Coronavirus Updates

Request Information from Booth


  • Select
  • Submit
  • Success

Cayse Llorens took a proud look out over the assembly of intellectual elite packed inside the Chicago Marriott Downtown. Standing in front of venture capitalists, researchers, PhDs, software engineers, startup founders, and executives, the Evening MBA student was introducing the first-ever Chicago Machine Learning Venture Capital Summit, launched by Built@Booth, the entrepreneurship and venture capital student group at Chicago Booth.

“Booth has a long history of redefining the status quo,” said Llorens. The event’s co-chair and head of event programming, Llorens was also recently a summer MBA associate at ChicagoNEXT and Chicago Venture Summit.

New technology and what it can do was the focus of the event, with a great deal of bandwidth dedicated toward reconciling the gap between the amount of data companies have access to and the ways they can use it to change how they operate. Following the big-data boom, companies now have tons of data, but no idea what to do with it all.

Suggestions were plentiful: panelists posited space after space where machine learning could grow, from individualizing online experience to predicting elections.

“For a startup,” said Llorens, “you have to think, what does your value add, because a lot of people know how to do machine learning now. So it shifts from ‘can I do machine learning?’ to ‘what can I do with machine learning?’”

Entrepreneurs were on site to answer that question. As the event began, a curious crowd gathered near a large screen, each smiling and frowning to demo a program that could use facial recognition to identify customers’ demographics and mood as they entered or left a store.

A pitch competition dominated the end of the event, featuring a diabetes management solution, a real estate app, and more, and IBM, Google, and Microsoft presented the capabilities of their new technologies.

“It’s not magic,” said Brian Clark, cofounder and CEO of Chicago-based Ascent Technologies, referencing the work it takes to make machine learning effective. “It’s an incredibly laborious process.” But it’s worth it, said Clark, because the efficiency that machine learning can create could change entire industries at a rapid pace.

The proliferation of ideas yet to be fully realized is part of why Llorens wanted to bring a wide range of people—students, alumni, and Chicago business leaders—together for the Chicago Machine Learning Venture Capital Summit: to gather those who want to create, those who want to invest, and those who want to become involved, all in the same place.

“There are all these building blocks that take some of these machine-learning algorithms that have been around for years and allow us to exploit them,” said Llorens, “and to give us things that we couldn’t have gotten yesterday.”

The Takeaways

Avirishu Verma, ’17, Analytics Expert, Opera Solutions Management Consulting Services

I loved connecting with machine-learning enthusiasts and learning about the latest developments in this field. The cool capabilities of IBM Watson, Microsoft Cognitive Services, and Google TensorFlow truly amazed me, and I can’t wait to try these tools for my own projects. During the panels and keynotes, it was great to hear both sides of the machine-learning story—from the entrepreneurs who are building machine-learning-based companies and from the VCs who are investing in them.

Eric Weisberg, Full-Time MBA student, Associate at Hyde Park Partners

As a former investor at J.P. Morgan, I have watched the [machine-learning] trend develop as more and more companies started mentioning big data, then eventually machine learning and AI, in their conference calls. Seeing over 500 people in one room for a five-hour conference on the subject confirmed that this is a real trend, and of particular note, a real trend in Chicago. The valley does not have a monopoly on machine learning and AI. . . . Chicago companies will find unique ways to leverage AI to build scalable businesses.