Put simply, Synthetic data is information – aka data – that’s been artificially manufactured rather than having been captured via real-world events.
Last month, Encord was one of a number of global tech companies invited by Amazon Web Services (AWS) to attend the event dubbed Project Stormcloud.
We've applied quantitative finance principles to help develop a platform that enables our customers to create and manage high-quality training data for computer vision.
When it comes to building AI systems, you’ve got to take data compliance considerations into account from day one; otherwise, your project will be finished before it even begins.
Machine learning engineers run into two problems with regards to labelling training data: quantity and quality. We have developed a quality feature that detects likely errors within a clients' project, using a semi-supervised learning algorithm.
We got together with Teton’s CTO Esben Thorius to learn about the role of computer vision in fall prevention and how Encord’s training data tooling has provided Teton with the flexibility needed to label and manage training data in the ways that result in the best model performance possible.
We caught up with Captur founder, Charlotte Bax, to hear more about the power of computer vision to assess the condition of remote assets, her experience using Encord, and her mission to bring applied AI to long-tail industries like operations.
Modern web browsers give you insufficient control over embedded videos. This is what we learned from solving frame synchronisation issues in web applications
CB Insights today named Encord, the platform for data-centric computer vision, on its annual AI 100 ranking, a list showcasing the 100 most promising private artificial intelligence companies in the world.
Most startup founders will agree that having a first-mover's advantage by reducing time to market is an essential part of their long-term success strategy.
We recently sat down with Dr. Hayee to hear more about his thoughts on the role of deep learning in medical diagnosis, his collaboration with Encord, and the changing landscape of medical AI.
It’s reminiscent of the early days of the internet: the promise of technology is clear, but society hasn’t yet seen widespread adoption of it. When we do, computer vision will touch every aspect of our lives.
Making your first hires and growing a team is a challenging and daunting process. Here are 4 key lessons that I have learned while scaling my startup from 0 to 25 employees.
This post is about hope–the hope that machine learning and computer vision can bring to physicians treating cancer patients.
The purpose of this tutorial is to demonstrate the power of algorithmic labelling through a real world example that we had to solve ourselves.
The purpose of this post is to review the paper we (including my cofounder Ulrik at Encord) recently published on this topic: “Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects”.
At Encord, we’ve spent weeks interviewing data scientists, product owners, and distributed workforce providers. Below are some of our key learnings and takeaways for successfully establishing and scaling a training data pipeline.
For context, I am a cofounder of Encord, a company in the current winter 2021 batch. We are focused on building software to improve data labelling for computer vision.
Today, we are thrilled to publicly launch Cord and announce our $4.5M seed round led by CRV and including the Y Combinator Continuity Fund, WndrCo, Crane Venture Partners, Harvard Management Company, and Intercom.
My co-founder Eric Landau and I recently closed a $4.5M seed round for our company Encord, focused on building software to automate data annotation for computer vision, led by CRV and including the Y Combinator Continuity Fund, WndrCo, Crane Venture Partners, and the Harvard Management Company.
The purpose of this post is to introduce the “micro-model” methodology we use at Encord to automate data annotation. We have deployed this approach on computer vision labelling tasks across a wide range of domains including medical imaging, agriculture, autonomous vehicles, and satellite imaging.
This article comes from my experiences co-founding Encord. Spoilers ahead for the most watched Netflix series of last year. Although if you are getting spoilers from this article, you probably don’t care about spoilers.
London-based Encord’s micromodels help get applications into production faster.
The latest trend in the ML community is the rise of what is termed data-centric AI. Simply put, data-centric AI is the notion that the most relevant component of an AI system is the data that it was trained on rather than the model or sets of models that it uses.
In 1956, John McCarthy, a young associate professor of mathematics, convened 10 mathematicians and scientists for a two-month study about “thinking machines”. The group decided to hold a summer workshop based on the assumption that if mathematicians and scientists could describe every aspect of learning in a way that enabled a machine to simulate it, then they could begin to understand how to make machines use language, form abstractions, and solve problems.