Rise of ChatGPT and Generative AI

  • By Rebecca Galicha
    • Jan 24, 2024
    • read
  • Twitter
  • Linkedin
ChatGPT

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. 

ChatGPT

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 AI models 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 tools in 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 AI models 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.

Extensive research and development is a key part of the development process to further the Generative AI technology. In order to offset R&D costs, Canadian companies can take advantage of the Scientific Research and Experimental Development (SR&ED) program which are designed to deduct SR&ED expenditures from income for tax purposes and provides an Investment tax credit (ITC) to reduce income tax payable or as a refund.  

If your company is doing R&D work, and would like to know if you are eligible for government funding, contact one of our experts today to find out! 

Sources: 

https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai

https://github.blog/2023-04-07-what-developers-need-to-know-about-generative-ai/

https://horizons.gc.ca/en/2023/08/01/the-future-of-generative-ai/

https://www.insiderintelligence.com/content/generative-ai-adoption-climbed-faster-than-smartphones-tablets

https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/services/data-and-intelligence/ethical_considerations_of_generative_ai.pdf?rev=c9b22c2626664710a1f0382d962fc3b7

Author

Headshot Rebecca Galicha
Rebecca Galicha

Senior Innovation Funding Consultant

Explore our latest insights

More arrow_forward

Optimizing The Cloud for Cost-Efficiency and Performance 

The cloud fundamentally changes how computing resources are provisioned, accessed, and managed. O...

Patents

Patents & SR&ED: Building a strategy for your R&D ...

Are you looking to patent your latest invention? You may want to consider this strategy and utili...

chatbots

How do Chatbots Work?

RecallM is the groundbreaking mechanism behind chatbots designed to address the challenge of crea...

IoT device

Next-Generation Network Protocols for IoT Devices

With an estimated 41.6 billion IoT devices generating 79.4 ZB of data by 2025, understanding prot...