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Luxury apartment building designed for seniors to use AI to monitor their health

Daily Mail - Science & tech

A luxury apartment building in New York, designed for seniors, will make use of Artificial Intelligence to monitor the health and wellbeing of its residents. Known as the Bristal, staying at the assisted living facility is on York Avenue costs between $12,800 and $20,150 per month, with each of the 132 studio apartments equipped with a private bathroom, kitchenette and an optional AI assistant. 'Foresite' is a predictive health and fall management system, that triggers alerts and warnings for medical professional and facility staff, coming pre-installed in all rooms. It isn't turned on by default, but comes with an upcharge on top of the up to $20,000 monthly rent, allowing for it to be activated as needed when a residents needs change, according to The Bristal. 'The pandemic has shown seniors that if you are living alone you are going to be isolated,' Faraz Kayani from the Bristal told the New York Post, adding that the AI and wider facility helps them stay connected to the community.


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

#artificialintelligence

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.



Opendoor moves into New York, New Jersey markets

ZDNet

Opendoor, the San Francisco-based real estate tech firm that uses artificial intelligence techniques to offer homeowners cash money for their homes, announced Tuesday it's now serving New York and New Jersey in select counties. This is the nine-year-old company's 46th market to date. "This speaks to our continued ability to scale our operational platform and our customer experience, nationwide," said Ian Wong, Opendoor's co-founder and chief technology officer, in an interview with ZDNet via Google Meet. Whatever your priorities -- from 5G to an amazing camera -- there's a phone here to meet your every need. Opendoor said it will buy homes on Long Island in New York, and the lower Hudson Valley area of the state, including Nassau County, Orange County, Rockland County, Suffolk County, and Westchester County.


How the Company Behind TikTok's Viral 3D-Printed Houses Wants to Help Solve the Affordable Housing Crisis

TIME - Tech

Watching a giant robotic arm methodically deposit layers of concrete may not sound like a transfixing experience. But on TikTok, videos of this exact process--otherwise known as 3D printing--are racking up tens of millions of views and helping people envision a world in which affordable 3D-printed houses are the new norm. Known as @thelayerlord on TikTok, Aiman Hussein has gained nearly 50,000 followers since joining the social platform last year to showcase the work of Alquist 3D, where he's the director of printing. His TikToks revolve around the process of 3D printing houses, with his most-watched videos showing how Alquist's printers systematically layer row after row of concrete to build up a home's exterior walls in a computer-generated pattern. Just like smaller-scale 3D printers, Alquist's machines have a dispenser that pumps out layers of material--in this case, concrete--on top of each other, to construct a physical object.


The environmental impact of the metaverse

#artificialintelligence

This article is part of a VB special issue. Read the full series here: The metaverse - How close are we? Some companies believe that the metaverse -- a yet-to-be-realized, internet-like series of connected worlds -- has enormous potential in the enterprise. For example, it could be used to improve work productivity by allowing employees to train or collaborate in workplace-like virtual environments. Or it could host home and office tours, a boon for a real estate market contending with pandemic travel restrictions.


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

ZDNet

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

arXiv.org 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.


Marginal Effects for Non-Linear Prediction Functions

arXiv.org Machine Learning

Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models and especially generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either in the shape of derivatives of the prediction function or forward differences in prediction due to a change in a feature value. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a model-agnostic interpretation method for machine learning models. This may stem from their inflexibility as a univariate feature effect and their inability to deal with the non-linearities found in black box models. We introduce a new class of marginal effects termed forward marginal effects. We argue to abandon derivatives in favor of better-interpretable forward differences. Furthermore, we generalize marginal effects based on forward differences to multivariate changes in feature values. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for marginal effects. We argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to partition the feature space to compute conditional average marginal effects on feature subspaces, which serve as conditional feature effect estimates.


Making the AI desert bloom

#artificialintelligence

Then you've probably seen a boab. Also known as the "tree of life", they can store 100,000 litres of water, which can feed, restore and invigorate nature even in the toughest of conditions. But the tree is not Australia's only boab -- in Melbourne, Boab AI is an investment company supporting a very different kind of ecosystem. "Artificial intelligence investment in Australia is a nascent but relatively new area where Victoria is leading the way," says Boab AI Managing Director Andrew Lai, a technology-focused venture capitalist who has helped hundreds of startups during his 15-year career. "Australia's share of world GDP is 1.8%, but our share of global AI funding is 0.22%, according to Pitchbook. So many people in Silicon Valley have become billionaires by investing in tech, but we just don't have that history and investment risk taking culture here. Most people just want to invest in real estate."