The U.S. Department of Energy wants to make investing in energy technology easier, less risky, and less expensive (for the government, at least).
A new initiative by the DOE’s office of Energy Efficiency & Renewable Energy (EERE) is looking for ideas on how to reduce barriers to private investment in energy technologies. Rho AI, one of 11 companies awarded a grant through the EERE’s US $7.8-million program called Innovative Pathways, plans to use artificial intelligence and data science to efficiently connect investors to startups. By using natural language processing tools to sift through publicly available information, Rho AI will build an online network of potential investors and energy technology companies, sort of like a LinkedIn for the energy sector. The Rho AI team wants to develop a more extensive network than any individual is capable of having on their own, and they’re relying on artificial intelligence to make smarter connections faster than a human could.
“You’re limited by the human networking capability when it comes to trying to connect technology and investment,” says Josh Browne, co-Founder and vice president of operations at Rho AI. “There’s only so many hours in a day and there’s only so many people in your network.”
Using the US $750,000 it received from the DOE, Rho AI has just two years to build, test, and prove the efficacy of its system. The two-year timeline for demonstrating proof of concept is a stipulation of the grant. With this approach, the DOE hopes to streamline the underlying process for getting new energy technologies to the market, instead of investing in particular companies.
“It’s a fairly small grant, relative to some of the larger grants where they invest in the actual hard technology,” Browne says. “In this case, they’re investing in ways to unlock money to invest in hard technology.”
Rho AI’s database will not only contain information about energy technology companies and investor interests, it will also track where money is coming from and who it’s going to in the industry. Browne imagines the interface will look something like a Bloomberg terminal.
To build the database, Rho AI will use Google Tensor Flow and Natural Language Toolkit—tools that can read and analyze human language—to scan public documents such as Securities and Exchange Commission filings and news articles on energy companies. The system will then use software tools that help analyze and visualize patterns in data, such as MUXviz and NetMiner, to understand how people and companies are connected.
In order to measure how well it’s helping investors and emerging clean technology companies find better business partners faster, Rho AI will compare the machine-built network with the real professional network of Carmichael Roberts, a leading venture capitalist in clean technology.
“This tool is intended to emulate and perhaps surpass the networking capability of a leading clean tech venture capitalist,” Browne says. “It should be able to match their network, and it should be able to very rapidly be ten times their network.”
Rho AI’s program should create a longer, more comprehensive list of possible investments than Roberts can—within seconds. The intention is for the final product to be robust enough that members of the private sector could and would adopt it after one year.
“If Rho AI is able to be successful in what they’re building, that will be in some sense self-scaling,” says Johanna Wolfson, director of the tech-to-market program at the DOE.
In other words, Rho AI could grow on its own and the industry could start seeing the effects of these connections. Investors and clean energy technology companies could find each other directly, while reducing the burden on the government to invest so much in energy innovation.
Improving the underlying pathway for getting new energy technology to market “actually can be done for relatively small dollar amounts, relative to what the government sometimes supports, in ways that can be catalytic, but sustained by the private sector,” said Wolfson.
Editor’s note: This post was corrected on August 8 to reflect the specifications of the DOE’s grant.
