How to discover AI code, know-how with CatalyzeX

When it comes to building an AI project, whether it’s for speech recognition or some other use case, data scientists and developers tend to spend plenty of time on Google, sifting through existing research that has already been conducted in the same area.

The goal of the effort is to understand which techniques and models have been applied in the space and which of those are good enough to refer to or build on. However, the problem is that with tens of thousands of research articles already on the internet, finding relevant technical material for the project at hand comes off as an extremely tedious task.

CatalyzeX accelerates AI code discovery

California-based CatalyzeX solves this challenge with a dedicated search engine to discover AI models and code. The solution, powered by the company’s in-house crawlers, aggregators, and classifiers, automatically goes through technical papers on sites such as Arxiv as well as code platforms to match and link machine learning models and techniques with various corresponding code implementations.

Here’s how you can use it to build your own AI project.

1. To begin, simply visit catalyzex.com and enter the project topic in the search bar. This could be anything from object detection to building a recommendation engine or algorithm for disease detection. Users could also use the recommendations suggested at the bottom of the search bar.
2. After searching, the platform lists most, if not all, available researches on the topic in question. For instance, a search for Covid-19 detection shows dozens of papers detailing various techniques, approaches, and frameworks for diagnosing the disease through chest X-rays. Users could go through the abstracts of these research papers right from the results page and then click through the most suitable one to investigate.
3. Once a research paper is opened, CatalyzeX aggregates all available information on it, starting from the names of the authors to the actual ML research, the findings, and the figures. Users could read through and understand what exactly the researchers wanted to accomplish as well as all related technical details.
4. Most importantly, this page also provides the link to the database and model code (right under the title) used for the research by the authors or contributed by the CatalyzeX community. So, if the project has some value, users could delve into the database and code implementation and build on top of it.

Above: An AI research paper with its code and database on CatalyzeX

In case the code and the database used for the research have not been made public, the platform also provides an option to contact the authors of the paper. All users have to do is follow the above-mentioned steps and click the “Ask Authors” button to reach out. Furthermore, users can even use the CatalyzeX browser extension to get links to code directly in Google search results. It further simplifies the search process, and is available on both Chrome and Firefox.