A Compass For Innovation

Innovation Lens empowers investors by offering curated reports and dashboard access that display the state of the art of existing scientific research and uses a patent-pending algorithm to generate statistically valid hypotheses about the most likely high-impact future research.

Investors, research institutions, and granting agencies alike will find Innovation Lens a helpful aid when deciding their investment priorities. Our algorithm identifies topics that are innovative and not yet covered in the literature, while also being close enough to recent research that they fit within a school of thought and a tradition.

We discovered patterns within the distribution of scientific research that predict where high-value research is likely to occur in the near future. We have demonstrated that the predictive value of these patterns is significantly higher than baseline (~300% better for topics covered in PubMed, and ~100% better in Physics and Computer Science). See validation details in our white paper here. Our patent-pending algorithm is able to reliably indicate topics that lie close enough to existing research to be well-supported, and far enough away to be innovative. Articles on these topics are more likely to be the highly-cited founders of a new line of research, and the harbingers of important technological development.

Large language models are high-dimensional vector spaces that contain huge amounts of information—nearly all the words humans have ever written down. These spaces and their properties contain many surprises. In some cases, novel vectors (which point to "locations" in the space of the language model) that do not correspond to any existing text can still contain valid information, because the space in which they exist is itself so information rich. This information can be leveraged to produce statistically valid inferences about future states of the vector space, based on the patterns that have held true in the past.

There are far too many articles on any given topic for investors to read directly. Even very small niches are packed with so many articles that it is very challenging to know where a significant new contribution can be made. This situation begs the question: how can automatic text analysis help us do better? Some other attempts to map the research space exist: Nomic’s Atlas is one notable example of a visualization similar to ours. However, Innovation Lens is the only service capable of suggesting “future abstracts” to suggest topics for future research.

Innovation Lens is a sophisticated “lens” with which to view the vast spaces of existing research, and bring into focus the topics that are ripe for innovation. It is computationally intensive, and GPUs aren’t free, so we have to charge to cover our costs. But the low cost of the service provides high value: our fee is orders of magnitude lower than a typical seed round investment, so when our insight provides more profitable investment priorities, it pays for itself many times over.

The underlying statistical research was done using more than 40 million articles from PubMed, arXiv, and the US Patent and Trademark Office database. We are constantly adding other scientific article repositories. Read our white paper here.

Please contact us at info@innovationlens.org with details of your use case.