Contrary to envisaged fears that emerging technology like Artificial Intelligence (AI) will put a lot of humans out of jobs, SAS, a global technology solution company has said the computerised system if well applied, will create more jobs and help improve human efficiency, using data analytics. The information on the role of AI in business growth was revealed at the SAS Road to Digital Transformation and Artificial Intelligence Workshop, which held in Lagos on Tuesday. Senior Business Solution Manager, Advanced Analytics and AI at SAS, Mr. Larry Orimoloye, argued that emerging technology like AI was actually transforming businesses creating more jobs and enhancing business efficiency. According to him, in a customer operation centre where humans are responding to customers' complaints, a lot of training and investment are being put into in to enable the staff respond accurately to customers' needs and challenges. However, he pointed out that this is something that machines like robots can easily do because they have been programmed to do so and they have the intelligence to it accurately.

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."

A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughout the sciences. Baik, Ben Arous and Peche showed that the spiked Wishart ensemble exhibits a sharp phase transition asymptotically: when the spike strength is above a critical threshold, it is possible to detect the presence of a spike based on the top eigenvalue, and below the threshold the top eigenvalue provides no information. Such results form the basis of our understanding of when PCA can detect a low-rank signal in the presence of noise. However, under structural assumptions on the spike, not all information is necessarily contained in the spectrum. We study the statistical limits of tests for the presence of a spike, including non-spectral tests. Our results leverage Le Cam's notion of contiguity, and include: i) For the Gaussian Wigner ensemble, we show that PCA achieves the optimal detection threshold for certain natural priors for the spike. ii) For any non-Gaussian Wigner ensemble, PCA is sub-optimal for detection. However, an efficient variant of PCA achieves the optimal threshold (for natural priors) by pre-transforming the matrix entries. iii) For the Gaussian Wishart ensemble, the PCA threshold is optimal for positive spikes (for natural priors) but this is not always the case for negative spikes.

Editor's Note: The rapid pace of technological innovation is changing the nature of warfare, and futurists are busy spinning out scenarios of a U.S.-China clash in twenty years involving nano-technology and fully autonomous weapons systems. Yet how will new technologies shape insurgency and counterinsurgency, which conjures up images of guerrillas hiding in Vietnam's jungles? My Brookings colleague Chris Meserole looks at two of the latest books on the subject and assesses how the balance between rebels and government may tilt. When U.S. Special Forces entered Afghanistan in 2001, Facebook didn't exist, the iPhone had yet to be invented, and "A.I." often referred to an NBA star. Seventeen years later, American special operations forces continue to ride horseback in rural Afghanistan, but information technology has advanced rapidly.

SAPPHIRE NOW took place on June 5-7 in Orlando with impressive numbers: more than 21,000 attendees from 102 different countries and 1,275 lectures, which was the SAP's main global event at the year. It was 3 days of much learning, where I had an opportunity to attend lectures, several demonstrations of products and applications, and meet interesting people. At the opening keynote of the event, called "The Next Move", SAP CEO Bill McDermott made the main announcements: the launch of SAP C/4 Hana and the SAP HANA Data Management Suite, the importance of SAP Leonardo, and also defined and listed what he considered the 10 main characteristics of an intelligent enterprise. McDermott commented on the importance of artificial intelligence to drive economic growth through the use of machines and the judgment of humans. "Great moments are born from great opportunities."

Google made headlines earlier this month when it pulled out of the U.S. Defense Department's flagship artificial intelligence program known as Project Maven, which leverages sophisticated algorithms to analyze drone footage. Until then, the project had been so secretive that few people knew Google was involved -- not even the former executive chairman of Google's parent company, Alphabet, who now sits on the Defense Department's Innovation Advisory Board -- let alone what it actually is. But Google's decision not to seek another contract for the AI project has thrust it into the spotlight as tech companies face a wave of protests over government contracts. "We believe that Google should not be in the business of war," more than 3,000 Google employees wrote in an April letter to company CEO Sundar Pichai that prompted the move. The growing resistance from Silicon Valley to working with the government, particularly the Pentagon, raises questions about the viability of Defense Secretary James Mattis's ambitious plans to leverage cutting-edge commercial technology for military purposes.

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.