This is a great question! AI detector (also known as AI generated content detectors) are a rapidly developing field with various implementation methods, but currently there is no "silver bullet" that can guarantee 100% accuracy.

Simply put, the core idea of AI detector is to search for different "fingerprints" or "style features" left by humans and AI when generating content. During the training and learning process, AI models will form specific patterns that are often imperceptible to humans.

The following are several mainstream implementation methods and technical principles currently available:

1、 Statistical feature-based detection method (currently the most mainstream)

This method treats the detection problem as a classification problem. The goal is to train a classifier that can distinguish between "human written" and "AI generated" text.

Feature extraction: Extracting various quantifiable features from text. These features are usually the areas where AI and humans exhibit differences unconsciously, such as:

Confusion level: measures the degree to which a text is "unexpected". AI models (especially early or weaker models) tend to generate high probability, common word combinations, so the overall perplexity of the text is usually low and uniform. Human writing is more creative and volatile, with higher levels of confusion and greater variability.

Emergencies: Measuring changes in vocabulary usage. Human vocabulary is more varied, while AI's vocabulary distribution may be smoother.

Word frequency distribution: Analyze the frequency of use of functional words (such as "de", "di", "de") and rare words.

Text complexity: such as variations in sentence length, paragraph structure, and diversity of grammatical patterns.

Semantic consistency: Check the consistency of viewpoints in long texts, as some AI may have inconsistencies when generating long texts.

Model training:

Collect a large dataset containing known human written text and AI generated text (e.g. text generated using GPT-3.5, GPT-4, etc.).

Train a classification model (such as logistic regression, support vector machine, or neural network) using the above features.

The trained model can predict the probability of whether the new text is "written by humans" or "generated by AI" based on its features.

Representative tools: Early GPT-2 detector, Stanford's DetectGPT (using a concept of "negative curvature", believing that AI generated text is in a special region on the probability surface of the model itself).

2、 Watermark based method

This is a more proactive and reliable technology that requires intervention during text generation.

Embedding watermark: intentionally introducing a subtle and specific pattern when generating text in AI models. For example:

Secret vocabulary list: pre-set a secret vocabulary list. When generating each word, the model will have a slightly higher tendency to select vocabulary from this list according to some rule (such as based on the previous word). Human readers may not feel it at all, but detectors can detect this pattern through statistical analysis.

Grammar or style tagging: Introducing extremely subtle and consistent grammatical structure preferences.

Detecting watermark:

The detector uses the same key (i.e. the secret vocabulary or rule) to check for statistical bias in the text. If it exists, it is determined to be generated by AI.

Advantages: If implemented properly, it is very reliable and difficult to remove by ordinary users.

Disadvantage: It requires the cooperation of AI model providers (such as OpenAI, Google, etc.) and cannot detect content generated by AI models without embedded watermarks.

3、 Internal State Analysis Based on Neural Networks

This method is more in-depth, directly analyzing the "thinking process" of the AI model itself that generates text.

Principle: At each step of AI text generation, the activation state of its internal neural network (i.e. which neurons are activated) forms a unique pattern. This pattern may differ from human writing.

Implementation: The detector is usually also a neural network trained to recognize this unique activation pattern. This method requires access to the internal layers of the AI model, so it is usually implemented by the model developer themselves.

4、 Zero sample or prompt based methods

This type of method does not require training specialized detection models, but cleverly utilizes generative models to detect themselves.

Assumption: AI models are more "familiar" with the content they generate.

Method: Give the same AI model a piece of text to be tested and ask it to continue writing or calculate the probability of generating that text. If the model can easily and with high probability continue writing text, then the text is likely to be generated by AI. DetectGPT utilizes a similar idea.

The main challenges and limitations currently faced by detectors

Accuracy is not 100%: All detectors have issues with false positives (mistaking human text for AI) and false negatives (not detecting AI text). Especially for high-level human authors or carefully modified AI texts, detection is very difficult.

Fast iteration speed: AI models update and iterate very quickly (such as from GPT-3.5 to GPT-4), and once the detector is released, the "pattern" it targets may quickly become outdated and require continuous retraining.

Adversarial attacks: Users can easily bypass many statistical feature-based detectors by simply requesting AI to "write in a human style, adding some unsmooth and erroneous elements," or paraphrasing the text generated by AI.

Poor generalization ability: a detector trained for GPT-3 may be invalid for the content generated by Claude or ERNIE Bot.

Ethical privacy issues: Large scale text detection may involve complex issues such as privacy and academic integrity.

Summary and Outlook

Implementing an AI detector is a typical dynamic adversarial process of 'one foot higher on the road, one zhang higher on the devil'.

In the short term, the combination method based on statistical features and watermarking is the most promising.

In the long run, this may not be a purely technical issue. The more likely future is:

Legislation and regulations: Require AI generated content to have mandatory watermarks or declarations added.

Human machine collaboration: The detector is not used as the final judgment, but as an auxiliary tool to remind humans to scrutinize more carefully.

Source governance: AI model developers have a greater responsibility to consider traceability from the model design stage.

Therefore, the implementation of AI detector is a comprehensive technical field that integrates natural language processing, statistics, cryptography (watermarking), and ethics.