Understanding the Emergence of Generative AI: A Look at the Butterfly Effect of the Transformer Model
The concept of the butterfly effect has become popular, expressing the idea that small events may have much larger consequences. In 2017, a group of researchers from the Google Brain team presented an obscure paper on a new architecture for sequence transduction models at a conference. They called this mysterious new design the “Transformer” model. Despite receiving little attention at the time, this presentation ended up causing a few waves, influencing a start-up called OpenAI to unleash the ‘generative AI tornado’ with its conversational ChatGPT AI platform in late 2022.
Why Did the Generative AI ‘Butterfly’ Emerge in Late 2022?
To understand the impact of the Transformer model and the evolution of generative AI, it’s essential to first understand the fundamentals of the technology. Generative AI relies on neural networks, which simulate the structure of the human brain. Neural networks learn from data and can be made up of dozens to millions of artificial neurons or units, arranged in a series of layers to process or learn about input data.
While neural networks have been around for decades, the revolution in generative AI only happened recently due to the rise of large language models (LLMs). These LLMs are neural networks that can process natural language and are trained on vast amounts of data. A type of LLM known as the Encoder-Decoder model is used for AI creation, which is essentially a translation between two languages known as ‘neural machine translation’ or ‘sequence transduction’.
The Transformer model introduced in 2017 solved two significant limitations of prior deep learning models for language: slow training speed and an inability to determine the relevance of individual words in the input data. This breakthrough allowed for the emergence of massive LLMs, such as the aging GPT-3 model with 175 billion parameters, which enabled the creation of systems like OpenAI’s ChatGPT.
What Do We Mean by an ‘AI Creation’?
AI creation is a translation between two languages, used by the Encoder-Decoder model which rely on parallel neural networks: an Encoder network and a Decoder network. The Encoder network learns the meanings of words from input language after being trained on data samples. The Decoder network takes these internal states and reverses the process, mapping them to words in the output language.
With LLMs, a ‘language’ can be something like a human language, a programming language, images, videos, or even protein structures. Historically, neural networks processing language needed to be done word by word, resulting in slow training times. However, the Transformer algorithm solved this problem by using attention mechanisms in place of recurrence and convolutions, allowing for parallel processing and faster training times.
Combining the parallel Transformer algorithm with increasingly faster AI chips have made it possible to build and train extremely large language models within reasonable timeframes. This breakthrough has led to the emergence of start-ups like OpenAI and debates on the consequences of generative AI.
Enter the Transformer
The Transformer algorithm has helped to bring about the generative AI revolution, making it possible to process language more efficiently. Since its introduction, AI practitioners have been able to build and train increasingly larger language models with faster training times. Paired with even faster AI-chips, the world has seen the rise of start-ups that could have significant impacts on society and the world at large.
Final Thoughts
The emergence of LLMs could shape the future of AI and the world. Whether it will be catastrophic or beneficial is still being debated. It is up to AI practitioners and society at large to determine how the technology will be used moving forward.
Editor Notes
Generative AI has the potential to revolutionize the way society functions through automated systems and applications. It is exciting to imagine the possibilities this technology can create, but there are risks to consider. Strong ethical considerations are necessary to ensure AI is used in a way that benefits society. Stay informed about the latest advances in AI with GPT News Room.
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