To understand what “multimodal” means, it helps to look back at how ChatGPT’s input and output methods have evolved over time.
When ChatGPT was first launched, it was purely text-based. This meant it only accepted prompts from users in text form, and its responses were all text as well. While we were amazed by this shiny new object called “Artificial Intelligence,” the user experience could sometimes feel somewhat boring and restrictive.
This all started to change with the introduction of multimodal capabilities. ChatGPT began to allow users to interact not just through text but also with images and voice. Interactions with ChatGPT not only became more fun but also more engaging and effective. This shift marked a significant step forward in the evolution of ChatGPT and AI, as the interactions feel much more natural and flexible.
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What is Multimodal AI and How Does It Work?
“Multimodal AI” refers to an AI system that can process and generate outputs from multiple types of inputs—like text, images, and audio—rather than being limited to just one. This contrasts with traditional computer systems, which usually work with a single mode of data, most commonly text only.
Multimodal capabilities apply to both the input and output of AI models. Take ChatGPT for example. It can now receive different types of inputs: you can type a question, show it a picture, or even ask a question using your voice. Likewise, it can respond in multiple ways, using text, images, and voice too.
These capabilities mirror how humans use various senses to understand the world and communicate. In addition, they also make interactions with AI increasingly natural and realistic, potentially to a point where distinguishing AI from a natural human interaction might become impossible.
ChatGPT’s Multimodal Support
In September 2023, when OpenAI announced that “ChatGPT can now see, hear, and speak“, it sent shockwaves through the AI industry. People were awed that the greatest AI tool of its time became even greater, with multimodal support marking a major leap forward in AI capabilities.
Since then, interactions with ChatGPT aren’t restricted to text only. ChatGPT can see, hear, and speak in these formats:
• Text: Traditional input/output format where users interact through written prompts and receive text-based responses.
• Images: Users can upload images, and ChatGPT can interpret and respond based on that, analyzing pictures or identifying objects within them. With DALL-E integrated since October 2023, it can also output images based on users’ prompts.
(Note: DALL-E is OpenAI’s advanced text-to-image generator.)
• Audio/Voice: Try speaking to ChatGPT, and you’ll be amazed at how human-like it sounds. ChatGPT’s voice capability understands natural languages (yes, multiple languages) and responds with voice outputs that are so natural and lifelike, that you might forget you’re talking to an AI.
And OpenAI isn’t ready to stop there. In the pipeline are other innovations like Sora, OpenAI’s answer to competitors’ text-to-video functionality. How Sora might eventually be integrated into ChatGPT, we don’t know yet. But it’s certainly something to keep an eye on.
Benefits of Multimodal AI
The shift to multimodal AI brings several advantages.
For one, it simplifies the AI interactions and makes it feel more intuitive. For instance, you don’t need to type everything—you can just show an image or speak. (This, by the way, enables you to apply the one-shot and multiple-shot prompting techniques using not only text but images too.) By considering different input types simultaneously, the AI can understand and respond to more complex queries, enhancing the overall interaction.
Multimodal capabilities also open up AI uses in more industries. In healthcare, for example, AI can assist doctors by analyzing medical images along with patient records, to provide more efficient and more comprehensive insights for patient care. Meanwhile, modern-day students get to enjoy a more engaging learning experience when they learn interactively with AI that can converse directly with them and understand the materials they show to the AI.
Challenges of Multimodal AI
Multimodal capabilities in the AI world are still in the developing stage, with leading AI companies, including OpenAI, actively exploring and refining them. At the current stage, tools that implement support for multimodality are impressive, but often face challenges with accuracy, consistency, and the seamless integration of different input types.
From the developers’ standpoint, implementing the multimodal capabilities and combining multiple data types require advanced processing power. Moreover, training AI to handle different kinds of inputs involves complex models and large amounts of diverse data. Technical processes like these can be computationally demanding and expensive, and potentially become a roadblock as AI companies weigh the benefits from such functionality against the costs.
There are also ethical and legal considerations, such as ensuring that AI systems respect user privacy when dealing with images or voice inputs. Security measures have to be put in place so that these systems are guarded against misuse.
While striving to develop multimodal capabilities into maturity, addressing these challenges is crucial to making multimodal AI safe and reliable for widespread use.
Conclusion
Multimodal AI represents a significant leap in the development of artificial intelligence. Implementing the ‘intelligence’ for the AI to understand and respond in multiple modes is not only a milestone in the journey to mimic natural human communication, but also greatly simplifies our interactions with AI and improves the user experience.
Fair enough, we are still in the early stages of developing and experimenting with multimodal AI. But even at this stage, the added support of various input modes (e.g., text and voice) has transformed how we interact with AI tools like ChatGPT and, possibly, the way we work.
We can look forward to witnessing even more exciting developments to come in the near future!
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