All you need to know about GPT-4
GPT-4: The Next Generation AI That Can ‘See’
OpenAI, the research organization behind some of the most advanced artificial intelligence systems in the world, has recently announced its latest creation: GPT-4. GPT-4 is a natural language processing (NLP) model that can generate coherent and diverse texts on almost any topic, given some input. But unlike its predecessor, GPT-3.5, which was mainly focused on text generation, GPT-4 can also process images and use them as additional input or output.
What makes GPT-4 so powerful and versatile is its internal architecture, which is based on a transformer-style neural network. A transformer is a type of neural network that uses an attention mechanism to learn how to focus on the most relevant parts of the input data, whether it is text or images. This allows the model to capture long-range dependencies and complex relationships between different modalities.
What makes GPT-4 different from previous versions?
GPT-4 is a large multimodal model, which means it can accept not only text but also images as input. Its previous versions, GPT-3 and GPT-3.5, could only take text inputs. This enables GPT-4 to comprehend, generate and manipulate human language with a level of precision and fluency that has never been seen before.
GPT-4 is also more creative than previous models, as it can produce novel and coherent texts based on diverse and complex inputs. It can also handle multiple tasks at once, such as answering questions, summarizing articles, writing captions for images, etc.
How does GPT-4 work?
GPT-4 works by using a complex and powerful neural network architecture called Transformer. A Transformer consists of two main components: an encoder and a decoder. The encoder takes an input (such as text or image) and converts it into a sequence of numerical vectors called embeddings. The decoder then takes these embeddings and generates an output (such as text) by applying attention mechanisms.
Attention mechanisms are mathematical functions that allow the model to focus on relevant parts of the input and output sequences while ignoring irrelevant ones. This way, the model can learn long-term dependencies and contextual information from large amounts of data.
GPT-4 is pre-trained on an extensive corpus of training data sourced from diverse and broad text repositories such as Wikipedia, Reddit, news articles, books, etc. This gives it a general knowledge of the language and various domains. However, pre-training alone is not enough to make the model perform well on specific tasks. Therefore, GPT-4 also needs to be fine-tuned on task-specific data sets (such as ChatGPT conversations) to adapt its parameters to the desired goal.
How can you start using GPT-4 in ChatGPT?
ChatGPT is one of the first applications that use GPT-4 as its underlying technology. ChatGPT is an online platform that allows you to chat with AI agents that have different personalities and interests. You can choose from various topics such as sports, movies, music, etc., or create your own custom topic.
To start using GPT-4 in ChatGPT, you need to sign up for an account on their website (https://www.chatgpt.com/). Once you do that, you can access their dashboard where you can select your preferred agent and topic. You can also customize your agent’s name, avatar, and voice.
After setting up your agent, you can start chatting with it by typing or speaking your messages in the chat box. You will see how GPT-4 responds with natural and engaging texts based on your inputs. You can also give feedback to your agent by rating its responses with emojis or stars.
GPT-4 has several advantages over previous NLP models:
- It can handle multimodal inputs and outputs, such as text-to-image, image-to-text, image captioning, image completion, etc.
- It can perform multiple tasks with minimal fine-tuning or domain adaptation, such as question answering, summarization, translation, dialogue generation, etc.
- It can generate longer texts with more diversity and creativity than ChatGPT.
- It can solve structured problems that require logic and reasoning skills, such as passing exams or playing games.
According to OpenAI’s tests, GPT-4 outperforms existing large-scale NLP models on various benchmarks and tasks. For example:
- On simulated exams designed for humans, such as bar exam questions or SAT math problems, GPT-4 achieves average accuracy scores between 70% and 80%, while ChatGPT scores around 50%.
- On natural language understanding tasks, such as GLUE (General Language Understanding Evaluation) or SuperGLUE, which measure how well a model can perform common NLP tasks like sentiment analysis or natural language inference, GPT-4 achieves state-of-the-art results.
- On natural language generation tasks, such as LAMBADA (Large-scale Autoregressive Model Benchmark for Automatic Data Analysis) or DART (Dataset for Aspect-based Reasoning Tasks), which measure how well a model can generate fluent and coherent texts given some context or query, GPT-4 surpasses ChatGPT by generating longer texts with more diversity and creativity.
Limitations and challenges:
- It still suffers from hallucination errors, which means it sometimes generates false or misleading information that is not supported by the input data.
- It still requires large amounts of data and computational resources to train effectively. According to OpenAI’s estimates, training GPT-4 took about 10 times more data than ChatGPT (1 trillion words vs 100 billion words) and about 100 times more computing power (10 exaflops vs 100 petaflops).
- It still poses ethical and social risks if used maliciously or irresponsibly. For example, it could be used to spread misinformation or propaganda online; it could be used to impersonate people or entities without their consent; it could be used to manipulate people’s emotions or opinions; etc.
Therefore, OpenAI has decided to release GPT-4 under strict conditions and regulations[²^. They have created an API service that allows selected researchers and developers to access GPT-4
Conclusion
GPT-4 is a game-changing technology that has revolutionized natural language processing
and generation. It has enabled applications such as ChatGPT to provide users with
an interactive and immersive experience of chatting with AI agents.
However, it is still flawed and has limitations that need to be addressed
in future research and development.
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