With the rise of visual data in inbound telemetry, computer vision has the potential to transform analytics. It automates the extraction of insights from videos and images.
By offering real-time visual data analysis and insights, computer vision is a transformative technology that will boost decision-making, automate data extraction and optimize processes across enterprises, according to Ayush Kumar (pictured), associate principal data scientist at IBM Corp.
“I think we are progressing to a future which is much more multimodal than where we are today,” Kumar stated. “We’ll see more information coming out as computer vision OCRs that we do on our own enterprise data. On the business intelligence side, I think there’s an important change and shift on how insights are consumed. Traditionally, we have more reports, which are more visual, but at the same time, we’ll have more agents, more pointed information and in-depth analysis that we’ll get out of these systems as well.”
Pradhan spoke with theCUBE Research’s John Furrier at the Media Week NYC: theCUBE + NYSE Wired 2024 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed why artificial intelligence sectors, such as computer vision, are game changers.
How computer vision will shape a multimodal future
Thanks to increased user engagement and diverse communication channels, such as interactive formats, audio, video and text, the future is expected to be multimodal. As a result, computer vision will play an instrumental role based on the automation of manual tasks, with model training techniques, such as quantization and pruning coming in handy, Kumar pointed out.
“We’ve been training these LLM models in terms of in-context learning or retrieval-augmented generation, and really training or fine-tuning these models is becoming more and more expensive,” he said. “I think in the future, the cost of training will actually reduce. We’ve seen effective techniques like pruning, quantization that bring the size of these models really to an extent where we can train these models.”
Since generative AI has materialized as a new category, disruptive enablement, process change and tech stack adoption are required. As a result, IBM is enabling the transition of this cutting-edge technology through data curation, multi-model and multicloud approaches, according to Kumar.
“There is a lot of change in terms of generative AI,” he said. “You see a lot of development in different places. The data side where we are looking at more data curation to feed into these models and LLMs. We have kind of figured out how data lakes work, data warehouses work, how do we enable enterprises with structured data, but when it comes to unstructured data, we are still trying to figure that out. With IBM we have multicloud and multi-model, but we also have a data curation process and tooling around it to help with the journey.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the Media Week NYC: theCUBE + NYSE Wired 2024 event: