Inside the race to build an 'operating system' for generative AI - VentureBeat

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Inside the race to build an 'operating system' for generative AI - VentureBeat

June 30, 2023 4:00 AM Credit: VentureBeat made with Midjourney Join top executives in San Francisco on July 11-12 and learn how business leaders are getting ahead of the generative AI revolution. Learn More Generative AI, the technology that can auto-generate anything from text, to images, to full application code, is reshaping the business world. It promises to unlock new sources of value and innovation, potentially adding $4.4 trillion to the global economy, according to a recent report by McKinsey.  But for many enterprises, the journey to harness generative AI is just beginning. They face daunting challenges in transforming their processes, systems and cultures to embrace this new paradigm. And they need to act fast, before their competitors gain an edge. One of the biggest hurdles is how to orchestrate the complex interactions between generative AI applications and other enterprise assets. These applications, powered by large language models (LLMs), are capable not only of generating content and responses, but of making autonomous decisions that affect the entire organization. They need a new kind of infrastructure that can support their intelligence and autonomy. Ashok Srivastava, chief data officer of Intuit, a company that has been using LLMs for years in the accounting and tax industries, told VentureBeat in an extensive interview that this infrastructure could be likened to an operating system for generative AI: “Think of a real operating system, like MacOS or Windows,” he said, referring to assistant, management and monitoring capabilities. Similarly, LLMs need a way to coordinate their actions and access the resources they need. “I think this is a revolutionary idea,” Srivastava said. Event Transform 2023 Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls. Register Now The operating-system analogy helps to illustrate the magnitude of the change that generative AI is bringing to enterprises. It is not just about adding a new layer of software tools and frameworks on top of existing systems. It is also about giving the system the authority and agency to run its own process, for example deciding which LLM to use in real time to answer a user’s question, and when to hand off the conversation to a human expert. In other words, an AI managing an AI, according to Intuit’s Srivastava. Finally, it’s about allowing developers to leverage LLMs to rapidly build generative AI applications. This is similar to the way operating systems revolutionized computing by abstracting away the low-level details and enabling users to perform complex tasks with ease. Enterprises need to do the same for generative AI app development. Microsoft CEO Satya Nadella recently compared this transition to the shift from steam engines to electric power. “You couldn’t just put the electric motor where the steam engine was and leave everything else the same, you had to rewire the entire factory,” he told Wired. What does it take to build an operating system for generative AI? According to Intuit’s Srivastava, there are four main layers that enterprises need to consider. First, there is the data layer, which ensures that the company has a unified and accessible data system. This includes having a knowledge base that contains all the relevant information about the company’s domain, such as — for Intuit — tax code and accounting rules. It also includes having a data governance process that protects customer privacy and complies with regulations. Second, there is the development layer, which provides a consistent and standardized way for employees to create and deploy generative AI applications. Intuit calls this GenStudio, a platform that offers templates, frameworks, models and libraries for LLM app development. It also includes tools for prompt design and testing of LLMs, as well as safeguards and governance rules to mitigate potential risks. The goal is to streamline and standardize the development process, and to enable faster and easier scaling. Third, there is the runtime layer, which enables LLMs to learn and improve autonomously, to optimize their performance and cost, and to leverage enterprise data. This is the most exciting and innovati...