The next version of GPT, OpenAI GPT-4, is anticipated to arrive around mid-March 2023. This represents an enormous step forward for AI models and may push their size limits even further.
OpenAI has a longstanding record of producing large AI models (GPT-1 had 117 million parameters, GPT-2 had 1.2 billion, and GPT-3 had 175 billion). It is likely that GPT-4 will follow suit.
Multimodal Large Language Models
Multimodal large language models (MLMs) are capable of handling various kinds of information, such as text, pictures, video and sound. This enables them to learn how to work within various data structures which makes them ideal for various applications.
At present, most LLMs are trained on text-only data, which hinders their abilities to perform tasks that require visual reasoning or grounding in reality. This explains why they may not be suitable for certain AI applications like visual question answering.
However, researchers have recently developed a way of creating multimodal large language models that are capable of learning and performing tasks requiring both linguistic proficiency as well as the capacity for visual perception and video processing. This technique is based on “model agnostic learning,” meaning it can be utilized with any LLM.
Microsoft’s Kosmos-1 multimodal large language model is one example of this type of technology in action. It utilizes “PrefixLM” to seamlessly merge images with text.
Kosmos-1, Microsoft’s multimodal model, has been trained to comprehend both language and image data. This enables it to perform various tasks such as image captioning, optical text recognition, and speech generation.
Multimodal approaches could enable AI systems such as ChatGPT and Midjourney to gain a better understanding of the world, something systems such as ChatGPT and Midjourney cannot achieve by only using language to interpret images.
According to a German news report, OpenAI GPT-4 may be capable of working in at least four modes: pictures, sound (auditory), textual content and video. This is an exciting development as it will enable AI to engage with us in more than one way.
The model was trained on web-scale multimodal corpora that contained interleaved text and images, image caption pairs, and text data. It performed well across various tests including language understanding, optical character recognition, image captioning, visual question answering, and speech generation.
Works Across Multiple Languages
GPT-3 was one of OpenAI’s most successful models, boasting 175 billion machine learning parameters that enabled it to generate texts that appeared remarkably human-like.
GPT-3’s success in its field notwithstanding, it was highly susceptible to model size limitations. While larger models offer greater precision, they typically demand more computing resources and are difficult to deploy.
Sam Altman, CEO of OpenAI, recently declared they will no longer prioritize increasing parameters in their models but instead concentrate on other elements that influence performance such as data and algorithms.
This shift is an indication that sparse models are becoming more prominent in the AI space, as they can scale up to trillions of parameters without incurring high computing costs. Furthermore, sparse models understand context better than dense ones do.
Business Use: Next-generation language models are invaluable tools for businesses, particularly when creating large amounts of content. Companies can scale up their customer support operations, content generation strategies, as well as sales and marketing activities with ease.
Artists and designers alike can gain from the advantages of this model. It takes text input and generates artwork on its own, saving time and money in the process.
OpenAI GPT-4 will also be better at summarizing information – an NLP task which has proven challenging for previous models to master.
GPT-4 is expected to be a multimodal model, capable of operating across different media such as text, images and video. This makes it even more useful for various industries and tasks.
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