Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. ABI Research finds the government and defense, banking, and technology market sectors to be the primary drivers and adopters of machine learning technologies. User and Entity Behavioral Analytics (UEBA) along with Deep Learning algorithm designs are emerging as the two most prominent technologies in cybersecurity offerings, especially in innovative hot tech startups.

Research into word embeddings is one of the most interesting in the deep learning world at the moment, even though they were introduced as early as 2003 by Bengio, et al. Most prominently among these new techniques has been a group of related algorithm commonly referred to as Word2Vec which came out of google research.[ In particular we are going to examine some desired properties of word embeddings and the shortcomings of other popular approaches centered around the concept of a Bag of Words (henceforth referred to simply as Bow) such as Latent Semantic Analysis. This shall motivate a detailed exposition of how and why Word2Vec works and whether the word embeddings derived from this method can remedy some of the shortcomings of BoW based approaches. Word2Vec and the concept of word embeddings originate in the domain of NLP, however as we shall see the idea of words in the context of a sentence or a surrounding word window can be generalized to any problem domain dealing with sequences or sets of related data points.

Researchers at Google and several universities recently made key progress in developing artificial intelligence (AI) that is itself able to write AI software, reports MIT Technology Review . In an experiment, AI researchers from Google Brain allowed software to develop the design for a machine learning system to recognize human speech. This software produced better results than software designs for machine learning that had previously been created by people, the researchers write in a scientific paper submitted to a conference. This paper has not yet been subject to peer review – so testing of these results by other researchers still remains to be done. Other research groups have also reported similar progress in recent months in the field of machine learning using artificial neural networks – the most promising technology in artificial intelligence in recent years.

Scality CEO Jérôme Lecat takes a look at how deep learning can transform businesses. If you work in information technology the chances are you have noticed regular articles in the media about artificial intelligence (AI), machine learning and deep learning. Some commentators make no distinction between these terms and they often use them interchangeably. But to attribute the same meaning to these names is an oversimplification which is unhelpful to those looking for new ways to add value to their businesses. While AI, machine learning and deep learning are often intertwined, they hinge upon different technologies and have their own unique attributes.

The enormous and raging wave of change that has hit our world in the last decade, has got some of us thinking and others reveling in their glory. The internet and evolving technological practices have increased possibilities. Man and machine collaboration has got us introduced to automated virtual work and communication systems everywhere in the world. Deep Learning has given birth to several real-life applications that have lessened human control and involvement in several spheres of life. The immense popularity of the Deep Learning UseCases blog was enough encouragement to look at more such UseCases.

Over the years, Deep Learning has become the most popular approach to developing Artificial Intelligence (AI) – machines that perceive and understand the world. It empowers geospatial ecosystem by providing real-time near-human level perception; integrates into analytical workflows and driving data exploration and visualisation – automating the entire process of creating scalable insights from large amounts of data. Such machines will be able to'understand' geospatial information themselves and with deep learning, able to self-obtain geospatial information from their surroundings as per required to do their jobs, processing it in real time. This is truly an extraordinary time. AI and Deep Learning have been applied to a vast range of industries, from healthcare, to finance, advertising and retail, to manufacturing and transport.

Until a few years ago, machine learning algorithms simply did not work very well on many meaningful tasks like recognizing objects or translation. Thus, when a machine learning algorithm failed to do the right thing, this was the rule, rather than the exception. Today, machine learning algorithms have advanced to the next stage of development: when presented with naturally occurring inputs, they can outperform humans. Machine learning has not yet reached true human-level performance, because when confronted by even a trivial adversary, most machine learning algorithms fail dramatically. In other words, we have reached the point where machine learning works, but may easily be broken.

Today is an exciting day for the advancement of AI at Microsoft. We have agreed to acquire Maluuba, a Montreal-based company with one of the world's most impressive deep learning research labs for natural language understanding. Maluuba's expertise in deep learning and reinforcement learning for question-answering and decision-making systems will help us advance our strategy to democratize AI and to make it accessible and valuable to everyone -- consumers, businesses and developers. We've recently set new milestones for speech and image recognition using deep learning techniques, and with this acquisition we are, as Wayne Gretzky would say, skating to where the puck will be next -- machine reading and writing. Maluuba's vision is to advance toward a more general artificial intelligence by creating literate machines that can think, reason and communicate like humans -- a vision exactly in line with ours.

This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. 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 is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.

Machine learning is gaining momentum and whether we want to admit it or not, it has become an essential part of our lives. As Adam Geitgey, Director of Software Engineering at Groupon, told JAXenter a few months ago, "anyone who knows how to program can use machine learning tools to solve problems." I think that in five years, machine learning won't be thought of as "magic" anymore. It will be a very common tool that nearly all programmers use to solve problems – just like how most programmers today know about databases and networking. Geitgey explained that even if you don't need a deep mathematical background to be able to apply machine learning, learning Python --"by far the most popular programming language today for machine learning"-- is a must.