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Dubai, UAE - January 6 2021: Artificial Intelligence (AI) and Machine Learning technologies will have the biggest impact on the real estate sector, according to the annual real estate tech survey conducted by DAMAC. The survey was conducted within the DAMAC Group, with nearly 90% of respondents from the Information Technology department and the remaining from the business excellence, data & analytics and technology teams.

Nearly a quarter of the respondents marked AI and Machine Learning to be the primary impact maker among technologies, followed by the Internet of Things (20%), cybersecurity (20%) and virtual & augment reality (17%), respectively in order of importance.

Among technologies that homeowners look for when buying property, it was revealed that smart homes with IoT is the most in demand, with nearly 30% of respondents ranking it as the priority. This is followed by touchless access control, digital transaction services and virtual immersive experience, respectively.

In response to such demands, DAMAC has introduced the DAMAC Central app for centralising all communications, collaborations, decision making, self-services for management, all departments and all staff in the organisation. The DAMAC Living app was also launched for community-related services for residents and tenants - which seeks to make a number of services and processes easier and more seamless for homeowners - from settling payments, to uploading documents for property handover, making amenity bookings and getting special discounts on services, among others.

DAMAC Chief Information Officer Jayesh Maganlal said: "The big takeaway for this survey is that there will be major shift in the real estate market sooner than later - from the digitisation of the buying or selling process, to the shifting attitudes of what people need and don't in terms of technology. While these shifts present great insights into how real estate will evolve, people will continue to need the support and guidance from the experts to help them navigate the journey towards homeownership. DAMAC has taken cognizance of these demands and has invested in the latest technological trends in order to elevate our customer and employee experience to the highest standards."

Furthermore, the majority of respondents agreed that 3D virtual tours are the most valuable technology for agents and brokers when approaching potential buyers with a property. Digital transaction services closely follow suit, with augmented reality and smart search engine taking the respective rankings according to order of importance.

Utilising VR and AR technology, DAMAC had in early 2020 launched a unique concept called 'A La Carte Villas' at DAMAC Hills, which enables buyers to personalise multiple aspects of their homes, including villa type, layout, landscaping, interiors, and furnishings, among others using a cutting-edge configuration app.

ENDS

DAMAC Properties has been at the forefront of the Middle East's luxury real estate market since 2002, delivering award-winning residential, commercial and leisure properties across the region, including the UAE, Saudi Arabia, Qatar, Jordan, Lebanon, Iraq, The Maldives, Canada, as well as the United Kingdom.

Since then, the company has delivered approximately 36,400 homes. Joining forces with some of the world's most eminent fashion and lifestyle brands, DAMAC has brought new and exciting living concepts to the market in collaborations that include a golf course by The Trump Organization, and luxury homes in association with Versace, Cavalli, Just Cavalli, Rotana, Paramount Hotels & Resorts, Rotana and Radisson Hotel Group. With a consistent vision, and strong momentum, DAMAC Properties is building the next generation of Middle Eastern luxury living.

DAMAC places a great emphasis on philanthropy and corporate social responsibility. As such, the Hussain Sajwani - DAMAC Foundation, a joint initiative between DAMAC Group and Hussain Sajwani, is supporting the One Million Arab Coders Initiative. The programme was launched by Vice President and Prime Minister of the UAE, and Ruler of Dubai, His Highness Sheikh Mohammed bin Rashid Al Maktoum, and is focused on creating an empowered society through learning and skills development.

Visit us at http://www.damacproperties.com

Follow DAMAC Properties on Facebook, Twitter,Instagram, LinkedIn and YouTube (@DAMACofficial).

For more information, please contact: Corporate Communications, DAMAC Properties: Tel: +971 4 373 2197 Email: corporatecommunications@damacgroup.com

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Damac Properties Dubai : AI and Machine Learning to Have Biggest Impact on Real Estate, finds DAMAC Survey - marketscreener.com

Title of the Special Issue/Thematic Track

Machine and Human Factors in Misinformation Management (VSI: IPMC2022 MISINFO)

- Damiano Spina (*), Senior Lecturer and DECRA Fellow, School of Computing Technologies, RMIT University, Melbourne, Australia. email: damiano.spina@rmit.edu.au

- Kevin Roitero, Postdoctoral Research Fellow, Department of Mathematics, Computer Science, and Physics, University of Udine, Udine, Italy. email: kevin.roitero@uniud.it

- Stefano Mizzaro, Full Professor, Department of Mathematics, Computer Science, and Physics, University of Udine, Udine, Italy. email: mizzaro@uniud.it

- Gianluca Demartini, Associate Professor, School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia. email: g.demartini@uq.edu.au

- Kalina Bontcheva, Full Professor, Department of Computer Science, The University of Sheffield, United Kingdom. email: k.bontcheva@sheffield.ac.uk

(*) Managing Guest Editor.

