GPT 4 vs GPT 3

OpenAI’s upcoming language model, GPT-4, is set to revolutionize the way we interact with AI in numerous ways, surpassing its predecessor, GPT-3, with its advanced capabilities.

With an impressive 1 trillion parameters, GPT-4 is expected to offer unparalleled accuracy and human-like dialogue. This article presents a detailed comparison between GPT-3 and GPT-4, highlighting the significant distinctions between these two models.

GPT-3, the current most advanced GPT model, boasts 175 billion parameters, approximately matching the number of synapses in a Hedgehog's brain. It has been applied in a wide range of applications, including chatbots, language translation, and even essay and poetry writing.

On the other hand, GPT-4 is rumored to have 1 trillion parameters. This increased parameter count will enable GPT-4 to offer even greater accuracy and human-like dialogue, potentially extending its understanding of the outside world and facilitating more informed decision-making.

GPT-4 may employ different architectures, such as the Transformer or the reformer architecture, which can enhance the model's efficiency and performance.

Key Differences between GPT-3 and GPT-4

One notable distinction between GPT-3 and GPT-4 is the latter's ability to address ChatGPT's slow response to user queries. GPT-4 is expected to provide prompt and more human-like answers.

Another significant difference is GPT-4's capacity to comprehend and navigate the external world. Unlike GPT-3, which lacks a coherent theory of mind and understanding of the existence of the outside world, GPT-4 shows potential for a more comprehensive understanding, empowering the model to make more informed decisions.

Moreover, GPT-4 is anticipated to have access to an even larger pool of training data, which will further enhance its accuracy and human-like dialogue. These improvements result from advancements in data collection, cleaning, and pre-processing techniques.

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Key Similarities between GPT-3 and GPT-4

It is important to note that GPT-4 will still encounter the inherent challenge of generating fictitious responses when it lacks the necessary information, similar to GPT-3.

The replication of human language and speech patterns is predicted to be even more effective in GPT-4 compared to GPT-3. Reinforcement learning from human feedback is key in achieving this goal.

GPT-4 is trained using feedback from human users, employing features such as thumbs up or thumbs down buttons, similar to ChatGPT's process. This approach reduces susceptibility to misinformation and harmful content.

Experts are exploring ways to implement a separate accuracy check routine, which could be integrated into GPT-4, rendering it even more sophisticated.