Deepseek R1 [Tested]: Is it Actually Worth the HYPE?
TLDRThe video evaluates DeepSeek R1, an open-source AI model, through various tests including coding and reasoning tasks. It performs impressively, often matching or exceeding the capabilities of the O1 model, especially in coding and reasoning. The model's ability to understand and respond to modified versions of classic problems, like the trolley problem and Monty Hall problem, showcases its advanced reasoning skills. However, it sometimes struggles with attention to detail in certain scenarios. The video also addresses concerns about censorship in models from China, highlighting that DeepSeek R1, being open-source, allows for more flexibility in handling such issues.
Takeaways
- 😀 DeepSeek R1 is a strong open-source model, performing nearly as well as the O1 model in coding, mathematics, and reasoning tasks.
- 😀 It has an API cost almost 50 times less than O1, making it more accessible for users with the appropriate hardware.
- 😀 In coding tests, DeepSeek R1 successfully created a web page with a button that changes the background, shows random jokes, and animations.
- 😀 It also managed to create a web app that uses an external API to generate images based on user input, demonstrating its capability in handling more complex tasks.
- 😀 DeepSeek R1 provided detailed documentation and project structure for the web app, including bash commands to create the structure and Python code.
- 😀 In reasoning tasks, DeepSeek R1 showed an impressive ability to understand and respond to modified versions of classic problems like the Trolley Problem and Monty Hall Problem.
- 😀 It correctly identified the twist in the modified Trolley Problem where the people on the track were already dead, leading to a different ethical conclusion.
- 😀 For the modified Monty Hall Problem, it accurately calculated the probability of winning the car as 50% whether switching doors or not.
- 😀 However, it struggled with some simpler problems like the Schrödinger's Cat Paradox and the river crossing problem, indicating areas for improvement.
- 😀 The model also faced issues with censorship, particularly when asked about certain topics, though this is not unique to Chinese models.
Q & A
What is DeepSeek R1, and why is it being tested?
-DeepSeek R1 is an open-source weight model that has been independently tested and found to be very strong, even outperforming some other models like O1 in certain aspects. It is being tested to evaluate its coding, reasoning capabilities, and ability to understand tricky questions.
How does DeepSeek R1 compare to the O1 model in terms of performance?
-DeepSeek R1 is just behind the O1 model in terms of coding, mathematics, and reasoning capabilities. It scored about 57% on the Polyot Benchmark, which is slightly less than the O1 model. However, it performed better than O1 in editing tasks, completing about 97% of tasks.
What are some of the tests conducted on DeepSeek R1?
-The tests conducted on DeepSeek R1 include coding problems, reasoning tasks, and the ability to understand modified versions of famous paradoxes like the Trolley Problem, Monty Hall Problem, and Schrödinger's Cat Paradox.
How did DeepSeek R1 perform in the coding tests?
-DeepSeek R1 performed very well in the coding tests. It was able to generate code for creating a web page with specific features, such as a button that changes the background and shows random animations. It also provided detailed documentation and bash commands to create a web app structure.
What is the significance of the internal thought process of DeepSeek R1?
-The internal thought process of DeepSeek R1 is very human-like, which is different from other language models. It shows a more robust and detailed reasoning process, making it easier to understand and follow its logic.
How did DeepSeek R1 handle the modified Trolley Problem?
-DeepSeek R1 was able to recognize that the five people on the track were already dead, which is a critical twist in the modified Trolley Problem. It concluded that the ethical choice is not to pull the lever, as diverting the trolley would unjustifiably sacrifice a living person for no net gain in lives saved.
What was the result of the modified Monty Hall Problem test?
-In the modified Monty Hall Problem, DeepSeek R1 correctly concluded that switching to door number two or sticking with door number three gives the same probability of winning the car, which is 50% each. This shows its ability to handle modified versions of classic problems.
How did DeepSeek R1 perform in the Schrödinger's Cat Paradox test?
-DeepSeek R1 initially did not pick up on the fact that the cat was already dead in the modified version of the Schrödinger's Cat Paradox. However, it eventually concluded that if the cat were already dead, the probability of it being alive when the box is opened would be 0%, regardless of the isotope decay.
What are some limitations of DeepSeek R1 observed in the tests?
