Last June Volume, a leading magazine on architecture and design, published an article on the GoogleUrbanism project. Conceived at a renowned design institute in Moscow, the project charts a plausible urban future based on cities acting as important sites for "data extractivism" – the conversion of data harvested from individuals into artificial intelligence technologies, allowing companies such as Alphabet, Google's parent company, to act as providers of sophisticated and comprehensive services. The cities themselves, the project insisted, would get a share of revenue from the data. Cities surely wouldn't mind but what about Alphabet? The company does take cities seriously.
Gary Matthews Jr., the retired professional baseball player who spent three seasons with the Angels, has sold a home in Corona del Mar for $3.69 million. The shake-sided home, built in 1961 and extensively updated, returned to market earlier this year for $3.995 million. Matthews Jr. bought the property a decade ago for $3.05 million, records show. Surrounded by walls and gates, the single-story house is entered through a front courtyard with a swimming pool, a stone fireplace and a separate spa. The pool and spa each have a waterfall feature.
Analytics leader SAS is helping customers gain more value from data with SAS Viya products, extending the value from the SAS Platform. These newest advances, such as embedded artificial intelligence (AI) capabilities, will further address the needs of organisations that are making analytics core to their business. A variety of industries, countries and organisation sizes have embraced SAS Viya products. With SAS, data scientists, analysts, developers, IT, domain experts and executives can all generate data-driven insights – from the same, consistent data, fostering greater collaboration and driving innovations faster. SAS continues to deliver new capabilities, such as image recognition, deep learning and natural language understanding into the SAS Platform.
Whether the Dodgers' playoff run ends with a World Series championship or an NLCS loss to the Chicago Cubs, right fielder Yasiel Puig will come home a winner. The slugging outfielder has bought a remodeled Encino estate for $2.65 million, public records show. That's about $350,000 less than what it listed for when it hit the market in June. Set behind a black iron gate, the home is approached by a long red driveway. A tiled entry gives way to hardwood in the living spaces, where expansive windows bring in natural light.
Perhaps, this is one of the main reasons why the industry is always on the lookout for professionals with dynamic skills, the ones who can reap higher customer satisfaction. Since people are more inclined to having fluidic conversations, cognitive capabilities, which can simulate how a human brain functions, reasons, and more importantly learns, become pivotal.Users characteristically do not want to get lostin the myriad of details, for example of a home loan or new credit card,while clicking several menus and sub-menus, especially when a simple question can fetch an instant answer. They also have above-benchmark performance when it comes to customer satisfaction, and deliver considerably higher customer acquisition rates as compared to the human workforce. An AI-driven chatbot instantly deflects such interactions, rather than making such customers wait needlessly.
In this article, I'll show you how I wrote a regression algorithm to predict home prices. Put simply, regression is a machine learning tool that helps you make predictions by learning – from the existing statistical data – the relationships between your target parameter and a set of other parameters. To show you how regression algorithm works we'll take into account only one parameter – a home's living area – to predict price. Once found, we can plug in different area values to predict the resulting price.
Predicting Portland home prices allowed me to do this because I was able to incorporate various web scraping techniques, natural language processing on text, deep learning models on images, and gradient boosting into tackling the problem. The Zillow metadata contained the descriptors you would expect - square footage, neighborhood, year built, etc. Okay, now that I was confident that my image model was doing a good job, I was ready to combine the Zillow metadata, realtor description word matrix, and the image feature matrix into one matrix and then implement gradient boosting in order to predict home prices. Incorporating the images into my model immediately dropped that error by $20 K. Adding in the realtor description to that dropped it by another $10 K. Finally, adding in the Zillow metadata lowered the mean absolute error to approximately $71 K. Perhaps you are wondering how well the Zillow metadata alone would do in predicting home prices?
It's a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science and predictive analytics problems through machine learning. With 73 million unique visitors per month, 20 TBs of data and 1.2 million statistical and machine learning models that runs every night to predict the next Zestimates, it is undoubtedly the best machine learning case study for real estate under the sun. While, million dollar seems like a big prize, it's the cost of having 10 data science engineers in Silicon Valley for eight months for 100,000$ a piece, whereas, to-date there are 2900 teams participating and competing for this prize from all around the world, with a typical size of three members per team, 8700 individuals it is just 114$ per engineer, which is equivalent to 14$ per month or 1.7$ per hour per data scientist. To submit your first kernel, you can fork my public kernel – how to compete for Zillow prize – first kernel and run it.
Last week we were in New York City for a real estate technology focussed forum organized by Knotel, Forbes and cre.tech. The invite-only event, which attracted leaders in real estate, PropTech and FinTech, took place in a Knotel-designed space in SoHo and included a authentic Syrian lunch prepared by refugees, followed by decadent deserts prepared by Lady M. Our guests enjoyed a panel discussion featuring Vishal Garg from Better Mortgage, Amol Sarva from Knotel and Susannah Vila from Flip. Travis Barrington is the Founder of cre.tech, a new media property focused on the commercial real estate technology scene. He routinely participates in real estate events and logs a lot of airline miles from his home base in San Diego.
We're standing in the epicenter of WeWork's cavernous New York City headquarters, where Fano, the company's Chief Growth Officer, has set up a demo area to show off new technologies for prospective clients. These offerings include building out custom office interiors, licensing software that companies can use to book conference rooms, analyzing data on how people are using those conference rooms, and providing on-site human community managers indoctrinated in WeWork's community-minded philosophies. It's a natural extension of WeWork's current business, according to Chief Operating Officer Jen Berrent, who explains that the idea of adding a services business is "something that there's demand for in the market." WeWork's closest model is Regus, a boring-but-practical shared office space business headquartered in Luxembourg.