For consumer companies, large-scale leveraging of customer and behavioral data to drive personalized customer experiences is turning into a virtual arms race. Marketing technology platforms of the last 10 years were built around campaign process that were still highly manual, requiring marketing execs to do all the testing, optimization and which makes the cycle time for learning and actually influencing marketing very slow. Now more and more marketers recognize the need to deploy advanced personalization capabilities that make the use of machine-learned optimization. And, Matt Fleckenstein, Chief Product Officer at Amplero, helps marketers achieve just that. With a track record for conceiving, building, and launching martech products and services it comes easy to him.

Algorithm

Anesthesiologists' jobs look safer than radiologists' jobs. In H. G. Wells's classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn't react sooner to the arrival of an "intelligence greater than man's"--in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: "We will peck them to death tomorrow, my dear." As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should?

The use of robotics and artificial intelligence in businesses is on the rise, but there are still significant challenges for organisations adopting the technologies. Two executives from global IT consulting and outsourcing group Capgemini spoke to IoT Hub about how best to meet these challenges and why the returns make the effort worthwhile. "The amount of data that's available now in places like social media and enterprises means it is becoming for efficient for machines to make decisions rather than humans, taking the human bias out of it and making decisions objectively," said Saugata Ghosh, senior manager of digital services at Capgemini. This trend, together with the maturity of robotic process automation (RPA) technologies over the last three to five years, has contributed to the growth in adoption of robotics and AI, Ghosh said. "If you look at the spectrum of robotic automation, at one end you have simple rules-based automation where the economics of those are such that they are quite easy to implement and have strong returns on investment," he explained.

A formal branch of artificial intelligence, machine-learning builds systems that learn directly from the data they are fed and effectively program themselves to analyse that data and make accurate predictions. Having already helped multiple business sectors create new models and drive competitive advantage, now it's the turn of the insurance industry. So just how is machine-learning changing the way insurers do business? "It gives insurers three distinct advantages," explains Max Richter, managing director in Accenture's UK insurance analytics group. "The first is to mine greater volumes of data, the second to scale analytics across the organisation by working smarter and faster, and lastly by answering more complex questions from'will this customer leave me at renewal?' to'what can I do about it?'" As such it is quickly becoming an essential tool for the insurance sector, specifically enabling companies to yield higher predictive accuracy as it can fit more flexible and complex models.

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.