ChatGPT showcased to the world the many wonders of a conversational AI chatbot based on a language model with access to vast datasets. With the progression of time and the accumulation of newer experiential data from ChatGPT’s analytics, OpenAI has now launched GPT-4, the latest version of their famed GPT line of generative AIs. With the arrival of ChatGPT’s successor, it has become apparent to several users that GPT-4 brings with it many improvements and is capable of carrying out a variety of tasks with a higher quotient of efficiency. Moreover, leading firms such as Microsoft have already integrated the new edition of the AI tool with their search engines, making it imperative to analyze and underline the differences between GPT-4 and ChatGPT. It must also be noted that access to GPT-4 remains restricted. Open access to GPT-4 is available only to users of ChatGPT Plus, Bing, and other OpenAI partners, with the larger public being encouraged to get on a waitlist to access the tool.
While numerous claims and reports abound concerning the vast degrees of improvement vis-à-vis GPT-4, it is noteworthy that the models still function on datasets that remain restricted to September 2021. Though the new chatbot does not have access to the vast reaches of the internet, it still shows considerable advancements in the areas of reasoning and coherence, allowing users to maintain long-form conversations, while also generating tangible analytics from data provided to it. However, that’s not all: GPT-4 also brings several other unique capabilities to its users. The upcoming sections delve into important facets of the new AI model and how it might just be more potent than its predecessor, which currently runs on GPT-3.5.
ChatGPT vs. GPT-4: Outlining Core Differences
GPT-4 is currently accessible only to users subscribed to OpenAI’s ChatGPT Plus program and their partner platforms.
Numerous new features and capabilities have made GPT-4 the talking point for tech experts and users alike, allowing the AI tool to carve out a niche for itself even before its widespread release. ChatGPT currently functions on an older model in the GPT series and remains aligned with its former capabilities. The key technical and operational differences between the two AIs are detailed below under the respective parameters.
Programming Capabilities
GPT-4 has showcased several programming capabilities and can generate accurate lines of code and even detect bugs. The AI tool is also capable of creating computer games from scratch with just a few detailed prompts from its users. Also, the language model has shown that it can create basic web pages by merely analyzing rough drawings uploaded by users to the interface. The parent company has also stated that developers will be able to build using the new iteration of the GPT language models. Despite ChatGPT being fairly skilled at programming, the GPT-4 AI showcases far more extensive capabilities than its former counterpart. Though impressive, there are concerns among seasoned developers that the GPT models might have trained on specific programming questions provided to it in the data sets, and its seamless ability to generate accurate code for certain prompts is merely indicative of its ability to memorize preprogrammed answers to specific questions.
Modeling Variations
Despite GPT-4 currently functioning to provide text-based outputs to its users, GPT-4 is multimodal and primarily runs on processing data provided to it to generate a text-based output. On the other hand, ChatGPT served only as a text-to-text generator that only accepted text-based information as inputs. GPT-4 now has the ability to view and analyze images and provides relevant pointers upon decoding the various aspects of the images. The AI tool also has the potential to understand deeper connotations and contextual settings, especially demonstrated by its ability to point out humorous elements in specific pictures. This also hints at the GPT-4 AI’s faculties that allow it to decipher relative meanings of words and figure out specific undertones in sentences despite these elements not being explicitly apparent.
Short-Term Memory
Another feather in GPT-4’s hat has been its ability to maintain conversations for longer and also remain committed to the discussion with fairly relevant information in response to successive prompts. This is due to GPT-4’s larger short-term memory, capable of analyzing and keeping track of nearly 64,000 words; on the other hand, ChatGPT’s short-term memory would be at its limit at just about 8,000 words. The latter’s inability to keep up with extended long-form prompts is especially punctuated when the expectations of the responses are fact-heavy and require more processing capabilities. However, GPT-4 is capable of engaging a far higher number of tokens relative to its predecessors and can also follow instructions to pull text and content from URLs pasted to the chatbot’s interface. These enhanced memory capabilities are bound to add weight to GPT-4’s processing and fidelity credentials.
Consistency
It is not unknown to users of ChatGPT that the AI chatbot can often run into problems when generating long responses. GPT-4 is more adept at doing this than its predecessor, maintaining constant flow, legibility, and relevance. Users can also create long-form text by providing specified data sets to the interface, signifying GPT-4’s primary nature of operating on a data-to-text model for its functioning. New developments and enhanced training have also enabled GPT-4 to allow its users to define a set structure for the responses they expect. This is also observed in the model’s capacity for processing and responding to multilingual prompts with an impressive degree of consistency. These tasks might also be scalable, allowing potential creators to repeat certain prompts a set number of times.
What to Expect: GPT-4 and Future Language Models
The future of AI and language model development currently relies on constant improvement and training.
The many improvements showcased by the GPT-4 AI when compared to its popular predecessor have set the stage for enhanced applications of AI for numerous tasks. However, it also remains apparent that the new AI iteration still remains disposed to a variety of constraints and limitations that others in the series were known for. Factuality and coherence remain consistent with GPT-4; however, it is still quite likely to generate facts out of thin air, often misleading its users. Similarly, the concerns surrounding AI being rather mechanical rather than intuitive will continue to gather steam. This is because language models function on constant analysis and memorization of data points, as opposed to thinking critically like a human being. Its applications in sensitive and high-accuracy tasks remain a pipedream, and only time will tell whether AI and chatbots will truly be able to achieve perfection.