AI Inference vs Training. What is AI Inference?

Understanding AI Inference

The world of artificial intelligence is vast and varied, with many different concepts and processes. One of these is AI inference, a crucial component of how AI models operate and provide value.

Definition and Importance

AI inference is the process where a trained neural network recognizes images, spoken words, and diseases, predicts text, or suggests choices based on its training. In simple terms, it involves taking smaller sets of real-world data and rapidly producing accurate results, making use of the training gained by a neural network.

Real-world examples

• Unlocking your phone with facial recognition:  When you use Face ID or similar technology to unlock your phone, AI inference is happening behind the scenes. Your face was never seen in this precise lighting and angle - and has to be analyzed anew, to ensure high security. The model has been trained on a massive dataset of faces and can infer with a high degree of accuracy, whether the face looking at the phone matches yours.
• Fraud detection in financial transactions:  Banks and financial institutions use AI to monitor transactions for suspicious activity. The AI model analyzes ongoing transaction details, spending habits, and other factors to infer whether a transaction might be fraudulent, helping to prevent financial losses.
• Medical imaging analysis:  In the medical field, AI is being used to analyze medical images like X-rays and MRIs. AI Model sees a completely new image and has to infer the presence of abnormalities or diseases based on its training.

Process of Inference

Once the AI model is trained, it can then recognize and make predictions on new data. This is the inference phase, where the model applies what it has learned to unfamiliar data. This phase can take place on various platforms, from the cloud to edge devices, depending on where the inference needs to occur.

For example, if you're using a large language model's API, like our LLM API, you're engaging with the inference phase of AI. The model has been previously trained on a vast corpus of text, and now it's using that training to generate responses to your prompts.

The efficiency and speed of inference can be greatly impacted by the hardware it's run on. We provide Cloud inference, so that the computation is run on our servers, and not your device. ChatGPT-4o is the latest step towards inference on devices, as it's lightweight and quick - but it'll take some time until we'll see a smartphone that can handle the inference all by itself.

What's next?

To sum it all up - AI Inference is the comprehension process - but for an AI Model. To learn the language of AI, we created this Academy Section, where you can learn both the basics of Prompt Engineering and the advanced stuff.

Looking ahead, the applications of AI are expected to grow. It's crucial for various sectors, including teaching, engineering, medical discoveries, and space exploration. All things considered, this means more opportunities for AI enthusiasts like you.

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References

‍[1]: https://www.oracle.com/artificial-intelligence/ai-inference/
‍[2]: https://www.cloudflare.com/learning/ai/inference-vs-training/
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If you aren't ready for an API yet, check out our free AI/ML AI Playground.

We're excited to see what amazing projects you will bring to life. Happy prompting!

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