About the talk
Artificial intelligence models are making their way into all sorts of products and services, including some very niche applications.
An example of a solution for a very specific problem is being developed by States Title, a startup headquartered in San Francisco. The company has employed machine intelligence to make residential real estate deals faster and more efficient – presenting its work at the recent AI Summit in New York.
According to Dominic Fahey, VP of Business Development at States Title, the predictive platform developed by the company can reduce the human labor involved in the title and escrow process by as much as 80 percent.
Hello, we're here at the humidity sensor for the first day of AI Summit New York and I'm talking to Dominique Fahey who's the VP for business 00:05 development at States title? And it's a relatively new company and is a very unusual way. It sounded 00:13 so far is Maxton call. He founded the company in 2016 after his own frustrations in 00:22 being a first-time home buyer. He saw the complexity the time that it takes and efficiencies in the process and realize that it needed a 00:32
transformation Max previously founded a vowel or which was an HR Predictive Analytics Enterprise and he threw that experience he saw that he could 00:40 apply at the title and escrow process materials were talking about machine intelligence. So what what is the benefit of using machine intelligence for 00:49 the processing machine intelligence to take out 80% The human labor in in the work of title and escrow 00:58 and it reduces the human errors improves the efficiency and then also provides a transformative experience for the home buyer 01:08
in addition to providing a better experience for our Associates because they're able to focus on the things that really require strategic human 01:17 decisions everybody. What's the key difference between machine intelligence and machine learning? Sure. So as we look at it 01:26 automation is when a human programs and machine precisely follow instructions for like a player piano. Whereas machine 01:36 learning is having a machine. Looking at data set and roll Trends and patterns through that date is at and being able to apply to a single problem 01:45
machine intelligence has derivatives of human intelligence, like learning and privatization. Whereas AI is artificial general intelligence 01:55 in the application that makes seeds human intelligence and you guys off to develop you want to 02:05 come with you to have patents for for for for some technology. I think it's something about that from several sources that 02:15 called you back in your company, like like like yours so been around for around a hundred fifty years and it's pretty much been done in the same way 02:25
in fashion in examining books and records are transformative application of machine intelligence to the title and escrow process is patented. The one 02:34 of our competitors can't fully copy, of course, and we believe that machine intelligence makes all the difference in the title and escrow. Steps to 02:43 take out as we talked about the inefficiencies in the human error. We also believe in the title and escrow process and machine intelligence will make 02:49 it a more transformative process for the home buyer differentiator, you know, like why 02:57
but 03:03 consumer demands are changing today. It takes on average home buyer 45 days to go through the entire process of buying a home with a mortgage We 03:13 Believe with the application of machine intelligence. We should be able to get to that transformative one click closed which would reduce the cost 03:22 increase the efficiency and provide a more delightful experience for the home buyer in addition as I mentioned earlier having The 03:29 title company of the future being able to focus and have its associates focus on the things that require strategic human decisions is really really 03:39
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