The emergence of generative artificial intelligence (AI) has been an intriguing phenomenon for many organizations. AI technology has been around for years, but the latest version of ChatGPT, a text-based AI chatbot, demonstrated amazingly human-like output and opened up many possibilities. AI should no longer be overlooked. It is time to develop strategies and utilize AI technologies to drive insights, innovation, and decision-making while addressing privacy and security concerns.
AI-powered algorithms and techniques can automate data processing, allowing for real-time analytics, pattern recognition, and predictive modeling. These capabilities will revolutionize information management practices. This makes it possible to uncover hidden insights, improve operational efficiencies, and drive strategic decision-making subject to the conditions that you properly train and fine-tune AI.
Similar to training new employees, you can also train generative AI for your organization. Essentially, generative AI generates its output by evaluating a vast collection of unlabeled, unstructured data on which you train it. It subsequently responds to prompts with output that aligns with the realm of probability as established by that dataset. Your organization should train and continuously fine-tune your private LLM using your corporate intelligence.
It can be quite challenging to train a private LLM since it should collect, analyze, and derive value from structured and unstructured data from applications, repositories, and endpoint devices. Most organizations struggle with ROT (redundant, obsolete, trivial) data, not knowing where it is located, whether the information is up-to-date, or who created or edited it. As a result, there may be too much data for training. You might not collect all corporate intelligence, and there is no guarantee that much of the content is accurate. Businesses generate too many copies of documents every day through routine actions like copy-paste, download, upload, attach, check-out, and even check-in. They also create many derivatives, which are different from the original but similar. These copies and derivatives are the root cause of the redundancy problem. You might ignore much of the valuable data on endpoints since it can be overwhelming to identify what is valuable. You need a well-defined strategy to address these issues as they compound daily. Otherwise, you will eventually end up in information chaos, and it will be extremely challenging to train your private LLM – more money, time, and effort but inadequate output.
Handling the redundancy problem requires you to identify copies and derivatives at a minimal cost. Within file systems, while a file remains in its location, its identifier is generally the combination of its name and location. Users or systems are making a judgment by a file name, location, and perhaps other metadata information associated with the file. This information is not permanent since the information may change as the files travel or are used. If you copy a file to another place, the copy becomes another independent file even though its name and content are the same. Identifying the copies becomes a difficult job. For file identification, you have to compare at least the hash of each file, analyze them with AI tools, or rely on the user's discretion. Fasoo developed Content Virtualization technology to overcome this limitation of existing file systems. Content Virtualization makes files independent of their physical location. A virtualized file has a unique identifier and a version number, and you can identify them by these parameters, regardless of location, name, or other metadata. You can treat all the copies as the same file. When users or systems update the content, all the copies in different storage locations, applications, and endpoints are updated automatically. Content Virtualization allows you to identify the entire lifecycle of a file, where it originated, how it changed, and who accessed it. The technology helps users reduce redundant copies dramatically and delete redundant copies with confidence. Your organization will not only reduce your threat surface by minimizing ROT data but alleviate the burden of setting a security policy on a file consistently and have accurate content usage with rich context, which is critical for analytics. Content Virtualization will benefit many organizations looking to train their private LLM by ensuring you only use current, valuable data when training it. This eliminates the issue of garbage in, and garbage out and helps drive growth using AI technologies.
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