Latest survey on Artificial Intelligence for Blockchains Market is conducted to provide hidden gems performance analysis of Artificial Intelligence for Blockchains to better demonstrate competitive environment . The study is a mix of quantitative market stats and qualitative analytical information to uncover market size revenue breakdown by key business segments and end use applications. The report bridges the historical data from 2017 to 2022 and forecasted till 2027*, the outbreak of latest scenario in Artificial Intelligence for Blockchains market have made companies uncertain about their future outlook as the disturbance in value chain have made serious economic slump. If you are part of the Artificial Intelligence for Blockchains industry or intend to be, then study would provide you comprehensive outlook. It is vital to keep your market knowledge up to date analysed by major players and high growth emerging players.
Financial technology startup firm Lendai announced Wednesday that it has raised $35 million in equity and debt seed funding. The purpose of the company is to enable foreign, non-residential borrowers investing in US real estate properties the ability to access immediate financing and competitive rates using its AI-based Triple Digital Underwriting System platform – making the underwriting process fast, easy and efficient. According to the company's announcement on Wednesday, this early round of financing is led jointly by Meron Capital and Cardumen Capital, with underwriting help from Discount Capital, Skywell Capital Partners, Mindset Ventures, and Viola Credit. Proceeds from the seed financing will enable Lendai to expand its reach and to help level the playing field for foreign investors who want to invest in US residential real estate properties. Concurrently, Lendai will use the seed funding to expand its services to more US states and launch new financing loan programs.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
Petropoulos, Fotios, Apiletti, Daniele, Assimakopoulos, Vassilios, Babai, Mohamed Zied, Barrow, Devon K., Taieb, Souhaib Ben, Bergmeir, Christoph, Bessa, Ricardo J., Bijak, Jakub, Boylan, John E., Browell, Jethro, Carnevale, Claudio, Castle, Jennifer L., Cirillo, Pasquale, Clements, Michael P., Cordeiro, Clara, Oliveira, Fernando Luiz Cyrino, De Baets, Shari, Dokumentov, Alexander, Ellison, Joanne, Fiszeder, Piotr, Franses, Philip Hans, Frazier, David T., Gilliland, Michael, Gönül, M. Sinan, Goodwin, Paul, Grossi, Luigi, Grushka-Cockayne, Yael, Guidolin, Mariangela, Guidolin, Massimo, Gunter, Ulrich, Guo, Xiaojia, Guseo, Renato, Harvey, Nigel, Hendry, David F., Hollyman, Ross, Januschowski, Tim, Jeon, Jooyoung, Jose, Victor Richmond R., Kang, Yanfei, Koehler, Anne B., Kolassa, Stephan, Kourentzes, Nikolaos, Leva, Sonia, Li, Feng, Litsiou, Konstantia, Makridakis, Spyros, Martin, Gael M., Martinez, Andrew B., Meeran, Sheik, Modis, Theodore, Nikolopoulos, Konstantinos, Önkal, Dilek, Paccagnini, Alessia, Panagiotelis, Anastasios, Panapakidis, Ioannis, Pavía, Jose M., Pedio, Manuela, Pedregal, Diego J., Pinson, Pierre, Ramos, Patrícia, Rapach, David E., Reade, J. James, Rostami-Tabar, Bahman, Rubaszek, Michał, Sermpinis, Georgios, Shang, Han Lin, Spiliotis, Evangelos, Syntetos, Aris A., Talagala, Priyanga Dilini, Talagala, Thiyanga S., Tashman, Len, Thomakos, Dimitrios, Thorarinsdottir, Thordis, Todini, Ezio, Arenas, Juan Ramón Trapero, Wang, Xiaoqian, Winkler, Robert L., Yusupova, Alisa, Ziel, Florian
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Sooner or later, the concept of digitization will completely take over all repetitive tasks. Today, with the help of big data, advanced technologies like automation, artificial intelligence, IoT, and machine learning are leveraging unimaginable amounts and types of information to work from. It is streamlining tedious, repetitive, and difficult tasks, which tend to slow down production and also increases the cost of operation. Owing to the evolution of technology, artificial intelligence startups are mushrooming like never before. The companies are driving the world into a new phase of digitization with a mixture of disruptive statistical methods, computational intelligence, soft computing, and traditional symbolic AI. Artificial intelligence is the combination of two amazing concepts namely science and engineering. With the infusion of disruptive trends and human intelligence, intelligent machines and intelligent computing programs are emerging. Slowly, the flare of innovations moved away from IT and entered into diverse industries including healthcare, education, finance, marketing, business, telecommunication, etc. Organizations realized that by digitizing repetitive tasks, an enterprise can cut the cost of paperwork and labor which further eliminates human error, thus boosting efficiency. Automating processes involve employing artificial intelligence solutions that can support digitization and deliver data-driven insights. Artificial intelligence startups emerge as a ready-made solution provider that supports every company's individual needs. AI startups in 2021 use big data to sophisticated AI models and leverage new solutions that could better serve customers. Analytics Insight has listed the top 100 artificial intelligence startups that are driving the next-generation development in technology. It democratizes the way investments are done by bringing sophisticated elite trading technology to laymen. Accrad is a health tech company that assists radiologists to reduce their workload with the precision of artificial intelligence. Radiologists work under different circumstances and deadlines and might find diagnosis through x-rays a bit difficult. Therefore, Accrad has come up with a futuristic solution to help with accurate and fast image diagnosis. The company has made x-ray processing more convincing and simpler. Its signature product CheXRad, a deep learning algorithm that identifies locations in the chest radiograph has the capability to predict 15 different diseases including Covid-19. Affable.ai is a data-driven influencer marketing platform where customers can find relevant and authentic influencers and manage marketing operations. By using cutting-edge computer vision algorithms on social media posts, the company delivers actionable insights about micro-influencers and their audience. Similar to how Google has sophisticated its search and promote relative ads to users, Affable.ai has also built one-click marketing at a shorter scale.
Artificial intelligence is poised to revolutionize the real estate industry and make the homebuying process much more transparent, AI-driven startup Localize believes. Headquartered in New York City, Localize was founded in Israel in 2012 and also has offices in Tel Aviv. The startup, which operates in Israel under the name Madlan, launched in the United States in 2019 and began working with real estate agents and brokerages earlier this year. It has developed an AI- and big data-based platform that enables both buyers and brokers to streamline house-hunting, a traditionally low-tech process. "Our goal is to reinvent homebuying," Localize President and Chief Operating Officer Omer Granot told The Media Line.
AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Artificial intelligence is disrupting the global real estate industry. Most of the high-end realtors are now incorporating data pipelines and algorithms into their decision-making process, and the results are telling. Information management is the key application of AI in the real estate industry. Let's find out how exactly AI and ML are changing real estate. AI has applications in estimating the market value of properties and predicting their future price trajectory.
The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the extent to which predictive accuracy differs for Kriging and machine learning techniques using big data. Thus, this study compares the predictive accuracy of apartment rent price in Japan between the nearest neighbor Gaussian processes (NNGP) model, which enables application of Kriging to big data, and the deep neural network (DNN), a representative machine learning technique, with a particular focus on the data sample size (n = 10^4, 10^5, 10^6) and differences in predictive performance. Our analysis showed that, with an increase in sample size, the out-of-sample predictive accuracy of DNN approached that of NNGP and they were nearly equal on the order of n = 10^6. Furthermore, it is suggested that, for both higher and lower end properties whose rent price deviates from the median, DNN may have a higher predictive accuracy than that of NNGP.