Deep learning has made great strides in recent years, with new systems and models such as GPT-3 offering higher quality interpretations of human language, allowing developers to use these concepts in more diverse applications. We can see these developments in our speech synthesis voice recorders and dual language translation apps, which have become incredibly good these days.
But what’s the next wave of features that this AI infrastructure can enhance? Hebbia wants to find out.
Hebbia today it’s a startup but actually a product studio, a sort of AI idea sketchpad founded by George Sivulka (a Stanford PhD student currently on leave) and a mélange of three other AI researchers and engineers from Stanford. The group, using the new deep learning techniques and models available today, is trying to push the boundaries of what knowledge graphs, semantic analysis, and artificial intelligence can ultimately do for human productivity.
Sivulka was inspired to focus on this field from the testimony of his friends’ experiences working in the knowledge economy. “A lot of my peers … they all do these white-collar jobs where they sit and read immense amounts of information all day,” Sivulka said. “People become banking analysts and dig into SEC forms for a line or two of information, or they go to law school or they become legal analysts and do the same thing … [They’re] simply bogged down by these walls of text, by this similar avalanche of information that is impossible to make sense of “.
(Tell me about it).
What he and his team want to do is empower human productivity by creating research, analysis and summary tools that can help you make sense of your personal universe of knowledge. “The idea is that Hebbia is building these thinking productivity tools that improve the way you work. These are things that actually control the input and output of information you face every day, ”Sivulka said.
It’s an ambitious vision, so they had to start somewhere. Their first product, which is what got me excited about the vision, is a Chrome plugin that has been in private beta and is released to the world more broadly today (note: for now it’s not listed in the Chrome Store yet. ). The plugin updates the search functionality in Chrome to go beyond just text pattern matching to start understanding what your query actually is is and how you might respond given the text on a page. Here is a demo of the plugin on ProWellTech:
So, for example, you could press Ctrl-F on a Wikipedia page and ask “Where did this person live?” and the plugin can determine that you are asking for positions and start highlighting text on that page with relevant information. It’s AI, and pretty beta AI too, so obviously your experience can and will be inconsistent right now. But as Hebbia tweaks its templates and improves its text comprehension, the hope is that browser search can be completely transformed and become a huge productivity boost.
Sivulka is a kind of firstborn prodigy. He worked at NASA as a teenager and graduated from Stanford in 2.5 years, finishing his master’s degree a little over a year later, and started a PhD before being undermined by Hebbia.
Hebbia’s vision has already attracted the attention of VCs only in its first months. Ann Miura-Ko at Floodgate led a $ 1.1 million pre-seed round which was joined by Naval Ravikant, Peter Thiel, Kevin Hartz, Michael Fertik and Cory Levy.
Sivulka notes that their Ctrl-F product is the main focus for the company right now and acts as a kind of gateway into the wider potential offered by knowledge graphs and personal productivity. “This is one of the last frontiers of what computers can do,” said Sivulka, noting that computation has already revolutionized many fields by digitizing data and making it easier to process. With Ctrl-F, “this is a core technology, [we’re] just scratching the surface of what we can do with this. “