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At the end of 2022, ChatGPT was released and quickly became viral as users were impressed by its advanced capabilities as compared to the functionalities provided by existing website chatbots or personal assistants at the time.
ChatGPT which stands for “Chat Generative Pre-Trained Transformer” is a form of Generative artificial intelligence (Generative AI). Generative AI is a branch of artificial intelligence adapted for creating different new content such as text, image, audio, etc. from existing data provided to the AI models.
Generative AI models typically use large datasets to train neural networks which could identify patterns within the data. The patterns are then used to generate the new content. These models are highly complex and require considerable computational resources – some examples of these models are Large Language Models (LLM), Generative adversarial networks (GAN), Transformer-based models, and Variational autoencoder models (VAEs). ChatGPT, in particular, uses a Transformer-based model designed for natural language understanding and generation tasks.
Uses of Generative AI
Generative AI models are applied across various domains, notably the following:
Content Creation
Generative text-to-image models like DALL-E are able to create artworks based on text input from an end-user. Image-to-text generative models, on the other hand, could be used to generate text captions based on image uploaded by an end-user.
Natural Language Processing
Generative AI can be used for different NLP tasks such as text generation, machine translation, and virtual assistants. With the power of current Generative AI models, the outputs (e.g., articles, translations, query responses) sound human-like.
Data Augmentation
Training machine learning models typically require large volumes of training datasets which may not always be readily available. Generative models can be leveraged to augment training data by generating synthetic data.
Entertainment + Gaming
The gaming industry can also benefit from Generative models in the form of creation of characters, storylines, and other game-related content.
Adoption of Generative AI
More and more users are adopting Generative AI in their everyday life. It is forecasted that use of this technology will surpass the adoption rate of mobile devices due to its low adoption cost. Before ChatGPT became widely popular, early industry adopters of generative models included healthcare (medical report generation), marketing (content creation and customer engagement), finance (automated financial reports), and customer support (chatbots and virtual assistants).
Concerns surrounding Generative AI
Despite the exponential adoption of Generative AI across different industries, various organizations have raised concerns regarding the ethical risk and misuse of the technology. Generative AImodels can be used to create highly convincing fake news, articles, or social media posts which could lead to mistrust and worsen the problem of online disinformation. In addition, the automation of content generation through generative AI can potentially displace jobs in industries reliant on human content creators such as the writing and movie industry.
In Canada, the federal government has issued guidelines for employees with respect to using Generative AI toolsin their operations. According to the guideline, the institutions should assess and mitigate risks, and restrict their use to activities where they can manage the risks effectively.
Generative-AI based Innovation
Although there are risks involved with the ubiquitous use of Generative AI, there is no doubt that the technology is here to stay. Recent innovations allow Generative AImodels to handle multiple modalities such as text and images. Translation of images from domain to another has also been possible through development of the Multimodal Unsupervised Image-to-Image Translation.