The rise of online misinformation is posing a threat to the functioning of the overall democratic process. Nowadays, it has been observed that there is an exponential growth of false information spread across the web and social network platforms; this expansion is also connected with the development of novel tools (e.g., large language models) that are able to process and generate large amounts of data. This has enabled the increase of large-scale counter-narratives and propaganda strategies in online communities, which have a major negative impact and can influence individuals and collective decision-making processes. To contrast this worrying trend, researchers are working on the development of data-driven and hybrid algorithmic methods with the aim of detecting misinformation and to control its spread. The proposed algorithms and solutions are complex and can be classified in different categories based on the underlying approach considered: fully automatic algorithms based on artificial intelligence, machine learning, and deep learning; human powered systems, either based on panels of experts or on crowdsourcing workers; and hybrid human-in-the-loop approaches, that try to fruitfully mix the above approaches. A better understanding on how humans and machines can effectively work together in the process of managing and fighting misinformation is needed.

The aim of this special issue is to accept submissions dealing with artificial, human, and hybrid techniques aimed at fighting the spread of misinformation.

Topics of interest include, but are not limited to:

- Predictive models to model and fight misinformation spread (e.g., trust and reputation models, formal models, online misinformation diffusion models, forecasting models).

- Machine learning, deep learning, transfer learning, reinforcement learning, graph based approaches, and probabilistic methods (e.g., classification, unsupervised / semi-supervised / supervised learning, applications, architectures, loss functions, training approaches) applied to fight misinformation.

- Infrastructures and resources for misinformation management (e.g., datasets, implementations, frameworks, architectures).

- Fairness, accountability, transparency, and safety of systems and processes to fight misinformation.

- Use of social media to study and combat misinformation online.

- Human computation and crowdsourcing methodologies to fight misinformation.

- Hybrid and multi-agent approaches to fight misinformation.

- Biases in artificial, human, and hybrid systems used to address misinformation.

- Adversarial approaches to misinformation (e.g., robustness of systems, automatic generation of misinformation).

- Information provenance and traceability.

- Filtering and recommendation systems for content dealing with misinformation (e.g., content-based filtering, collaborative filtering, recommender systems).

- User-centered (e.g., user experience, effectiveness, engagement) and system-centered (e.g., metrics, experimental design, benchmark) evaluation.

- Fighting Multimedia misinformation (text, audio, image, and video; deep fakes).

- Fighting Multi- and cross-lingual misinformation.

- Generation of explanations and explainable algorithms to deal with misinformation.

- Regulation, policies, and socio-economical perspectives on misinformation and approaches to fight misinformation.

- Influence and psychological aspects of misinformation.

- Social network analysis, influencer detection of misinformation, and fake news spreader profiling.

- Corpora, annotation, and test collections (including tools and resources) to build and evaluate systems and processes to fight misinformation.

Submit your manuscript to the Special Issue category (VSI: IPMC2022 MISINFO) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures. Please note IP&Ms strict no pre-print policy outlined in the author guidelines.

The authors of accepted papers will be obligated to participate in IP&MC2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.

Please see this infographic for the manuscript flow:https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference

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Machine and Human Factors in Misinformation Management - Information Processing and Management Conference - Knovel

Deep Learning market report contains detailed information on factors influencing demand, growth, opportunities, challenges, and restraints. It provides detailed information about the structure and prospects for global and regional industries. In addition, the report includes data on research & development, new product launches, product responses from the global and local markets by leading players. The structured analysis offers a graphical representation and a diagrammatic breakdown of the Deep Learning market by region.

Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017-2023.

