Generative AI, a system that can automatically generate anything from text to images as well as complete application code, is changing how businesses operate in the world. It is predicted to uncover new avenues of value and creativity, possibly increasing $4.4 trillion in value to the world economy, as per an analysis by McKinsey.

However, for many businesses who are attempting to make use of the power of generative AI is only just beginning. They will face several challenges when changing their systems, processes, and their cultures to accommodate the new paradigm. They must act quickly before their rivals get an advantage.

One of the biggest challenges is how to manage the complicated interactions between the generative AI applications as well as other corporate assets. These applications, powered by substantial model languages (LLMs), can generate content, respond, and make autonomous decisions that impact the entire company. They require a different kind of infrastructure to support their autonomy and intelligence.

Ashok Srivastava, chief data officer at Intuit, a firm that has been making use of LLMs for many years in the tax and accounting sectors, In an interview with VentureBeat in a lengthy interview that this system could be compared to an operating system that can be used to generate AI “Think of a real operating system, like MacOS or Windows,” said the expert stated, referring to the assistant, management, and monitoring capabilities. In the same way, LLMs need a way to coordinate their efforts and get access to the resources they require. “I think this is a revolutionary idea,” Srivastava declared.

The operating system analogy helps demonstrate the magnitude of changes that generative AI brings to businesses. It’s not only about adding a layer of frameworks and software over existing systems. It’s also about giving the software the power and authority to control its processes by deciding which LLM to employ in real-time to respond to an individual’s request and when to turn the conversation over to an expert human. This is, in essence, an AI managing an AI, as per Intuit’s Srivastava. It’s also about allowing developers to use LLMs to build intelligent AI applications quickly.

It’s similar to how operating systems transformed computing by taking away minor details and allowing users to complete complex tasks effortlessly. Enterprises must apply the same principles to generative AI application development. Microsoft President Satya Nadella recently compared this change to the transition of steam engines to electrical 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,” Nadella said to Wired.

What is the process of developing an operating system to run generative AI

As per Intuit’s Srivastava, There are four primary layers that businesses must be aware of.

The first is the data layer that ensures that the business has an accessible and unified data system. This means having a knowledge base with the most relevant information regarding the business’s specific domain, for instance, the tax codes and accounting guidelines for Intuit. Additionally, it includes an effective process for data management process that ensures the protection of customer privacy and conforms to rules and regulations.

The second layer is the development layer, which gives employees a standard and consistent method to develop and implement generative AI applications. Intuit refers to GenStudio as a platform that includes models, templates, frameworks, and libraries to aid in LLM application development. It also includes tools for rapid development and testing LLMs in addition to security and governance guidelines to reduce the risk of a potential incident. The aim is to simplify and standardize the process of development and allow rapid and easy scaling.

Thirdly, there’s the runtime layer. It allows LLMs to improve and learn independently, to improve their performance and efficiency, as well as to draw value from the enterprise data. This is a very interesting and exciting field, Srivastava stated. In this area, new open frameworks like LangChain are at the forefront. LangChain offers an interface through which developers can connect to LLMs via APIs and connect them to tools and data sources. It allows you to chain multiple LLMs in a row or specify when one model should be used compared to another.

Fourthly, there is the layer of user experience, which provides satisfaction and value to users who use the AI applications. AI applications. This includes creating user interfaces that are uniform, user-friendly, and enjoyable. This also involves keeping track of user feedback and behavior and adjusting the LLM outputs.

Intuit has recently revealed an operating system that consists of all of these layers. It’s called GenOS, which makes it one of the few businesses to adopt a fully-fledged Gen OS for its operations. The announcement received little attention because it is primarily internally owned by Intuit and is not accessible to third-party developers.

What other companies are playing the game in the AI space? AI market

Although companies such as Intuit are creating their own generation OS platform internally, there’s a dynamic and growing collection of open software platforms and frameworks that are helping to advance the current state of LLMs. These platforms and frameworks enable entrepreneurs to build more autonomous, intelligent, and generative AI applications in various fields.

A key trend is that developers are leveraging the efforts of a few firms that have developed what are known as foundational LLMs. They are figuring out ways to profitably utilize and improve these foundational LLMs that have already been trained using massive quantities of information and billions of variables from other organizations but at a substantial cost. The models, such as OpenAI’s GPT-4 and Google’s PaLM 2, are called fundamental LLMs since they offer the general-purpose framework for developing generative AI. However, they do have their limitations and trade-offs, which are based on the kind and quality of the data they are based on, as well as the specific task they were designed to perform. For instance, certain models concentrate on text-to-text generation, while others concentrate on image-to-text generation. Some models are better at summarizing, and others excel in recognizing tasks.