep learning models on just a single server, the team, led by IBM Research's director of systems acceleration and memory Hillery Hunter, managed to efficiently scale up distributed deep learning (DDL) using multiple servers.

You've probably encountered the term "machine learning" more than a few times lately. Often used interchangeably with artificial intelligence, machine learning is in fact a subset of AI, both of which can trace their roots to MIT in the late 1950s. Machine learning is something you probably encounter every day, whether you know it or not. The Siri and Alexa voice assistants, Facebook's and Microsoft's facial recognition, Amazon and Netflix recommendations, the technology that keeps self-driving cars from crashing into things – all are a result of advances in machine learning. While still nowhere near as complex as a human brain, systems based on machine learning have achieved some impressive feats, like defeating human challengers at chess, Jeopardy, Go, and Texas Hold'em.

Machine Learning consulting, Data Science consulting - if you've already decided that you need it, now comes the choice time. How do you find one that is worth your time and money? Truth be told, AI, Machine Learning or any other buzzword you may use, is not a holy grail and a cure for all the world's data problems. If you have been told otherwise, you've been lied to. However, Machine Learning plugins to your company can increase the productivity of your employees by 2x or 3x as achieved my many of our clients.

Traditionally, computing power is associated with the number of CPUs and the cores per processing unit. During the 90s, when WinTel started to invade the enterprise data center, application performance and database throughput were directly proportional to the number of CPUs and available RAM. While these factors are critical to achieving the desired performance of enterprise applications, a new processor started to gain attention – Graphics Processing Unit or GPU. For many of us, GPUs remind of the video cards that were designed for graphic-intensive games. These were purely optional, which didn't influence the buying decision of an average user investing in a PC or server.

I've been following IBM's AI efforts with interest for a quite a while now. In my opinion, the company jump-started the current cycle of AI with the introduction of Watson back in the 2000s and has steadily been ramping up its efforts since then. Most recently, I wrote about the launch of PowerAI, IBM's software toolkit solution to use with OpenPOWER systems for enterprises who don't want to develop their AI solutions entirely from scratch but still want to be able to customize to fit their specific deep learning needs. Today, IBM Research announced a new breakthrough that will only serve to further enhance PowerAI and its other AI offerings--a groundbreaking Distributed Deep Learning (DDL) software, which is one of the biggest announcements I've tracked in this space for the past six months. Anyone who has been paying attention knows that deep learning has really taken off in the last several years.

With IBM setting new deep learning records, how long can it maintain a position of dominance within the space? IBM says it has broken new ground and achieved a new deep learning record by discovering a way to accelerate data processing, which is crucial to the technology's functionality. This breakthrough regards the time it takes to train deep learning platforms to recognise images, an ability at the core of training technology to process information like humans. The time required by IBM to train a ResNet-101 neural network was a record breaking seven hours, trumping Microsoft's ten day process taken to train the same system. In recent testing the tech giant achieved a record recognition accuracy rate of 33.8% when processing 7.5 million images, beating the previous 29.8%