AI code discovery platform CatalyzeX raises $ 1.64 million

California-based CatalyzeX, a startup offering platform AI / ML code discovery and know-how, announced today that it has raised $ 1.64 million in a seed round of funding led by Unshackled Ventures, Darling Ventures, Kepler Ventures, On Deck, Abstraction Capital, Unpopular Ventures and Basecamp Fund. The company said it plans to use the round – which also saw the participation of more angels – to further accelerate the development of its product and democratize AI for builders around the world.

Over the years, tens of thousands of AI studies have been conducted that have built a huge stock of technical material for various use cases and industries. However, it has long been a challenge for developers and data researchers around the world to find relevant information from this huge piece for an upcoming project. They would often end up spending hours on Google, searching for papers that could contain code snippets / models to build on (only 10-12% share code) and other know-how that could accelerate the development of their AI project.

CatalyzeX for AI code discovery

CatalyzeX AI code discovery

Above: CatalyzeX AI code discovery platform

Image credit: CatalyzeX

Encouraged by this challenge in their own professional careers, brothers Gaurav and Himanshu Ragtah decided to launch CatalyzeX in 2019. The startup offers a website that curates AI research articles and online research, giving developers a one-stop-shop to discover ML techniques and know-how, along with the corresponding code, for their respective projects.

“CatalyzeX’s offerings are powered by crawlers, aggregators and classifiers we have built internally to automatically go through technical papers as well as code platforms daily and to match and connect machine learning models and techniques with various similar code implementations,” Gaurav said. Venturebeat in an email. “We also allow code submissions and feedback from members of the CatalyzeX network.”

The free access platform is a kind of search engine where a developer selects the recommendations or inserts a problem query, such as. cancer detection, in the search box. The results show all relevant available ML models / techniques – with full paper and code implementation – that could help with the problem. If the code is not publicly available, the platform also allows you to get in touch with the authors to request it or get answers to further questions.

In addition to this, CatalyzeX also offers a browser extension that automatically displays links to code implementations for ML techniques and papers that appear in Google search results.

“Since code is the lingua franca of builders and creators, not walls of text, and given the sheer volume of AI research evolving every single day, it saves a lot of time and effort for developers and technical non-experts to discover and evaluate viable opportunities to harness artificial intelligence in their products and processes, “the co-founder added.

Focus on addressing the current status quo, growing user base

While platforms like 42papers and Deepai.org also offer AI research and know-how, CatalyzeX claims to differentiate itself with a much larger stock for model / techniques and code discovery. The platform currently serves over 30,000 users each week with more than 500,000 code implementations.

Gaurav stressed, however, that the real challenge is not to beat these sites, but to address the current status quo, which is highly fragmented and holds back significant technology development from reaching the real world.

This, he said, will be done by speeding up the development of the product, and bringing it to more developers and data researchers around the world. The co-founder did not share specific product development plans, but he noted that part of the funding will go to hiring product designers and engineers who would work on upgrading the platform.

“We also have planned integrations and partnerships with multiple code collaborations and AI research platforms,” ​​he added, noting that they also explore revenue generation opportunities, such as introducing a paid level of advanced search filters and integrating with development / deployment environments or connecting high-skill talent with global opportunities in artificial intelligence.

According to PwC, artificial intelligence could contribute up to $ 15.7 trillion to the global economy by 2030. Of that, $ 6.6 trillion is likely to come from increased productivity and $ 9.1 trillion from consumption side effects.

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