Generative AI became particularly popular and known to the general population with the introduction of ChatGPT. However, this AI solution has been on the market longer than imagined. 

Its power in transforming research, content creation and discovery, and customer service has sent businesses running to implement this technology. Most organizations are restructuring with the current AI wave to implement generative AI, but how is it possible? Regardless of the business domain or application use case, generative AI offers opportunities to organizations looking to tap into it. 

In this article, we will learn what generative AI is and how it can be implemented into various digital products relevant to businesses of different kinds. 

What is Generative AI? 

Generative AI is a branch of artificial intelligence that has the most widespread business applications and relevance because of its versatility. It leverages machine learning algorithms to create new, original content from learned data. After learning adequately from a large and diversified dataset, the algorithm is poised to produce text, video, images, audio, and code that are original but reflective of the learning material. With these capabilities, machines can now perform tasks traditionally associated with human creativity, like writing, composing music, or generating animations. 

Deep learning models like neural networks that can simulate the decision-making characteristic of the human brain are used in developing generative AI. These networks can cherry-pick the most relevant components of the input data and learn from them to generate accurate content per the user's instructions. 

With tools like ChatGPT and Dalle-E, generative AI technology has gained prominence over the past years. The number and capabilities of such tools are steadily growing, demonstrating the potential of generative AI technology to revolutionize content creation, automation, and business usage. 

Why Generative AI? 

As we have seen, generative AI is a game-changing technology, and the earlier a business implements it in its operations, the better. The significance of generative AI primarily lies in its ability to automate and enhance the creative process. Contrary to the popular opinion that this technology could potentially replace creatives, the tool is more of a helper. It serves various applications, from designing products to drafting documents, just from a prompt. 

The underlying machine learning algorithm is able to achieve these functionalities by simply learning from examples and generating similar yet unique results. This capability is quite crucial for scaling content creation and driving innovation across industries. 

Also, the ability to create customized content makes generative AI ideal for personalizing user experiences. This personalization can be applied by customer service and marketing teams across various industries. Therefore, generative AI stands out for its potential to redefine how individuals and organizations approach their business problem-solving and creative processes.            

Developing generative AI models

Implementing generative AI in various business processes begins with understanding the business problem and developing the relevant generative AI solution. The most technical and often challenging aspect of implementing generative AI is the development phase. It involves model design, training, evaluation, and deployment. Each of these stages requires careful planning and implementation to ensure effective alignment of the AI technology with the business needs.  

Contextualizing business problems with generative AI 

As powerful as generative AI models are, they are only helpful when used appropriately to solve the right problem. Therefore, it is essential that we first understand the business problem that we are trying to solve with the generative AI model. Problem analysis will bring to light the possible application areas, alignment with the business objectives, and the expected results. Only after contextualizing the problem and understanding how the technology applies in our industry can we develop the model. 

Understandably, each business across various sectors has different needs and are trying to use generative AI to solve different problems. For instance, a market research agency might need a generative AI system to read through large volumes of research materials, generate insights from these documents, and present the results in summarized and readable formats. Such a use case would require training a generative model on a dataset that captures this functionality and testing it to ascertain accuracy and usability.     

Model design and data collection

Model development is critical to implementing generative AI models for whichever business application. This development starts with model design and data collection. Design simply entails defining the model architecture relevant to the application and the nature of the data it will generate. For instance, when developing text-based generative AI models, we go for large language architectures like transformers, while we choose vision model architectures if we intend to generate images or animations. 

Different architectures that match these computational models are available for use when implementing generative AI technology. Whether neural networks, diffusion models, or any other type, the choice of model architecture sets the foundation for the resultant generative AI model.  

After picking the right architecture, data collection follows. Generative AI models require extensive, diverse datasets to learn from. For instance, training a Stable Diffusion model for image generation would need a minimum of a thousand to 2 billion well-labeled images to train. Besides being voluminous, this data must be representative of the task and domain of the application.

