If you keep hearing about artificial intelligence but aren't quite sure what it means or how it works, you're not alone. There's been much confusion among the general public about the term, not helped by dramatic news stories about how "AI" will destroy jobs, or companies that overstate their abilities to "use AI." A lot of that confusion comes from the misuse of terms like AI and machine learning. So here's a short text-and-video guide to explain them: Think of it like the difference between economics and accounting. Economics is a field of study, but you wouldn't hire a Nobel Prize-winning economist to do your taxes.

The types of anime-style characters artificial intelligence was making in 2015 weren't good. But now in 2018, the machines have improved. As Twitter user Yoshiwo Konogi points out, it's amazing at how good AI has gotten at this type of art. Earlier this year, the deep learning AI character creation system Crypko went into beta.

The same thing happens in vision, not just in humans but in animals' visual systems generally. Brains are made up of neurons which "fire" by emitting electrical signals to other neurons after being sufficiently "activated". These neurons are malleable in terms of how much a signal from other neurons will add to the activation level of the neuron (vaguely speaking, the weights connecting neurons to each other end up being trained to make the neural connections more useful, just like the parameters in a linear regression can be trained to improve the mapping from input to output). Our biological networks are arranged in a hierarchical manner, so that certain neurons end up detecting not extremely specific features of the world around us, but rather more abstract features, i.e. patterns or groupings of more low-level features. For example, the fusiform face area in the human visual system is specialized for facial recognition.

Summary: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources. Recently we wrote that training data (not just data in general) is the new oil. It's the difficulty and expense of acquiring labeled training data that causes many deep learning projects to be abandoned. It also matters a great deal just how good you want your new deep learning app to be.

Facebook Inc. today launched a developer preview of its long-awaited PyTorch artificial intelligence software framework, which helps accelerate the deployment of AI-based applications. Announced in May, PyTorch was created by Facebook's AI research group as a machine learning library of functions for the programming language Python. It's primarily designed for use with deep learning, which is a branch of machine learning that attempts to emulate the way the human brain functions, and has led to major breakthroughs in areas such as language translation, image and voice recognition. PyTorch is supposed to help speed up the development of these kinds of AI capabilities, and has previously been used to build more realistic avatars for Facebook's Oculus virtual reality headset. Researchers at UC Berkeley have also used PyTorch to speed up their work on image-to-image transformation, for example.

The latest version is 2.0.1. TensorFlow -- Machine Learning Software, is an open source software library for machine learning. It was developed by the Google team for sorts of perceptual tasks. Also, to conduct sophisticated research on machine learning and deep neural networks. TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors.

A chocolate brown dog looks down the barrel of the camera lens. A spider is suspended above grass by its web. They are gorgeous photographs, but they don't actually exist – artificial intelligence dreamt them up. Andrew Brock of Heriot-Watt University and colleagues at Google's DeepMind created a generative adversarial network (GAN), a type of algorithm that pits two AIs against each other, to produce new images. The GAN, known as BigGAN, was first trained on thousands of images linked to particular words, such as dog or butterfly.

This is the final piece of my three-part blog on the topic human intelligence vs. AI, and how AI is being used successfully to address various problems in cybersecurity. In the first part of the blog, I offered a definition of intelligence and its three components: a store of knowledge, mechanisms to acquire knowledge, and the ability to use knowledge for problem-solving. We differentiated between the notions of general AI (which does not exist today) and narrow AI (which certainly does). In Part 2, we reviewed the relationship between the terms AI, machine learning, expert systems, and deep learning, and looked at several narrow AI systems in domains outside of cybersecurity. Now, let's look at the application of AI in cybersecurity.

IMAGE: The figure shows a personalized structural connectome; the strength of each connection between all possible brain regions is used to train a deep neural network to predict one of two... view more Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians' decision making processes, this work also highlights how artificial intelligence is driving change in the medical field. Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death.

Facebook's development department has finished a first release candidate for v1 of its PyTorch project – just in time for the first conference dedicated to the Python package. For those not familiar with the tool, its main features are NumPy-like tensor computation with GPU acceleration and a special deep neural network implementation. The preview contains a new set of compiler tools that at runtime rewrite PyTorch models to be more efficient. The just-in-time compiler should also be able to export models that are able to run in a C only runtime. Optimisation is optional and can be done either by tracing native Python code with torch.jit.trace or using a Python subset called Torch Script.