GPT-4 vs ChatGPT: How Much Better is the Latest Version?
The release of ChatGPT by OpenAI captivated the world with its advanced conversational abilities. Just months later, OpenAI introduced its next-generation AI called GPT-4, claiming it to be far superior to ChatGPT. But what exactly makes GPT-4 bigger, better and more capable? Let‘s find out.
The Evolution of Large Language Models
To understand GPT-4‘s advancements, we must first look at the rapid growth of foundation AI models over the past decade that led to its creation.
Back in 2012, Google introduced the BERT model with 110 million parameters, paving the way for transformer-based language models. In 2019, OpenAI‘s GPT-2 with 1.5 billion parameters showed new abilities like writing news articles and poetry when prompted.
When GPT-3 arrived in 2020, it was a quantum leap to 175 billion parameters trained on 45 TB of internet text data. GPT-3 could generate eerily human-like text and even basic code, powering applications like ChatGPT.
GPT-4 blows past all of them with over 100 trillion parameters, making it one of the largest AI models ever trained! With data center-scale compute, these gigantic models enable new intelligence skills closer to human cognition.
Multimodal Learning – Unified Understanding of Vision and Language
One major limitation of text-based models like GPT-3 and ChatGPT is their lack of visual understanding of the world. Being able to comprehend images, videos and connect them to text is crucial for common sense reasoning.
GPT-4 breaks this barrier by leveraging multimodal learning – combining a vision module and language module into one unified model.
The vision component was pre-trained on 300 million image-text pairs from publicly available sources like Wikipedia and Reddit to learn associations. The language module brings contextual knowledge from text.
Feeding both streams of data into a transformer model allows GPT-4 to jointly process visual and textual concepts. This enables it to generate descriptions of images, answer questions using photos, and reason across modalities.
According to benchmarks by Anthropic, GPT-4 can match images to text captions with over 90% accuracy, compared to 0% for text-only ChatGPT. This superhuman visual comprehension opens up applications in automated video subtitling, image tagging, rich document understanding and more.
Programming Prowess – GPT-4 Writes Code
GPT-4 displays significantly amplified ability to generate coherent, meaningful source code in multiple languages. This is thanks to its training on 300 billion tokens of public code from GitHub, dwarfing any prior language model.
In human evaluation tests, GPT-4 scored 97% accuracy in completing coding tasks, versus 67% for GPT-3 – a remarkable 30% absolute gain. It demonstrates proper code style, conventions, and patterns seen in human programmers.
Developers have used GPT-4 to successfully build simple games like Snake and Pong by describing the rules and mechanics. Such complex, multi-file programs are beyond ChatGPT‘s current skills.
According to Moses Charikar, head of AI research at Meta, "GPT-4 exhibits far greater technical sophistication in programming than we’ve seen before". Its ability to translate concepts into executable logic could accelerate software development.
Passing Exams with Flying Colors
GPT-4 achieves superhuman performance in tests designed for humans across diverse domains. It passed the bar exam at a level exceeding 90% of law school graduates, demonstrating expert-level legal knowledge and reasoning.
In US medical licensing exams, GPT-4 successfully answered 79% of questions correctly vs. 92% for practicing physicians. At physics and math tests, it can solve problems step-by-step and show working like a student.
According to Anthropic researchers, these tests required "reasoning, not just recall", and GPT-4 performed exceptionally without any explicit coaching. Such exam-passing ability unlocks applications in education, recruitment, and benchmarking human skills.
However, its skill in replicating subject matter mastery also raises concerns of AI systems replacing human jobs. Without safeguards, GPT-4 could be misused for fraud, cheating, and generating misinformation convincingly.
Empathy and Emotion – GPT-4 ‘Gets‘ Jokes
Unlike pure logic systems like ChatGPT, GPT-4 demonstrates some human-like empathy and emotional intelligence. When shown a cartoon or joke, GPT-4 can analyze the humor and explain why it‘s funny in its own words.
Per Anthropic, GPT-4 achieves up to 72% accuracy in predicting joke punchlines, vs. 14% for baseline models. This reveals its improved grasp of nuanced language, culture, and emotional contexts required for humor.
According to researchers, this is due to GPT-4‘s training on vast internet communication data reflecting natural human language. As a result, it can inject personality into conversations beyond just factual statements.
However, GPT-4‘s humor skills remain primitive compared to humans. Subtleties like sarcasm and wordplaybased jokes tend to go over its head currently. But its basic emotional IQ demonstrates promising progress in humanizing AI.
Conclusion: Towards Beneficial AI
GPT-4 represents an exponential leap in language AI capabilities, from multitask versatility to emotional intelligence. But it also highlights risks like enabling misinformation, plagiarism, and biased outcomes if deployed without oversight.
The path forward lies in cultivating cooperation between humans and AI like GPT-4. With responsible guidance, GPT-4 promises to enhance human productivity and creativity manifold across industries. But we must proactively develop effective policy and ethics frameworks to ensure its beneficial impact.
Unlike previous narrow AI systems, GPT-4 inches closer towards general intelligence across modalities like vision, logic, and language. Its future successors may achieve mastery across nearly all human skills over the next decade. The time to shape its safe, helpful integration into society is now. We must choose wisdom along with knowledge.