While collecting data to train generative AI models, ethical issues like privacy and security emerge. Therefore, it is important to develop frameworks for anonymizing and securing sensitive information. With the data and model architecture ready, we jump straight to model training!     

Model training 

Training a generative AI model involves feeding it with the collected data and iteratively adjusting its parameters to improve the output. The model is exposed to the prepared dataset to enable it to understand the interrelationship between the data points and perform the related generative tasks. 

Depending on the approach taken, the training process can be resource-intensive. In a typical training lab where generative AI models are trained from scratch, hundreds of GPUs run non-stop to enable the model to learn adequately from the data. However, for organizations that cannot afford this costly and time-consuming training approach, there are existing foundational models like GPT-4 that they can adapt to their various business needs using techniques like fine-tuning. 

Advanced training techniques like transfer learning, embedding, and reinforcement learning from human feedback (RLHF) are also employed to refine the model’s performance further. These techniques improve the accuracy of the generative AI model and align them with human expectations and needs like helpfulness, harmlessness, and ethics.      

Model evaluation and deployment

After training, it is only fair to test the performance of the model before deploying it for use. This model testing is technically achieved through evaluating the model’s performance against predefined metrics and benchmarks. Quantitative measures like recall, precision, and accuracy, as well as comparisons of the model with human performance, are key evaluation methodologies. 

If the model fails to pass the predefined evaluation benchmarks, it is retrained by augmenting the dataset and tuning the hyperparameters till the desirable result is achieved. Generative AI models that pass evaluation are deployed, making them accessible to end-users. Integrating these models into AI applications that users can interact with is part of the deployment phase.

However, the lifecycle of these models extends beyond deployment. To maintain the relevance and accuracy of these generative AI models, it is important to engage in post-deployment practices like continuous monitoring and reevaluation. Post-deployment maintenance also adapts the model to new data and user feedback, further improving it.      

Using Generative AI models responsibly 

As much as generative AI unlocks infinite possibilities in the field of technology, their responsible use is paramount to ensure they serve society positively. This responsible use involves addressing social biases that may arise from training data or model assumptions, which may lead to systematic unfairness. Human audits, representational analysis, and fairness metrics are some of the approaches taken to mitigate such biases. The strategies taken to correct the biases can include but are not limited to, diversifying the training data, engaging in adversarial training, and adjusting model priorities. 

Intellectual property (IP) and privacy issues also demand attention when implementing generative AI. To avoid legal pitfalls, the creators, trainers, and users of AI-generated content must respect copyright laws and user privacy. Therefore, it is advisable to verify the copyright status of the training data. Since these models are trained on data from different jurisdictions, understanding the evolving regulations of different regions is also considered an IP best practice.

As we approach Artificial General Intelligence (AGI), the stakes are even higher. While AGI promises immense benefits and improvement of the generative AI, it also presents severe risks. The potential of surpassing human capabilities carries more harms, like ethical violations, that must be addressed early enough. Strategies like alignment with human values and policy development must be put in place to control AGI outcomes. AGI, like generative AI, must follow clear behavioral rules and feedback mechanisms as part of using these technologies responsibly. 

Final thoughts on implementing generative AI 

Generative AI has the potential to revolutionize industries and change how businesses operate by automating tasks and generating original content. The underlying principle of this technology is learning from datasets similar to the task to be accomplished and generating new and original content based on the learned patterns.      

Implementing generative AI requires a conscientious approach that balances technical prudence and ethical responsibility. Before deciding on the model architecture or training methodologies to employ, it is important to understand the business problem that the model seeks to solve. Sufficient training data should then be collected and used to train a model that satisfies the predefined business goals. 

As we integrate these models into various sectors, it is important to consider their ethical implications and societal impacts. As such, the implementation of Generative AI should be guided by principles that prioritize fairness and privacy while preventing misuse.

Labelbox is a complete solution combining the best tools and fully managed services for generative AI. Evaluate multiple models simultaneously, analyze and rewrite responses, and generate new, highly-specific datasets for RLHF and advanced training. Get started with a free trial of the platform and see how Labelbox helps you implement generative AI.