What is Training data?

What is Training Data?

Machine Learning algorithms learn from data. They find relationships, develop understanding, make decisions, and evaluate their confidence from the training data they’re given. And the better the training data is, the better the model performs. In fact, the quality and quantity of your machine learning training data has as much to do with the success of your data project as …
Five AI Market Trends for 2021: Shifting Approaches to Data, Use Cases, and More

Five AI Market Trends for 2021: Shifting Approaches to Data, Use Cases, and More

The field of artificial intelligence (AI) continues to evolve at a rapid pace as companies learn and grow from their challenges and successes. More and more, companies are aligning on the foundational role that data plays in AI implementation. As a consequence, there are numerous exciting trends emerging within the data subdomain worth watching. These trends illustrate both the barriers …
Brandwatch Case Study

Brandwatch Becomes More Agile in Delivery of Digital Intelligence Insights to Customers

 “Compared to our freelancing network, Appen’s platform allows us to iterate quickly with our experimental design and data collection. This is an incredible benefit to us. ” – Hamish Morgan, VP of Data Science, Brandwatch The Company Founded in 2006, Brandwatch offers a digital consumer intelligence platform that delivers actionable insights to marketers, analysts and market researchers. In tracking what …
Appen Data Annotation Services

What is Data Annotation?

Text, audio, image, or video becomes training data for machine learning through data annotation, with the help of people and technology. 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 via data annotation. But what …

Appen’s Annual State of AI Report Finds a Shift to Internal Efficiencies

AI budgets up significantly and decisions moving from C-suite to technologists, away from “silver bullet” solutions to improvements to internal operations SAN FRANCISCO — June 15, 2021 — Appen Limited (ASX:APX), the leading provider of high-quality training data for organizations that build effective AI systems at scale, today released its seventh annual State of AI report. This year’s report reveals …
what is data labeling

What is Data Labeling?

Everything You Need to Know About Data Labeling – Featuring Meeta Dash Artificial intelligence (AI) is only as good as the data it is trained with. With the quality and quantity of training data directly determining the success of an AI algorithm, it’s no surprise that, on average, 80% of the time spent on an AI project is wrangling training data, …
Building AI in the EU

How to Get Started: Building Trustworthy AI in the European Union

Artificial Intelligence (AI) has tremendous potential to increase productivity, uncover new insights and create new experiences. But such a high potential technology demands that organizations build AI with a responsible lens in mind to make sure it nets a positive impact. The European Commission (EC) has taken steps to transform such recommendations into law and has recently expanded its product …
How to Reduce Bias in AI

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 …
Overcoming AI Deployment Challenges

Overcoming AI Deployment Challenges

5 Tips for Handling the Biggest AI Obstacles You may have heard the stat: over 80% of artificial intelligence (AI) projects never make it to deployment. While we expect to see that number decrease in the coming years, in the meantime companies face very real, and common, barriers to success. Lack of high-quality data, insufficient technical expertise, misalignment within the …