One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.

In a recent article, we demystified some of the technical jargon that's being thrown around these days like "artificial intelligence", "SaaS, "the cloud", and "deep learning". While the techies can debate among themselves the difference between "machine learning" and "deep learning", we're going to consider the two terms synonymous and henceforth just talk about "deep learning". So just what is "deep learning"? We wanted to understand more, so we came across this excellent TED talk given by Jeremy Howard which finally explains in layman's terms just what deep learning is. If you have 20 minutes, watch the video now and no need to read any further. If you don't have time to watch the video, here's what we learned. When you use Google Images to search for a "grey cat", Google Images shows you grey cats. Is this because Google can recognize what a grey cat looks like? This is simply because Google searches text to find grey cat images. So how can we train Google to identify grey cats by only looking at images? Here's how we do it. Let's start with a sample of 10 million random pictures from Facebook and teach Google how to learn. The first part entails scanning this massive set of pictures using an algorithm developed by a software developer at Google. What does this algorithm do? It looks at the relationships of pixels in a digital photo and tries to find objects of a similar shape. Let's try this with a simple example. Let's say the pictures were black and white and composed of circles, triangles and squares. You could quite easily imagine an algorithm that could first identify the differences in color (every color is actually a unique code in software) and then start to map sharp differences in color that would denote shapes. The shapes could then be described by the direction of the lines as either circles, triangles, or squares. You could even go ahead and make them color pictures. The computer can now point out a "red triangle" or even a "beige circle". Without even having to do much coding, the computer now has the intelligence of a small child when it comes to identifying shapes. Now let's take this to the next level. Let's take a sophisticated deep learning algorithm and feed it 100 million pictures from Facebook. Let's tell the algorithm to try and find similar objects in this "big data" set and then group them. These groups are displayed to a developer who can then label them. Humans would perhaps be the most obvious and frequent object that the computer would identify. The developer would then be shown 50 humans the computer identified and could start to label sets within the group like "old person", "baby, "Chinese person" or "freckled person".

It will allow developers to write deep learning applications as standard Spark programmes that run on top of existing Spark or Hadoop clusters. Intel has launched a deep learning library – the open-source BigDL for Apache Spark cluster-computing framework. BigDL, which is already running in the Databricks Spark Platform, allows users to write their deep learning applications as standard Spark programmes that can directly run on top of existing Spark or Hadoop clusters. It allows the exporting of artificial intelligence expertise to data scientists that currently work across several applications in various fields. BigDL is modeled after Torch, an open source deep learning framework used in scientific computing.

Step 6 is to look at cool projects. Eigenfaces and facial recognition are really cool use cases and have many practical security applications.

Joab Rosenberg is the former deputy head analyst for the Israeli government and CEO of Epistema. Nature magazine announced in late January that a computer designed by Google's DeepMind defeated a human master in the ancient Chinese board game, "Go." This impressive achievement once again raised the expectations for a predicted future in which computers will have artificial intelligence, with major media outlets worldwide touting this anticipated future. One of the major questions raised in response to DeepMind's achievement is what are the outer limits, if any, of intelligent machines? In November of last year, Dr. Kira Radinsky, a computer scientist and "machine learning" expert, argued in the Israeli newspaper "Ha'aretz" that computers will be able to accurately predict the outcome of the Israeli-Palestinian conflict.

This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This will be an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We will introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms.

We are excited to announce that an AWS Deep Learning AMI for Ubuntu is now available in the AWS Marketplace in addition to the Amazon Linux version. The AWS Deep Learning AMI, now available on AWS Marketplace, lets you run deep learning in the Cloud, at any scale. Launch instances of pre-installed, open source deep learning frameworks, including Apache MXNet, to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques. The AWS Deep Learning AMI lets you create managed, auto-scaling clusters of GPUs for large-scale training, or run inference on trained models using the latest versions of MXNet, TensorFlow, Caffe, Theano, Torch, and Keras. With the addition of an Ubuntu version, you have the choice to run on the operating system of your choice.

We have seen a fast growing interest in current activities around AI startups and research in the last couple of months. Headlines like "2016 was the year AI came of age", "AI was everywhere in 2016", and "The Great A.I. Awakening" were all over the media in the ending weeks of 2016 and we are curious about what 2017 will bring. I found particularly interesting that the current applications, future potential, and possible risks even attracted interest beyond the tech community through TV shows like Westworld, coverage on traditional media and even Obama's farewell address. Sadly, for many of us tech enthusiasts here in Europe, we sometimes feel like there is way less movement on this side of the Atlantic than in the Silicon Valley. However, with major acquisitions like DeepMind, Magic Pony Technology, Movidius, Vision Factory, and Dark Blue Labs, Europe has shown that it is actually leading the way in AI and machine learning.

We are excited to announce that an AWS Deep Learning AMI for Ubuntu is now available in the AWS Marketplace in addition to the Amazon Linux version. The AWS Deep Learning AMI, now available on AWS Marketplace, lets you run deep learning in the Cloud, at any scale. Launch instances of pre-installed, open source deep learning frameworks, including Apache MXNet, to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques. The AWS Deep Learning AMI lets you create managed, auto-scaling clusters of GPUs for large-scale training, or run inference on trained models using the latest versions of MXNet, TensorFlow, Caffe, Theano, Torch, and Keras. With the addition of an Ubuntu version, you have the choice to run on the operating system of your choice.

AI are going to control more of our lives in the future, so making sure they work together is of great importance. What happens when an immovable object meets an unstoppable force? That is an age-old conundrum Google is trying to solve, as when you have two artificial intelligence systems that are programmed to complete conflicting tasks, how do you stop them from fighting about it? To that end, Google is using its DeepMind subsidiary to figure out how to have AIs play nicely together. DeepMind is running experiments on robotic "social dilemmas" and published the results in a new report, The Verge reported.