I recently conducted an industry roundtable. It was made up of a good group of executives. They were very opinionated and thoughtful. I learned a lot on that call.
But one thing stuck out at me. I asked about the future of mortgage lending technology and Jeff Bradford, Founder and CEO of Bradford Technologies, Inc. said this:
“It’s all about deep learning. Deep learning is like artificial intelligence,” Bradford explained. “There is so much work going on in this space right now and it’s open source technology, so anyone can use it. There’s a guy that used the software to create a self-driving car inside of a month. Big companies have been trying to develop a self-driving car for years. Once the software learns something it, doesn’t make a mistake.”
Jeff Bradford is a renowned expert in appraisal analytics and accuracy in collateral valuations, and the mastermind behind computer-aided appraising, the most far-reaching and significant advance in the appraisal segment in decades. He frequently presents and speaks at industry events, on topics that include technology, valuation processes and valuation standards. He is a strong proponent of open industry standards and was one of the chief architects of the MISMO Appraisal XML standard.
Jeff started his career teaching early developers how to program the Macintosh at Apple Computer. He also specialized in computer-aided engineering at Structural Dynamics Research, and performed and managed computer aided analysis projects for FMC Central Engineering Labs.
Eventually he started Bradford Technologies. Bradford Technologies is an innovator of valuation tools and solutions for residential appraisers. The company pioneered computer-aided appraising, was the first to incorporate statistical support in both mainstream and alternative valuation products, and currently provides one of the most adopted technologies for residential appraisers. AppraisalWorld, the company’s online appraiser community with over 20,000 members provides services focused on building trust and reliability in the appraisal industry.
Jeff is a very smart guy. I’ve come to know and admire his intellect over the years, so when he talked about deep learning I had to know more. I came across this article that talked about when Ray Kurzweil met Google CEO Larry Page. As the story goes, he wasn’t looking for a job. A respected inventor who’s become a machine-intelligence futurist, Kurzweil wanted to discuss his upcoming book How to Create a Mind. He told Page, who had read an early draft, that he wanted to start a company to develop his ideas about how to build a truly intelligent computer: one that could understand language and then make inferences and decisions on its own.
It quickly became obvious that such an effort would require nothing less than Google-scale data and computing power. “I could try to give you some access to it,” Page told Kurzweil. “But it’s going to be very difficult to do that for an independent company.” So Page suggested that Kurzweil, who had never held a job anywhere but his own companies, join Google instead. It didn’t take Kurzweil long to make up his mind: in January he started working for Google as a director of engineering. “This is the culmination of literally 50 years of my focus on artificial intelligence,” he says.
Kurzweil was attracted not just by Google’s computing resources but also by the startling progress the company has made in a branch of AI called deep learning. Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.
The basic idea—that software can simulate the neocortex’s large array of neurons in an artificial “neural network”—is decades old, and it has led to as many disappointments as breakthroughs. But because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before.
With this greater depth, they are producing remarkable advances in speech and image recognition. Last June, a Google deep-learning system that had been shown 10 million images from YouTube videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats. Google also used the technology to cut the error rate on speech recognition in its latest Android mobile software. In October, Microsoft chief research officer Rick Rashid wowed attendees at a lecture in China with a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his own voice uttering them in Mandarin. That same month, a team of three graduate students and two professors won a contest held by Merck to identify molecules that could lead to new drugs. The group used deep learning to zero in on the molecules most likely to bind to their targets.
Isn’t this just amazing? Just imagine the mortgage applications for this technology. For years we’ve talked about using technology to automate mortgage processes or forms, but this technology could actually revolutionize all of mortgage lending and how we think about processing a loan. Hopefully someone in our space will catch on to deep learning and use it to transform mortgage lending for the better.