A 2021 Gartner report projected the growth of the Artificial Intelligence (AI) market at a CAGR of 21.3% to a market share of $62 billion by 2022. One of the most intriguing AI applications vigorously trending at this point is GPT-4. It has taken the tech world by storm, giving rise to all kinds of myths and speculation.
After the blazing success of GPT-3, GPT-4 was bound to intrigue technologists and business leaders determined to stay abreast of innovation. Of course, apprehensions abound, but one thing is certain: GPT-4 will be a quantum leap from its predecessors in language prediction. This blog highlights the meaning of GPT and the significance of GPT-4 in the business world.
What is GPT?
GPT stands for ‘Generative Pre-Trained Transformer’ and refers to an array of language processing methodologies that can evolve and learn in real-time, with or without supervision. A GPT model learns from the provided training datasets and the internet to generate new texts in response to a prompt. GPT is tremendously popular among Machine Learning (ML) researchers due to its amalgamation of Natural Language Processing (NLP) and Natural Language Generation (NLG) to effectively understand the thought patterns and nuances of human language and reproduce the same.
A Brief History of GPT-1, 2, and 3
Open AI launched the first rendition of Generative Pre-Training in 2018 based on Transformer architecture that processes ML models at an unprecedented speed. The creators of GPT-1 used semi-supervised learning to train the algorithm based on a mammoth BooksCorpus database of 7000 unpublished books. While the training of the model is completely unsupervised, its refinement of results to predict the next word is supervised. The Transfer learning language model of GPT-1 set the stage to perform generative pre-training with a broader range of parameters and datasets.
GPT-1 finds its applications in rudimentary predictive texts by looking at the last word in the dataset.
In the later part of 2019, OpenAI came up with another rendition of GPT based on the same Transformer decoder but with changes in the total number of decoders and dimensionality. Working with 1.5 million parameters and trained on a dataset of eight million webpages, GPT-2 outsmarts all the language models functioning with domain-specific datasets. Moreover, compared to its predecessor, GPT-2 can seamlessly handle ten times the data and produce more promising results of language-related tasks such as translation, summarization, and question answering.
GPT-2 improved customer service automation, generated chatbots and improved human-AI integration in NLP.
GPT-3 created history with its whopping 175-billion datasets as it crossed the 10-billion parameter mark of the then-largest Microsoft’s Turing NLG model. It is one of the highest-order breakthroughs of OpenAI, honing expertise in generating texts that closely mimic and sound like a sentient being. The model deploys both Natural NLP and NLG to understand the nuances of human language and can generate realistic texts based on anything comprising a text structure. In addition, GPT-3 requires significantly less input text to generate massive volumes of relevant and sophisticated text.
ChatGPT is the most popular application of GPT-3 that optimizes human-AI interactions, learns from past errors under zero supervision, and changes incorrect premises of response. Designed to improve customer service, ChatGPT has become a go-to for marketers for detailed consumer information and to protect them from any deceitful or misleading response. Dall E is another application developed by OpenAI that uses the 12-billion parameters to generate images from text prompts.
The applications of GPT-3 are far-reaching, ranging from generating code snippets and creating memes to finding the presence of neurodegenerative diseases in patients by detecting language impairment in speech.
What’s New in GPT-4?
GPT-4 is a significant departure from the previous language models in certain areas and intelligent optimization in others. Let’s find out what’s new in GPT-4:
1. Model Size
Large models have higher accuracy but do not always provide accurate information for every data point, mentions a 2021 research paper from the University of California. Sam Altman, the CEO of OpenAI, holds the same opinion, which is why GPT-4 won’t exceed its predecessor in size. Deployment of smaller datasets is more cost-effective, requires fewer computing resources, and has simpler implementation processes.
2. Optimized Parameters
Optimal hyper-parameters (HP) refer to pre-setting the values of a given ML algorithm to increase its efficiency. Parameters of large models are rarely optimized due to the sheer operating cost involved. In 2021, a swift collaboration of OpenAI and Microsoft led to the development of a new parameterization (μP) technique that stabilizes model training irrespective of their sizes. The idea is to find the best hyper-parameters for a smaller set and transfer them to a larger model without compromising on model behavior. Thus, it is certain that GPT-4 will incorporate optimal hyper-parameterization.
3. Less Prompting
GPT-3 is a master of prompt programming. The language model can extract its own action plan from a simply written input. However, these results vary in quality as GPT-3 needs to analyze the quality of the prompt, limiting the system’s true potential. GPT-4 is supposed to be far more robust in developing a self-assessment process and eventually making prompting obsolete.
4. GPT-4 Will be Text-Only
Despite all the apprehensions of developing a multimodal model to combine textual and visual information, GPT-4 will remain a text-only model like its predecessor. Dall E-2 works on a multimodal model, and any subsequent versions must cross the benchmarks set by its performance. As OpenAI is primarily concerned with clearing the limitations of text-based models, GPT-4 will likely be a text-only platform.
When is GPT-4 Releasing, and How Will it Impact the Market?
A 2023 New York Times report expected GPT-4 to release in the first quarter of 2023. So here is a look at how GPT-4 can potentially impact the market:
- Language processing will change marketing dynamics, automating the tasks of writing posts about different marketing strategies.
- Customer support will be more streamlined and serve multidimensionally by generating more human-like responses to customer queries.
- GPT-4 will have a groundbreaking impact on education by creating personalized experiences with its advanced language learning methodologies.
- Generating large volumes of relevant business content in more than one language will become a cakewalk. GPT 4-generated blogs, articles, social media posts, and product descriptions will perfectly mimic the human usage of language.
- The downside is that there is a high chance of fake news getting manufactured, hiding behind the garb of human-like writing style. This will make it difficult to distinguish fact from fiction.
To sum up, GPT-4 will raise the bar for automated texts, but it’s a far cry from achieving a human-like understanding of language. However, GPT-4 will come with a larger context window (memory) to handle complex tasks with perfect accuracy and correct human errors more easily.
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By Bishwadeep Mitra
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