GPT-4: Taking AI Language Models to the Next Level and How It Differs from GPT-3.5 Turbo

Artificial intelligence (AI) has made significant strides in the past few years, churning out state-of-the-art language models that have revolutionized how we interact with machines. Among the most notable advances is OpenAI's third generation of transformer models, notably GPT-3, with its latest model GPT-3.5 Turbo which powered the ChatGPT technical demo acting as a trailblazer for AI adoption. But AI continues to develop at a rapid pace and just 3 months from the launch of ChatGPT we find ourselves on the cusp of another groundbreaking milestone: GPT-4.

What is GPT-4?

While GPT-4 is currently in beta (it'll be coming to TypeEngine soon so keep an eye out), we can already see the powerful improvements that GPT-4 has over GPT-3. GPT, which stands for Generative Pre-trained Transformer, is a series of deep-learning models designed to understand and generate human-like text. GPT-4 is the next iteration of this powerful AI language model series, further pushing the boundaries of AI capabilities.

How is GPT-4 Improving on GPT-3?

Although some of the specifics of GPT-4 are speculative as OpenAI has decided not to release as much data about the model and its composition, there are confirmed and inferred improvements over its predecessor, GPT-3.5 Turbo:

1. Model Size - Whilst not confirmed, each new iteration of GPT has seen a substantial increase in model size, meaning it can store more parameters, which in turn can result in better performance. GPT-4 will certainly have continued this trend, contributing to it being even more powerful than the impressive GPT-3.5 Turbo.

2. Fine-Tuning - OpenAI has invested substantial effort in producing models fine-tuned for specific tasks. This means GPT-4 will have an even greater range of applications and more specialized capabilities as well as it's more general generative capabilities. We've started to see some so far such as BING Search Chat and integration into the Stripe documentation.

3. Generalization - Improvements in models like GPT typically result in a greater ability to generalize and perform new tasks. GPT-4 is already proving to be more versatile in completing tasks that it hasn't directly trained on, making it more human-like in problem-solving.

4. Context Understanding - One of the limiting factors in GPT-3.5 Turbo is its context window, which caps input length at around 4096 tokens. With advancements in architecture, GPT-4 will be able to support 23,768 tokens providing the opportunity for an even more in-depth understanding of long text inputs and contexualisation of complex systems.

5. Vision - The extremely interesting addition to GPT-4 is "Vision" which allows GPT to "see" and understand images. This then allows the AI to respond based on that image and a prompt. In the technical demo, they used this to create a website from a drawing on a napkin. This opens up substantially more applications than GPT-3. As OpenAI puts it "Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs."

6. Languages - GPT-4 has been confirmed to be exceptionally better at not only translating and responding in languages other than English but understanding problems in different languages, even those with "low resources" in the training data. This results in GPT-4 having a strong 3-shot accuracy across a vast spread of languages.

7. Accuracy - Whilst GPT-4 still has similar limitations surrounding its "hallucinations" which is where it confidently responds incorrectly, GPT-4 is 40% better at responding factually then GPT-3.5 Turbo and appears to have taken a step forward in improving reliability.

The introduction of GPT-4 promises to mark a significant step forward in artificial intelligence and natural language processing and ushers in an exciting age of AI transformation that GPT-3 paved the way for. We at TypeEngine have high expectations for the applications and capabilities of GPT-4 and are working hard to produce tools that take advantage of its awesome power.

As we continue to embrace AI and use it in our day-to-day lives, we hope that it helps contribute positively to the wider world and society and continue to aim to do our bit to ensure that AI is used responsibly.