Today's Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from Blue Prism, HCL Technologies, Noble.AI, Tata Consultancy Services and 4Cite. To equip young talent with the digital skills and experience needed for the future job market, Blue Prism, a global leader in Robotic Process Automation, has collaborated with the EY Foundation, a UK charity helping young people access employment opportunities, to provide paid work experience and mentors through the EY Foundation ten month Smart Futures programme. HCL Technologies (HCL), a leading global technology company, announced that it has been named a Top Employer 2020 in the United Kingdom, Sweden, Germany, the Netherlands, Poland, France and South Africa. Dentsu Aegis Network has acquired 4Cite Marketing, a leading people-based identification and data services technology company.
A very strong Java programmer in the other technologies might work out, too. Our platform automates the entire process of building predictive models starting from raw business data through data and feature engineering to machine learning all the way to production. We have offices in the USA, Japan, and Poland. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects. Unique to the dotData Platform is its AI-powered feature engineering, which eliminates the most time-consuming and labor- and skill-intensive aspects of the full data science process by discovering and evaluating millions of features derived from relational, transactional, temporal, geo-locational, or text data.
KRAKOW, POLAND - 2019/01/23: In this photo illustration, the Google Hangouts logo is seen displayed ... [ ] on an Android mobile phone. Here are five things in technology that happened this past week and how they affect your business. According to a report released this week, Google is in the process of creating its newest messaging app and the company's development team is looking to combine the capabilities of multiple apps that the tech giant already provides into one. The report detailed that the new messaging app would combine the features from Hangouts Meet, Hangouts Chat, and Drive, but also include Gmail features. This would be Google's tenth app.
A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints Marek Smieja a,, Łukasz Struski a, Mário A. T. Figueiredo b a Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland b Instituto de T elecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, PortugalAbstract In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach, S 3 C 2 (Semi-Supervised Siamese C lassifiers for C lustering), is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method. Keywords: semi-supervised clustering, deep learning, neural networks, pairwise constraints 1. Introduction Clustering is an important unsupervised learning tool often used to analyze the structure of complex high-dimensional data. Semi-supervised clustering (SSC) methods tackle this issue by leveraging partial prior information about class labels, with the goal of obtaining partitions that are better aligned with true classes [1, 2, 3, 4, 5, 6]. One typical way of injecting class label information into clustering is in the form of pairwise constraints (typically, must-link and cannot-link constraints), or pairwise preferences (e.g., should-link and shouldn't-link), which indicate whether a given pair of points is believed to belong to the same or different classes. Most SSC approaches rely on adapting existing unsupervised clustering methods to handle partial (namely, pairwise) information [7, 8, 4, 5, 6, 9].
In a previous post, I expressed my happiness that I got to present at ML in PL in Warsaw. I had the opportunity to take a step back and reflect a bit on the ethics of what we do as practitioners of data science and builders of machine learning models. It's an important topic and doesn't receive the attention that it should. The algorithms we build affect lives. I have researched this topic quite a lot, and during that time I have found a number of stories that made a huge impression on me.
Every year before Christmas, consumers face similar dilemmas: what gifts to buy for their loved ones? Selection based on your own tastes can end in failure and returns of gifts. When trying to predict what their loved ones want to find under the Christmas tree, people now more often use new technologies, notes Dr. Artur Modliński from the Department of Management of the University of Lodz. These technologies are applications - artificial intelligence that predicts the hottest holiday trends with advanced algorithms. At the same time, shopping websites often use artificial intelligence to track the behaviour of Internet users; they observe what products they search for, what they most often click, what topics and categories they search for - and what they buy.
I was super happy that I had the opportunity to present at a world class Machine Learning event in Warsaw, Poland. People from research organizations from all over the world attended ML in PL. I had been looking forward to all of the deeply technical talks, but I was grateful to the organizers that we could start the day by taking a step back and reflecting a bit on the ethics of what we do. It's an important topic and doesn't receive the attention that it should. As Machine Learning people, we work on technologies that are super powerful.
Let's get two things out of the way: First of all, there are a lot of reasons to get a boob job, none of which are anyone else's business. Second, the perfect boob does not exist. But if you were a plastic surgeon hoping to be the Michelangelo of one person's idealized breasts, it would help to have a shared language of what's aesthetically important. Most plastic surgeons accomplish this over the course of several consultations, talking to the patient about what will make them happy. In an attempt to improve this process, a team of researchers in Poland used eye-tracking technology to see what parts of the boob people looked at when assessing the symmetry and relative attractiveness of breasts.
When looking at breasts, both men and women stare at the area around the nipples the most, plastic surgeons have determined using eye-tracking technology. The findings may help improve the outcomes of both cosmetic and reconstructive surgery by providing a more objective measure of breast aesthetics. Three quarters of'gaze time' was focused on the lower breast and nipple areas, which received the most attention from men and women, the researchers found. Furthermore, people are most likely to glance towards the so-called nipple-areola complex, which was found to be the'most common point of initial fixation'. 'Thanks to objective analysis of observer's gaze pattern, eye-tracking technology may provide a better insight into the visual perception of breast aesthetics and symmetry,' said paper author Piotr Pietruski of the Memorial Hospital, Warsaw.
Samsung Electronics' Global Research & Development (R&D) Centers play a key part in developing artificial intelligence (AI) capabilities for real-world usage. A credit to the work this advanced R&D branch of Samsung undertakes, both Samsung R&D Institute Poland and Samsung Research America AI Center have recently won two prestigious global challenges. This year, Samsung R&D Institute Poland won first place in two categories, the first being text-to-text translation from English to Czech and the second – an end-to-end system translating English speech into German text. For the text-to-text translation category, researchers worked to develop a model to translate the transcript of a spoken English-language TED Talk into Czech. Developing their winning model required the Samsung team to develop large, filtered corpora from which to work and generate as much synthetic data as possible.