April 28, 2017
Machine learning and artificial intelligence have become hot topics in enterprise, entrepreneurial and technology circles. So much so that in his founder’s letter yesterday, Alphabet CEO Larry Page touched on the importance of the technology, noting that they began working on it “long before others.” Late last year, Google also released Google Cloud Machine Learning, which provides modern machine learning services, with pre-trained models and a service so that developers everywhere can generate their own tailored models.
There is no doubt that these developers have endless applications in endless industries for machine learning. As we mentioned in this blog, ML solutions can drastically improve the way we work. From mining and manufacturing to robots and self-driving cars, it can lower operating costs, create efficiencies in production and prevent mechanical failures.
This could be why there has been such growth in investment in ML and AI technology. In fact, 48 percent of the 79 venture capital deals in AI done in the first 7 months of 2016 were for funding seed-level companies and, from 2006 to 2014, there was a 40 percent increase in deep-learning publications that included an author with a corporate affiliation. Focus on the advancement of AI and ML is coming from entrepreneurs and enterprises.
Because of this interest from our primary partners, this week, TechNexus sponsored University of Chicago’s Entrepreneurship and Venture Capital Student Group Built@Booth’s first annual Machine Learning Venture Capital Summit.
The event allowed VCs, machine learning startup CEOs and researchers to connect and share their points of view on the future of ML and AI. It was also an opportunity for select startups to pitch their ML businesses for the chance to receive mentorship from top experts, a coveted sit down with a VC focused on ML investments.
After pitching to a sold out crowd of a deeply interested ML experts, the following three companies also won a three-month membership at TechNexus:
Coming this summer, Heretik leverages machine learning to solve inefficiencies during legal contract review. Deeply integrated with leading e-Discovery platform Relativity, our software simplifies the import process, automates contract categorization, and identifies key contract sections to save your team valuable time & resources.
Enodo Score is a patent pending predictive analytics platform for the commercial real estate industry that objectively quantifies the investment potential of multifamily investment properties. Using real-time data from more than 30 public and private sources covering approximately 1,500,000 multifamily properties nationwide, Enodo Score’s machine learning algorithm untangles each of the factors that drive returns in multifamily investments- and we provide this insight in a simple, map-based interface to show users how property and market related factors will affect any property’s investment quality.
The bSMART app helps predict blood glucose levels two hours into the future and make suggestions on insulin dose adjustments. It will have an interface to log blood glucose numbers, carbs intake, insulin dose and activity levels. The app will run a proprietary data analytics algorithm and predict how the blood glucose levels will be in the next two hours given certain amount of carbs, insulin and activity levels. The predictions are usually within 20 percent of the range 75 percent of the time, which is industry standard. The app also allows users to test out what-if scenarios to help them administer the right amount of carbs, activity levels and insulin dose.
As these three companies prove, machine learning has endless applications and will positively impact industries across the board. What industry do you think will see the most advancement?
April 28, 2017
We’re looking for the select startups that think like we do and want to engage seriously with our corporate clients and global ecosystem of innovators. If you’re ready, we want to hear from you.