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.
The emergence of Artificial Intelligence (AI) and Machine Learning (ML) has transformed the role of digital technology in business operations. These cognitive technologies have boosted the value addition of computing and digital processing power, from pre-programmed process automation to turning data into actionable insights. AI is allowing businesses to be ultra-responsive in real-time, achieve system transparency and gain valuable insights into customers, markets and processes. The applications of AI are not only vast in themselves, but the technology can also be integrated with machines and human users through multiple interfaces. For instance, Audio AI allows humans to interact with machines using natural language processing algorithms.
ZipRecruiter, the Santa Monica based employment marketplace named One of the World's Most Innovative Companies in 2019 by Fast Company just released their much-anticipated Future of Work Report 2020. The Artificial Intelligence (AI) jobs gold rush is spreading to more states. This is good news for the future of work. Jobs that require AI, machine learning, robotics, and engineering skills will continue to dominate as AI-enabled systems replace manual labor. In this article, I will walk you through some of the highlights.
The tech industry's great new hopes – artificial intelligence (AI), big data and blockchain – are making their presence felt in an unexpected new field: Indian real estate. As the world starts to rely more on these innovations, India is catching up, and making room. There are AI labs coming up in established tech hubs like Bengaluru and surprise destinations like Kolkata. Now, Hyderabad is set to house India's first blockchain district. Experts point out that this signals an impending boost for the housing market there.
Caspar.AI honored for achievements in AI Technology for Real Estate CB Insights today named Caspar.AI to the fourth annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world. Featured in CB Insights real-estate AI category, Caspar is reshaping the real estate industry to allow for smart, sustainable design that both improves the residents' living experience and reduces overall costs for property management. Real estate developers partner with Caspar to build differentiated smart properties, drive additional revenue, save costs, and enhance resident experience. "We are delighted to be awarded as the top AI company for real estate," said Dr. Ashutosh Saxena, Founder & CEO of Caspar.AI & Former Faculty in the Department of Computer Science at Cornell University. "People spend two-thirds of their time at home. There is a massive opportunity for AI to reimagine how people live in their homes. Our Caspar Sense and Caspar Adapt technology, understand the resident activities and automatically adapts home to their preferences. "It's been remarkable to see the success of the companies named to the Artificial Intelligence 100 over the last four years.
The newest roommate app, Badi, has made its official debut into the New York market. The artificial intelligence app took Europe by storm in September 2015. England, Germany, and Spain to be exact. The company has changed the lives of 2 million users by providing about 300,000 room listings. Badi uses an algorithm based on information such as age, gender, interests, and lifestyle preferences to assist users in finding rooms on the platform.
The ongoing adoption of AI and automation technologies will transform the way people work and cause a disruption, particularly in industries like real estate, finance and banking. Since the start of the industrial revolution, the world has seen skills shift in a way that has resulted in ever-changing jobs and newer skills required to keep pace with emerging technologies. According to McKinsey, these technologies "will bring numerous benefits in the form of higher productivity, GDP growth, improved corporate performance, and new prosperity, but they will also change the skills required of human workers." The real estate sector is in a great position to leverage AI and automation technologies to increase productivity, reduce costs and minimize errors. The number of workers who are engaged in manual tasks that only require basic cognitive skills will likely decline.
No industry is immune to technological advances, but real estate is one niche that has been traditionally slower to adopt new trends. PropTech is booming and changing the way we buy, sell, and interact with our properties. I have recently been to ProbTech events to deliver keynotes, and more and more companies in the industry are asking for my advice on tech transformation. So, with this post, I would like to share the key trends every real estate professional and property manager needs to be aware of. The real estate and property management industries are uniquely positioned to benefit from big data.
As the amount of data grows, AI could be used to classify and store documents and even proactively alert when a new appraisal is due or a property explodes in popularity, for example. Machine-learning enabled search could help people quickly analyze the massive amount of search data that exists by pre-processing and tagging useful meta-data for all property-related documents in a company's repository. How can companies keep up? According to the Altus Group report, two of the biggest challenges facing firms in collecting or utilizing more data to drive decision-making are a lack of internal capability and lack of appetite from the company to invest in the required technology. To solve for these challenges, consider a PwC report's digital transformation strategies for real estate organizations, which include these four steps: