I set out to find the answers to the following questions: What is Chat GBT and how does it work? How can we, as educators, make use of it to save time and innovate in our classrooms? What are its limitations and what does it promise for the future? Perhaps you’ve been asking yourself the same questions. Let’s find out together.
Chat GBT and Bard Claude Llama are all examples of large language models. They’re designed with an easy user interface to sound like we’re having a conversation with another human being, masking all of the complex computation happening in the background. The word ‘chat’ in Chat GBT stands for ‘generative pre-trained Transformer.’ It’s generative because it can create new outputs based on the input data we give it. It’s pre-trained on vast quantities of data, which are then tweaked by human engineers.
You can think of a neural network, like Chat GBT, as multiple layers of interconnected nodes that share and compute data and information. These neural networks evolve and learn over time, resulting in emergent properties. Some of these large language models suddenly demonstrate abilities that weren’t programmed in with the original data set.
The explosion in artificial intelligence over the last couple of years is the result of advances in gaming technology, which provided the processing power needed, and increasing access to vast fields of data. Companies like OpenAI have scraped the internet, incorporating all the tech they can access into their training sets.
Language, like a Galton board, follows underlying rules that allow us to effectively communicate with one another. Language models, such as Chat GBT, are trained to understand the nuances of context upon which language is predicated. They give the impression of a human operator and can pass the Turing test because they have so much data to draw upon.
Chat GBT 2 was released in 2019 with 1.5 billion parameters. In 2020, Chat GBT 3 came out with 175 billion parameters, and Chat GBT 4, the current model, has 1.7 trillion parameters. These large language models can be fine-tuned for specific purposes, such as protein folding, weather prediction, or identifying hot spots for wildfires.
As educators, we can use large language models like Chat GBT to lighten the administrative load, assist in curriculum planning, deliver innovative lessons, provide personalized feedback, and assess student work. However, there are limitations to consider, such as the principle of garbage in, garbage out, biases in the data, data protection concerns, and the phenomenon of hallucinations.
Despite these limitations, the future holds exciting possibilities. Imagine a world with personalized learning pathways for all students, where models can respond in real-time to their interests and needs. Combined with augmented or virtual reality, we could create immersive learning experiences. The inputs accepted by language models may broaden to include audio, images, and video, creating a multi-sensory platform.
In conclusion, large language models like Chat GBT are revolutionizing education. While there are challenges to overcome, the potential for enhancing teaching and learning is immense. By embracing this technology, we can create a more engaging and personalized educational experience for students.
[Note: The article has been edited and optimized for clarity and coherence.]
