In a study that used artificial intelligence to analyze design elements, such as artwork and wall colors, in pictures of living rooms posted to Airbnb, a popular home rental website, the researchers found that people tended to follow cultural trends when they decorated their interiors. In the United States, where the researchers had economic data from the U.S. Census, they also found that people across socioeconomic lines put similar efforts into interior decoration. "We were interested in seeing how other cultures decorated," said Clio Andris, assistant professor of geography, Penn State and an Institute for CyberScience associate. "We see maps of the world and wonder, 'What's it like living there,' but we don't really know what it's like to be in people's living rooms and in their houses. This was like people around the world inviting us into their homes."

The science of service centers has advanced with hold-time estimates, call-back options and voice-recognition technologies. Yet once the customer reaches an agent, odds are high that the agent will not be able to solve the problem in one go. Unsolved problems lead to more complaints, greater customer churn and wasted time of employees trying to calm upset customers. Artificial intelligence (AI) promises to substantially improve the experience. Early efforts are helping companies improve the overall customer experience, while reducing costs--in staff time, service escalations such as field technician visits, and defecting customers--in the bargain.

WHEN the first printed books with illustrations started to appear in the 1470s in the German city of Augsburg, wood engravers rose up in protest. Worried about their jobs, they literally stopped the presses. In fact, their skills turned out to be in higher demand than before: somebody had to illustrate the growing number of books. Fears about the impact of technology on jobs have resurfaced periodically ever since. The latest bout of anxiety concerns the arrival of artificial intelligence (AI).

Accordingly, a prime step in text mining applications is to extract interesting patterns and features, from this supply of unstructured data. Feature extraction can be considered as the core of social media mining tasks such as sentiment analysis, event detection, and news recommendation [2]. In the literature, sentiment analysis tends to be used to refer to the task of classifying the polarity of a given piece of text at the document, sentence, feature, or aspect level [23]. There are various applications on a variety of domains which utilize sentiment analysis, in this regard one can mention applying the sentiment analysis for political reviews to estimate the general viewpoint of the parties [43], predicting stock market prices based on sentiment analysis by utilizing the different financial news data [5], and making use of the sentiment analysis to recognize the current medical and psychological status for a community [23]. Machine learning algorithms and statistical learning techniques have been rising in a variety of scientific fields [9, 10]. A number of machine learning techniques have been proposed to perform the task of sentiment analysis. As one of the powerful sub-domains of machine learning in recent years, deep learning models are emerging as a persuasive computational tool, they have affected many research areas and can be traced in many applications. With respect to the deep learning, textual deep representation models attempt to discover and present intricate syntactic and semantic representations of texts, automatically from data without any handmade feature engineering.

In the early 2000s, the U.K. government started pushing a new angle to narrow the inequality gap: getting people access to financial services, such as a bank account, money advice and affordable credit. It saw a strong link between financial exclusion and child poverty, and a 2004 Treasury report found more than 65 percent of the unbanked were on the lowest salaries. That's when Frédéric Nze had the idea that eventually led to Oakam, a small loans company with a mission to underwrite customers who typically struggle to get a loan. In 2017, a parliamentary committee called on Britain's financial regulator and banks to give it a greater priority. More than 1.7 million people in the U.K. do not have a bank account, and 40 percent of the working-age population have less than 100 pounds in savings.

In this Feb. 1, 2019 photo, surveillance cameras are seen near the spot where "Empire" actor Jussie Smollett allegedly staged the attack in Chicago. Chicago police tapped into a vast network of surveillance cameras _ and some homeowners' doorbell cameras _ to help determine the identities of two brothers who later claimed they were paid by "Empire" actor Jussie Smollett to stage a racist and homophobic attack. CHICAGO (AP) -- Police tapped into Chicago's vast network of surveillance cameras -- and even some homeowners' doorbell cameras -- to track down two brothers who later claimed they were paid by "Empire" actor Jussie Smollett to stage an attack on him, the latest example of the city's high-tech approach to public safety. Officers said they reviewed video from more than four dozen cameras to trace the brothers' movements before and after the reported attack, determining where they lived and who they were before arresting them a little more than two weeks later. Smollett reported being beaten up by two men who shouted racist and anti-gay slurs and threw bleach on him.

Looking at the benefits data can provide, the smart city concept is incomplete without its use. Today, smart cities are unlocking the potential of data in terms of preventing and predicting crime and terrorism. Not to mention, the two elements can bring devastating changes to life in cities. According to sources, today, the most successful smart cities are those that are utilising the gathered data to predict and prevent crime and terrorism. Along the way, they are strengthening the connected infrastructure that is key to establishing a secure urban environment.

The hardware segment is projected to witness the highest growth rate during the forecast period. Algorithm Segment Review Based on algorithm, it is classified into deep learning, querying method, natural language processing, and context aware processing. The deep learning segment is projected to grow at the highest CAGR during the forecast period, owing to increase in use of signal reduction, data mining, and image recognition, which are integral components of most AI protocols. Global AI in healthcare Market: Key Geographic Segment Based on region, the AI in healthcare market is divided into North America, Europe, Asia-Pacific, and LAMEA. North America accounted for the largest market share in the AI in healthcare market in 2016, and is expected to retain its dominance throughout the forecast period.

Demand forecasting, for all of its importance in business, has had a mixed run in retail. Even in fairly predictable categories in general merchandise, it's far too easy for retailers to start the current year's plan by loading in all the assumptions made from the year before, rather than starting clean with a new demand forecast. In fact, according to RSR Research's benchmark, even though 68% of better-performing retailers ("Retail Winners") and 53% of all other retailers believe that starting with a demand forecast as the basis for the next year's plan is very valuable, only 49% of Winners and 29% of their peers actually do so today. Part of the reason why is because forecast error in retail is high, as high as 32% according to some estimates. And, the more sporadic or non-repeatable the demand is, the more forecast error occurs – thus, grocery retailers operating a replenishment strategy have a far easier time using a forecast than a fashion retailer introducing a high-fashion item that responds to a new trend. Additionally, not all products face the same demand profiles.

As part of Data Science Nigeria's (DSN) vision to build a world-class Artificial Intelligence (AI) ecosystem, it has released a first-of-its-kind AI …