See the future of research before it happens

InnovationLens offers a way to gain an overview of existing scientific research and automatically generate hypotheses for future research.

Professional scientists and students alike will find InnovationLens a helpful aid when deciding their next research project. 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. Other professionals who are interested in near-future trends, like hedge fund analysts and venture capitalists, also find our service useful. And officials in granting agencies and university research funding offices use our algorithm to optimize their resources for high-value research.

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 two baselines. Our 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.

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. We reverse engineer certain vectors in order to produce human-readable text.

Most students and researchers are aware that there are too many articles on any given topic to read. Even very small niches are packed with so many articles that it is very challenging for a new researcher to know where a significant new contribution can be made. And the situation is not much better for seasoned researchers. 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, InnovationLens is the only service capable of suggesting “future abstracts” to suggest topics for future research.

InnovationLens is a sophisticated way to give researchers a “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: for the price of a few lattes, we offer a panoramic view of ideas that would not all occur to a researcher looking for their next topic.

The underlying statistical research was done using half a million articles from the Computer Science section of ArXiv. We will continue to validate and publish our results for other domains as well.

Not every suggestion will be useful. It is best to generate a few dozen "high-value topics" as working hypotheses, and then use your own expertise and intuition to choose a few to examine more deeply. Innovation Lens is a hypothesis generation tool, and cannot replace the work of human researchers.

The sheer volume of scientific articles imposes some design decisions in a consumer-facing product such as InnovationLens, which has to balance completeness with speed and usability on a web interface. If you are interested in backtesting for other purposes, or you want API access to use our algorithm for larger datasets than can be displayed on web browsers, please contact us with your use case.

We occasionally offer one-time coupons for new users to try the system free of charge. But we cannot guarantee results about the future. By using our system, you acknowledge that it is provided as-is, based on your choice of input parameters.

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