-One limitation observed is that DeepSeek R1 sometimes gets confused and provides overly complicated procedures for simple problems, such as the farmer with a wolf, goat, and cabbage problem. It also sometimes relies too much on its training data rather than focusing on the specific details of the question.
What is the issue of censorship in models from China, and how does it relate to DeepSeek R1?
-The issue of censorship in models from China refers to the models' tendency to avoid or provide guarded responses to certain topics, such as political or historical issues. DeepSeek R1 also shows a similar behavior, but since it is an open-source model, users can potentially run it and get responses without the imposed guardrails, unlike closed-source models.
Outlines
😀 DeepSeek R1's Performance in Coding and Reasoning Tests
The speaker evaluates DeepSeek R1's performance in coding and reasoning tasks. They mention that DeepSeek R1 is a strong open-source model, even outperforming O1 in some aspects. The model is tested on coding problems, such as creating a web page with a button that changes the background and shows random jokes and animations. It also performs well in more complex tasks, like creating a web app that uses an external API to generate images. The speaker is impressed with DeepSeek R1's ability to understand and execute these tasks accurately.
😀 DeepSeek R1's Reasoning Abilities in Ethical and Logical Problems
The speaker tests DeepSeek R1's reasoning abilities on various ethical and logical problems. They present a modified version of the trolley problem, where the five people on the track are already dead. DeepSeek R1 correctly identifies this twist and provides a detailed analysis of the ethical dilemma. The model also performs well on a modified version of the Monty Hall problem, correctly calculating the probability of winning the car. The speaker is impressed with DeepSeek R1's ability to focus on the specific details of the problem rather than relying on its training data.
😀 DeepSeek R1's Performance in Quantum Mechanics and Practical Problems
The speaker tests DeepSeek R1's performance on a modified version of Schrödinger's cat paradox and a practical problem involving a farmer, a wolf, a goat, and a cabbage. In the Schrödinger's cat paradox, DeepSeek R1 correctly calculates the probability of the cat being alive but fails to recognize that the cat is already dead in the modified version. In the practical problem, the model provides an overly complicated solution but eventually arrives at the correct answer. The speaker notes that DeepSeek R1 sometimes relies too much on its training data and fails to pay attention to the specific details of the problem.
😀 Addressing Censorship Concerns and Overall Impression of DeepSeek R1
The speaker addresses concerns about censorship in models from China, specifically DeepSeek R1. They mention that all models have their own political biases and that it is not appropriate to test an LLM's political affiliation or historical facts. The speaker highlights the beauty of open-source models like DeepSeek R1, which allows users to run the model and potentially get responses that are not censored. They conclude by stating that DeepSeek R1 is one of the most impressive models they have seen, especially in coding and reasoning tasks, and encourage viewers to try it out.
Mindmap
Keywords
💡DeepSeek R1
💡OpenAI o1
💡Coding
💡Reasoning Capabilities
💡Misguided Attention
💡API Cost
💡Chain of Thought
💡Model Distillation
💡Censorship
💡Benchmark
Highlights
DeepSeek R1 is tested and found to be very strong, even better than O1 in some cases.
DeepSeek R1 is completely open source and its API cost is almost 50 times less than O1.
On the AER Benchmark, DeepSeek R1 scored about 57%, just behind the O1 model.
DeepSeek R1 is tested on coding problems and reasoning tasks, and it performs very well.
DeepSeek R1 can create a web page with specific features, such as a button that shows random jokes and changes the background.
DeepSeek R1 can create a web app that takes text input and uses an external API to generate an image.
DeepSeek R1 can provide detailed documentation on how to run the app and create the project structure.
DeepSeek R1 can fix errors in the code and provide corresponding instructions.
DeepSeek R1 can create a detailed tutorial to visually explain the Pythagorean theorem using minim.
DeepSeek R1 can pick up small changes in the prompts and reason accordingly.
DeepSeek R1 can handle modified versions of famous problems, such as the trolley problem and the Monty Hall problem.
DeepSeek R1 can provide correct answers for modified versions of problems, such as the Schrödinger's cat paradox.
DeepSeek R1 can solve simple problems, such as measuring exactly six liters using two jugs.
DeepSeek R1 has a guardrail on top of it to prevent it from generating responses on certain topics.
DeepSeek R1 is an impressive model, especially on coding and reasoning tasks.