Most of the autonomous vehicles that you're likely to encounter in the near future are either Level 2 or Level 4 autonomous. Level 2, which you'll find in a Tesla on the highway, means that the car drives itself in specific situations but expects you to be paying attention the entire time. Level 4 y...

A year and a half ago I wrote about a start-up working on dynamically-tuned, self-optimizing Linux servers. That company is now known as Concertio and they just launched their "AI powered" toolkit for IT administrators and performance engineers to optimize their server performance. Concertio Optimizer Studio is their product making use of machine learning that aims to optimize Linux systems with Intel CPUs for peak performance by scoping out the impact of hundreds of different tunables for trying to deliver an optimal configuration package for that workload on that hardware. Here's how they say their Optimizer Studio software works: Patent-pending Concertio Optimizer Studio is an easy-to-use software tool that uses AI techniques to dynamically discover near-optimal configuration settings for peak performance. Optimizer Studio software uses a workload classification engine to detect different phases of execution of a workload while system applications run.

As you should know, feed-dict is the slowest possible way to pass information to TensorFlow and it must be avoided. The correct way to feed data into your models is to use an input pipeline to ensure that the GPU has never to wait for new stuff to come in. Fortunately, TensorFlow has a built-in API, called Dataset to make it easier to accomplish this task. In this tutorial, we are going to see how we can create an input pipeline using it and how to feed the data into the model efficiently. This article will explain the basic mechanics of the Dataset, covering the most common use cases.

As shown in the figure above, the computing power increased by 10,000 times since the year 2000. The cost of storing the data has also gone down by around 3000 times since the year 2000. There has been an exponential growth of data created due to the rise of the internet, the smartphone revolution and the social media. Data is ubiquitously available now. These three ingredients created a milieu for a perfect storm for deep learning.

Some authors excel at long form stories and some are best in the short story. A few are great at both. Hugh Howey is interesting because most of his books are comparatively short, and even his most famous work (Wool) was published as a series of novellas. This series of short works covers most of Howey's short stories collected in one place, and as you may expect, it's hit or miss. There are some good ones and some that miss the mark, and a few new Wool stories too.

Artificial intelligence (AI), security orchestration and the Internet of Things (IoT) are disrupting our industry. Some bombastic pundits are even predicting a Jetsons-like world where computers and IoT devices replace workers with machines. Yet most futurists believe computing will only evolve to the level of a digital assistant. Tasks, they say, will be split between artificial and biologic intelligence. Forensics is a prime example of a human compliment to both AI and security automation.

Slowly our inner person consciousness that observes all around and inside us will become conditioned based on AI based technologies. We can't stop this God-like massive super intelligent system that we keep on building. For all rational conclusions I can confide in the advantages of AI super intelligence but one crucial question that hovers over our time right in front of our eyes "How alive would this AI will be?"; or could it be likely that man was never fully conscious –only partially and relatively more conscious than animals, trees and stones.

Few emerging technologies generate as much buzz as augmented reality (AR), virtual reality (VR), and chatbots. Even so, it seems like these new technologies explode onto the public radar, then vanish as quickly as they arrive. Behind the scenes, though, companies and researchers are making ongoing breakthroughs in all three of these areas. And when AR and VR combine with AI-powered chatbots, magic happens. Augmented reality has been around for a few years, notably in games such as Pokémon Go. It didn't really grab the public's attention on a large scale until the last generation of iPhones was released, though.

As was also clearly stated by Vladimir Putin on September 4, 2017: "whichever country leads the way in Artificial Intelligence research will be the ruler of the world". According to Thomas Kuhn's old, but still useful, epistemological model, every change of the scientific paradigm – rather than the emergence of new material discoveries – radically changes the visions of the world and hence strategic equilibria. Hence, first of all, what is Artificial Intelligence? It consists of a series of mathematical tools, but also of psychology, electronic technology, information technology and computer science tools, through which a machine is taught to think as if it were a human being, but with the speed and security of a computer. The automatic machine must representman's knowledge, namely show it, thus enabling an external operator to change the process and understand its results within the natural language. In practice, AI machines imitate the perceptual vision, the recognition and the reprocessing of language -and even of decision-making – but only when all the data necessary to perform it are available.