Generative AI is the latest era in AI/Machine Learning (ML) that is opening up new opportunities and tackling previously unaddressed challenges. Create new content and ideas such as conversations, stories, images, videos, and music. It utilizes a large-scale model, pre-trained on a huge amount of data, commonly called a foundation model (FM).
Using AI to maximize your organization’s data strategy can improve employee productivity and collaboration, enable smarter decision-making, and result in real, quantifiable business value. increase. It empowers developers to reimagine applications and create new customer experiences while accelerating time to revenue and transforming businesses.
Evolution of Generative AI
Despite the excitement around AI, we are still in the early stages of that transformation. The current wave of generative AI is focused on creating new content based on snapshots of existing data. This is extremely valuable in improving and automating business processes.
However, the real value of generative AI lies in the next stage of enabling insights and driving decisions. All of these early generative AI applications are content-oriented and focused on processing vast amounts of data to produce fairly accurate results. However, the accuracy of these results is not sufficient for decision making.
The first generation of these applications perform very well in the enterprise-to-customer market where quickly sifting through large amounts of data to produce a condensed or summarized view is a major advantage.
Generated content often serves as a starting point for more focused human involvement in completing the job that should be done. Time-consuming, low-value tasks like building outlines, writing code, building test datasets, and performing data analysis are accelerated. Humans are more productive by working on tasks that are not mission critical and can be undone and redone with relatively little effort. Copilot X is a great example of a feature where output timeliness is more important than content accuracy.
quantity and quality
However, the true value of AI comes from its ability to derive meaningful insights and drive result-based decision-making. In such cases, quality and accuracy are paramount.
To drive the transformation that AI can enable, from low-impact, low-risk content generation for low-fidelity and low-accuracy use cases to high-impact, high-risk analytics that drive decision-making and drive high needs. should migrate to fidelity and accuracy.
For businesses to evaluate the return on investment (ROI) of these new AI capabilities and determine how they can uniquely differentiate themselves with their customers, B2B applications must drive outcomes and justify the ROI. I have. This should be done in a secure manner within the context of the existing data ecosystem.
Democratizing AI
Rapid advances and adoption of AI have democratized access to the core technology at the heart of this solution: the underlying model. The tremendous pace of innovation in large language models (LLMs) in the open source community has sparked the creation of several open source LLMs that democratize access to core technologies for building AI businesses.
Smaller models can run on low-power hardware such as the iPhone, lowering the barrier to tinkering and creativity. The availability of these small but sufficiently high quality models has encouraged innovation and unlocked potential for individuals and institutions around the world. Ultimately, the best models are those that can be continuously and rapidly learned, so they can be iteratively fine-tuned over a short period of time.
This makes it difficult to train and experiment with the work of a few very large companies or major research organizations alone, at night, on bulky laptops or even personal computing devices like iPhones. Barriers to entry have been lowered. These advances will encourage companies to assess the impact of AI in the light of their business.
Multimodal platforms hold the key to propelling AI-powered apps
For companies to succeed, they need access to multiple underlying models, including multiple modalities of text, image and video that can be fine-tuned based on their own data to deliver unique business-relevant insights. They want to get a base FM and make it easy to build differentiated apps using their own data. Data is more important than ever and is the cornerstone of any successful AI initiative. This includes a reliable and performant data platform that supports unstructured operational and non-operational data with tighter integration with analytics and ML platforms that enable prescriptive analytics. .
Achieving this in real time requires deeper integration with the latest data stored in operational databases. The database should be adept at handling multiple forms of data without incurring performance or latency overheads.
By building on a multimodal, low-latency data platform, enterprises can realize their vision of prescriptive analytics and drive AI-powered applications.
