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."

Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.

The art of making perfumes and colognes hasn't changed much since the 1880s, when synthetic ingredients began to be used. Expert fragrance creators tinker with combinations of chemicals in hopes of producing compelling new scents. So Achim Daub, an executive at one of the world's biggest makers of fragrances, Symrise, wondered what would happen if he injected artificial intelligence into the process. Would a machine suggest appealing formulas that a human might not think to try? Daub hired IBM to design a computer system that would pore over massive amounts of information--the formulas of existing fragrances, consumer data, regulatory information, on and on--and then suggest new formulations for particular markets. The system is called Philyra, after the Greek goddess of fragrance.

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.

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model's behavior and predictions, which are helpful for determining the reliability of the model's prediction. However, such methods do not fix and improve the model's reliability. In this paper, we teach our models to make the right prediction for the right reason by providing explanation training signal and ensuring alignment of the models explanation with the ground truth explanation. Our experimental results on multiple tasks and datasets demonstrate the effectiveness of the proposed method, which produces more reliable predictions while delivering better results compared to traditionally trained models.

Frank n Raf Market Research LLP As per Frank n Raf latest Research report, The global industrial robotics market size is expected to reach USD 41.23 billion by 2020., increasing at a CAGR of 7.0% through the forecast period. The accelerated expansion of the automotive industry worldwide and increasing adoption of robotics in the non-automotive industry including chemicals, food & beverage, rubber & plastics, and electronics/electrical are stoking the growth of the market. Companies executing industrial robots are frequently realizing substantial financial advantages, which is pointing to a surge in installation of robots in modern manufacturing plants. Combination of robots with production processes help increase productivity, reduces overheads, contributes a high degree of flexibility, improves quality, and reduces waste to a large range as compared to the outcome of manual labor, which consequently drives the market. Industrial robots have been effective for the formation of a new ecosystem distinguished by rewarding, lucrative, and high-paying jobs.

The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.

WINTER PARK, Fla.--(BUSINESS WIRE)--Feb 20, 2019--COPC Inc., a global consulting firm that helps companies improve operations to transform the customer experience, and Execs In The Know, a global community of customer experience professionals, have announced the release of the 2018 Corporate Edition of the Customer Experience Management Benchmark (CXMB) Series. The report, The CX Journey: Understanding Corporate Strategies and Best Practices, provides customer experience management insights from the corporate perspective. A key finding is that since 2017, companies have dramatically increased their use of artificial intelligence (AI)-powered solutions for customer service. "Our new corporate report shows that companies see tremendous potential in AI-powered solutions for customer care, both in applications that are customer-facing and in those that assist call center agents with their work. However, we also know from previous research that customers want a quick and easy way out of any AI-powered solution to reach a live person. Our findings overwhelmingly show that companies are keenly aware of this necessity in any customer-facing application. And while customers still want that personal interaction, we think that AI-powered solutions will find their appropriate place in the service journey," said Kyle Kennedy, president and chief operating officer, COPC Inc.

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In these cases, the estimation of the actual query location is also subject to uncertainty. In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain. The algorithm employs a Gaussian process model that takes probability distributions as inputs. Theoretical results are provided for both the proposed algorithm and a conventional UCB approach within the uncertain-inputs setting. Finally, we evaluate each method's performance experimentally, comparing them to other input noise aware BO approaches on simulated scenarios involving synthetic and real data.

Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.