Investors poured a record high $1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

Sophos has announced the acquisition of Irish security firm Barricade, adding behavior-based analytics to its endpoint offering. Barricade offer a technology platform that it claims can enhance the ability to identify malicious or suspicious behaviour by using machine learning and artificial intelligence. It said that this works by extending the capabilities of rule-based detection technologies, that will be increasingly challenged to keep up with the growth of sophisticated and complex attack patterns. Sophos will maintain the offices in the Republic of Ireland with Barricade CEO David Coallier and the team of developers, data scientists and engineers joining the Sophos Cloud group. Coallier said: "We are proud of the technology we have built and are pleased to join the team at Sophos focused on artificial intelligence and machine learning based security analytics. Driving the development of our technology into a comprehensive security solution that every IT professional can use presents us with the next phase in our exciting journey."

Please join us for an exciting day discussing the impact artificial intelligence will have on business and important key industries within the UK, including manufacturing, the financial sector and creative industries. From writing new Beatles-esque songs to making increasingly accurate medical diagnoses, few industries appear safe from the disruptive impact of rapidly developing Artificial Intelligence technology. In fact, a recent Australian study predicted that as many as 60% of students are pursuing careers that will be rendered obsolete by the time they graduate. With 80-100 people due to attend the AI conference, we will look at the wide reaching social and economic impact that deep learning and artificial intelligence is likely to have traditionally conservative'human to human' industries. There is little doubt that AI and deep learning represent a hugely positive opportunity for business, we will discuss these opportunities, what businesses need to do next and the economic transformation we may be facing in our lifetime.

The Snasci Logo comprises of three smaller rings, intersected by a large ring. Symbolically, this represents an adaptation of the Three Laws of Robotics by the science fiction author Isaac Asimov. The rules first appeared in his short story "Runaround" (1942). Quoting from the "Handbook of Robotics, 56th Edition, 2058 A.D.", the laws are: Whilst these laws are broadly acceptable for a robot, they are too narrow for an Artificial General Intelligence. An artificial General Intelligence must deal with scenarios that go beyond physical interaction with humans.

Last week, Microsoft announced a speech recognition breakthrough: a transcription system that can match humans, with a word error rate of 5.9 percent for conversational speech. This new system is built on an open source toolkit that Microsoft already developed. A major new update to the toolkit, now called the Cognitive Toolkit, was released today in beta. Formerly called the Computational Network Toolkit (CNTK), the MIT-licensed, GitHub-hosted project gives researchers some of the building blocks, such as neural networks, to develop their own machine learning systems. These machine learning applications can run on both CPUs and GPUs, and the toolkit has support for compute clusters.

Lush combines three languages in one: a very simple to use, loosely-typed interpreted language, a strongly-typed compiled language with the same syntax, and the C language, which can be freely mixed with the other languages within a single source file, and even within a single function. Lush has a library of over 14,000 functions and classes, some of which are simple interfaces to popular libraries: vector/matrix/tensor algebra, linear algebra (LAPACK, BLAS), numerical function (GSL), 2D and 3D graphics (X, SDL, OpenGL, OpenRM, PostScipt), image processing, computer vision (OpenCV), machine learning (gblearning, Torch), regular expressions, audio processing (ALSA), and video grabbing (Video4linux). If you do research and development in signal processing, image processing, machine learning, computer vision, bio-informatics, data mining, statistics, or artificial intelligence, and feel limited by Matlab and other existing tools, Lush is for you. If you want a simple environment to experiment with graphics, video, and sound, Lush is for you. Lush is Free Software (GPL) and runs under GNU/Linux, Solaris, and Irix.

There are few things hotter in tech right now than artificial intelligence. You'll hear people with titles like "chief experience officer" and "thinkfluence concierge" talk about "neural networks" and "machine learning" and "natural language processing." The idea is, you can talk to your computer as if it were a person. Eventually, the idea goes, you can hold full conversations with an AI chat bot, asking it to answer complex questions and undertake complicated tasks. But nearly two decades ago, our current era of AI overload started with two simple sentences.

Cybersecurity companies are increasingly looking to artificial intelligence tech to improve defense systems and create the next generation of cyber protection. These trends are driving demand for automated cybersecurity, i.e. AI-driven software that can use machine learning and other technologies to differentiate benign or harmful activity on a system or network. We used CB Insights data to understand when artificial intelligence began to be linked to cybersecurity, and we identify 13 companies to watch at the intersection of AI and security. To inform our analysis in the charts below, we used the Trends tool on the CB Insights Platform, which analyzes millions of media articles to track technology trends.

Ford reported a sharp drop in October sales in the U.S. Unit sales declined 12% as passenger car sales slumped. Ford postponed its release on Tuesday due to fire delays at its headquarters in Michigan. Ford is working on a new technology that uses an array of sensors that monitors activity going on behind a car that is backing up and stops it if the driver doesn't notice a pedestrian or another car. SAN FRANCISCO -- Ford Motor is working on a suite of new driver-assist safety features for its production cars that stop short of offering full autonomy. Among the technologies being developed at the automaker's Research and Innovation Center in Aachen, Germany, include camera- and laser-enabled systems that can take over the steering wheel in an emergency to avoid high-speed collisions, as well as mapping-triggered dash alerts that warn drivers they're traveling down a one-way road.

This post is by Max Kaznady, Data Scientist, Miguel Fierro, Data Scientist, Richin Jain, Solution Architect, T. J. Hazen, Principal Data Scientist Manager, and Tao Wu, Principal Data Scientist Manager, all at Microsoft. Today's businesses collect vast volumes of images, video, text and other types of data – data which can provide tremendous business value if efficiently processed at scale and using sophisticated machine learning algorithms. Example applications include real-time labeling and monitoring of sentiment in tweets, itemization of equipment and materials at construction sites through video surveillance, and real-time fraud detection in the financial domain, to name a few. In a previous blog post, we described how to set up DNNs in the cloud using a high performance GPU VM and MXNet. In this sequel, we outline a pipeline process for training and scoring with DNNs in a large-scale production environment.