Informa’s customers want to understand what new technologies will be most relevant to their businesses. This is also more “hype” around technologies. Therefore, it is increasingly important for companies to stay informed about emerging technologies.

What it does

Marble Grapes display the most relevant technologies for each of Informa's 6 industry-specific clients.

How we built it

We developed a neural network to algorithmically predict the estimated “noise” of a technology. This information is then displayed in a dynamic dashboard for Informa’s market analysts.

Challenges we ran into

Time constraints were a significant problem. There was limited accessibility of meaningful data. There were also minor syntax issues with Javascript ES6.

Accomplishments that we're proud of

Interviewing Informa, and understanding the problem in a deep way. We're also proud of developing a website that is intuitive to use.

What we learned

We learned that it is hard to access meaningful data, despite having a good solution in mind. We also learned that 4 young adults can eat a surprising amount of grapes in a short period of time.

What's next for Maple Grapes

We'd like to improve the accuracy of the algorithm by increasing the body of historical data for technological successes and failures. We'd also to account for a social media impact score, by doing sentiment analysis.


Faith Dennis (UC Berkeley), Shekhar Kumar (University of Toronto), Peter Zheng (City University of New York), Avkash Mukhi (University of Toronto)

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