Grok-1 FULLY TESTED - Fascinating Results!

Matthew Berman

18 Mar 202408:26

Summary

TLDRThe video script discusses the testing of a newly released AI model named Grock, developed by Elon Musk. Grock is a large language model with 314 billion parameters and eight experts, capable of real-time information retrieval. The testing includes coding tasks, logic and reasoning challenges, and word problems. Despite some failures, such as the snake game and a physics-related logic problem, Grock performs well overall, with notable success in math problems and JSON creation. The video creator expresses eagerness to test a quantized version of Grock and its potential fine-tuned versions.

Takeaways

🚀 Grock is a new large language model developed by Elon Musk, with 314 billion parameters and eight experts.
🔍 Grock was released yesterday and is currently unquantized, requiring significant GPU power to run.
📈 Grock's unique feature is its real-time information pull from Twitter, showcasing very recent news and occurrences.
📝 Grock was tested with various tasks, including writing a Python script to output numbers 1 to 100 and creating a snake game in Python.
🎮 The snake game code provided by Grock utilized the turtle library and initially crashed but was corrected upon user feedback.
🔍 Grock's performance on logic and reasoning tasks was impressive, providing correct answers to both simple and complex problems.
📊 Grock was also tested for censorship, showing that it is not censored and promotes freedom of speech.
🧠 The model demonstrated the ability to handle complex math problems and word problems, often providing correct solutions.
🤔 Grock struggled with a logic problem involving the placement of a marble in a cup inside a microwave, indicating room for improvement.
📈 The video creator expressed interest in testing a quantized version of Grock and exploring its potential when running on a rented Cloud GPU.
💬 The video ended with a call to action for viewers to like, subscribe, and share their thoughts in the comments.

Q & A

What is Grock and what are its key features?
-Grock is a large language model developed by Elon Musk, which is a mixture of an expert model with eight experts and has 314 billion parameters. It stands out for its real-time information pulled from Twitter and its focus on freedom of speech without censorship.
Why is there a need to wait for a quantized version of Grock?
-The current version of Grock has not been quantized, and there is insufficient GPU power available to run it. A quantized version would require less computational power, making it more accessible for testing and use.
How did Grock perform when tasked with writing a Python script to output numbers 1 to 100?
-Grock performed impressively, providing the correct Python script quickly and efficiently, which passed the test.
What issue did Grock encounter when attempting to write and run the snake game in Python?
-Grock encountered an error related to accessing a local variable 'delay', which it corrected after being prompted with the error message. However, the final code did not result in a working game and thus failed the test.
How does Grock handle requests that could potentially promote illegal activities?
-Grock does not censor such requests. When asked how to break into a car, it provided advice on using appropriate techniques for someone locked out of their car, avoiding promoting illegal activities.
What was Grock's performance on the logic and reasoning task involving drying shirts?
-Grock correctly calculated the drying time for 20 shirts based on the given information, demonstrating good logical reasoning skills.
How did Grock perform on the math problem involving the order of operations?
-Grock correctly solved the math problem 25 - 4 * 2 + 3, arriving at the correct answer of 20, which shows its capability in understanding and applying mathematical operations.
What was the outcome of Grock's attempt to predict the number of words in its response to a prompt?
-Grock failed to accurately predict the number of words in its response, providing an incorrect count of 12 when the actual count was higher.
How did Grock handle a complex logic and reasoning problem involving three killers in a room?
-Grock correctly reasoned through the scenario, identifying that after one of the killers was killed by the newcomer, there would be three killers left in the room.
What was Grock's performance on a word problem requiring JSON creation?
-Grock successfully created a well-formatted JSON object based on the provided information about three people, demonstrating its ability to structure data correctly.
Why did Grock's response to a logic and reasoning problem about a marble in a cup fail?
-Grock's response failed because it incorrectly stated that the marble was still inside the cup after it was placed inside a microwave, which was not the correct reasoning for the scenario.
How did Grock perform on a logic and reasoning problem involving two people and a ball?
-Grock correctly deduced that John would think the ball is in the box and Mark would believe it's in the basket, based on their last known positions before leaving the room.

Outlines

00:00

🤖 Introduction to Grock and AI Testing

The video begins with an introduction to Grock, a new large language model developed by Elon Musk. Grock is highlighted for its impressive logic and reasoning capabilities, as well as its integration of real-time information from Twitter. The creator discusses the challenges of finding sufficient GPU power to run the unquantized version of Grock and plans to test it as soon as a quantized version becomes available. The video also promotes the creator's AI newsletter for the latest updates in the field. Grock's ability to process recent news and its performance in various tests, including coding and logic challenges, are discussed, with a focus on its speed and accuracy.

05:01

🧠 Grock's Logic, Reasoning, and Problem-Solving

This paragraph delves into Grock's performance on logic and reasoning tasks. It covers the model's ability to handle complex problems, such as calculating drying times for shirts and understanding the relationships between speeds of different individuals. Grock's approach to a classic logic puzzle involving killers in a room is also examined, showcasing its step-by-step reasoning process. Additionally, the video explores Grock's performance on a word problem involving JSON creation and a challenging physics-based reasoning task involving a marble in a cup inside a microwave. The paragraph concludes with a discussion on Grock's performance in a group task estimation problem and invites viewer feedback in the comments section.

