July 2020 - Appen

2020 State of AI and Machine learning Survey

State of AI 2020: Report Now Available

What You Need to Know About the Sixth Annual Industry Report We introduced the State of AI and Machine Learning Report over six years ago when AI and machine learning adoption was still in its infancy. Since then, the industry has grown significantly, with budgets, executive visibility, and ownership all increasing year-over-year. Despite rapid growth, there is more room for …

The Role of Quality Assurance in Artificial Intelligence

What You Need to Know About Quality Assurance for Your AI Models Launching an artificial intelligence (AI) model that’s accurate, reliable, and unbiased is undeniably a challenge. Organizations that manage to achieve success in AI initiatives are likely aware that the quality assurance (QA) process is very different with AI than traditional QA processes. Quality assurance plays a critical role …

Appen Machine Learning FAQ

Explore this machine learning FAQ for an overview of machine learning and artificial intelligence, including details about different methods and how you can invest. What is machine learning? Machine learning is the process of teaching a machine how to learn by providing it with guidance that helps them develop logic on its own and giving them access to datasets you …

Allen Institute for AI; Enhanced Research Experience to Scholars

We were able to quickly go through different iterations of annotation tasks with our crowd workers and understand what was working and what wasn’t. The fact that the quality control features are already built into the Appen platform makes task setup even easier. – Madeleine van Zuylen, Data Science Analyst, Allen Institute for AI The Company The Allen Institute for …

What is Data Annotation?

Building an AI or ML model that acts like a human requires large volumes of training data. For a model to make decisions and take action, it must be trained to understand specific information. Data annotation is the categorization and labeling of data for AI applications. Training data must be properly categorized and annotated for a specific use case. With …

How to Reduce Bias in AI

Top Eight Ways to Overcome and Prevent AI Bias Algorithmic bias in AI is a pervasive problem. You can likely recall biased algorithm examples in the news, such as speech recognition not being able to identify the pronoun “hers” but being able to identify “his” or face recognition software being less likely to recognize people of color. While entirely eliminating …