But the real excitement lies in the runtime layer. This is where LLMs can learn and improve on their own, optimizing their performance and leveraging enterprise data. And leading the way in this area is LangChain, an open framework that allows developers to seamlessly integrate LLMs into their applications. With LangChain, developers can chain multiple LLMs together and specify when to use each model.
Of course, none of this matters if the user experience isn’t top-notch. That’s why the user experience layer is crucial. It’s all about delivering value and satisfaction to the customers who interact with generative AI applications. This means designing user interfaces that are intuitive, consistent, and engaging. It also means constantly monitoring user feedback and behavior to ensure that the LLM outputs are adjusted accordingly.
Intuit, a company known for its innovation, has recently announced its own platform called GenOS. This platform encompasses all the layers mentioned above, making it one of the pioneers in embracing a full-fledged gen OS for its business. While the platform is mostly internal to Intuit, there is a vibrant ecosystem of open software frameworks and platforms that are advancing the field of LLMs.
One interesting trend is the use of foundational LLMs. These are models that have already been trained on massive amounts of data by other organizations. Developers can leverage these models through APIs and customize them for their specific needs. Techniques like fine-tuning, domain adaptation, and data augmentation allow developers to optimize the performance and accuracy of LLMs for their target domain or task.
Another way developers are enhancing LLMs is by using frameworks that enable querying of both structured and unstructured data sources. This means that LLMs can access relevant data based on user input or context. For unstructured data, embeddings are used to convert it into a format that LLMs can process efficiently. Vector databases, like Pinecone, are becoming increasingly popular for storing these embeddings.
The future of enterprise LLM intelligence is exciting. Companies like OpenAI and Google are continuously improving their models to make them more intelligent and versatile. However, enterprise companies like Intuit have the advantage of fine-tuning existing models or building their own specialized models. They are also exploring self-guided, automated LLM agents that can remember tasks and work through them independently.
The possibilities in the generative AI space are endless. With the right tools and frameworks, developers can create intelligent and autonomous applications that revolutionize various domains. It’s an exciting time to be in the world of generative AI.
The future of generative AI is here
The race to build an operating system for generative AI is not just a technical challenge, but a strategic one. Enterprises that can master this new paradigm will gain a significant advantage over their rivals, and will be able to deliver more value and innovation to their customers. They arguably will also be able to attract and retain the best talent, as developers will flock to work on the most cutting-edge and impactful generative AI applications.
Intuit is one of the pioneers and is now reaping the benefits of its foresight and vision, as it is able to create and deploy generative AI applications at scale and with speed. Last year, even before it brought some of these OS pieces together, Intuit says it saved a million hours in customer call time using LLMs.
Most other companies will be a lot slower, because they’re only now putting the first layer — the data layer — in place. The challenge of putting the next layers in place will be at the center of VB Transform, a networking event on July 11 and 12 in San Francisco. The event focuses on the enterprise generative AI agenda, and presents a unique opportunity for enterprise tech executives to learn from each other and from the industry experts, innovators and leaders who are shaping the future of business and technology.
Intuit’s Srivastava has been invited to discuss the burgeoning GenOS and its trajectory. Other speakers and attendees include executives from McDonalds, Walmart, Citi, Mastercard, Hyatt, Kaiser Permanente, CapitalOne, Verizon and more. Representatives from large vendors will be present too, including Amazon’s Matt Wood, VP of product, Google’s Gerrit Kazmaier, VP and GM, data and analytics, and Naveen Rao, CEO of MosaicML, which helps enterprise companies build their own LLMs and just got acquired by Databricks for $1.3 billion. The conference will also showcase emerging companies and their products, with investors like Sequoia’s Laura Reeder and Menlo’s Tim Tully providing feedback.
I’m excited about the event because it’s one of the first independent conferences to focus on the enterprise case of generative AI. We look forward to the conversation.
In the world of artificial intelligence, there is a race to develop an advanced ‘operating system’ to power the new generation of generative AI. Such an operating system is designed to provide the computational resources necessary to create powerful and autonomous AI programs that are tailor-made for problem solving and data exploration.
At its core, generative AI is a type of machine learning that enables a system to generate new data on its own, without the need for human input. The most cutting-edge applications of this technology are in the fields of robotics, healthcare, and the financial industry.
The development of a ‘black box’ operating system for generative AI is being led by several major tech companies. These include Google, Microsoft, IBM, and Amazon. While each of these companies has its own approach to building the perfect AI operating system, one common factor is the use of deep learning. This involves the use of powerful algorithms to extract patterns from large amounts of data.
These algorithms enable the system to understand what type of data is relevant to a particular task, and how to make sense of it. This makes it possible to create AI agents that can recognize patterns and generate useful insights from data, even when it is complex and incomplete.
The stakes are high for these tech giants, as the first company to successfully develop a working AI operating system will be in an excellent position to reap the rewards. The use of deep learning for generative AI has been around for a few years, but these companies are now pushing the boundaries of what is possible to provide more advanced and comprehensive solutions.
In addition to the race to develop a powerful and comprehensive AI operating system, there is also a race to apply this technology to the real world. Companies such as Google and Apple are already exploring ways to use generative AI in their own products and services.
For example, Google is working on using generative AI for natural language processing and speech synthesis in its digital assistant. Apple is investing in autonomous systems and robotics, while IBM is experimenting with AI-driven decision-making and predictive analytics.
Ultimately, the race to develop a groundbreaking operating system for generative AI is an exciting challenge, and one that has the potential to revolutionize the way we think about and interact with AI. The advances made in this field in the coming years will be critical in determining which company is able to capitalize on the amazing potential of this technology.
