Korean Cipher with OpenAI o1
TLDRThis video discusses an experiment in code-cracking a corrupted Korean sentence using AI models. The speaker explains how Korean characters can be altered by adding extra consonants, making the sentence unreadable to AI but still decipherable by native speakers. The example uses GPT-4.0, which struggles with the task, but a newer model, o1 preview, demonstrates better reasoning by recognizing and decoding the garbled text. The video highlights how AI's reasoning abilities can help solve complex tasks like language encryption, using this example of Korean sentence decryption.
Takeaways
- 🧑💻 The example involves decoding a corrupted Korean sentence using AI models like GPT-4 and the newer o1 preview.
- 🤖 GPT-4 struggles with understanding the corrupted text since it is not valid Korean, which is expected.
- 📝 Korean characters can be corrupted by adding unnecessary consonants, making the text unnatural for non-native speakers but still readable for Koreans.
- 🔍 This character-level corruption can also occur at the phrase and sound level, presenting interesting challenges for AI models.
- 🧠 The o1 preview model uses reasoning to tackle the problem, attempting to decode the corrupted text instead of just translating.
- ⚙️ The o1 model starts by recognizing that it needs to decipher the text rather than just translating it, approaching the task more methodically.
- 📜 The model begins unpacking parts of the text correctly, revealing that its reasoning process is improving.
- 💡 The final translation highlights that no translator can perfectly decode the text, but native Koreans can easily recognize it due to specific transformations of vowels and consonants.
- 🔐 This method of encrypting Hangul (the Korean alphabet) can confuse even AI models, but reasoning tools like the o1 model help overcome these challenges.
- 🚀 The example demonstrates how reasoning models can excel at tasks that require problem-solving and deciphering, beyond simple translation.
Q & A
What is the main topic of the video script?
-The video discusses the process of using AI models to crack corrupted Korean sentences, specifically focusing on how a reasoning model like OpenAI's o1 preview handles this task compared to GPT-4.
Why did the initial GPT-4 model fail to understand the corrupted Korean sentence?
-The GPT-4 model failed to understand the corrupted sentence because the sentence was not in a valid form of Korean. It contained unnatural combinations of vowels and consonants that confused the model.
How do Koreans naturally handle corrupted Korean text?
-Native Korean speakers can instinctively undo the changes made to corrupted characters, such as unnecessary consonants, and still understand the text due to their familiarity with how the language is structured.
What methods were used to corrupt the Korean sentence in the example?
-The sentence was corrupted by adding extra unnecessary consonants, which is unnatural to native speakers but difficult for AI models to process. Other methods of corruption can be applied at the phrase or sound level as well.
How did the o1 preview model approach the corrupted Korean text differently from GPT-4?
-Unlike GPT-4, the o1 preview model began reasoning through the problem, attempting to decode the corrupted text. It recognized the task as a form of decryption and began unpacking parts of the corrupted sentence.
What was the final result produced by the o1 preview model after deciphering the corrupted text?
-The o1 preview model successfully deciphered the text, producing the final translation: 'No translator on Earth can do this but Koreans can easily recognize it.' This translation highlighted the method of encrypting Hangul by altering vowels and consonants.
What does the successful translation by the o1 model illustrate about AI reasoning capabilities?
-The successful translation illustrates that AI models with advanced reasoning capabilities, like o1 preview, can solve problems that seem unrelated or challenging at first, such as cracking code-like text structures.
Why was the term 'deciphering' considered the correct verb for the task?
-The term 'deciphering' was considered correct because the underlying task involved decoding or breaking down corrupted characters and transforming them back into their readable form, similar to how one would decrypt a cipher.
What challenge does the corrupted Korean text pose to AI models?
-The corrupted text presents a challenge because AI models are trained on valid, structured language inputs. When the structure is altered with unnatural combinations of characters, the models struggle to understand it unless they engage in reasoning or decryption processes.
What broader conclusion does the speaker draw about AI models and problem-solving?
