AI is taking over rapidly in this technology-driven world. The evolution of AI has led to many fields, Generative AI is one of those fields. It is widely used nowadays as it is fast and versatile. When we hear about it, the first thing that comes to our mind is that it generates. That is what the main purpose serves though. Generative AI creates new ideas and content like photos, videos, animation, 3D models, etc when a variety of input is provided.
The history of generative AI began in 1966 with Eliza, the first chatbot created by Joseph Weizenbaum at MIT. Eliza simulated conversation by rephrasing user statements, paving the way for modern chatbots like ChatGPT.
Recurrent Neural Networks (RNNs) emerged in 1986, gaining popularity for their ability to retain previous inputs in their internal state, unlike traditional feed-forward neural networks. This advancement followed the trend started by Eliza, enhancing chatbots' capability for continued conversation.
Introduced in 1997, Long Short-Term Memory (LSTM) networks are specialized RNNs that use input and output gates to manage information retention, overcoming traditional RNNs' short-term memory limitations for handling long sequences.
Introduced in 2014, Gated Recurrent Units (GRUs) simplify RNNs with two gates: the update gate for balancing information and the reset gate for discarding unnecessary data, enhancing computational efficiency and retaining long-term dependencies.
Introduced in 2014, the attention mechanism enhanced LSTM and GRU architectures by improving context retention in natural language processing. This significant paradigm shift in sequence modeling offered a new perspective compared to earlier models.
The rise of large language models (LLMs) began in 2018 with Google's BERT, a foundational model that processed text bidirectionally. Following BERT, OpenAI released GPT-2, and later models like T5 and GPT-3 emerged, expanding the landscape of language processing capabilities.
Our Generative AI course emphasizes real-world application. You'll work on projects like developing a chatbot using GPT algorithms, creating text-to-image visuals with DALL-E, and deploying models with cloud services like AWS or Azure, connecting AI models to real systems