Here’s the translation to American English:
During the NYC AIAI summit, Roboflow CEO and co-founder Joseph Nelson addressed a crucial yet often overlooked topic in the realm of artificial intelligence: vision. In a context where language models dominate the headlines, Nelson emphasized the importance of visual understanding—the way machines interpret the physical environment—as an essential component for developing intelligent systems that operate under real-world conditions.
Nelson demonstrated how visual artificial intelligence is already revolutionizing various industries, from instant automation in sports events like Wimbledon to quality control in electric vehicle production. He explained that Roboflow has a community of one million developers working on creating visual AI applications both at the production level and in open-source projects, highlighting the extensive adoption of this technology.
In his talk, Nelson highlighted three key aspects of visual AI. First, he referred to edge cases in computer vision, uncommon situations that restrict models’ ability to capture the complexity of the real world. Second, he raised the question about the future of visual models: whether a single model will dominate or whether there will be a collection of smaller models designed for specific tasks. Lastly, he underscored the need for visual AI to operate in real-time and with data generated by the systems themselves, which is critical for its effective implementation.
Nelson also emphasized that vision is one of the primary senses of human beings. Through practical examples, he illustrated how equipping software systems with visual perception allows them to answer specific questions, such as the number of people in a room or the proper manufacturing of a batch of products.
Roboflow is emerging as a key player in this domain by offering tools and platforms that enable companies to build and deploy visual AI. More than half of the Fortune 100 companies already utilize Roboflow, particularly in sectors that require high precision in their operations.
Looking toward the future, Nelson highlighted Roboflow’s commitment to open source as a way to advance the understanding of vision. The company has launched a package called Supervision, aimed at helping developers integrate detections into broader systems, and provides various open-source tools to simplify the process of visual AI.
In terms of implementation, Roboflow supports large companies like Rivian, which have utilized its models to ensure product quality. It has also made notable advancements in practical applications, such as Walmart’s self-scanning kiosks, which use cameras to automatically identify products in customers’ carts.
Nelson concluded his presentation with a powerful message: for artificial intelligence to fulfill its potential in the real world, it is crucial that it is capable of seeing and understanding that environment. Visual understanding thus emerges as a critical line of action in the evolution toward visual intelligence, emphasizing the decisive role of developers in this process.
Referrer: MiMub in Spanish
