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Nvidia is on a quest to find the most disruptive artificial intelligence startups. This quest is part of a larger contest dubbed Nvidia Inception, which is screening more than 600 entrants to cull the best AI startups in three big categories. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank- style event this week as part of the search to find the best AI startups. Huang and a panel of judges listened to pitches from 14 AI startups across three categories. These were filtered from the more than 600 contestants who entered the Nvidia Inception contest. The winners will walk away with $1.5 million in cash at a dinner on May 10 at Nvidia’s GPU Technology Conference.
The idea is to make construction safer by using AI on jobs that safety experts can’t physically get to in a day, said Josh Kanner, CEO of Smartvid.io. Construction generates more than $10 trillion a year in revenues, but accidents lead to more than 1,000 deaths a year. But Smartvid.io figured out that photo and video data is often wasted, and Smartvid.io is unlocking the value of that content to analyze it for safety information. Smartvid.io automates the process of importing video into its app. Then the company’s safety AI analyzes the data for safety issues. All of the images are made searchable, and safety managers can make use of them. The system has integrated work flows and alerts. Workers get a suggestion from Smartvid.io, and they can rate that suggestion, which feeds back into the AI model.
Tel Aviv-based company is applying AI to the task of detecting malware. About 1 million new variants of malware are spread every day. Often, a new family of malware is only about 30 percent different from the code of something that came before. Many antivirus vendors focus on detecting known malware in a library, using reactive technology. But Deep Instinct believes the better solution is deep learning, which can be used to detect unknown malware in real time. It doesn’t detect virus signatures, sandboxing of content, or heuristics. Instead, it only looks at the binary raw details of the file in question. And Deep Instinct doesn’t require frequent updates, said Eli David, chief technology officer at Deep Instinct. It trains the deep learning neural network on hundreds of millions of files. In short, it focuses on prevention, not reaction.
Home insurance companies have to collect data on more than 100 million homes in the U.S. That isn’t easy, and Cape Analytics is using geospatial imagery, computer vision, and machine learning to help. Cape Analytics can collect aerial images that reveal a lot about a home, if they are properly analyzed, said Busy Cummings, vice president of sales. The initial focus is on the property insurance industry, but many others, from tax assessors to inspectors, also need a lot more data about homes. Home owners are often unreliable sources of information about their own homes, partly because they many not know the answers and partly because they may understand how to game the system to get lower insurance rates, Cummings said. Public records are also a frequently used source of data, but they are often outdated. And using inspectors is costly and often time-consuming.
The Munich, Germany-based company is building the software systems needed to measure how the railroads are being used and gain insights into that data, said Vlad Lata, chief technology officer at Konux. The data on railways isn’t very reliable, so Konux makes a sensor that can be placed on concrete labs that hold tracks in place. One of Konux’s customers is Deutsche Bahn, the German train company, which has more than 70,000 switches on its tracks throughout Europe. Those switches require about five manual inspections a year, each requiring about three people for a minimum two-hour inspection. Deutsche Bahn spends about $9,100 per switch, and that comes to about $630 million a year.
Digital Genius brings deep learning and AI to customer service operations. All of us spend about 43 days of our lives on the phone with customer service. It’s a $350 billion a year industry, and $300 billion of that cost lies in the salaries of people who are handling the calls. Digital Genius started out by creating a chatbot that follows a rule-based system to help offload customer service. But the company found that chatbots have limited usefulness. Then Digital Genius shifted more of its focus to deep-learning algorithms. “We built chatbots before they were cool, learned lessons of where they don’t work, and now we can solve problems much more quickly,” said Mikhail Naumov, cofounder of Digital Genius. […]