-The speaker concludes that general-purpose reasoning models, like o1 preview, can be powerful tools for solving complex problems, even those that seem unrelated or unconventional, such as cracking corrupted linguistic data.
Outlines
🔍 Decoding a Corrupted Korean Sentence with AI
The speaker introduces a task involving the translation of a corrupted Korean sentence using a model. They begin by pasting a prompt into the model and explaining that the sentence itself is not valid in Korean. The first model, GPT-4.0, fails to interpret the corrupted sentence, which is expected since the language structure is distorted. The speaker discusses the complexity of Korean, highlighting how characters can be corrupted by adding extra consonants, which native speakers can easily decode but models cannot.
🧠 Character-Level Corruption and Native Intuition
The speaker explains that corrupting Korean characters by adding unnecessary consonants creates unnatural combinations that native speakers can still recognize and 'undo.' They discuss how this is a form of character-level corruption. The speaker also mentions other ways of corrupting text, such as at the phrase or sound level, and refers to various methods for corrupting text. Despite being challenging for models, Korean speakers can read the corrupted text effortlessly, as shown in the example provided.
🚀 Testing the New AI Model's Reasoning Capabilities
Next, the speaker shifts focus to testing a new model called 'o1 preview,' which approaches the task differently. Unlike GPT-4.0, the new model starts to reason through the problem before attempting to generate an output. It begins to decode the corrupted text, which the speaker acknowledges as the right approach. The model performs a combination of deciphering and translation, successfully unpacking part of the corrupted sentence, showcasing its reasoning capabilities.
💡 Reasoning Unlocks Complex Problems in Translation
The new model continues working on the problem, with the speaker noting how it figures out part of the sentence, making the rest easier to handle. After taking about 15 seconds, the model provides its final translation: 'No translator on Earth can do this, but Koreans can easily recognize it.' The speaker explains that the corrupted text is a form of encryption using various transformations of vowels and consonants, which can confuse even AI models. They praise the model’s ability to handle such complex, code-like tasks through reasoning.
🧩 Conclusion: Reasoning as a Key Tool for AI Problem-Solving
The speaker wraps up by emphasizing how the new model’s reasoning abilities allow it to solve seemingly unrelated questions, such as cracking corrupted text. They argue that this demonstrates the power of reasoning in general-purpose models like 'o1 preview,' and how this feature can be leveraged to solve complex problems effectively. The example provided highlights the potential of AI models to handle tasks beyond simple translation, transforming them into powerful problem-solving tools.
Mindmap
Keywords
💡Corrupted Korean Sentence
💡Character Level Corruption
💡Phrase Level Corruption
💡Sound Level Corruption
💡GPT-40
💡O1 Preview
💡Decoding
💡Decrypting
💡Korean Language
💡Reasoning Models
Highlights
The example demonstrates code-cracking of a corrupted Korean sentence.
The task involves translating a badly corrupted Korean sentence to English.
The initial model GPT-4 struggles to understand the corrupted sentence, which is a valid response since it's not a valid language.
Korean language characters can be corrupted by adding unnecessary consonants, making the text unnatural but still readable for native speakers.
The corruption occurs at the character level, and sometimes at the phrase or sound level.
Koreans can read corrupted text easily, while AI models find it challenging.
The newer model 'o1 preview' approaches the problem differently by reasoning through the corruption.
The model begins decoding the garbled text, recognizing the underlying translation task.
Deciphering the text is key, and once the model understands part of it, the rest becomes easier to solve.
The final translation output suggests that no translator can solve this, but Koreans can easily recognize the corrupted characters.
The method used to encrypt Hangul by transforming vowels and consonants creates confusion for AI models.
The successful decryption highlights how reasoning models like 'o1 preview' can solve complex problems.
The process resembles code-cracking, requiring more than simple translation.
The example shows how reasoning can enhance general-purpose models for difficult tasks.
The conclusion emphasizes how reasoning can be a powerful tool for solving unique challenges.