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Global Deep Learning Market Global Drivers Restraints Opportunities Trends and Forecasts up to 2023

Market OverviewDeep learning can be considered as a subset of machine learning and consists of algorithms that allow a software to self-train to execute tasks such as image and speech recognition by exposing multilayered neural networks to bulk data. It can have a profound impact on various industries such as finance automotive aerospace telecommunication and information technology oil and gas industrial defense media and advertising medical and others. The increasing research and development activities in this domain is expanding the end use areas for the technology.

The factors that contribute to the high market share are parallelization high computing power swift improvements in information storage capacity in automotive and healthcare industries. A few major applications for deep learning systems are in autonomous cars data analytics cyber security and fraud detection. It has become imperative for both small and big organizations to analyze and extract meaningful information from visual content. Advanced technologies such as graphic processing units are highly accepted in scientific disciplines such as deep learning and data sciences.

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Valuable insights are extracted from bulk data by using deep learning neural networks to improve customer experience and generate innovative products. The development in artificial intelligence capabilities in natural language processing computer vision areas and image and speech recognition are driving the growth for deep learning.The use cases for deep learning is diverse ranging from detecting gene abnormalities and predicting weather patterns to identifying fraudulent insurance claims stock market analysis robotics drones finance agriculture. Deep learning systems have wide applications in the banking and financial sector.

It helps bank employees expand their capabilities so that they can focus more on customer interactions rather than regular banking transactions. The deep learning software can offer solutions based on a clients background and history and thus can provide evidence and context-based reasoning for every problem. Industries worldwide are generating enormous data which require high processing power and this data is being generated at an unprecedented rate and volume. This has created an enormous opportunity for deep learning powered applications. A plethora of start-ups are coming up with vertical specific solutions and global corporations are supporting these start-ups to innovate faster.

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Market AnalysisAccording to Reportocean Research the Global Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017-2023. The market is driven by factors such as faster processor performance large training data size and sophisticated neural nets. The future potential of the market is promising owing to opportunities such as development in big data technologies expanding end-user base and extensive R&D. The market growth is curbed by restraining factors such as implementation challenges rigid business models dearth of skilled data scientists affordability of organizations and data security concerns and inaccessibility.

Segmentation by SolutionsThe market has been segmented and analyzed by the following components: Software and Hardware.

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Segmentation by End-UsersThe market has been segmented and analyzed by the following end-users: Medical Automotive Retail Finance IT & Telecommunications Industrial Aerospace and Defence Media and Advertising Oil Gas and Energy and Others.

Segmentation by RegionsThe market has been segmented and analyzed by the following regions: North America EMEA Latin America APAC and Latin America.Segmentation by ApplicationsThe market has been segmented and analyzed by the following applications: Image Recognition Voice Recognition Video Surveillance and Diagnostics Data mining and Others.

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BenefitsThe study covers and analyses the Global Deep Learning Market. Bringing out the complete key insights of the industry the report aims to provide an opportunity for players to understand the latest trends current market scenario government initiatives and technologies related to the market. In addition it helps the venture capitalists in understanding the companies better and take informed decisions.> The report covers drivers restraints and opportunities (DRO) affecting the market growth during the forecast period (2017-2023).> It also contains an analysis of vendor profiles which include financial health business units key business priorities SWOT strategies and views.> The report covers competitive landscape which includes M&A joint ventures and collaborations and competitor comparison analysis.> In the vendor profile section for the companies that are privately held financial information and revenue of segments will be limited.

Region/Country Cover in the Report

North America EMEA Latin America and APAC

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Key Players Covered in the Report

Microsoft CorporationIBM CorporationAmazon Web ServicesNvidia CorporationDeepmind Technologies Ltd

This report covers aspects of the regional analysis market.The report includes data about North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa.This report analyzes current and future market trends by region, providing information on product usage and consumption.Reports on the market include the growth rate of every region, based on their countries over the forecast period.

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The report analyzes companies across the globe in detail.The report provides an overview of major vendors in the market, including key players.Reports include information about each manufacturer, such as profiles, revenue, product pricing, and other pertinent information about the manufactured products.This report includes a comparison of market competitors and a discussion of the standpoints of the major players.Market reports provide information regarding recent developments, mergers, and acquisitions involving key players.

