The AI Summit uncovered the potential of generative AI, with a spotlight on ethical AI, benefits to creatives, and the critical role of humans in the loop.
Experience "Christmas in July" with a ChristmasGAN project. Explore Generative Adversarial Networks, image translation, and creative Christmas touches.
Uncover CVPR 2023 top takeaways, including new roles for humans in the loop, self-supervised learning and zero-shot models, and humans in generative AI.
These 4 use cases examine why using drones to collect data makes industrial inspections safer, more accurate, and more efficient than manual inspections.
No time for a webinar? This blog post recaps our discussion on ethically designed AI systems with the 4 steps you need to achieve ethical AI.
Insurers are using AI to lower customer acquisition costs, identify new opportunities, and enable sales with personalized coaching and tools.
Are you ethically sourcing training data for your AI models? And what does “ethically sourcing” mean, anyway? Read this post to explore the issue.
These four use cases examine why using drones to collect data makes drone inspections safer, more accurate, and more efficient than manual inspections.
The nuances of language can be difficult for a machine to understand, hence the need for human input to accelerate testing and ensure quality control.
Sentiment analysis can turn the abundance of online information into actionable insights, but machines can’t do everything by themselves.
Humans are necessary while automating decisions and processes with AI, machine learning, and RPA. Experts discuss the need for humans in the loop (HITL).
No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.
Your training data operations are like assembly lines: data is your raw material, and you have to get it through production steps to structure it for AI. You need skilled people ...
Anonymous crowdsourcing is a common alternative to an in-house team for AI development. It can be a cheap option for training machine learning algorithms but it’s rarely as ...
Given the challenges of hiring and managing a team to complete the arduous data work behind AI, many companies are turning to outside help.
AI innovators rely on external teams to structure data for ML algorithms. But scaling quality data requires the right people & processes in your tech stack.
