Act Two, Scene One — Multimodal AI
Multimodal Artificial Intelligence represents the next mind-blowing development in AI that will allow businesses to further push the envelope of what’s possible. Problems that were impossible to solve just two years ago because of technology limitations are now being revisited and actively being solved. The implications for business are profound.
As the recent post by Sequoia Capital captured very aptly, we are entering Generative AI’s Act Two. Continuing this analogy, multimodal is the opening scene to this second act.
What is Multimodal AI?
Multimodal refers to the use of multiple modes of input, such as text, speech, and images, to train machine learning models. Humans are able to easily distinguish between these forms of media. We understand that an apple, whether described in writing, spoken about, or shown as an image or a video is fundamentally the same object. Up until recently, AI systems were not able to make these connections.
One of the key implications of multimodal AI in the enterprise is that it can help make sense of unstructured data that doesn’t fit into a traditional databases or spreadsheets made up of these different data types. By using multimodal AI to analyze the unstructured information, businesses can gain insights that were previously impossible to obtain.
Another key implication is helping improve a businesses’ customer experience. By analyzing customer interactions across multiple channels, such as email, chat, and social media, it’s possible to obtain a more complete understanding of customers’ needs and preferences. This information can then be used to personalize marketing campaigns, improve customer service, and to glean valuable insights into current product offerings.
Multimodal AI can also help businesses stay ahead of competitors by enabling them to quickly adapt to changing market conditions. By analyzing data from multiple sources, such as social media trends, news articles, and even relevant videos, businesses can gain insights into emerging trends and adjust their strategies accordingly.
Implementing multimodal AI in an enterprise requires a well-defined strategy and a clear understanding of the business’s needs. Here are some steps that businesses can take to implement multimodal AI:
1. Identify the business problem: The first step is to identify the business problem that you want to solve using multimodal AI. This could be anything from improving customer experience, to automating repetitive tasks, to something as narrow as speeding up video processing.
2. Define success criteria: Define the criteria for success for your multimodal AI implementation. This could include metrics such as accuracy, speed, and cost-effectiveness.
3. Collect and preprocess data: This involves cleaning, labelling and formatting the data so that it can be used to train machine learning models.
4. Deploy and monitor the model: Deploying the model in your enterprise environment involves integrating it with your existing systems and monitoring its performance to ensure that it continues to provide accurate predictions.
5. Iterate and improve: Iterating and improving your model over time involves collecting feedback from users, retraining the model with new data, and continuously monitoring its performance.
It’s a good idea to start with a small-scale implementation of multimodal AI to test its effectiveness before scaling up. Implementing multimodal AI can be a complex process, but it has many benefits for businesses that are willing to take the time and invest exploring this new frontier.
Multimodal AI has the potential to transform the way businesses operate by providing new insights into unstructured data, improving customer experience, automating repetitive tasks, streamlining operations, and enabling quick adaptation to changing market conditions.
Contact our team today to learn how multimodal enterprise AI tools can be deployed in your business!