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Deep Learning market 2022 Industry Growth Analysis by Key Players, Segments, Competitive Landscape and Forecast to 2030 - Taiwan News

AI projects benefit children immensely since they help them to increase their knowledge and talents for their bright future

Traditional education has undoubtedly suffered, as online learning has grown in popularity, and some parents have even turned to home schools. Learning, on the other hand, is not restricted to the classroom. At home, students and beginners may do a variety of enjoyable and simple artificial intelligence (AI) and machine learning (ML) projects. The majority of these projects require a computer or laptop, as well as internet connectivity. These projects provide your child with an enjoyable learning experience, that introduces them to artificial intelligence and machine learning at a young age.

In an age, where changing your face to appear like your favorite dog or trying to seem royal by wearing a crown, is all the rage on the internet. This project allows you to do just that design your face app filters, and apply them to your face. This is a fun AI and machine learning project for kids, that teaches them how to use AI to recognise their face with a computer camera, then appropriately fit the filter to their face and even tilt it in line with how their face tilts.

In this project, you will have to anticipate the selling price of a new home in Boston, for this assignment. The prices of properties in various locations of the city are included in the projects dataset. The datasets for this experiment, are available at the UCI Machine Learning Repository. Aside from the costs of various properties, youll also obtain statistics on the peoples ages, the citys crime rate, and the locations of non-retail companies. Its a terrific assignment for youngsters, to put their knowledge to the test.

Developing a chatbot, is one of the top AI-based initiatives. Create a rudimentary customer service chatbot first. The chatbots, which may be found on many websites, can be used as inspiration. After youve made a basic chatbot, you may improve it and make a more complex version. The chatbots specialty may then be changed and its functions can be enhanced. AI allows you to construct a variety of innovative chatbots.

This is a popular artificial intelligence project concept. This research aims to build on a ground-breaking current deep Learning application: face emotion identification. The deep Learning face emotion detection and identification system are used to recognise and understand human facial emotions. It can identify happy, sad, angry, terrified, surprised, disgusted, and neutral human emotions in real-time.

This is one of the greatest beginners AI project ideas. The stock market is a favourite of machine learning scientists. Because it is tightly packed with information, this is the case. You may get a variety of data sets and start working on a project right away. This project would be perfect for students interested in working in the financial business, since it may give them significant insight into a variety of elements of the industry.

This is a simple AI-based space combat game, where you just use hand gestures to operate your vessel. PictoBlox, a computer or laptop with a camera or webcam, and an internet connection are all necessary. The foundations of the game are simple: you move your finger around, to control the movement of the spaceship. The game uses artificial intelligence to analyse your hand, and its movements via the camera, which then drives the games spaceship forward.

The next AI and machine learning project for students, are to control a 2-wheel drive robot with hand gestures rather than a computer, smartphone, or joystick. If you havent made the robot yet or are unsure how to do so, go here. A two-wheeled robot, a camera-equipped laptop or PC, PictoBlox, and an internet connection, are all necessary. Youll use machine learning to teach the model to recognise hand gestures, so it can move forward, turn left and right and stop in this project.

Students may use the speech recognition capability of PictoBloxsartificial intelligence extension, to operate household appliances in this project. The students will use PictoBlox, to create a room with appliances like lights, a fan, and a radio, then write a script in PictoBlox to operate them using voice commands. After youve completed the project, you may turn it into a real-life room with lights, fans, and radios.

Because of the present global scenario and the rise of online classrooms, the conventional attendance method may be difficult or cumbersome. This cutting-edge machine learning project enables your teacher to take class attendance, using facial recognition. It collects sample photographs of your face using machine learning, and the next time the computer scans a face, it uses AI to compare it to the stored samples, marking you as present if there is a match.

You will create a plagiarism detector for a project, that can detect similarities in text copies, and calculate the proportion of plagiarism. Users can register for this software, by generating a valid login id and password. The file will be separated into content and reference links, after the uploads are complete. The checker will next go through the whole document, checking for grammatical problems, visiting each reference link, and scanning the content of all of the links for matches, to your material.

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Top 10 Low-Cost AI Projects for Your Kids to Work on - Analytics Insight

A key requirement of machine learning is to label the data correctly to ensure the best results, but the process is long and time-consuming. This also brings about an issue when dealing with extremely large data sets in unsupervised or semi-supervised learning. The saviour here is active learning with strategies that assist developers in prioritising the data and selecting the most useful samples to label to have the highest training impact. Furthermore, it promises to reduce the samples needed by choosing the right examples.

