Over the next 20 years, there will be few industries and professions that will have not been significantly impacted by the presence and power of Artificial Intelligence (AI). The next AI frontier is real estate which, if one follows the money, makes perfect sense: In 2017, real estate accounted for a whopping 13.4 percent of the GDP. The challenge for AI, however, is whether it can competently assume the roles of humans in a profession that is heavily dependent on providing the personal touch to the buyer and seller in every transaction. Tell that to Skyline AI, which offers an AI platform with a diverse range of real estate-centric applications that are supposed to make the professional more efficient and the clients more satisfied. Skyline's AI platform professes to be able to: While these tasks may replace human function, they are still far short of the relationship building that has thus far been the exclusive domain of humans.
Data integration methods or tools have undergone a major overhaul in the last few years. Not so long ago, traditional manual methods were employed to integrate data. But as the volume of data increased, these methods became outdated due to their labor-intensive, time-consuming, and error-prone nature. Companies now require in-depth business knowledge, a strong understanding of a diverse set of data schemas, and cognizance of underlying data relationships to perform data integration. With time, organizations have shifted their reliance to newer techniques to bolster data integration.
Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone's real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by. The first step in making a high-quality portfolio is to know what skills to demonstrate.
Artificial intelligence, big data and machine learning will increasingly be put to use monitoring the energy performance of buildings and helping owners cut costs, according to Keith Gunaratne, the founder and managing director of technology firm EP&T. Founded in 1993, EP&T specialises in the development of energy conservation technologies in the commercial sector. One of its products, Edge Zeus - an AI and machine-learning platform that allows energy performance to be monitored and controlled through a mobile device – has been deployed in 12 buildings in the portfolio of the Abu Dhabi Financial Group, or ADFG. In one of the buildings – Abu Dhabi's Seaside Tower – the technology has led to a 29 percent reduction in energy consumption and cost savings of over AED 626,000 in a 12-month period. "What we would like to see in the local market is an accommodative environment that fosters the take-up of data science as a solution that remains attractive to the c-suite," Gunaratne said.
Imagine you are living in a townhome complex where expenses like water, landscaping and parking lots are managed by the Homeowners Association (HOA). All is well in the world until one day, the HOA instructs you to get rid of your hot tub because they have found that the meter you share with your neighbor is consuming significantly more water than any other townhome in the complex and they suspect because you have a hot tub, it must be your fault! If this sounds a little too specific to be a consequence (or a particularly nasty case of hot tub jealousy), that is because this nightmarish situation was exactly what my friend Sadie faced in 2017. Sadie fought the good fight and persevered to where she ultimately kept her hot tub after proving her neighbor had not one, but TWO leaking toilets that had been gradually getting worse and worse (for YEARS). Upon hearing of this -- as an unabashed data nerd -- I was of course thinking, "how could data from Sadie's meter have been used to detect and prevent this!?"
Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, announced today that-in partnership with Onyx-Cognivas Pty.-it is launching two privately-led security deployments in South Africa to support community safety initiatives. The state-of-the-art security technology will protect two prominent high-rise residential apartment buildings in the upmarket Sandton area, a high income residential, financial and business suburb of Johannesburg with a population of 225,000. The rollout plan is to deploy this technology across several apartment blocks, a hotel and commercial properties in the precinct-with the objective of deploying a "private Smart City". In addition, advanced custom sensory applications are planned to be installed in a well-known petroleum group with convenience stores/service stations throughout South Africa. The announcement was made by Jay Hutton, VSBLTY co-founder and CEO, who said, "We are excited to provide complete Smart City-like security solutions in Sandton. This state-of-the-art technology uses the power of machine learning and computer vision."
This August, the Department of Housing and Urban Development put forth a proposed ruling that could potentially turn back the clock on the Fair Housing Act (FHA). This ruling states that landlords, lenders, and property sellers who use third-party machine learning algorithms to decide who gets approved for a loan or who can purchase or rent a property would not be held responsible for any discrimination resulting from these algorithms. The Fair Housing Act (FHA) is a part of the Civil Rights Act of 1968. This stated that people should not be discriminated against for the purchase of a home, rental of a property or qualification of a lease based on race, national origin or religion. In 1974, this was expanded to include gender, and in 1988, disability.
Machine learning (ML) is the current paradigm for modeling statistical phenomena by harnessing algorithms that exploit computer intelligence. It is common place to build ML models that predict housing prices, aggregate users by their potential marketing interests, and use image recognition techniques to identify brain tumors. However, up until now these models have required scrupulous trial and error in order to optimize model performance on unseen data. The advent of automated machine learning (AutoML) aims to curb the resources required (time and expertise) by offering well-designed pipelines that handle data preprocessing, feature selection, and model creation and evaluation. While AutoML may initially only appeal to enterprises that want to harness the power of ML without consuming precious budgets and hiring skilled data practitioners, it also contains very strong promise to become an invaluable tool for the experienced data scientist.
Now that the majority of households in the U.S. own, watch and use a smart television set or TV-connected device -- with artificial intelligence-powered ability to control how they watch or use these products with their voice -- what's the next big thing consumers will do with these products? The answer: Monitor and control other aspects of their homes, from adjusting room temperatures to knowing who is at their front door. That was the consensus viewpoint of a new interfaces panel at last Tuesday's first-ever "Next: The Connected Future […] Source: Smart TVs are "Prime Real Estate" for Powering Households
An hour of earnings reports highlighted how every business will go digital, becoming software-based and utilize artificial intelligence. Michael Dale Hayford, CEO of NCR, said the company is looking to create software defined stores in its retail business. And banking, which is becoming more about the ATM than the branch. NCR reported third quarter revenue of $1.78 billion, up 15% from a year ago, with net income of $105 million. The company saw strong growth in both ATM and point of sale terminals.