In this special guest feature, Bob Fletcher from Verne Global reflects on the recent HPC and AI on Wall Street conference.
“While obviously the New York financial community was the focus for this conference one of the best presentations came from out-of-town. Thomas Thurston, CTO at San Francisco based WR Hambrecht Ventures shared an amazing use case for DNN training. Most VCs spend their time networking and digesting hundreds of interview-style start-up pitches every quarter to finally filter them down to a limited number of investment candidates. It’s an incredibly time intensive process and the opportunities to either miss a good investment candidate, or plump for the wrong candidate are high.
Thomas’s team of 9 data scientists have turned this process on its head and they mine a wide variety of data sources – industry, social media, ‘dark data’, etc which are then pushed through the machine learning blender to determine the best start-ups to invest in. Unfortunately (but unexpectedly) he didn’t share too many specifics on the data or DNN models, but he did share some compelling results data. Compared to normal, traditional VC and corporate investment team average start-up success rates WR Hambrecht achieved:
Typical VC portfolios – 3X improvement
Corporate investment portfolios – 3X improvement
Internal corporate projects cancelled due to internal issues – 3X improvement
Picking successful internal corporate technical projects – 8X improvement Pretty impressive! Perhaps the days of the Harvard MBA VC are numbered? I’m not betting against them yet – they will likely hire data science teams of their own to replicate Thomas’s excellent approach.”