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Roi Krakovski
Co-Founder & CEO at Usearch | Ph.D. | Former IBM | Search & Data
Roi Krakovski is a technology professional and entrepreneur who currently serves as the Co-Founder and CEO of Usearch.12 He has a strong background in artificial intelligence, data science, and search technologies.
Education and Professional Background
Krakovski holds a Ph.D. and has previously worked at IBM.2 His academic background includes studies at Ben-Gurion University of the Negev.1 This combination of academic and industry experience has likely contributed to his expertise in the field of search and data technologies.
Current Role and Company
As the Co-Founder and CEO of Usearch, Krakovski is involved in developing innovative search and data solutions. Usearch appears to be focused on internet search technologies and leveraging AI for data collection and analysis.24
Areas of Expertise
Krakovski demonstrates expertise in several key areas:
- Artificial Intelligence: He frequently discusses AI applications, particularly in data collection and analysis.2
- Coding and Software Development: Krakovski has been coding since he was 13 and shares insights on how Large Language Models (LLMs) are changing coding practices.3
- Web Scraping: He has shared content related to web scraping techniques.5
- Search Technologies: Given his role at Usearch, Krakovski is deeply involved in developing and improving internet search technologies.4
Thought Leadership
Krakovski is active on LinkedIn, where he regularly shares insights and thoughts on various technology topics. He discusses the importance of collecting the right data for AI applications2, the impact of generative AI on the tech industry6, and how search technologies are evolving.4 This activity suggests that he is engaged in thought leadership within his field.
Roi Krakovski's diverse background in academia, industry experience at IBM, and his current role as a startup founder position him as a knowledgeable figure in the realms of AI, search technologies, and data science.
Highlights
Converting large volumes of unstructured text into a unified Excel file has always been a challenging task. This difficulty remains, whether we use large language models (LLMs) or not.
Since LLMs are not yet mature enough to handle this task at scale, we need to be particularly creative in our approach.
- AI should run on top of your data (a consensus everyone now agrees on).
- Most of the data used repeatedly originates from public sources (probably later combined with proprietary knowledge or unique processes to provide a competitive edge.)
- No enterprise possesses volumes of data comparable to those in web archives and public data.
- Meaning AI will primarily be applied to public data.
And this marks the rise of automated web data pipelines: From the web to your database.
