Let's talk about the practical application of artificial intelligence to cybersecurity. Or rather, let's read about it. My friend Sean Martin has written a three-part series on the topic for ITSP Magazine, exploring AI, machine learning, and other related topics. I provided review and commentary into the series. The first part, "It's a Marketing Mess! Artificial Intelligence vs Machine Learning," explores probably the biggest challenge about AI: Hyperbole.

When researchers decide to train their latest and greatest artificial intelligence (AI) systems there's one big, fundamental flaw they have to deal with – AI's, by nature – and design – are amnesiacs, that is to say they have great difficulty in retaining knowledge between tasks. It's the equivalent of trying to train someone something new when they're always forgetting stuff. So as you can imagine if you're trying to teach an AI something new it can get quite frustrating, there's even a special term for it – "Catastrophic forgetting." In a new demonstration of just how close the associations between AI's and our own brains have become though Google's DeepMind engineers, the same crazy cats who recently published a breakthrough Artificial General Intelligence (AGI) architecture, who've been teaching their AI's to dream and fight with each other, and annihilate online gamers, have just created an AI that can retain its knowledge between tasks – turning raw memory into long term experiences that stay with the program, even as it moves onto other things. And they published a paper on it.

A key trend in contemporary healthcare is the emergence of an ambitious new cadre of corporate entrants: digital technology companies. Google, Microsoft, IBM, Apple and others are all preparing, in their own ways, bids on the future of health and on various aspects of the global healthcare industry. This article focuses on the Google conglomerate, Alphabet Inc. (referred to as Google for convenience). We examine the first healthcare deals of its British-based artificial intelligence subsidiary, DeepMind Technologies Limited,1 in the period between July 2015 and October 2016.2 In particular, the article assesses the first year of a deal between Google DeepMind and the Royal Free London NHS Foundation Trust, which involved the transfer of identifiable patient records across the entire Trust, without explicit consent, for the purpose of developing a clinical alert app for kidney injury.

Machine learning and deep learning are changing the face of digital marketing, and they are forcing websites to make changes to accommodate them. Google is leading the pack, by using both machine learning and deep learning as part of its algorithms for website rankings in search engine results. Google has publicly stated that RankBrain, its machine learning, artificial intelligence system that is used to help process search queries, is now the third ranking factor in search engine results rankings. Artificial intelligence is an all-encompassing term that describes machines that can demonstrate intelligence. Technically speaking, AI means that a device can perceive its environment and take actions based on that environment, giving the impression that a device is making decisions by itself.

The Lambda DevBox Black gives your team four NVIDIA's Pascal GPUs at a significantly reduced price point.4x NVIDIA GTX 1080 ti 11GB GPUs (Pascal) 64 GB of DDR4 System Memory Intel Core i7-6850K 40 PCI-e lane hexacore processor Comes pre-installed with TensorFlow, CUDA, CuDNN, Caffe, and Torch.

HEN shown photographic images under a free-viewing (i.e task-free) paradigm, human eyes preferentially fixate on image locations which are visually salient. Multiple studies [1]-[5] have demonstrated that this fixation mechanism is bottom-up, predominantly driven by image content and richness of detail (color, texture etc.). This explanation, while satisfactory for photographic images, seems inadequate for certain categories of images such as line drawings. In particular, one class of line drawings - hand-drawn sketches - are sparse and largely devoid of detailed content. In addition, they are typically binary images containing virtually no color-based information (see Figure 1). Even so, multiple studies have demonstrated a "fixations-intonothing" phenomenon [6]-[9], wherein the eye fixations on the same stimulus by multiple subjects fall on empty regions, yet exhibit enough regularity to make gaze-based inferences. One possible explanation is that the first eye fixation conveys all there is to know ('Gestalt') about the underlying scene semantics [10] and the regularity in rest of the fixations is a statistical anomaly. However, a more intriguing explanation is that these empty region fixations aim to implicitly verify the overall consistency of the scene content depicted in the sketch [11], [12]. Which of these explanations is correct?

Deep learning uses layers of neural networks to look for patterns in data. When a single layer spots a pattern it recognizes, it sends this information to the next layer, which looks for patterns in this signal, and so on. So in face recognition, one layer might look for edges in an image, the next layer for circular patterns of edges (the kind that eyes and mouths make), and the next for triangular patterns such as those made by two eyes and a mouth. When all this happens, the final output is an indication that a face has been spotted. Of course, the devil is in the details.

Facebook on Wednesday unveiled Big Basin, its latest GPU server geared for deep learning. Like its predecessor Big Sur, its design will be open sourced through the Open Compute Project. Compared to Big Sur, the new server allows Facebook to train machine learning models that are 30 percent larger, thanks to two factors: an increase in memory from 12 GB to 16 GB, as well as the availability of greater arithmetic throughput. "Right now we use AI to recognize people and objects in photos," Kevin Lee, technical program manager for Facebook, explained to ZDNet ahead of the Open Compute Summit. "Chances are if you use Facebook, you're using AI models that have been trained with Big Sur."

In 2011, scientists made one of the most important discoveries in the history of AI development. They found that graphics processing units (GPUs) are far better at simulating biological learning than central processing units (CPUs). In retrospect, it seems obvious. Human brains are much more like GPUs than CPUs. Both brains and GPUs rely on parallel processing that simulates and predicts real world physics. In light of this, AI developers created powerful deep neural networks (DNNs) that emulate human brain function.

OpenAI research has a short introduction on their newest research "Learning to Communicate". There are many trends that I watch for in the field of Deep Learning. Two trends that are related and I believe going to be very promising areas are language learning and multi-agent communication. If you have not been watching, this week has had a tremendous release of papers involving the former and culminating with OpenAI's post, stitching it all together! Let me explain though what transpired in this amazing week.