(posting this on behalf of my cofounder: Amin Ahmad).

A friend of mine asked me recently why I decided to start Vectara? Now that we’ve left stealth mode and launched publicly, I thought some of you might also be wondering, and so I hope the following paragraphs will serve to provide insight into those reasons.

You are probably aware that the 2010s were marked by rapid advances in artificial intelligence, fueled by the dominance of deep neural networks as the tool of choice for machine learning. Perhaps no field advanced as rapidly as language understanding, which, at the dawn of the decade and despite the existence of a huge body of linguistics research and decades of sustained effort, could not pretend to the feats of comprehension regularly displayed by young children.

Around 2016, my research group at Google, led by visionary Ray Kurzweil, demonstrated the use of dual encoders for embedding-based response prediction in email. In 2017, I began work with two colleagues on zero-shot dual-encoder models for answer retrieval over large, arbitrarily-defined text corpora. The introduction of transformers a few months later pushed the system’s performance to viability, and in 2018, Google Talk to Books was launched, becoming the first large-scale application to be powered by such an architecture^(1).

Over the next few years I worked with a wide range of product teams on applying zero-shot dual encoders, and, together with the fantastic success of BERT and GPT, it became clear to me that transformer-based models understand language in a qualitatively different way than previous techniques. Just as importantly, I saw the potential for deep learning, which had served, till then, to further concentrate power in the hands of a few powerful organizations, to instead do just the opposite and level the playing field for the rest of the world^(2,3).

These observations, then, are the inspiration for Vectara. Building software, platforms, and APIs has always been my passion, and as an engineer, the greatest validation of my work is to see it adopted and put to good use by others. Together with my cofounders, Tallat Shafaat and Amr Awadallah, we built a platform that puts the power of large language models and neural search into the hands of developers everywhere.

Computers that can comprehend us may well represent one of our final steps towards the singularity.

Amin Ahmad

https://github.com/vectara

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^(1)If the reader is aware of earlier examples, I welcome any corrections. Please email me at amin@vectara.com.

^(2)The release of OpenAI’s Whisper model under an MIT license and the success of HuggingFace are two indicators of this trend.

^(3)I’ve previously written on this topic under “The Disruptive Potential of NLU”