Multi-Modal AI in Action and Key Considerations for the Next Wave of Data Intelligence

Multi-modal AI models are expected to have a major impact on businesses in the near future. However, implementing them represents an IT challenge in that they tend to be more complex than their single-modal counterparts due to the need to process diverse types of data. IT must be able to manage the increased complexity while maintaining scalability and efficiency.

multi-modal AI
(Credit: NicoElNino / Alamy Stock Photo)

The arrival of multi-modal AI signals a new era of intelligence and responsiveness. Defined by the integration of natural language, vision, and multisensory processing into AI systems, this paradigm shift promises to redefine how these tools understand, interact with, and navigate the world around them. 

While single-modal AI excels at specific tasks related to one data type, multi-modal AI enables more comprehensive understanding and interaction by leveraging cross-modal information. This allows for more context-aware, adaptive, and human-like AI behaviors, unlocking new possibilities for applications that require understanding across modalities. However, multi-modal AI also brings increased complexity in model development, data integration, and ethical considerations compared to single-modal systems.

This latest rapid evolution of AI systems could have a major impact on businesses’ capabilities, especially due to the number of organizations that are already using AI. For instance, in 2023, 73 percent of US companies were estimated to be using AI in some aspect of their business (PwC), and the global AI market is expected to exceed $1 trillion by 2028 (Statistica).

We will continue to see an even greater shift towards the use of multi-modal AI, signaling a progression from traditional generative AI to more adaptable and intelligent systems capable of processing information from diverse sources. So, how does this type of AI look in the “real world” today, and what are the key concerns to keep in mind when implementing it? 

Multi-Modal in Action

As we look to the future of multi-modal AI, we can expect to see exciting progress in contextual chatbots and virtual assistants that reference visual information, automated video generation guided by scripts and verbal cues, and new immersive multimedia experiences driven dynamically by user interaction and interests. As an example, in the AEC sector, multi-modal AI is being leveraged to create intelligent systems that can analyze and interpret building information models (BIM), satellite imagery, and sensor data to optimize site selection, design, and construction processes, leading to more efficient and sustainable projects.

Some of these multi-modal AI models in action currently include GPT-4V, Google Gemini, Meta ImageBind, and others. By leveraging the complementary strengths of different modalities—ranging from text and images to audio and sensor data—these systems achieve more comprehensive, contextually rich representations of their environment. 

The implications of multi-modal AI extend far beyond the realm of technology, which has already begun to impact industries such as entertainment, marketing, and e-commerce. In these sectors, the integration of multiple modes of communication—text, images, speech— creates more personalized, immersive experiences. From interactive advertisements to virtual shopping assistants, multi-modal AI has the potential to redefine user engagement.

While this type of AI is increasing, and there are numerous benefits to that, there are also key concerns to weigh, such as integration and quality, ethics and privacy, and model complexity and scalability.

Data Integration and Quality 

Data quality has always been essential to achieving strong results in AI projects, and that is no different for multi-modal AI. Combining data from different modalities can be challenging due to variations in formats, scales, and levels of noise. 

Organizations tackle the complexities of cleansing, collecting, storing, and consolidating their unstructured data while still making it accessible under certain permissions. Once that data is successfully integrated and cleansed across modalities, then multi-modal AI projects can be successful. Moreover, it's essential to have a unified platform in place for AI initiatives and data insights.

Industries such as media and publishing already see wide-ranging content generation and publishing opportunities through the use of multi-modal AI. They are already aware of potential risks, such as particular images or mischievous instructions causing unexpected behaviors in an image-to-text AI system. There is also the possibility of "prompt injection," where subversive instructions are subtly fed into the prompt image to undermine or attack the AI system. These scenarios further strengthen the argument for early adopters to have comprehensive data and risk management policies in place before new application testing and development. 

Ethical and Privacy Considerations

Multi-modal AI systems may involve sensitive data from different sources, raising concerns about privacy and ethics. Moreover, maintaining data quality - even with substantially larger and more varied data sets, likely with multi-modal models - is essential to prevent biases and inaccuracies that may arise from individual modalities.

It's important to incorporate mechanisms for data anonymization, consent management, and bias detection to ensure the ethical use of multi-modal AI technologies. For example, one solution many businesses consider is instituting an ethical policy around how an organization uses AI models. This policy should be regularly reviewed to ensure it’s working as it was intended to. 

Model Complexity and Scalability

Finally, multi-modal AI models tend to be more complex than their single-modal counterparts due to the need to process diverse types of data. Managing the increased complexity while maintaining scalability and efficiency poses a significant challenge. 

To overcome this, organizations can develop architectures and algorithms that can effectively handle multi-modal data without sacrificing performance. For instance, rigorous, high-quality training data and methods versus solely model scale. Microsoft’s Phi-2 model has pointed the way towards what can be achieved through this approach.

Ultimately, multi-modal AI signals a major shift in how we approach AI. By addressing these challenges, developers can create more robust and reliable multi-modal AI systems that can effectively leverage diverse sources of information and achieve successful results.

Related articles:

About the Author(s)

Jim Liddle, Chief Innovation Officer, Nasuni

Jim Liddle is the Chief Innovation Officer at Nasuni, the leading file data services company. Jim has over 25 years of experience in storage, big data, and cloud technologies. Previously, Jim was the founder and CEO of Storage Made Easy, which was acquired by Nasuni in July 2022. In addition, Jim was European sales and operations director for the big data company GigaSpaces and, before that, was the European general manager for Versata, a NASDAQ-listed business process and rules management company.

Stay informed! Sign up to get expert advice and insight delivered direct to your inbox

You May Also Like

More Insights