As it turns out, AI is starting to truly become mainstream and 2017 looks like it's going to be full of new technologies and platforms. During the past year we've seen many new companies dealing with this, including the grand opening of Open AI (which has already shipped some pretty interesting papers), quite a few startups on the subject, a huge amount of FUD concerning self-driving cars and, of course, the rise of the mighty chatbot. Just like it happened with the popularization of countless technologies before, there's always people who buy way too much into the hype and out come the tinfoil hats. There's already talk of how AI is making humans obsolete and that we're like two days away from giant floating heads in the sky demanding we show them what we got. The reality, however, is somewhat different.

Machine Learning is a subfield of computer science by which computers have the ability to learn without being explicitly programmed. Machine learning is a method used to develop complex models and algorithms that lend themselves to prediction. Machine learning is deployed where the system deals with large scale of data. Deployments of machine learning leads to improved speed and accuracy of the functions performed by the system. Machine learning is majorly deployed for solving classification and regression problems.

THERE IS SOMETHING familiar about fears that new machines will take everyone's jobs, benefiting only a select few and upending society. Such concerns sparked furious arguments two centuries ago as industrialisation took hold in Britain. People at the time did not talk of an "industrial revolution" but of the "machinery question". First posed by the economist David Ricardo in 1821, it concerned the "influence of machinery on the interests of the different classes of society", and in particular the "opinion entertained by the labouring class, that the employment of machinery is frequently detrimental to their interests". Thomas Carlyle, writing in 1839, railed against the "demon of mechanism" whose disruptive power was guilty of "oversetting whole multitudes of workmen".

In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

Your dog remembers more than you might think. A new study that tested the memory of man's best friend found that dogs exhibit something akin to episodic memory -- a process that's been well documented in humans, but difficult to prove in other animals. In experiments, the dogs were able to recall human actions even when they weren't expecting to be tested on what they observed, according to a report published Wednesday in the journal Current Biology. The findings show that episodic memory, thought to be linked to self-awareness, may extend well beyond humans to species outside of the primate lineage. Scientists have long wondered whether other animals have something like episodic memory, which allows us to recall specific past events even though they may not have been particularly important when they happened.

Bletchley Park, the site of secret code-deciphering projects during World War Two, could become the centre for a new generation of codemakers and codebreakers. There are plans for a training college to teach cybersecurity skills to 16-19 year olds at the Buckinghamshire site. Former Home Secretary Lord Reid said it had become vital to build up the "talent pool" for cyber-defence. The college in a wartime building at Bletchley is intended to open in 2018. The project, developed by a not-for-profit group from the cybersecurity industry, is planning a National College of Cyber Security, which would open in autumn 2018.

Its first operatives famously cracked coded messages encrypted by the Nazis, hastening the end of the second world war. Now Bletchley Park is planning a new school for the next generation of codebreakers in order to plug a huge skills gap in what is fast emerging as the biggest security threat to 21st-century Britain. The College of National Security, a first for the UK, is scheduled to open in 2018 in a specially adapted premises on the Bletchley Park site. The sixth-form boarding school will be free to the 500-odd applicants, with a mix of venture capital, corporate sponsorship and very possibly state funding underwriting the multimillion-pound costs. The school will teach cyber skills to some of the UK's most gifted 16- to 19-year-olds.

Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application. These models however lack transparency due to their complex nonlinear structure and to the complex data distributions to which they typically apply. As a result, it is difficult to fully characterize what makes these models reach a particular decision for a given input. This lack of transparency can be a drawback, especially in the context of sensitive applications such as medical analysis or security. In this short paper, we summarize a recent technique introduced by Bach et al. [1] that explains predictions by decomposing the classification decision of DNN models in terms of input variables.

Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key properties used to interpret automata and propose a modification of a state-merging approach to learn variants of finite state automata. We apply the approach to problems beyond typical grammar inference tasks. Additionally, we cover several use-cases for prediction, classification, and clustering on sequential data in both supervised and unsupervised scenarios to show how the identified key properties are applicable in a wide range of contexts.

For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled one. A bootstrap technique is used to identify sub-samples rich in the well modeled process, and classify each event according to the frequency of it being part of such sub-samples. Comparisons with general MVA algorithms will be shown, as well as a study of the asymptotic properties of the method, making use of a public domain data set that models a typical search for new physics as performed at hadronic colliders such as the Large Hadron Collider (LHC). The most popular classification algorithms based on supervised learning require a well modeled signal and a well modeled background. For the classifier to learn how to separate the two classes, it is crucial that both models are known. The case in which either signal or background has an unknown PDF is, however, acquiring importance in many classification problems that arise in the realm of particle physics, due to the fact that every passing day more and more known models are ruled out by the data. Two scenarios are mainly interesting for particle physics: a very well known background modeled from simulation, in presence of an unknown rare signal; a very well known signal modeled from simulation, contaminated by a background of origin unclear and/or not simulable. In both scenarios, it is desirable to manipulate the fraction of the unknown process, without modifying the kinematic distributions of the very well known one.