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Have you ever thought about using your engineering skills and education to help others in developing countries or in impoverished areas of big cities in the United States? From 6:30-8 p.m. Wednesday, Nov. 20, representatives from the Peace Corps, City Year and Teach for America will hold a joint panel session to provide more information and to answer questions about service corps opportunities. The panel session will be held in Mallinckrodt Multipurpose Room on the lower level. Questions? Email firstname.lastname@example.org.
By Ethan Green and Zachary Berman
Imagine it’s your first day at a new job. Your boss drives you out to a 50-acre field of grass, hands you a single blade of grass and says, “Find all the blades of grass that are just like this one.” What do you do?
Even an expert would say, “I can find all of the similar blades, but it will take at least a week,” but your boss says you don’t have a week. That’s where simMachines comes in.
What is simMachines?
simMachines may be the fastest similarity search engine in existence. While standard search engines, like Google, look for specific keyword matches, similarity search takes in an object, such as a picture, and returns other objects that share characteristics with the first object.
Arnoldo J. Muller-Molina, the brain behind simMachines, has a simple idea – find objects that are similar and find them faster than anyone else.
When we first talked with Arnoldo, he showed us a demo of how his search engine could take a picture of a shoe and find other shoes exactly like it – the same color, heel and style. But similarity search can do much more than find a pair of shoes.
What can big data do?
Similarity search can recommend a new song, discover what a customer might buy next or even analyze data from rockets shot into space. Data scientists all over the world are analyzing larger amounts of data than ever before, and as the problems get bigger, finding the solutions takes longer.
As a user listens and votes on more songs, Pandora gets better at recommending new titles; but the more data Pandora has, the longer the search for a new song could take. Arnoldo and his team have created a number of different similarity searches that are faster than ever before, and now he is building a company with his solutions.
The CELect Project
As part of the CELect Entrepreneurship Course, we – Ethan Green, Austin French (MBA ‘14) and Zachary Berman – have gotten the chance to work with simMachines. We are conducting a message audit and performing a full market analysis to help Arnoldo take his incredibly fast similarity search engine to the world of big data. We want to make sure simMachines’s message accurately depicts the value simMachines can provide to its customers through its products. In order to market such sophisticated products, understanding the customers’ needs is critical.
After our meeting with Arnoldo, we were blown away by just how much similarity search can do, and how much he has accomplished. We believe that with the right structure and market positioning, simMachines will be extremely successful in bringing its proprietary similarity search to companies across the globe.
— Ethan Green and Zachary Berman are both seniors in the BS/MS program in computer science in the School of Engineering & Applied Science with second majors in entrepreneurship through the Olin Business School.