A former cabinet minister has likened Russia's role in Syria to the Nazi regime in 1930s Spain, ahead of an emergency Commons debate on the humanitarian situation in Aleppo. Andrew Mitchell accused Russia of "shredding" international law with its bombing campaign in the country. The Tory MP also accused Russian forces of committing a war crime by attacking a UN relief convoy last month. The three-hour emergency debate will be held later in the day. The northern city of Aleppo has become a key battleground in Syria's bloody five-year civil war.

Question: I own a single-family home in a common-interest development. One of the reasons we purchased this house was because we knew it had covenants, conditions and restrictions, and felt that we won't have to worry about policing our neighbors. That's what the board is supposed to do. But after only a year, I'm very frustrated. There are a few condominiums here, so parts of our complex have a higher density.

The Expressive Intelligence Studio is developing a new approach to freeform conversational interaction in playable media that combines dialogue management, natural language generation (NLG), and natural language understanding. In this paper, we present our method for dialogue generation, which has been fully implemented in a game we are developing called Talk of the Town . Eschewing a traditional NLG pipeline, we take up a novel approach that combines human language expertise with computer generativity. Specifically, this method utilizes a tool that we have developed for authoring context-free grammars (CFGs) whose productions come packaged with explicit metadata. Instead of terminally expanding top-level symbols — the conventional way of generating from a CFG — we employ an unusual middle-out procedure that targets mid-level symbols and traverses the grammar by both forward chaining and backward chaining, expanding symbols conditionally by testing against the current game state. In this paper, we present our method, discuss a series of associated authoring patterns, and situate our approach against the few earlier projects in this area.

This vendor-written tech primer has been edited by Network World to eliminate product promotion, but readers should note it will likely favor the submitter's approach. Everyone says there is an information security talent gap. In fact, some sources say the demand for security professionals exceeds the supply by a million jobs. Their argument is basically this: attacks are not being detected quickly or often enough, and the tools are generating more alerts than can be investigated, so we need more people to investigate those alarms. We believe that, even if companies aroaund the world miraculously hired a million qualified InfoSec professionals tomorrow there would be no change in detection effectiveness and we would still have a "talent gap."

Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM's performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.

As enterprise enters further into the digital age, data has become the strategic asset that knowledge workers, small or large, rely on to guide their decisions. However, managing such large volumes of data has exposed some unprecedented challenges for the enterprises. Enterprises have learned that the data that they hold, comes in a variety of formats, resides in different and distributed systems and is specific to the organization and its domain. Setting these challenges as the backdrop, IBM's Watson division has built solutions that not only allow for data connectivity but also the analysis of unstructured data and its customization to an enterprise domain. IBM Watson Explorer is Watson's flagship product for text analytics and discovery.

Chatterbox Labs is a Cognitive data science company focused on delivering real world business outcomes that drive multi-million dollar outcomes. As a company we often find ourselves explaining the key differences between RPA and Cognitive. Fundamentally, RPA and Cognitive are two completely different offerings that both have merit, however, we describe RPA as the "Finger Tapping" as opposed to the "Brain Mapping" of Cognitive. RPA is a different and lower value proposition compared to Cognitive. Cognitive is built and ready to address 90% of the worlds data that is unstructured and not monetized…..RPA cannot address this market Cognitive is focused on processing 148K documents per second and achieving accuracy of 80% which is higher than human accuracy.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

Economist Milton Friedman was in favor of replacing the Federal Reserve with a computer. Maybe he was on to something. To the contrary, as a monetarist he wanted the Fed to increase the stock of money at a constant, 3%-5% rate a year, a task he thought a computer quite capable of performing. The Fed's lagged response to the 2008 financial crisis and an anemic recovery since then have given a new sense of urgency to the quest for a monetary rule or rules. For economists, the main benefit of a rule is to make monetary policy transparent and predictable. Everyone knows where the ship is headed and can plan accordingly.

Identity fraud, in which a slice of your identity ranging from new credit cards to entire bank accounts is taken over by criminals, rose by 49 per cent in 2015 on the previous year. That totalled almost 170,000 cases, according to data collected by Cifas, the financial industry's non-profit fraud advisory service. The reason for the rise is that more and more we use the internet for financial transactions, but have very few ways to verify our identity without cumbersome systems involving human interaction, which are also vulnerable to fraud. Cifas' 2015 Fraudscape report shows that 86 per cent of identity fraud happened online, with bank accounts and credit or debit cards most targeted, closely followed by loans and communications, typically mobile phone accounts. Traditionally, companies dealing with such problems have acted after the fact, trying to unravel complex or opportunistic frauds by working back through audit trails.