On April 19, Built@Booth organized the inaugural Chicago Machine Learning Venture Capital Summit. The team, led by Cayse Llorens, managed to bring together entrepreneurs, investors and student for a 5-hour conversation on machine learning, Artificial Intelligence and their application in the business realm.
Booth’s first conference focused on machine learning attracted over 600 attendees to the downtown Marriott’s conference center. The summit featured a number accessible yet technical lectures such as “Cutting Edge Concepts from Applied Machine Learning Research” by Booth’s professor Sanjog Misra. However, most of the evening focused on finding ways to apply machine learning techniques to solve practical issues and commercialize the technology.
In this spirit, panelists from several leading Chicago-based analytics start-ups warned that actual problem-solving is crucial for real value-creation rather than letting technology be a hyped fad. Szabolcs Paldy from Discover Financial Services expressed a hope that companies have learnt the lesson from the ‘big data’ frenzy 5 years ago and will approach the emerging AI opportunity pragmatically. Not every problem requires AI and not every pile of data is useful.
Several speakers also expressed opinion that good machine learning models are unlikely to provide sustainable competitive advantage to ML-focused start-ups unless they manage to build superior data sets. A wave of open-sourcing in the industry makes many powerful tools easily available and commoditized. Gaining access to better data than competitors thus becomes critical.
An important part of the summit was a pitch competition. Three entrepreneurs had an opportunity to showcase what type of problems machine learning can help solve. Booth alumnus Abraham Pabbathi talked about his early prototype of a diabetes management app that leverages ML to predict blood glucose levels. Other entrants talked about Enodo Score, a predictive analytics platform for real estate investors, and Heretik, an M&A due diligence analytics tool for automated analysis of contractual data.
Given this year’s success, the summit’s organizers already contemplate the format of the next year. Cayse says: “There's clearly a lot of interest in commercializing ML and AI and we are very excited to be part of such important conversation. Initial feedback from summit’s participants is very positive. Importantly, our sponsors see excellent value in it and already expressed interest in participating the next year. Many people would like the summit to take a full day.”
Recently, Booth started to pay a lot of attention to machine learning and related data analytics tools. Classes such as Machine Learning, Big Data, Augmented Intelligence or Digital and Algorithmic Marketing offer hands-on glimpse into the still not widely understood field. The Chicago Machine Learning Venture Capital Summit adds a welcome connection to real-life applications and businesses. Our school’s effort to claim leadership among MBA programmes in this extremely hot area is highly commendable.