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4 Ways to Use Multimodal AI For a Data-Driven Culture

If you've been staying on top of recent developments in the world of business and technology, you will have noticed the recent rise in artificial intelligence, language models, and machine learning models over the last couple of years. AI is a great help when it comes to speeding up business operations.

However, AI is not anywhere near its most advanced form yet, and the next big step in the world of AI for business is multimodal AI.

Multimodal AI will revolutionize AI by combining multiple types of input. Keep reading for our four ways to use AI to create a truly data-driven culture in your organization.

What is Multimodal AI?

It can be difficult to understand innovative concepts like AI, especially given their far-reaching applications. Nowadays, companies can even use AI social media post generators to create the most engaging content for their brands.

However, with AI apps for business overhauling the business landscape, knowing what multimodal AI is and how it can be used is important.

At the moment, most AI models use just one type of input. This is usually natural language, as most current models have been trained on natural language understanding.

On the other hand, multimodal AI is all about using multiple modes, so multiple types of input. This can include language, audio, visual or sensory inputs simultaneously to produce a more complex and nuanced mental image.

This allows the AI model to combine elements such as facial recognition, video input, and wider textual information to enhance your AI-powered tools. With this, integrating multimodal AI in a business, such as an artificial intelligence call center, for instance, can help to elevate your AI offering, as it will be able to learn from a much wider range of sources than it can currently.

4 Ways to Use Multimodal AI

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With multimodal AI representing such a significant shift in how we will all view and use artificial intelligence in the future, it's a good idea to start thinking about how your organization will make the most out of multimodal AI. To help you, we've put together 4 key steps to using and implementing multimodal AI in 2024.

1.    Identify your goals

At the moment, generative AI is used sporadically across most businesses. For example, ChatGPT is used to generate text content for websites or assist companies in choosing a .ai domain on OnlyDomains.

When it comes to multimodal AI, however, you need to have a clear and cohesive plan in order to get as much as you can out of this exciting leap forward in machine learning. Start by identifying the business goals that can be improved using a more complex AI model.

An example of a business goal that can be achieved with the use of multimodal AI is in the healthcare sector. Multimodal generative AI can assist human perception in providing accurate diagnoses; it can combine the textual input provided by electronic health records with the audio input of a patient's symptoms, for instance.

Multimodal AI isn't just useful for healthcare, however. Everything from supply chain optimization to the automotive industry can be streamlined with the use of multiple inputs over a single data type.

This not only ensures multimodal generative AI is useful for a range of tasks in the world of business, but it can also elevate everything from your team's approach to project time tracking and presentations to the way you create e-commerce product descriptions.

2.    Find your data

Once you've identified the business goals you want to achieve with multimodal AI, you'll need to find the data on which to train the models. To take advantage of multimodal AI's unique benefits, you'll need a range of different data types.

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Multimodal systems will use anything from sensory inputs to gesture recognition, so it shouldn't be difficult for you to find data. The key is to ensure that it remains relevant to your specific goals and intended outcomes. If you want to simplify this, you can use automation software to speed up the processing of data for your AI model.

3.    Get on top of the technical requirements

There's no getting around this fact: multimodal generative AI is going to bring huge benefits to your organization, and with this will come certain technical challenges. While you might have been able to implement unimodal systems fairly easily-with applications such as an AI contact center not requiring much onboarding-multimodal AI might need more specialist support. 

With the wide range of data involved, from speech recognition to image processing, you'll need a large computational capacity on site. You'll also need a huge amount of data storage, as well as data scientists and AI experts who are confident in managing this data and the AI model itself.

4.    Integrate your data sources

This key step is specific to multimodal AI. One of the biggest benefits of multimodal models is that they can combine data inputs, meaning that you have to integrate different data types such as text, audio, and video.

Without integrating these different forms of data, you might as well be using the traditional form of generative AI, leaving you without the contextual understanding and data-driven outcomes that come with the use of multimodal AI.

Integrating your data will likely require you to invest in a data center upgrade so as to overcome any gaps in your system, as well as purchasing more advanced data integration tools to ensure that you are up to date with the most recent technological developments.

Multimodal AI: The Route to a Data-Driven Culture

The world of AI is always quickly evolving and developing. However, the rise of multimodal generative AI could be one of the most transformational shifts since the recent explosion in its popularity.

By combining multiple inputs at once, multimodal AI brings with it a more holistic understanding of the world that better reflects all available data. Therefore, it promises to be a crucial tool for any business looking to make data-driven decisions. If you want to implement a truly data-driven culture, follow our advice for using multimodal AI today!

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Published Thursday, April 11, 2024 7:31 AM by David Marshall
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