Various strategies can be used depending on the applications and needs of the model. However, when it comes to learning active learning, the practice is generally a part of bigger machine learning modules, which is why we have created a one-stop guide to mastering active learning online through resources varying from online video tutorials to blog posts and academic papers.

YouTube

Computerphile is a popular YouTube channel that discusses computer science-related topics. Their tutorial on active learning is taught by Dr Michel Valstar, who holds a PhD in Computing and is currently a professor at the University of Nottingham. The tutorial is a foundational element for the basics of active learning, taught through diagrams and illustrations of the concepts.

ICML, the International Conference on Machine Learning, is one of the fastest-growing AI conferences that discuss the latest academic papers. During their 2019 conference, Robert Nowak and Steve Hanneke taught the basics of active learning theory and the popular algorithms to apply (the video is now available online). In addition, the tutorial focuses on sound active learning algorithms and how they can be used to reduce the labels on training data. Robert Nowak holds the Nosbusch Professorship in Engineering at the University of Wisconsin-Madison. Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute in Chicago, specialising in AI and ML.

Applied AI is a great resource for learning AI/ML online through core concepts and real-life applications. The channels collective views cross 12 million and are popular for the basic concepts thorough teachings. Their tutorial on active learning in ML breaks down the principles of the concept along with real-life examples and mathematical explanations.

PyData is an educational program of NumFOCUS, a US-based not for profit organisation that provides a forum for the international community of data science to share their ideas through conferences. Speaking at one of their events is Jan Freyberg, a machine learning software engineer at Google Health. In a detailed talk, Freyberg discusses active learning in the interactive Python environment, given the ease and comfort in the ecosystem.

Devansh is a Computer Science and Computational Math Double Major at the Rochester Institute of Technology. Through this YouTube tutorial, he comprehensively discusses the basics of active learning, its works and compares it to SSL and GANs. He further explains the concept in detail regarding its use and active learnings acquisition function.

Ranji Raj, holding a masters degree in data science, takes on Youtube to publish tutorials and classwork related to machine learning. His video on active learning gives an in-depth introduction to the subject while discussing important concepts through diagrams and demonstrations. Raj also has consequent coursework on his GitHub page for data scientists interested in learning further.

Scaleway is a French cloud computing company that creates Youtube videos consisting of short machine learning tutorials and real-world applications. In their webinar on active learning, the company collaborated with Kairntech, an AI modelling and dataset creation platform, to discuss the various applications of active learning. The video discusses training datasets and how active learning can be applied for classification. It also glossed over common issues and how to overcome them.

Blog tutorials

Ori Cohen is a PhD holder in CS, currently working as a senior director of data science at New Relic. His Towards Data Science blog post on active learning is an extensive tutorial that discusses the various scenarios possible while using active learning, the algorithms that can be used, the sample selection methods and the codings used for all.

A blog post on Data Camp, an online interactive learning platform, explains in depth the A-Zs of active learning in a moderate level of difficulty. The tutorial discusses the concept in detail with definitions, examples and visuals, and teaches how one can apply active learning on their datasets through a particular example.

Written by a CS and EE student at IIT, India, this post is an in-depth tutorial on using active learning with Python. The tutorial is technical, explaining the code and its concepts through codes and steps. In addition, the post discusses various inputs, outputs, and the Python codes needed to apply active learning correctly.

Alexandre Abraham, a senior research scientist at Dataiku and a Ph D holder in computer science, has written an extensive tutorial on active learning packages on his Medium blog post. The blog post analyses the active learning packages available through a feature comparison, their covered approaches, and their coding aspects. There are three main packages and different methods that data scientists can leverage.

Papers

The paper in discussion is written by Kai Wei, an assistant professor at UCLA, Rishabh Iyer, an assistant professor at the University of Texas, and Jeff Bilmes, a professor at the University of Washington. Their paper studies the problem of selecting a subset of data to train a classifier and how individuals can apply the active learning framework to mitigate the issue.

Online courses

The DeepLearning.AI course in ML data lifecycle has a fourth module, tagged Advanced Labeling, Augmentation and Data Preprocessing, that focuses on semi-supervised learning, dataset labelling, and the role played by active learning within. The instructor, Robert Crowe, works at TensorFlow by Google and has multiple degrees in AI, ML and data science.

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Top online resources to learn Active Learning - Analytics India Magazine