There is More Than Generative AI, and Yes, it is Latency-Sensitive

*****Precision in discussing terms like "generative AI," "LLM" (Large Language Models), "SLM" (Small Language Models), "Tuning," and "Inference" is vital to prevent misconceptions and promote clarity, mainly as most current discussions focus on generative AI and LLMs like those from OpenAI. However, other inference AI platforms are being deployed.*****

Contrary to popular belief, not all inference processes are latency-insensitive. Most people are discussing generative AI, and these text-based generative AI models can tolerate a much higher latency. Numerous edge applications depend on real-time processing for optimal functionality. These solutions address these needs by situating AI inference platforms at the network's edge, near where data is generated. This strategic placement dramatically decreases latency, enabling near-real-time applications to process data locally instead of relying on distant cloud data centers.

Manufacturing and Automotive Industries

In manufacturing, edge AI rapidly processes data from sensors and cameras to identify defects or enhance production lines. Automakers employ computer vision on assembly lines to spot vehicle flaws before they leave the factory. This low-latency (less than 100 us), always-on technology minimizes delays that could compromise quality assurance, integrating local processing with cloud support for sophisticated AI tasks.

Recruiting and Sports Industry

In recruiting, AiScout's app utilizes edge-to-cloud capabilities to bridge amateur athletes with professional scouts. This platform allows athletes to record and upload their performances, which are then processed and analyzed in the cloud, considerably reducing the time and expenses typically associated with scouting.

Healthcare Sector

Edge-to-cloud technology is transforming healthcare, as seen in digital pathology. AI aids pathologists in more accurately and swiftly analyzing medical images, improving patient outcomes. This technology is supported by secure, rapid image sharing across hospital networks, bolstered by 5G and robust cloud processing capabilities.

T-Mobile and Las Vegas Collaboration

In a pioneering move to reduce pedestrian fatalities, T-Mobile and Las Vegas have deployed a high-resolution camera system at crosswalks. This system assesses the traffic light status and approaching traffic conditions in real-time when a pedestrian steps onto a crosswalk. If the light is not red, it computes whether to switch it to red within a few milliseconds, considering vehicle distance, speed, and network connectivity. Any significant delay compromises the model's effectiveness, underscoring the critical need for rapid, data-driven decisions to enhance pedestrian safety.

Inference at the Edge empowers businesses across various industries to boost their operations and achieve faster, more reliable outcomes. As technology evolves, these solutions will remain pivotal in driving innovation and efficiency in the business world.

Precision in discussing terms like "generative AI," "LLM" (Large Language Models), "SLM" (Small Language Models), "Tuning," and "Inference" is vital to prevent misconceptions and promote clarity, mainly as most current debates focus on generative AI and LLMs like those from OpenAI.