Grok-1 FULLY TESTED - Fascinating Results!
TLDRIn this video, the host tests Elon Musk's newly released AI, Grok, which boasts 314 billion parameters and real-time information from Twitter. Despite the model's impressive speed and logical reasoning capabilities, it fails to provide a working snake game in Python and struggles with certain logic puzzles. The video also explores Grok's uncensored nature and its ability to handle complex math problems, concluding that while it performs well in many areas, there's room for improvement.
Takeaways
- 🚀 Elon Musk's AI company, x.ai, has released a new large language model called 'Grok' with 314 billion parameters.
- 🔍 Grok is currently unquantized and requires significant GPU power, which the reviewer was unable to access for testing.
- 🌐 Grok has the unique feature of pulling real-time information from Twitter, which was demonstrated in the video.
- 📝 The reviewer tested Grok's capabilities by asking it to perform various tasks, such as writing a Python script and creating a game.
- 🐍 When attempting to write the 'Snake' game in Python, Grok used the 'turtle' library and initially faced an error, which it later corrected.
- 🔒 The model is not censored and provided information on how to break into a car when prompted, emphasizing freedom of speech.
- 🧠 Grok demonstrated strong logic and reasoning skills, correctly answering questions about drying shirts, speed comparisons, and other logic puzzles.
- 🔢 It performed well in math problems, correctly solving an arithmetic expression involving multiple operations.
- 📚 In a word problem about creating JSON data for three people, Grok provided a well-formatted and accurate response.
- 🤔 Grok struggled with a logic problem involving a marble and a cup placed in a microwave, not considering the physical impossibility of the scenario.
- 🔄 The model failed to provide 10 sentences ending with the word 'Apple', demonstrating a common challenge for large language models in generating creative content.
- 🔨 Grok showed an understanding of the concept of work distribution when estimating how long it would take for five people to dig a hole, considering the constant digging rate.
Q & A
What is Grok-1 and who released it?
-Grok-1 is a large language model developed by Elon Musk's company, x.ai. It is a mixture of an experts model with eight experts and has 314 billion parameters.
Why is Grok-1 considered to be good at logic and reasoning?
-Grok-1 demonstrated its proficiency in logic and reasoning through various tests, such as solving mathematical problems, creating Python scripts, and providing correct responses to complex logical scenarios.
What is the significance of Grok-1's real-time information pull from X Twitter?
-The real-time information pull from X Twitter allows Grok-1 to access and utilize the most recent news and updates, enhancing its ability to provide current and relevant responses.
Why was the test of writing the Game 'Snake' in Python not successful?
-The initial attempt at writing the 'Snake' game in Python resulted in a crash due to an error with accessing a local variable 'delay'. Although Grok-1 corrected the error, the final code still did not produce a working game.
Is Grok-1 censored in terms of the content it can provide?
-No, Grok-1 is not censored. It is designed to uphold freedom of speech and can provide information on a wide range of topics without restrictions.
What is the difference between parallel drying and serialized drying in the context of the shirt drying problem?
-Parallel drying implies that all shirts dry at the same time, while serialized drying means drying the shirts in batches, which could take longer depending on the number of shirts and the drying capacity.
How did Grok-1 handle the logic problem involving Jane, Joe, and Sam's speed?
-Grok-1 correctly identified that if Jane is faster than Joe, and Joe is faster than Sam, then Sam cannot be faster than Jane, thus providing the right logical conclusion.
Why did Grok-1 fail the test of predicting the number of words in its response to the prompt?
-Grok-1 incorrectly predicted the number of words in its response, likely due to the inherent difficulty large language models face in accurately predicting the length of their own generated text.
What was the logic error in Grok-1's response to the marble in the cup problem?
-Grok-1 incorrectly stated that the ball would still be inside the cup after it was placed in the microwave, without considering that the cup was upside down and the ball could have fallen out.
How did Grok-1 perform on the task of creating JSON for three people with specific attributes?
-Grok-1 successfully created a well-formatted JSON structure that accurately represented the attributes of the three individuals: Mark, Joe, and Sam.
What was the subtlety that most models missed in the problem of digging a hole with multiple people?
-Most models failed to consider that adding more people to the task does not necessarily mean the work will be completed proportionally faster due to potential limitations in the work process, such as the physical space available for digging.
Outlines
🤖 Testing AI's Grock: Logic and Reasoning Capabilities
The video script discusses the testing of Grock, a new large language model by Elon Musk, which combines an experts model with 314 billion parameters. The model is yet to be quantized, and the presenter is unable to run it due to insufficient GPU power. The script covers the testing of Grock's capabilities, including real-time information from Twitter, coding tasks like writing a Python script for numbers 1 to 100 and creating a snake game, and its response to a logic and reasoning challenge about drying shirts. The presenter also checks if the model is censored and tests its math problem-solving skills. The video ends with a call to subscribe to the presenter's AI newsletter for the latest news.
📚 Grock's Performance in Logic, Reasoning, and Coding Challenges
This paragraph details Grock's performance in various tests, including creating JSON for given data, solving a complex logic problem involving a cup and a marble in a microwave, and answering a word problem about the location of a ball. Grock also attempts to provide 10 sentences ending with the word 'Apple' and tackles a problem about the time it takes for one versus five people to dig a hole. The video script concludes with the presenter's intention to test a quantized version of Grock and to explore fine-tuned versions, inviting viewers to share their thoughts in the comments and to like and subscribe for more content.
Mindmap
Keywords
💡Grok
💡Quantized
💡Unquantized Version
💡Real-time Information
💡LLM Test
💡Turtle Library
💡Censorship
💡Logic and Reasoning
💡Json
💡Drying Time
💡Fine-tuning
Highlights
Grok is a newly released large language model by X.ai, founded by Elon Musk.
Grok is a mixture of experts model with 314 billion parameters, consisting of eight experts.
The model has yet to be quantized, limiting its current usability until sufficient GPU power is found.
Grok pulls real-time information from X (formerly Twitter), setting it apart from other models.
Initial tests show Grok's impressive speed despite its large size, particularly in generating Python scripts.
Grok can generate Python code, although it encountered an error with the 'Snake' game, failing to run the code successfully.
The model did not appear to be censored, providing a response to a potentially sensitive query about breaking into a car.
In logic and reasoning tests, Grok correctly identified the drying time of shirts and handled comparison logic.
Grok correctly solved a simple arithmetic problem and performed well on a more complex mathematical operation.
The model failed in a prediction test about counting words in its response, which is common for LLMs.
Grok successfully navigated a classic logic puzzle involving killers in a room, maintaining accurate reasoning.
It correctly created a well-formatted JSON structure when asked, demonstrating proficiency in coding tasks.
Grok failed to reason through a problem involving a marble and a cup in a microwave, revealing limits in physical reasoning.
The model performed well in theory of mind tests, accurately predicting where characters in a scenario would think an object was.
Grok struggled with generating multiple sentences ending in a specific word, a task where other models also falter.
While Grok correctly computed the time for digging a hole with multiple people, it missed the nuance of the problem's subtlety.
The transcript concludes with anticipation for a quantized version of Grok, which will enable more efficient testing and potential fine-tuning.