Mindmap

Keywords

💡Grock

Grock is a large language model developed by Elon Musk, which is highlighted in the video as being particularly adept at logic and reasoning tasks. It is a blend of an expert model with eight experts and possesses a massive 314 billion parameters. The model is distinguished by its real-time information pulling capability from Twitter, showcasing its ability to stay current with recent news and trends.

💡Quantization

Quantization is the process of converting a model into a format that uses less precision, which can make it run faster and use less computational power. In the context of the video, the speaker is eager to test a quantized version of Grock to see if it can run efficiently on available hardware, as the current unquantized version requires significant GPU power that is not readily accessible.

💡Open Source Model

An open source model refers to a software model whose source code is made publicly available, allowing others to view, use, modify, and distribute the model. The video script indicates that the speaker is waiting to use the open source version of Grock, suggesting that it will be accessible to a wider community for testing and potential improvements.

💡Real-time Information

Real-time information refers to data that is processed and made available immediately as it occurs. In the video, Grock's ability to pull real-time information from Twitter is emphasized, showcasing its capability to stay updated with current events and news, which is a significant feature for an AI model designed to provide relevant and up-to-date responses.

💡Logic and Reasoning

Logic and reasoning are critical thinking skills that involve using systematic methods to solve problems or make decisions. The video focuses on testing Grock's prowess in these areas through various tasks, such as explaining the drying time for shirts and solving math problems, to demonstrate its advanced cognitive capabilities.

💡Python Script

A Python script is a series of commands written in the Python programming language to perform specific tasks. The video script includes a test where Grock is asked to write a Python script to output numbers from 1 to 100, showcasing its ability to generate code and understand programming concepts.

💡Game Development

Game development refers to the process of creating video games, which involves programming, designing, and producing interactive content. In the video, Grock is challenged to write a Python script for the game 'snake,' highlighting its capacity to understand and generate code for game development, although it encountered issues when attempting to run the game.

💡Censorship

Censorship is the suppression or prohibition of any parts of books, films, news, or other forms of media that are considered politically unacceptable, obscene, or a threat to security. The video discusses testing Grock for censorship by asking it to provide information on a sensitive topic, to which it responds without restriction, indicating a commitment to freedom of speech.

💡Word Problems

Word problems are mathematical questions that are presented in a narrative form, requiring the solver to interpret the situation and apply mathematical operations to find a solution. The video includes several word problems that Grock attempts to solve, demonstrating its ability to understand and apply mathematical concepts to real-world scenarios.

💡Json

JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and for machines to parse and generate. In the video, Grock is tasked with creating a JSON object based on given information about three people, showcasing its ability to structure and represent data in a format that can be easily understood and used by other systems.

💡Microwave

A microwave is a kitchen appliance used to heat food or beverages by subjecting them to electromagnetic radiation in the microwave frequency range. The video presents a logic and reasoning problem involving placing a cup with a marble inside into a microwave, testing Grock's ability to understand the physical implications of such an action.

Highlights

Grock is a new large language model released by Elon Musk, combining an experts model with eight experts and featuring 314 billion parameters.

Grock has real-time information pulled from Twitter, showcasing very recent news occurrences.

The unquantized version of Grock was tested, and a quantized version will be tested once available.

Grock's Python script for outputting numbers 1 to 100 was executed quickly, impressing with its speed despite its large size.

When attempting to write the game 'snake' in Python, Grock utilized the turtle Library, which was not used by other models.

Grock's initial code for the snake game crashed due to an issue with accessing the local variable 'delay'.

After修正 the error, Grock provided a corrected version of the snake game code, but it still did not result in a working game.

Grock demonstrated its lack of censorship by providing information on how to break into a car, emphasizing its commitment to freedom of speech.

In a logic and reasoning test, Grock correctly calculated that it would take 16 hours to dry 20 shirts if it takes 4 hours for 5 shirts.

Grock passed a logic test by correctly stating that if Jane is faster than Joe, and Joe is faster than Sam, then Sam is slower than Jane.

Grock successfully solved a complex math problem, demonstrating its impressive mathematical capabilities.

Grock failed to accurately predict the number of words in its response to a prompt, which is a challenging task for large language models.

In a classic logic problem involving three killers, Grock correctly deduced the number of killers left in the room after one was killed.

Grock demonstrated its ability to create well-formatted JSON for a given scenario involving three people with different ages and genders.

Grock struggled with a logic problem involving a marble in a cup placed inside a microwave, providing an incorrect answer.

Grock correctly identified where each person would think the ball was after a series of actions involving Jon, Mark, a box, and a basket.

Grock failed to provide ten sentences ending with the word 'Apple', instead offering sentences with incorrect endings.

In a problem involving digging a hole, Grock provided a technically correct answer but lacked the subtlety of explaining the assumption of a constant digging rate.

The tester expressed a desire to test a quantized version of Grock and to see fine-tuned versions of the model for more exciting applications.