AI capabilities are advancing rapidly, but the results are mixed. While chatbots and digital assistants are improving generally, the results can be laughable, perplexing and perhaps even unsettling. Google's recent demonstration of Duplex, its natural language technology that completes tasks over the phone, is noteworthy. Whether you love it or hate it, two things are true: It doesn't sound like your grandfather's AI; the use case matters. One of the striking characteristics of the demo, assuming it actually was a demo and not a fake, as some publications have suggested, is the use of filler language in the digital assistant's speech such as "um" and uh" that make it sound human.
In doing so, they utilise artificial intelligence or AI to increase their business value and create barriers for new entry to beat the competition. "We have to transform our business to create a barrier for new entry by using AI BIM – artificial intelligence building information modelling – for our design, construction process and even our after-sales service to customers. We set up our application to serve all of our customers' demands," said AP (Thailand) Plc's chief business group officer for condominiums, Vittakarn Chandavimol, in a recent interview with The Nation. AP (Thailand) set up its design and construction standards by collaborating with its building partners to ensure they know how to meet the company's AI BIM requirements.. The system reduces paperwork by communicating through an iCloud system.
Kaggle is an AirBnB for Data Scientists – this is where they spend their nights and weekends. It's a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science and predictive analytics problems through machine learning. It has over 536,000 active members from 194 countries and it receives close to 150,000 submissions per month. Started from Melbourne, Australia Kaggle moved to Silicon Valley in 2011, raised some 11 million dollars from the likes of Hal Varian (Chief Economist at Google), Max Levchin (Paypal), Index and Khosla Ventures and then ultimately been acquired by the Google in March of 2017. Kaggle is the number one stop for data science enthusiasts all around the world who compete for prizes and boost their Kaggle rankings.
Drones have become a hot topic these days and cities, companies, organizations are deploying it for various purposes. Companies like real estate agencies, wedding photographers, and farmers are using drones, commercially. For the safe and secure flights in Canada, the state has come up with new drone rules. Earlier the rules included, drones weighing more than 250 gms, would operate 9 km away from airports, no more than 90-meter altitude, no more than 500 meters from the operator and should remain within the sight. Now, new Transport Canada rules are being proposed that includes dividing drones into weight classes and permits and liability insurance for heavier drones.
There are many ways to reduce trading risk through hedging. Funds typically use futures and options to hedge each trade. Similar to insurance, this safety net comes at a price. Using an AI-based strategy, though, there is a way to protect a position at a much lower cost. Before McDonald's could introduce Chicken McNuggets, they had to hedge against the cost of chicken.
ITRealty intelligently analyzes the multiple listing services (MLS) that brokers and agents use to find/list properties, establish contractual offers of compensation among brokers, and accumulate and disseminate information to enable appraisals. "It does not matter anymore how far in the past comparable properties were sold", says Yuriy Setko, "Our algorithms will analyze where the market was for that particular type of property, in that particular neighborhood in the past, and apply the time adjustment percentage to the selling price to give you that property's market price as if it was sold yesterday." You usually have a good number of comparables to see if the asking price is right". It also helps to precisely determine the market price when putting up a property for sale. ITRealty tracks price drops on MLS, along with other listing analysis algorithms, to find "motivated sellers", as well as drawing supplementary data on real estate not found on MLS.
Despite unpredictable economic and political headwinds, the European real estate sector continues to flourish, albeit in some regions more than others. Competition for deals is fierce and speed is often of the essence: so much so that, according to research1 recently conducted by Drooms, over 50 per cent of real estate professionals in Europe are compromising on the quality of their due diligence in their rush to complete deals quickly. However, modern technology has a solution for those seeking to complete real estate transactions more efficiently. Our research also revealed that where time pressures on due diligence have led to a potential decrease in quality of the process, parties to a transaction have found a solution in technology enabled with artificial intelligence (AI), such as virtual data rooms. According to a Real Capital Analytics (RCA) report published in February 2018, Europe's commercial property investment market returned to growth in 2017, when it registered the third strongest annual expansion on record.2
The rise of artificial intelligence threatens to eliminate jobs once considered impossible to automate. One series of papers by Oxford researchers ranks jobs by their estimated susceptibility to automation. Among those most rated likely to vanish – because they involve work that AI can increasingly accomplish less expensively – are real estate brokers, insurance claims adjusters and sports referees. Could anything good come of mass unemployment? History tells us that when technology squeezes people out of jobs, they revolt.
This article is based on a presentation originally given by Daniel Faggella, CEO & Founder at Emerj (formerly TechEmergence) to a group of real estate executives at a Grupo4s "Future of Real Estate" event in San Francisco, in March of 2018. The real estate market in the US is currently a seller's market, with demand outstripping supply, and housing affordability going down steadily for 2018 (Source: Gallup, May 2018). As more efficient means of buying and selling properties are being made possible with the help of machine learning, other AI-based applications are creeping their way into maintenance, energy management, and more. In the article below, we'll explore the applications of machine learning in real estate. We'll examine the buying and selling process, in addition to a more in-depth look at facilities management and building automation systems. We'll also examine other industries that might serve as a proxy for future real estate innovation, helping executives to imagine future possibilities before they impact real estate itself. To help business leaders find the real estate applications and ML insights that matter most to them, we've broken this article out into the following sub-sections: Our interviews with hundreds and hundreds of ML researchers (for our AI in Industry podcast, and elsewhere) show that they agree on very little in terms of big-picture dynamics of ML. There is no consensus on which industries will be transformed first, no consensus about AI risks in the coming 20 years, and no consensus on a definition of artificial intelligence. However, the one thing they do agree on is that AI will change a lot of how business is done in the coming decade or two, as the Internet did in the past two decades.
Many urban planners, artificial intelligence researchers, civil engineers and public officials aspire to create "smart" cities. Their goal is to deploy advanced technology to better study, monitor and manage urban growth and infrastructure, thereby helping cities become more livable, safe and sustainable, more functionally and economically efficient. Making cities smarter is not a new idea. But with digital computers now able to store, process and interpret increasingly large amounts of data, and with significant advances being made in automation and artificial intelligence (AI), the potential for understanding, analyzing and taking quick action to enhance how cities operate has grown. Geographic information system (GIS) technology was the first computer-based tool for achieving smarter urbanism.