Understanding AI 101: What is Inference in Machine Learning and...
The rapidly evolving field of Artificial Intelligence (AI) has led to significant advancements in Machine Learning (ML), with “inference” emerging as a crucial concept. But what exactly is inference, and how does it work in a way that you can make most useful for your AI-based applications?
Understanding Inference
In the context of ML, inference refers to the process of utilizing a trained model to make predictions, draw conclusions, or generate text about new, unseen data. This stage is the culmination of the ML pipeline, where the model is deployed to produce outputs based on the patterns, relationships, and insights it acquired during the training phase of AI. Inference is a critical step, as it enables ML models to be applied in real-world scenarios, such as:
- Virtual assistants
- Chatbots
- Self-driving cars
- Medical diagnosis systems
Examples like speech recognition will only be useful in real-time applications if the system can provide exceptionally fast inference speeds. Defining Inference: A Simple Analogy
Imagine you’re having a conversation with a language model, and you ask it to complete a sentence: “I love reading books about _______.” The model has been trained on a vast amount of text data, including books, articles, and conversations. Based on this training, the model uses its knowledge to make an inference: “I love reading books about science fiction.” The model didn’t simply memorize the answer; instead, it used the patterns and relationships it learned from the training data to generate a response that makes sense in the context of the sentence.
In the example about reading books, the model’s inference is based on its understanding of language patterns.
How Inference Works
Inference involves a series of complex steps, including:
Types of Inference
There are several types of inference used in ML, including:
- Statistical inference
- Causal inference
- Bayesian inference
If you want to get much deeper into this and read about the foundation of these methods check out the paper, Attention Is All You Need by Vaswani et al. from 2017 or this transformer neural network explanation video.
Real-World Applications of Inference
Inference has numerous real-world applications, including the following. Check out the examples powered by Groq:
A Summary of Inference To Get Started
In essence, inference is the process of applying learned knowledge to make predictions or decisions about new, unseen data. It’s a fundamental concept in ML, and understanding it is key to unlocking the full potential of AI applications. By grasping the basics of inference, you’ll be better equipped to explore the exciting world of AI and its many applications, from language translation and text summarization to image recognition and speech synthesis.
After understanding inference, a great next step is to begin exploring prompting techniques or understanding what a token is in ML.
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