To our users,
Today we are excited to announce that Algorithmia has completed a Series A financing of $10.5M led by Google’s new fund focused on AI and machine learning investments with the participation of Madrona Venture Group, Rakuten Ventures, Osage University Partners, and Work-Bench.
Financing rounds are rarely a goal state (or at least they shouldn’t be) but an important milestone in allowing a company to continue to build out its mission. Read More…
TL;DR The most accurate machine learning systems to date are those that use a “human-in-the-loop” computing paradigm.
Though we have seen huge advances in the quality and accuracy of pure machine-driven systems, they tend to fall short of acceptable accuracy rates. The combination of machine-driven classification enhanced by human correction, on the other hand, provides a clear path forward in acceptable accuracy. Below we will describe a real-world use case of building and scaling these type of systems.
One of the most rewarding parts of working at Algorithmia is that we get to collaborate with amazing university researchers across the globe.
Last May, Richard Zhang, Philip Isola, and Alexei A. Efros from the University of Berkeley Vision Lab published their work “Colorful Image Colorization.” This paper describes a novel use of a convolutional neural net (learn more about deep learning) for colorizing black and white pictures.
With over 317 million active users a month, Twitter has become a wealth of data for those trying to understand how people feel about brands, topics, and more. Mining Twitter data for insights is one of the most common natural language processing tasks.
Dear Friends of Algorithmia,
Every year we like to take a small step back and reflect on what we achieved in 2016. Last year we went from private beta to a top Seattle startup.
2016, as we hoped it would be, was a big year for us. We provided more than 30,000 developers with access to over 2,700 algorithmic microservices – a humbling achievement for our small, dedicated team.