The Future of Security: How Multimodal AI is Transforming Our Safety

In the fascinating world of artificial intelligence (AI), one phrase seems to pop up frequently: "multimodal." What does it mean, and why does it matter? Let’s take a stroll through the evolving landscape of security technology and see how this buzzword is not just a passing trend but a real game-changer.

The Multimodal Shift

Chip Huyen, a leading voice in AI and a seasoned computer scientist, nails it when she talks about how traditional models in machine learning (ML) have mostly focused on one type of data — be it text, image, or audio. Yet, just as humans seamlessly switch between reading a book, listening to music, or watching a movie, AI is now learning to juggle these modes too. The result? Large Language Models (LLMs) and Large Multimodal Models (LMMs) are setting the stage for major advancements in security systems.

Better Than Before, Yet Still Lacking

For the past half-century, we’ve relied heavily on electronic security systems. While these systems have improved over time, they still depend on human oversight that can be costly and sometimes less reliable. Imagine that an access-controlled door alarms — a person still needs to check the live video to decide if an officer should be dispatched. Frustrating, isn’t it?

Even with rising occurrences of AI-enabled video analytics, which help cut down false alarms, humans remain in the loop. Security personnel often have to assess every unusual activity. For instance, when it comes to tailgating detection, officers usually have to intervene post-incident, which can lead to delays in addressing potential threats.

Unpacking New Technologies

Picture this: a state-of-the-art security system can not only predict and prevent unauthorized entry at a door but can also announce “One entry at a time, please” to deter any hopeful tailgaters. While this is promising, these systems are still limited in scope. They typically operate at a single door without correlating incidents happening elsewhere on the premises.

Here’s where modern LLMs and LMMs come into play. These advanced systems can process and analyze a staggering amount of data at lightning speed, breaking free from the constraints of previous security systems. This leads us to some exciting developments in incident response:

1. Real-Time Correlation

Imagine instantly integrating various data types — text logs, video feeds, audio signals, and sensor metrics — all at once! This capability allows security teams to form a well-rounded picture of what’s really happening in real-time.

2. Comprehensive Understanding

These AI systems synthesize multiple inputs to highlight patterns or relationships that might go unnoticed. For example, if a series of unusual behaviors occur at different access points, the system can identify that a potential breach is developing.

3. Smart Policy Application

Thanks to predefined rules and learned behaviors, AI can assess situations, spot anomalies, and most importantly, detect threats before they escalate.

4. Clear Communication

Lastly, imagine a scenario where complex data is condensed into easy-to-understand narratives. These systems can generate reports in plain language tailored to different audiences, ensuring everyone, including non-experts, is in the loop.

Conclusion

With these advancements, the future of physical security is beginning to look brighter. While we still face challenges, the integration of multimodal AI technologies fosters a proactive approach to mitigation that was not possible before.

The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.