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Artificial Intelligence for Blockchains Market SWOT Analysis by Size, Status and Forecast to 2022-2028 - Blackswan Real Estate


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.

Fintech lender Lendai raises $35M for AI-based platform to enable foreign investors to buy US real estate


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.

Technology Ethics in Action: Critical and Interdisciplinary Perspectives Artificial Intelligence

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.

Forecasting: theory and practice Machine Learning

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.

Data Analyst (Looker) - UK, Remote


Plentific is one of the fastest-growing property technology companies in the UK and Germany, with offices in the US and Turkey. Its mission is to improve lives by making property work better for everyone. The platform empowers landlords and property managers to manage their properties and internal trades workforce, source local trade talent, build community cohesion and provide better services than ever before. The end-to-end solution offers a flexible approach to compliance management, repairs reporting and delivery, ensuring landlords and property managers can be confident that their property meets necessary standards. Following a major funding round, Plentific has recently expanded into the US multi-family market and has plans to enter the wider professional and commercial real estate sector in Europe this year.

US-Israeli Startup Uses Artificial Intelligence to Revolutionize Real Estate Industry - The Media Line


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.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

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.

How AI Is Changing The Real Estate Landscape


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.

POINTER: Constrained Text Generation via Insertion-based Generative Pre-training Artificial Intelligence

Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation. However, these models cannot be directly employed to generate text under specified lexical constraints. To address this challenge, we present POINTER, a simple yet novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. This procedure is recursively applied until a sequence is completed. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable. Since our training objective resembles the objective of masked language modeling, BERT can be naturally utilized for initialization. We pre-train our model with the proposed progressive insertion-based objective on a 12GB Wikipedia dataset, and fine-tune it on downstream hard-constrained generation tasks. Non-autoregressive decoding yields a logarithmic time complexity during inference time. Experimental results on both News and Yelp datasets demonstrate that POINTER achieves state-of-the-art performance on constrained text generation. We intend to release the pre-trained model to facilitate future research.

A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN Machine Learning

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.