Artificial Intelligence (AI) has fascinated computer scientists, visionaries, and even the general population for decades. So much so that for years AI has been a regular plot device of several science fiction novels, television shows, and movies. However, AI is no longer a fancy buzzword, it is a technology with widespread applications in various aspects of our daily lives. From our smartphones, autonomous driving cars to tools used in various industries, AI is making a difference everywhere. With the advent of generative AI applications such as ChatGPT, Bard, Midjourney, GitHub Copilot, Stable Diffusion and many more, AI is now becoming ubiquitous and breaking barriers that were previously considered unbreakable.
The speed with which generative AI is developing is unprecedented. ChatGPT was released in November 2022, and within four months, OpenAI released its next Large Language Model (LLM) ChatGPT 4, which is significantly more advanced and powerful. At the same time, Google quickened its AI initiative and released its own generative AI chatbot called Google Bard. Several more generative AI tools such as Meta’s Llama, OpenAI’s Dall-E, Anthropic’s Claude, and many similar tools are becoming widely popular.
Before proceeding further, it would be prudent to look at how generative AI tools are built and how they work. While generative AI may seem to have appeared abruptly, it has been in the making for decades. It employs an artificial neural network imitating the billions of neurons in the human brain. This is possible through deep learning which goes deep into the layers of the neural network to make intelligent decisions of its own. To perform tasks such as creation of text, images, videos and computer code, generative AI processes extremely large sets of data.
The popularity of generative AI tools can be attributed to the fact that for the first time the average user was able to properly converse with AI tools and utilize it in everyday tasks. Generative AI tools can not only write texts, create digital art, answer a broad variety of questions but also generate code and complex designs. From students to professionals and even business leaders, these tools can be used for routine work as well as help in innovating and building new things. While consumers have been the first to adopt generative AI tools for increased productivity, businesses too have been looking at ways to adopt them to stay relevant and competitive.
“With the exponential advances in technologies like IoT, 5g and 6g, WiFi6 and Web3.0, sensor technology and wearables, and now Generative AI, ChatGPT and ML, the amount of new data available for analysis and actionable customer insight will explode. Velocity and speed-to-market now becomes the competitive differentiators. Companies must plan today for a convergence point 2 to 3 years out to be able to ingest, integrate, analyze, and transport information in real time or be left behind. As a result, many legacy systems in existence today will need to be rearchitected to meet this near future demand.”
– Joseph Mendel, Managing Director, V-Soft Digital Consulting
The biggest challenge that businesses face in the generative AI age is that most tools such as ChatGPT and Google Bard are only trained with public data and data sets that are provided to them. They do not have access to an enterprise’s internal data or private business information necessary to optimize the workflows. However, the benefits generative AI offers businesses in terms of automation and productivity are too great for businesses to ignore. But before that, business leaders must ensure that their internal company data is ready for generative AI algorithms.
Define clearly what type of content the AI model should generate. Once the objective is defined, subsequent decisions regarding data selection, preprocessing and model training can be taken care of smoothly.
Data is the lifeblood of any AI model and the quality of data provided to it directly impacts generative AI system’s performance. The dataset collected to train AI model should be diverse, representative align with the enterprises’ objective. This dataset may include documents, images, code, or any relevant information but they should be structured, clean and labeled to ensure that the quality of the generated content remains good.
For many enterprises, data is already well-organized, but continues to reside in silos and not in a centralized manner. Data pipelines should be optimized to collect data from disparate sources and feed them to the AI model efficiently.
Preprocessing data before it is fed to the generative AI model is important. This can include data cleaning, text-tokenization, resizing of images and similar tasks. This would ensure that the generative AI model can understand the data format and effectively learn from it.
Training a generative AI model involves feeding prepared data to it and iteratively adjusting the parameters to further optimize the performance. The data should be continuously evaluated, and its quality assessed by human experts. Iterate on the model and its training process to address any shortcomings should be carried out based on the evaluation.
Generative AI has drawn some criticism for generating inappropriate, offensive, and biased content. Therefore, it is important to implement safeguards such as content filters and bias detection mechanisms to ensure that the generated content meets the ethical and quality standards.
Most modern companies have the potential to take advantage of the generative AI revolution. However, only those enterprises that would truly benefit have prepared their data for generative AI. Preparing enterprise data for use with generative AI is a comprehensive process that demands careful planning, data curation, preprocessing, model selection, and iterative refinement. As generative AI continues to advance, mastering the art of data preparation will remain a cornerstone of success in the ever-evolving landscape of artificial intelligence.