{"id":6052,"date":"2023-09-01T16:53:27","date_gmt":"2023-09-01T20:53:27","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=6052"},"modified":"2023-09-08T14:05:39","modified_gmt":"2023-09-08T18:05:39","slug":"generative-ai-how-to-care-for-and-properly-feed-chatty-robots","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/generative-ai-how-to-care-for-and-properly-feed-chatty-robots\/","title":{"rendered":"Generative AI \u2013 How to Care For, and Properly Feed, Chatty Robots"},"content":{"rendered":"

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Solutions Review\u2019s <\/strong><\/i>Contributed Content Series<\/u><\/strong><\/i><\/a> is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Ontotext<\/a>‘s Doug Kimball offers commentary on how to properly interface with generative AI.<\/strong><\/i><\/span><\/p>\n

Developments in generative AI (GenAI) have reached a crescendo at what feels like hyper-speed. It has captivated our minds, imagination, and conversations over the last several months with its seemingly magical superpowers. Enterprises worldwide are analyzing Generative AI capabilities and seeking ways to leverage them for a variety of use cases to improve their competitive edge and incorporate automation and efficiency.<\/p>\n

Terms related to GenAI such as hallucinations and Large Language Models (LLMs) have become lingua-franca for any and every business conversation. As a result, students, business professionals, developers, marketers, and others have begun exploring these \u201cchatty robots\u201d and discovered there is a lot to like, and some things to be concerned about. LLMs in particular have remarkable capabilities to comprehend and generate human-like text by learning intricate patterns from vast volumes of training data; however, under the hood, they are just statistical approximations.<\/p>\n

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Interfacing with Generative AI<\/strong><\/h2>\n

So, What Exactly are Generative AI and LLMs?<\/strong><\/h3>\n

Generative AI refers to computational models that are trained on massive amounts of text data and output in the form of text, images, video, audio, new data, and even code. An LLM, on the other hand, is a neural network model built by processing text data. Analyzing how this data relates to other text-based data allows the data to be associated with similar text.<\/p>\n

In simple terms, these models predict which word best follows previous words by taking a broader context of the words before it. LLMs can even take tone and style into account where responses can be modified by incorporating personas such as asking ChatGPT (powered by an LLM) to explain the concept of data governance through a Taylor Swift style lyric.<\/p>\n

Challenges & Limitations<\/strong><\/h3>\n

So while innovation in AI technologies may introduce new capabilities and uncover new opportunities, it quickly runs into problems associated with data governance and quality, trust, bias, and ethics. For example, if input training data is of bad quality, the results from AI algorithms will be substandard too. LLMs routinely generate superficial and inaccurate information, are non-deterministic, unreliable, and suffer from being trained using stale data. They are also incapable of providing provenance or pointers to data sets that let users know how the results were obtained.<\/p>\n

As a result, and even if they sound realistic, these LLMs routinely produce bad responses based on outdated training data, exhibit random hallucinations, create bias, and lack real-world context. This is because they don\u2019t care about conflicting or ambiguous information, they often make up an answer randomly based on certain parameters. But as one could imagine, this can produce catastrophic results with buggy pipeline code, bad and\/or suboptimal implementation logic, inappropriate answers, or just plain toxic information. They run the risk of using trademarked, copyrighted, or protected data as they scour public data and can be easily exploited and manipulated to ignore previous instructions. Worse, LLMS can be made to execute malicious code, commands, or unintended actions by creative prompting.<\/p>\n

Additionally, data is the fulcrum of AI, and the data used to train LLMs must be properly governed and controlled. Otherwise, any LLM deployed in production would run the risk of basing their decisions on poor quality data, exposing privacy, intellectual property, bias, and ethical issues. Evaluating the trustworthiness of LLMs is notoriously difficult as well as they don\u2019t have any ground truth label. This causes organizations to struggle to identify and benchmark as to when the model can be trusted.<\/p>\n

Building Governance, Security, and Trust with LLMs<\/strong><\/h3>\n

Before jumping on the generative AI bandwagon, organizations should do their homework and clearly understand the risks, challenges, and negative consequences of leveraging LLMs. Otherwise, they are like a black box, where very little is known as to how they arrive at answers and responses and organizations can lose control of private data, GenAI pipelines can get compromised, or applications can be attacked in subtle ways by hackers. To avoid this, enterprises should consider:<\/p>\n