Artificial intelligence hasn’t quite replaced humans, but the technology is making business better.

AI can’t replace humans, but the technology is making inroads in more and more business sectors.

In an oft-quoted interview with Life magazine in 1970, Marvin Minsky, an MIT researcher and pioneer in artificial intelligence, predicted that scientists were about three to eight years away from creating a machine as intelligent as the average human. Such a machine would “be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight,” Minksy said, and it would learn at such a “fantastic speed” that it would reach genius level within just a few months.

Fifty years later, Minksy’s vision of a machine on par with the human brain still hasn’t been realized — but popular AI tools, such as Google’s search engine and Apple’s Siri, have become part of everyday life, and machines are learning how to master an array of complex tasks, from operating self-driving cars to spotting tumors to monitoring crops.

“It’s no longer, ‘is artificial intelligence going to work?’ It does work, and there’s numerous applications that are out there — medical imaging, credit fraud detection, movie selections — that use sophisticated artificial intelligence algorithms to make business better,” says Jeff McFadden, chief technology officer for Xonar Technology, a Largo-based company that designed an AI-enabled security surveillance system to detect weapons. “It’s really moved out of that research area and moved into where it’s really a commercially viable technology set.”

The leaps forward have been made possible by powerful computer processing engines and advances in machine learning techniques. Computers with enough horsepower can crunch large data sets and use a series of algorithms to extract patterns and glean insights from that information. In Al recommendation systems, like that employed by Netflix, the algorithm looks at an individual’s viewing history, considers the preferences of other members with similar tastes and evaluates other information to come up with a viewing suggestion. An algorithm in an intelligent credit card fraud detection service, on the other hand, flags suspect purchases by looking for outliers or anomalies that depart from a consumer’s normal purchasing behavior.

McFadden’s Xonar uses an algorithm to spot concealed weapons based on their ultra-wide band radar signature. The system transmits an electromagnetic pulse toward a person as they walk through it. The pulse bounces back to a receiver. That reflected wave is then analyzed by machine’s algorithm to see if it matches the shape, density and other characteristics of various weapons in its learning library. It’s more discriminating than traditional technologies, which rely primarily on metal detection, and Xonar can differentiate between a knife or handgun and other harmless metal items, such as keys or money clips, making for fewer “false positives.” It’s also less obtrusive. McFadden says friends who’ve breezed through the system in place at the entrances of Ruth Eckerd Hall in Clearwater didn’t even realize it was there.

Inspired by neural networks in the brain, these “deep learning” systems can remember and build on observational patterns they find in data. In essence, they become smarter over time, but it’s vital to control what data the systems receive. “The big AI in general, deep-learning specifically, learns everything. It learns what you want it to learn, but it learns what you don’t want it to learn,” McFadden cautions.

In some cases, AI can outperform its human counterparts. A recent study in the journal Nature found that a Google AI system did a better job in predicting breast cancer from mammograms than radiologists did. And a new AI software engineered by the British company DeepMind has created a system of algorithms called AlphaFold that can rapidly and reliably predict the 3-D shape of proteins — a task that usually takes months or years. The breakthrough is expected to speed up and reduce the costs of pharmaceutical development.

Here’s a closer look what companies and researchers across Florida are doing with AI technology and why the experts say it will never completely replace humans.

REAL ESTATE/DEVELOPMENT

Real Estate Modeling

Designer: Olivia Ramos (Deepblocks)

Product: Deepblocks, early property analysis software

Growing up in Cuba, where she lived until she was 10 years old, Olivia Ramos spent lots of time in the office where her mother worked as an architect. “At the time, they had no computers, so it was all a bunch of pencils and rulers, and I fell in love with all the little gadgets,” she recalls.

Two decades later, Ramos is perfecting her own gadget — a high-tech software application called Deepblocks that uses data and deep learning to streamline and automate the process of early property analysis. Developers using the software can zoom in to a specific parcel, set their building parameters — square footage, number of units, parking, etc. — and the program spits out a 3-D visual of the project and an analysis with a projected return on investment that takes into account everything from market demographics (such as rent-to-income ratios) to local zoning rules.

“Zoning data, the rules of the city, are usually 400- page PDFs and are really expensive to go through and understand,” says Ramos, who has a master’s degree in architecture from Columbia University and a master’s in real estate development from the University of Miami. “We developed models that understand that data and extract that data from those documents. You just select a piece of land, and it tells you what you can do.”

The software can shave considerable time off development planning. It took one customer a year to do 21 iterations of a particular parcel that Deepblocks can help do in a few hours, and users can do as many models as they want, Ramos says. It currently includes parcel data for more than 1,000 U.S. cities and zoning data for 30 cities.

The Miami startup has a staff of six, including CEO and founder Ramos, and has raised $2 million through two funding rounds. It’s raising $3 million in a third round. Real estate pros can buy a subscription to Deepblocks for $1,620 a month or $12,600 for a year. The software has seen a “big growth in adoption” amid the COVID-19 pandemic because people can’t travel as easily to visit potential markets, Ramos says.

The goal is for the software to make suggestions on opportunities in the market and determine the highest and best use of any property, Ramos says. When that happens, she believes Deepblocks will help tackle even bigger problems, such as a lack of affordable housing.

“It’s really, really hard to make an affordable housing project profitable, and it requires a lot of government help, so if we use the inefficiencies and understand what to build and how to build and where to build it, then that pro jects a lot of savings on the front end,” she says. “Every single penny you save in affordable housing in cost, it’s going to make that project more likely to happen.”

By Amy KellerFlorida Trend

Published Feb. 18, 2021 | Updated Feb. 19, 2021