Recently, OpenAI teased the release of GPT-4 through its official social media channels, including ChatGPT. GPT-3 has already caused a stir in how we accomplish numerous daily tasks, and its potential impact is widely recognized. OpenAI’s GPT-4 can be considered the next step in the evolution of language AI. Building on the success of ChatGPT, we now witness a debate on GPT-3 vs GPT-4 to predict even more advanced natural language processing and text generation capabilities.
Let’s look at what we can expect from GPT-4 compared to GPT-3 in terms of productivity and creativity.
In May 2020, OpenAI unveiled GPT-3 in a paper called “Language Models are Few Shot Learners.” This massive neural network was groundbreaking, and after OpenAI released a beta API, excitement grew as people discovered its abilities. GPT-3, despite not being specifically trained for certain tasks, could generate code from web page descriptions, write custom poetry and songs, and even ponder the future and meaning of life. It had become a meta-learner, learning how to learn through being trained on a vast amount of internet text data.
Users build products with GPT-3 using natural language, and the system can understand and carry out their requests. This marked the third year OpenAI released a new GPT model, with GPT-1 debuting in 2018 and GPT-2 following in 2019. With this pattern in mind, the release of a potential GPT-4 may be imminent. Given the paradigm-shifting impact of GPT-3, it’s exciting to consider what advancements GPT-4 will bring to AI.
GPT-3 (Generative Pretrained Transformer-3) is a large language model developed by OpenAI. It has the following capabilities:
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Compared to previous GPT models, GPT-3 has the following differences:
GPT-4 is the most recent and advanced model in OpenAI’s Generative Pretrained Transformer series. It’s a big machine learning model trained on a large dataset to produce text that resembles human language. It is said that GPT-4 boasts 170 trillion parameters, making it larger and stronger than GPT-3’s 175 billion parameters. This upgrade results in more accurate and fluent text generation by GPT-4.
The ability to comprehend and produce various forms of natural language text, both formal and informal, is a standout feature. This versatility makes it useful for language translation, text summarization, question answering, and other applications. GPT-4 can also learn from diverse data sources, allowing for fine-tuning to specific tasks and domains, making it highly adaptable.
Aside from its remarkable language processing skills, GPT-4 has potential in tasks such as image and video generation due to its Transformer architecture. This architecture has demonstrated effectiveness in various machine-learning tasks, including computer vision.
GPT-4 represents a major natural language processing technology advancement with potential applications across many fields. Though it’s not yet available, once released, it’s expected to be a valuable tool for anyone working with natural language text.
Related Article – Openai GPT4: What We Know So Far and What to Expect
In a 2022 interview, Sam Altman, the CEO of OpenAI, revealed the company’s plans for GPT-4. He stated that the model wouldn’t significantly surpass the size of GPT-3, which currently has 175 billion parameters. While it may have slightly more parameters, the focus for GPT-4 is to extract more value from similar numbers by improving performance through other means. This approach aligns with OpenAI’s mission to enhance the capabilities of AI models while maintaining responsible and sustainable development practices.
Reports indicate that GPT-4 will have a trillion parameters, leading to more accurate and faster responses from ChatGPT. However, this parameter increase may also result in higher costs for OpenAI.
The 100-fold increase in parameters from GPT-2 to GPT-3 brought about quantitative and qualitative differences. GPT-3’s capabilities surpass those of GPT-2, and OpenAI will likely continue this trend with GPT-4, making it even larger in size and leading to new, unparalleled advancements. It is possible that the differences in GPT-3 vs GPT-4 could bring us closer to creating a neural network capable of true reasoning and understanding.
This trend of “bigger being better” aligns with researcher Richard Sutton’s statement that general methods that leverage computation have been the most effective in AI research over the past 70 years. Only time will tell if this continues to hold true.
GPT-3’s performance in NLP tasks such as machine translation and question answering was impressive when trained with a few examples. However, its performance was not as good when it was required to perform a task it had not seen before. Despite its vast neural network, it could not perform tasks based purely on intuition, which even humans struggle with.
However, while talking about GPT-3 vs GPT-4, GPT-3 excels in few-shot multitasking, with researchers observing that its performance improved faster with the increasing number of parameters, proving its ability as a meta-learner. If GPT-4 follows the pattern of its predecessors and boasts even more parameters, it’s expected to be an even better multitasker, potentially breaking the notion that deep learning systems require many examples to master a single task.
GPT-3 has demonstrated an understanding of conversation continuation without explicit instruction. The possibilities of what GPT-4 could achieve in this regard are intriguing. It could prove that language models can learn to multitask with just a few examples, almost matching human capability.
GPT-3, the cutting-edge language model developed by OpenAI, was made available to external developers in 2020 through its beta API playground. One of its defining features is the ability to communicate with it using natural language, allowing incredible tasks to be performed with a simple prompt. For example, if you tell GPT-3, “The following is a story about the universe that a wise man is telling a young boy,” it would write a story in an easy-to-understand language, making the wise man appear knowledgeable about the universe. This process of communicating with GPT-3 is known as prompt programming.
However, there is a catch. The quality of the output produced by GPT-3 can vary greatly depending on the quality of the prompt given. A poor prompt will result in a poor output, leaving it unclear whether the fault lies with the system or the person who wrote the prompt. This issue raises questions about the robustness of GPT-3 and highlights the need for future models, such as GPT-4, to be less reliant on good prompts.
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Moreover, GPT-3 doesn’t have the ability to self-assess and realize that it may have made an error. A true artificial intelligence should be able to self-assess and express doubt or a lack of understanding. For example, if GPT-4 could say, “I don’t know,” or “Your prompt is not very clear,” it would be a significant step towards a more intelligent system. The development of such self-assessment capabilities will likely be crucial in unlocking the full potential of future language models like GPT-4.
While comparing GPT-3 vs GPT-4, GPT-3 is a remarkable language model but has limitations. Unlike a person, it has limited memory and can only process information within the context of a 500-1000 word window. This means it can’t remember past inputs, making it challenging to complete long-form tasks such as writing a novel or coding a program. Additionally, GPT-3 struggles to maintain coherence over extended periods, often repeating ideas or veering off into unrelated topics.
This is due to its architecture, which is based on attention but lacks convolution and recurrence. Although transformers have proven to be a powerful tool, there is room for improvement.
Future models like GPT-4 could overcome these limitations by incorporating advances in the transformer architecture, providing a larger context window, and allowing users to input various media types, including text, images, video, and audio.
GPT-4 is anticipated to bring about enhancements in its ability to emulate human behavior and language patterns in response to user inputs. With improved optimization, GPT-4 is expected to display a heightened capacity to comprehend human intentions, even in instances where there may be inaccuracies, surpassing the capabilities of its previous versions.
Related article – Openai GPT4: What We Know So Far and What to Expect?
OpenAI’s commitment to continually advancing algorithms such as RLHF (Reinforcement Learning from Human Feedback) suggests that GPT-4 could incorporate it with improved effectiveness. RLHF involves human trainers refining AI models through supervised fine-tuning. Enhanced human supervision in training may reduce the risk of toxic or biased content generated by GPT/ChatGPT and minimize instances of misinformation.
In conclusion, while GPT-4 represents a significant advancement in language AI technology for businesses, it will take time for widespread adoption. The first few years may see continued reliance on human expertise in certain areas. Nevertheless, the potential for improved performance and efficiency offered by GPT-4 makes it a worthwhile investment for the future.
Here are some predictions after comparing GPT-3 vs GPT-4:
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