Being able to spell, checking your profile for typos and using correct grammar could determine whether you get a match on a dating site, a new study has revealed. Researchers worked with members of an online dating service and asked them to read fake profiles that had language errors as well as ones that had no mistakes. The study of more than 800 people on a dating site in the Netherlands found that errors such as writing'teh' for'the' and putting capital letters in the wrong place suggested sloppiness and a lack of attention to detail. If a profile has spelling mistakes and grammatical errors they were more likely to be perceived as less intelligent and so were less likely to be a match. 'In the case of online dating, inattentiveness can be interpreted as a lack of effort and interest in putting time and effort in constructing a dating profile.

This is a continuation of our series on machine learning methods that have been implemented in JASP (version 0.11 onwards). In this blog post we train a machine learning model to find clusters within our data set. The goal of a clustering task is to detect structures in the data. To do so, the algorithm needs to (1) identify the number of structures/groups in the data, and (2) figure out how the features are distributed in each group. For instance, clustering can be used to detect subgenres in electronic music, subgroups in a customer database, or to identify areas where there are greater incidences of particular types of crime.

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The Virginia Chapter of ISACA Herndon monthly meeting will feature a presentation by Eric Poynton, Founder of Awake Security entitled "Machine Learning in Security." An over-reliance on AI and Machine Learning can lead to Risk. The methodologies that compensate for ML's weaknesses might atrophy with such a fixation.

We've come a long way in finance and accounting. From purely manual bookkeeping carried out in large, dusty, paper ledgers, through to Excel-based solutions and advanced accounting systems, we've been plunged into the exciting world of intelligent automation – powered by the "golden triangle" of robotic process automation (RPA), artificial intelligence (AI), and smart analytics. Representing a complete game-changer in improving finance processes, particularly promising under the umbrella of AI, is the concept of machine learning (ML). ML algorithms enable a specific task to be performed without it being explicitly programmed but rather through learning by example. The solution is based on statistical models created from sample data provided to the algorithm.

From virtual assistants to content moderation, sentiment analysis has a wide range of use cases. AI models that can recognize emotion and opinion have a myriad of applications in numerous industries. Therefore, there is a large growing interest in the creation of emotionally intelligent machines. The same can be said for the research being done in natural language processing (NLP). To highlight some of the work being done in the field, below are five essential papers on sentiment analysis and sentiment classification.

Scientists at the TSU Laboratory of Biophotonics, working with Tomsk National Research Medical Center (TNIMC) oncologists, have developed a new approach to the diagnosis of adenocarcinoma, a malignant tumor of the prostate gland, that uses artificial intelligence to identify oncopathology and determine the stage of the disease. Using machine learning, a computer model was taught to distinguish between healthy tissues and pathology with 100 percent accuracy. The gold standard for the diagnosis of cancer is histology, during which tissue from a patient is examined for malignant changes. So that the samples can be stored for a long time, they are dehydrated and packed in paraffin. Then experts make thin sections and examine these slides under a microscope.

Its impact can be far more profound and felt across the entire organisation, affecting the ways team interact and the way leaders lead. If AI is to deliver on its true potential for your organisation, it needs cultural as well as technical change. In this Breakfast Briefing, Part of BIMA's Age of AI series in partnership with Microsoft, an expert panel will help you explore the building blocks of an AI culture, including: So how do you create quality data available to all? Empowerment: Many of the most profound impacts of AI come from people closest to the business. So how do you encourage greater collaboration?

Our personalities impact almost everything we do, from the career path we choose to the way we interact with others to how we spend our free time. But what about the way we drive--could personality be used to predict whether a driver will cut someone off, speed, or, say, zoom through a yellow light instead of braking? There must be something to the idea that those of us who are more mild-mannered are likely to drive a little differently than the more assertive among us. At least, that's what a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is betting on. "Working with and around humans means figuring out their intentions to better understand their behavior," said graduate student Wilko Schwarting, lead author on the paper published this week in Proceedings of the National Academy of Sciences.

Decades ago, Japan faced an unavoidable, long-term economic challenge. Even as its economy reached record highs in the late 1980s (fueled by strong auto sales, the rise of innovative companies like Nintendo, and real estate speculation), it was preparing for the coming day when more than a quarter of its population would be over age 65. Today, Japan's median age is more than 10 years older (47) than that of the US (36). To offset the economic realities of a rapidly aging workforce, Japan made the decision to become a world leader in robotics. Advanced robotics in manufacturing, healthcare, consumer electronics, and soon personal services are now deeply entrenched in the Japanese economy, a movement created out of a need to maintain productivity and GDP growth.