Hi, I set my API to gpt-4, and asked it:
Please provide a brief summary of the main advancements and differences between GPT-3 and GPT-4, with specific focus on their architecture, capabilities, and potential applications. Additionally, highlight any notable limitations or concerns related to the use of these language models.
It replied with:
As of now, GPT-3 (Generative Pretrained Transformer 3) is the latest version of the powerful language model developed by OpenAI. It has 175 billion machine learning parameters and has made significant advancements over its predecessor, GPT-2, in terms of its ability to generate human-like text, answer questions, translate languages, and even write poetry.
GPT-3’s architecture is based on transformer models, which use self-attention mechanisms to generate predictions. It can understand context and make connections between different parts of a text. GPT-3 has been used in various applications, including content creation, programming help, tutoring, language translation, and more.
However, GPT-3 does have limitations. It can sometimes generate incorrect or nonsensical responses and lacks the ability to fact-check its outputs. Furthermore, it can inadvertently generate biased or offensive content, raising ethical concerns. It also lacks explainability, meaning it cannot provide clear reasoning for its outputs.
As for GPT-4, as of the time of this response, OpenAI has not released a GPT-4 model. Therefore, it’s not possible to provide a comparison or detail any advancements or differences between GPT-3 and GPT-4. It’s expected that GPT-4, whenever it is released, will aim to overcome the limitations of GPT-3 and bring further advancements in the field of AI language models.
As a point of comparison, I asked the same question to ChatGPT Plus, and got this more appropriate response:
Certainly, here’s a brief overview of the main advancements and differences between GPT-3 and GPT-4, focusing on architecture, capabilities, and potential applications:
GPT-3
Architecture:
- GPT-3 has 175 billion machine learning parameters.
- Uses a Transformer architecture.
Capabilities:
- Text completion, summarization, translation, question-answering, and more.
- Can execute basic programming tasks.
- Somewhat controllable output via prompt engineering.
Potential Applications:
- Chatbots, content generation, data extraction, simple coding tasks.
Limitations:
- Limited context window (~2048 tokens).
- Requires careful prompt design for specific tasks.
- Can produce incorrect or nonsensical answers.
- Ethical concerns like generating harmful or biased content.
GPT-4
Architecture:
- A more substantial number of parameters (exact number not publicly disclosed).
- Continued use of Transformer architecture but with optimizations.
Capabilities:
- Improved accuracy, comprehension, and contextual awareness.
- Better at task generalization.
- Enhanced capabilities to understand and generate more complex text.
Potential Applications:
- Similar to GPT-3 but with broader and more reliable performance across applications like natural language understanding, programming, etc.
Limitations:
- Still limited by context window, although slightly improved.
- Even with the advancements, the model is not perfect and can still produce incorrect or nonsensical answers.
- Ethical concerns remain similar to those in GPT-3, although there are improvements in flagging harmful or biased content.
Both GPT-3 and GPT-4 represent significant advancements in natural language processing and have a wide array of commercial and academic applications. However, despite these advancements, there are concerns regarding the reliability, ethical implications, and environmental impact of deploying such large-scale models.
So what’s up with this? Are we getting what we’re paying for here??
