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AI, LLM Training, and Operations Efficiency

Learn how to leverage AI, technology and human capital for the future.

What is AI Bias?

Let's break down AI bias: what it is, why it happens, and how we help companies fine-tune their language models to eliminate it.

What is AI Bias?

How Adversarial Training Enhances AI Alignment

Uncover the powerful technique of adversarial training, crucial for shielding AI models from deceptive attacks. This in-depth blog delves into its workings, applications, and ethical considerations, empowering you to build robust and secure AI systems.

How Adversarial Training Enhances AI Alignment

Diffusion Models: A Beginners Guide (2024)

Diffusion models are a category of latent variable generative models that includes diffusion probabilistic models and score-based generative models. Understand their structure, limitations, and applications.

Diffusion Models: A Beginners Guide (2024)

The Future of Crowd Work: Q&A with Dr. Mark Whiting

Dive deep into an important conversation around challenges in the current crowd work ecosystem, worker incentive design, and the future of crowd work with Dr. Mark Whiting.

The Future of Crowd Work: Q&A with Dr. Mark Whiting

Equitable Pay for All: Understanding Geographic Wage Disparity in Remote Work

Join us as we seek equitable solutions for compensation in a globally connected workforce. We discuss why it's not always a straightforward "yes" or "no" answer and explore ways to find a fair balance.

Equitable Pay for All: Understanding Geographic Wage Disparity in Remote Work

Should You Pay Per Task or By Hour? Optimizing Worker Productivity for High-Quality Data

Expert labelers favor payment per task over hourly wages for high-quality data annotation despite published research. Gain insights into the contrasting influence of pay-per-task and hourly wage compensation structures for data labeler productivity.

Should You Pay Per Task or By Hour? Optimizing Worker Productivity for High-Quality Data

Understanding Human-in-the-Loop (HITL) in Machine Learning

Human-in-the-loop (HITL) is an approach that combines human expertise with AI capabilities at various stages of development. It's a collaborative approach where humans provide input, validate outputs, and refine AI models. This collaboration ensures better accuracy, adaptability, and ethical considerations in AI systems.

Understanding Human-in-the-Loop (HITL) in Machine Learning

What is Data Labeling? Explaining Use Cases, Career Paths, and Impact in AI

Learn all about data labeling in AI development. Uncover its significance, techniques, and pivotal role in training machine learning models. Discover how you can contribute towards the advancement of AI by being a data labeler.

What is Data Labeling? Explaining Use Cases, Career Paths, and Impact in AI

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