What is Active Learning?
A machine learning approach where the model queries the user or an oracle for input on uncertain data.
More about Active Learning:
Active Learning is a semi-supervised machine learning technique. Instead of passively receiving all training data, the model actively queries for the most informative data points when faced with uncertainty. This approach can reduce the amount of labeled data required for effective learning, as the model focuses on the most ambiguous and informative instances.
In the context of chatbots, active learning can help in refining responses by seeking feedback on uncertain interactions or by prioritizing the annotation of specific user queries.
Frequently Asked Questions
How does Active Learning reduce the need for labeled data?
By focusing on the most uncertain data points, active learning ensures that the model receives the most informative examples. This can lead to faster convergence and improved performance with less labeled data compared to traditional methods.
Is human intervention always required in Active Learning?
While not always, active learning often involves human experts to label or clarify the data points the model finds ambiguous, ensuring more accurate learning from those instances.
From the blog
Custom model training and fine-tuning for GPT-3.5 Turbo
Today OpenAI announced that businesses and developers can now fine-tune GPT-3.5 Turbo using their own data. Find out how you can create a custom tuned model trained on your own data.
Herman Schutte
Founder
Create an AI version of yourself for your coaching business
Harnessing the power of Artificial Intelligence is no longer reserved for tech giants or sci-fi enthusiasts. As a coach, what if you could scale your expertise, offering guidance at any hour without extending your workday?
Herman Schutte
Founder