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Multimodal AI: A Whole New Dimension of Decision-Making

Since debuting to the general public in late 2022, generative AI has become an integral part of technology landscapes. While best known for rapidly generating complex content in text form, the tech is by no means confined to natural language. For example, it can also create strikingly realistic images.


Now, a new chapter in the generative AI success story is beginning – with the advent of multimodal models that can process text, images, and other data modalities simultaneously. These models integrate disparate information from various data types in much the same way as humans do. The result? A deeper, more comprehensive understanding of the world, plus the ability to use this understanding to master more challenging tasks.


Multimodal Models: A Brief Overview


Essentially, multimodal models are machine learning (ML) models that can process information from different data modalities, such as images, videos, and text. The primary aim of multimodal AI is to overcome the limitations inherent in traditional unimodal systems, which focus on just one type of data source.


While models that operate across different data modalities are by no means new, they’ve typically been unidirectional and trained to perform very specific tasks – for example, converting speech to text or text to image.


Today’s multimodal AI approach goes much further. By incorporating the context and supporting information needed to make accurate predictions, it delivers a more holistic and nuanced understanding of data. In fact, the approach is so powerful that Gartner expects multimodal AI models to outperform their unimodal counterparts in over 60% of generative AI applications.


Understanding Multimodal Models


To understand how multimodal models work, we need to consider their core elements. These are as follows:


  • Input

  • Model processing

  • Output


In a first step, users provide inputs, which can be in the form of language (written or spoken prompts), images, video, or audio.


Next, these inputs are sent to the AI model for interpretation. Specialized models or algorithms process each modality and extract relevant features or information – for example, image processing is handled by convolutional neural networks (CNNs), and text is classified using transformers. Once the individual modalities have been processed, the resulting data is merged using the multimodal data fusion method.


Finally, the model generates the output. This can take the form of text-based responses via an app or the speakers of smart glasses. Because the model and the output processing are inherently dynamic, different outputs can be generated using the same inputs.


Boosting Understanding, Robustness, and Flexibility


As already mentioned, one considerable benefit of multimodal models is that they can develop a more profound and nuanced understanding of their information inputs. In this respect, they mimic the human ability to combine information from the various senses.


What’s more, combining different sources of information enhances the accuracy and reliability of the models. This is due in part to the strengths of one modality offsetting the weaknesses of another – for example, an image may resolve ambiguities in linguistic input or vice versa. In addition, if the model extracts the same information across multiple modalities, this will tend to confirm the validity of that information.


And finally, because multimodal models aren’t limited to just one data source, they can be applied more flexibly to a far wider range of scenarios and tasks than is possible with a unimodal approach.


Multimodal Models in Action: Some Selected Use Cases


So, how can multimodal models be applied in the real world? One very promising use case is personalized product discovery. Here, the tech can leverage individual users’ personal preferences to help customers find the most relevant products. This can be taken a step further by using multimodal models to generate personalized product descriptions.


In the field of medical diagnosis, the tech can provide invaluable support for healthcare professionals. To find out precisely what’s wrong with their patients, doctors have to consider many different kinds of information. By bringing together all the relevant sources – including health records, physical examinations, lab tests, and medical images – multimodal models can help physicians make the right diagnosis and draw up the corresponding treatment plan.


Another fruitful area of application is in automated vehicles. Self-driving cars can use multimodal data, such as camera images, and radar and light detection and ranging (lidar) data, to interpret their surroundings and take corresponding action.


Understanding the Challenges of the Tech


But while multimodal models offer a wealth of opportunities, they also pose challenges. Developing and training models of this kind entails integrating different data formats and sources. As a result, it can be a highly complex, resource-intensive process.


As is often the case in advanced AI-based scenarios, there’s also the issue of data availability and quality. Setting up a multimodal model calls for high-quality, annotated data across all the various modalities involved. And meeting that requirement can be both tricky and cost-intensive.


Finally, there’s the question of integration and fusion. Effectively melding information from disparate modalities requires careful consideration of the relationships and interactions between all the various data sources, and this presents an ongoing challenge.


Shapes of Things to Come


These hurdles notwithstanding, multimodal models seem poised to reshape the AI landscape. By seamlessly combining information from text, images, audio, and video sources, they promise a wider-ranging, more differentiated understanding of the world – an understanding that’s strikingly similar to that achieved by human cognition.


And it’s not just the data sources that are many and varied; when it comes to practical applications, multimodal models have the potential to impact everything from personalized digital experiences to advances in fields like healthcare, autonomous systems, and more besides.


Any Questions or Comments? 


Want to find out more about multimodal models and what they have to offer your business? Then, feel free to reach out to me. And if you have thoughts of your own about this trending tech, join the discussion by leaving a comment below.


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