An AI system's ability to constantly adapt and improve its performance by learning from new data over time.
More about Continuous Learning:
Continuous Learning pertains to the ongoing process where AI systems learn and refine their models based on new data and feedback, even after their initial training. Unlike traditional machine learning models that are trained once and then deployed, continuous learning systems evolve, adapt, and improve as they encounter new scenarios and receive more data.
This approach ensures that AI models remain relevant, accurate, and efficient, especially in dynamic environments where data patterns can change.
Frequently Asked Questions
How does Continuous Learning benefit AI systems?
Continuous Learning allows AI systems to adapt to changing conditions, trends, or user behaviors, ensuring that their performance remains optimal and they don't become outdated.
Does Continuous Learning require human intervention?
While the learning process can be automated, human oversight is often beneficial to validate changes, provide labeled data, or address complex scenarios the system might not handle autonomously.
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.
Automate your customer support and marketing with Zapier and SiteSpeakAI
With the power of Zapier's 6000+ available apps and integrations, you can now connect your chatbot to your favorite tools and completely automate every aspect of your customer support and brand marketing.