Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving topic discovery has focused on this notion of time. However, especially in settings where content is contributed by a community or a crowd, an orthogonal notion of time is the one that pertains to the level of expertise of the content creator: the more experienced the creator, the more advanced the topic. In this paper, we propose a novel time-evolving topic discovery method which, in addition to the extracted topics, is able to identify the evolution of that topic over time, as well as the level of difficulty of that topic, as it is inferred by the level of expertise of its main contributors. Our method is based on a novel formulation of Constrained Coupled Matrix-Tensor Factorization, which adopts constraints well-motivated for, and, as we demonstrate, are essential for high-quality topic discovery. We qualitatively evaluate our approach using real data from the Physics and also Programming Stack Exchange forum, and we were able to identify topics of varying levels of difficulty which can be linked to external events, such as the announcement of gravitational waves by the LIGO lab in Physics forum. We provide a quantitative evaluation of our method by conducting a user study where experts were asked to judge the coherence and quality of the extracted topics. Finally, our proposed method has implications for automatic curriculum design using the extracted topics, where the notion of the level of difficulty is necessary for the proper modeling of prerequisites and advanced concepts.

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

WHEN TAY MADE HER DEBUT in March 2016, Microsoft had high hopes for the artificial intelligence–powered "social chatbot." Like the automated, text-based chat programs that many people had already encountered on e-commerce sites and in customer service conversations, Tay could answer written questions; by doing so on Twitter and other social media, she could engage with the masses. But rather than simply doling out facts, Tay was engineered to converse in a more sophisticated way--one that had an emotional dimension. She would be able to show a sense of humor, to banter with people like a friend. Her creators had even engineered her to talk like a wisecracking teenage girl. When Twitter users asked Tay who her parents were, she might respond, "Oh a team of scientists in a Microsoft lab. They're what u would call my parents." If someone asked her how her day had been, she could quip, "omg totes exhausted."

Artificial Intelligence (AI) is described as the science of creating intelligent machines capable of performing real time tasks at a level of human expert emphasizing nearly every business operation across various business sectors. Traditional review methods in insurance sector posed several threats related to policy making, premium rates fixing and risk of grouping policy holders. The evolution of AI as an insurance technology is mitigating these risks and also supporting the insurers in decision making. The increased level of personalization and better outcomes to the customers offered by AI are the major driving factors for the rise of AI in insurance sector. New research report on the global Artificial Intelligence In Insurtech market is a complete overview of the market, covering various aspects product definition, segmentation based on various parameters, and the prevailing vendor landscape.

This is the second post in "AI role in CyberSecurity" series by #AILabPage, first post is available here It's easy to describe & define Artificial intelligence compare to what actually it is. Now to put it in one liner "AI is kind of intelligence demonstrated by machines to do the same task done by any human using natural intelligence". In other words same task performed by Human with Natural Intelligence and Machine with Artificial Intelligence should produce same results. Speed, quality and productivity are the measuring units here. Cybersecurity protect internet-connected systems, including hardware, software and data, from cyber attacks.

A newly launched AI Global Governance commission (AIGG), tasked with forming links with politicians and governments around the world to help develop and harmonise rules on the use of AI, has suggested that at least one key regulation should be that any decisions made by an AI system'must be tracked back to a person or an organisation'. Although the view was only the early product of meetings yesterday ahead of the AIGG launch event, which is backed by the UK Parliament's APPG AI group and the Big Innovation Centre, it could become something of a standard ethical line for the many legal projects now developing in this area. Earlier this month the Law Society launched its own Public Policy Commission on Algorithms and Justice, for example, one of several AI ethics initiatives around the world. Ensuring that any algorithmic decision is traceable and can be tracked back to a person or organisation could provide society with a greater sense that at least someone is responsible for the actions of an automated system, and that important decisions were not being made in a regulatory vacuum and without any recourse for legal action against a party that caused harm to another. In fact, one could argue that not being able to assign responsibility to the actions of an algorithm would in effect undermine the justice system and put AI's outputs on a par with'an act of nature', i.e. beyond the ability of society to apply rules. The AIGG meeting also stressed that regulators needed to move a lot faster than they are, nationally and globally, because AI technology and its use was now moving a lot faster in terms of its development and actual use in society.

'Insider Threat' is a formidable risk to business because it threatens both customer and employee trust. Accidental or malicious misuse of a firm's most sensitive and valuable data can result in customer identity theft, financial fraud, intellectual property theft, or damage to infrastructure. Because insiders have privileged access to data in order to do their jobs, it's usually quite difficult for security professionals to detect suspicious activity; a process even more challenging to automate (and deploy at scale across the large organisations that most need it) as – so I will suggest – computers fundamentally lack semantic understanding of the meaning of the'bits' they so adroitly process. Conversely, in this talk I will outline a new approach to'Insider Threat' detection that draws inspiration from the Traffic Analysis' of encrypted Axis signal traffic' undertaken at Bletchley Park in WW2. A novel approach that (i) conceives companies as complex autonomous autopoietic entities and (ii) deploys state of art computational analysis of the communication flows that so define the company to flag potentially aberrant employee behaviour; intelligence that can be leveraged to help detect HR problematics before they arise.

Kamikaze drones fired from bazooka-like launchers are helping the US military hunt down and destroy lethal enemy drones with deadly precision. The interceptor craft crash into drones to take them down mid-flight and may even carry an explosive charge to bolster their destructive power. They form part of a new weapon system presented at the Pentagon earlier this month that deals specifically with the threat of attacks using shop-bought drones. The system, which can be mounted to an off-road vehicle, also features advanced radar technology and a computer-powered machine-gun. Kamikaze drones fired from bazooka-like launchers could help the US military hunt down and destroy enemy drones.

A team of researchers hope lab-grown brains from 550,000-year-old Neanderthals will be able to pilot the movements of a crab-like robot. The unbelievable experiment is using Neanderthal DNA to grow pea-sized brains masses, which are hooked-up to robots to test the capabilities of the electrical signals detected within the tissue. Researchers from the University of California, San Diego (UCSD) School of Medicine are simultaneously growing brain tissue from human DNA to plug into the same crab-like machines. They hope the difference in robot movements achieved by the lab-grown brains of modern man and Neanderthals, who diverged from human beings around 550,000 to 765,000 years ago, will offer vital clues about the minds of our early ancestors. The lab-grown brains cannot achieve conscious thoughts or feelings – but can mimic the basic structure of a developed brain, and reveal key differences in how the nerve cells function.

LONDON – A medical chatbot said to perform as well as or even better than human doctors has sparked a war of words in Britain, in a clash over how much the cash-strapped public health service should rely on artificial intelligence. AI company Babylon, which is already working with the National Health Service, claimed its chatbot scored higher marks than real live doctors in "robust tests." The British firm said it quizzed the AI using sample questions for trainee exams set by Britain's Royal College of General Practitioners (RCGP), the professional body for family doctors. The programmed chatbot, a key feature of Babylon's "GP at Hand" app, scored 81 percent when sitting the test for the first time, while the average pass mark over the past five years for doctors was 72 percent, according to the company. Ali Parsa, its founder who presented the findings in London earlier this week, hailed the results as "a landmark." "(They) take humanity a significant step closer to achieving a world where no one is denied safe and accurate health advice," he said in a statement.