Generative Artificial Intelligence or Generative AI is the application of artificial intelligence or machine learning algorithms and models using deep learning techniques and technologies to generate new data. Examples of these data include texts, images, and audio. These data usually relied on human inputs and skills but Generative AI makes their creation time-efficient and cost-effective.
Some of the well-known examples of Generative AI models include the GPT-3 and GPT-4 large language models developed by OpenAI which powers its ChatGPT chatbot and DALL-E image creation services and the BERT large language model used by Google.
It is important to note that there are three main Generative AI models. These are Generative Adversarial Networks or GANs, transformer-based models, and variational auto-encoders. GANs have been used to generate a variety of data such as texts and images. Transformer-based models have been applied to a variety of natural language processing tasks.
Variational auto-encoders or VAEs are used for complicated generative models of data and for fitting them to huge datasets. Some of its uses include generating images of fictional people and producing high-resolution digital artwork.
Pros of Generative AI: Advantages and Applications
Generative AI fundamentally works by using existing content to generate new content. However, from a more in-depth look, it is driven by unsupervised and semi-supervised machine learning algorithms. The machine learning model is trained on a large dataset and this model is used to generate new and original data that resembles the data in the training sets.
The main advantage of Generative AI fundamentally centers on reducing dependence on human involvement in data and content creation. Nevertheless, because of its capabilities, the technology has marked a new era in data generation and content creation that is set to disrupt several processes in different sectors, industries, and markets.
Below are specific advantages and applications of Generative AI:
1. Data Generation and Data Interpretation
One of the more specific advantages and applications of Generative AI is that it can generate new data in a more time-efficient and cost-effective manner. This is useful for accomplishing tasks such as data organization, data processing, data augmentation, data synthesis, and data generation for unrepresented or underrepresented groups.
Furthermore, it can help in analyzing data and understanding complex systems. Some of its notable applications include converting satellite images to map views to investigate new locations, transforming medical images into photo-realistic images, and generating marketing data based on collected data about the target market or consumer behaviors.
The aforesaid applications can help improve data-based decision-making processes. Generative AI reduces the time needed for data processing, synthesis, and analysis. Furthermore, it also reduces dependence on human-generated data, which can be both time-consuming and expensive, while also reducing human-generated errors.
2. Content Creation and Creative Applications
It is important to highlight the fact that Generative AI can be used for different creative applications centered on content creation. Specific models allow the creation of new and original content such as texts, images, and music. The ChatGPT application from OpenAI has the capabilities to write long-form texts, compose lyrics, and even write and debug codes.
There are models that have been made available to the public either for free or as a commercial service. Dall-E is a free albeit limited service from OpenAI that can create original and realistic images and art from text descriptions and natural language. Canva has integrated an AI-based functionality for editing and creating images in its app.
Several applications and services have been developed for automating software development. These services can automate app creation and code debugging. Nevertheless, considering its creative applications, Generative AI has opened time-efficient and cost-effective opportunities for individual creative professionals and organizations to create content.
3. Time-Efficiency and Cost-Effectiveness
Another notable advantage of Generative AI is that it makes processes fast and inexpensive. Remember that it facilitates and expedites data generation and data analysis without the need for human involvement. This is beneficial for data-driven organizations or data-driven processes in which time and budget constraints are the main operational hurdles.
The technology is also disruptive. Organizations can benefit from using the technology to trim down marketing-related or sales-involved expenses, administrative costs, and even costs related to their information technology requirements. Businesses can operate with a minimal workforce. This is beneficial for startup companies and scaling up their operations.
Note that there are commercial services that are set to replace certain occupations such as writers, graphic designers, photo and video editors, researchers, administrative personnel, and entry-level programmers. Self-prompting applications or AutoGPTs are AI agents that can create, organize, and execute tasks without the need for prompting.
Cons of Generative AI: Disadvantages and Issues
The introduction of Generative AI models and related services has created debates about the limits of technology and its impact on society. Remember that the technology is disruptive. It threatens to make certain occupations obsolete or reduce the earning potential of affected professionals because organizations tend to choose more cost-effective alternatives.
Furthermore, considering its applications and other potentials, the technology has also raised concerns about misuse and abuse. ChatGPT and other AI-based writing applications have been used to write academic papers. Image generators have also been accused of copyright infringement and violations of personal data and privacy rights.
Below are specific disadvantages and issues of Generative AI:
1. Need for Quality Control and Data Accuracy
One of the main disadvantages of Generative AI is that the data or content a particular model generates or produces still needs to be inspected and audited. There are several instances in which a particular model has produced low-quality outputs that showed inaccuracies, lack of relevance, and questionable results, among others.
ChatGPT is a prime example. It cannot provide responses to prompts about more recent events. It also produces ambiguous and repetitive answers to particular questions. Google Bard was criticized for an advertisement with a wrong claim that the James Webb Space Telescope was used to take the very first pictures of a planet outside the Solar System.
It is important to note that the quality of outputs produced by a particular generative model is determined by the quality of its datasets or training sets. A specific model can reflect the biases present in the training data. This can result in biased results if the training set is biased. These results affect both the quality and reliability of outputs.
2. Ethical and Legal Issues and Considerations
The introduction of ChatGPT in November 2002 was welcomed with both enthusiasm and criticism. It remains one of the more robust chatbots in terms of its capability to process natural language and provide human-like conversational responses. Ensuing public use has demonstrated that it can be used for academic and workplace dishonesty.
Issues about infringement of intellectual property rights such as copyrights and trademarks have also emerged. Remember that Generative AI models are trained using large datasets. These training sets are scrapped from the internet. The contents produced by text, image, and music generators are based on previous works of other creators and artists.
Using a Generative AI service can expose an individual or organization to potential legal responsibilities. For instance, besides intellectual property infringement, a specific service may generate new data that could potentially violate privacy rights. A possible example would be generating personal or sensitive information.
3. Complexity and Technical Challenges
Another disadvantage of Generative AI is that the technology in itself can be difficult to understand. A lot of people do not know how it works and how to use or implement it. This can be counterproductive to its advantages and applications. A small business might refuse to implement it in its operation because it is a complex and unfamiliar technology.
Free services such as ChatGPT and Dall-E have limitations. For example, during peak usage, ChatGPT tends to suffer from downtimes. Dall-E is also free but a particular user can generate up to 50 free images in the first month and its usage will be limited further to 15 images per month. Paid services offer more guarantees and flexibility.
However, since 2020, numerous AI companies and services have mushroomed, making it difficult to choose the most reliable ones or those that provide the best value for specific requirements. Implementing in-house generative AI capabilities poses technical challenges because models can be computationally expensive and inefficient.
