In just a few months, artificial intelligence (AI) has advanced from basic tasks like classification, clustering, dimensionality reduction, and prediction to proficient content generation. Generative AI, which focuses on AI models’ ability to create new content, spans various formats such as text, images, video, code, audio, and synthetic data. This brief overview aims to highlight the key differences between traditional AI and generative AI, exploring the foundational models driving generative AI, tracing its evolutionary history, and discussing its diverse applications, risks, and considerations.
The rise of generative AI, powered by foundational models, does not make traditional AI obsolete; instead, it enhances the toolkit and helps address new challenges. Traditional AI, utilizing supervised and unsupervised machine learning as well as deep learning models, remains valuable for structured and generalized tasks. In contrast, generative AI excels in tasks like content creation, code generation, summarization, and interactive chatbots.
Generative AI gained significant attention on November 30, 2022, when OpenAI introduced ChatGPT, a chatbot that attracted 100 million users within its first two months. However, the development of generative AI has been ongoing for years, marked by milestones such as the introduction of generative adversarial networks in 2014 and Google’s creation of the transformer architecture in 2017. The transformer architecture revolutionized language-based generative AI by enabling the analysis of word sequences and capturing contextual meaning beyond individual words.
The emergence of generative AI was exemplified by large language models (LLMs) like Google’s BERT, which emerged in 2018 based on the transformer architecture. LLMs can generate summaries, facilitate multilingual translation, and create original content. Generative AI encompasses a broad range of outputs, including text, image, code, audio, video generation, and structured synthetic data creation.
Foundation models, including LLMs, are crucial to generative AI. When fine-tuned with specific topic-related data, these pre-trained models support various applications. For example, FinBERT, a BERT model pre-trained on financial texts, enhances the accuracy of financial analyses and outputs.
While the potential of generative AI and foundation models is vast, associated risks require a human-in-the-loop approach. Risks include hallucinations, output inaccuracies, misuse leading to deepfake creation or cyber-attacks, biased responses, lack of transparency, intellectual property concerns, data privacy issues, infrastructure requirements, and copyright infringement implications.
Effective governance frameworks are essential for governments and businesses to build trust in AI systems. Regulations and guidelines on AI governance are critical for managing risks and ensuring positive outcomes in the face of generative AI’s disruptive nature. Responsible deployment of generative AI offers promise in addressing global challenges and driving positive transformations, but it requires careful oversight and regulation to mitigate potential negative impacts.
