Have you ever wondered how your brain frames sentences while you are speaking? Let me explain it. When you speak a sentence, you do not start from scratch. Rather, the selection of each and every word in a relevant and grammatically correct sentence is influenced by two crucial factors:
The context,
The previously used words in that sentence
For example, consider two sentences: "There is an apple" and "There are some apples". Here you can see that when your sentence starts with "There is..." the brain predicts that the next words should be used in such a way that it defines a single apple. But when the sentence starts with "There are...", the brain predicts that the next few words should be so selected that it should talk about more than one apple. Clearly, the brain framed the sentence depending upon the context (here the context is an apple or some apples lying in front of the observer) and each word is selected depending upon the previously used words in the sentence. Thus, there is some sort of coherence among the words in a sentence and to predict the later words the brain needs to look back at the words that have already been used in the sentence to predict the next word correctly. We might not feel that happening because our brain works at lightning speed without us even realizing.
Now, to implement such capabilities in machines, scientists and researchers have developed different architectures. One important early architecture is called Recurrent Neural Networks (RNNs) - the networks that have memory! The word "recurrent" means that information can loop back and be used again, like remembering what you just said to help you decide what to say next.
RNNs have predictive capabilities specially used to work with sequential data (Sequential data are the kind of data where the data points can be placed in a series depending upon a particular relation followed across the sequence). They process information step by step, just like reading a sentence word by word, and they remember what they've seen before. The RNNs are trained on huge sets of sequential data where the training inputs are the previous data (also called seed data) and the training outputs are the value that needs to be predicted. RNNs have proven efficiency in recognizing patterns in sequential data and predicting the next element in the sequence.
While modern AI systems like ChatGPT and Google's search use newer architectures called Transformers (which are better at handling very long sequences and work faster), RNNs are still useful in several areas:
Stock price prediction: The prices that stocks of a particular organization hit are generally not random. Some sort of coherence is present which can help us closely predict the future price of the stock depending upon historical price points.
Musical note generation: In music, for a tune to sound melodious, the notes played in the tune should be coherent. Notes cannot be played at random in a musical piece.
Sensor data monitoring: RNNs are used to detect unusual patterns in sensor data. The data predicted by RNNs are compared with the readings captured by the sensor. If there is a huge difference, it can signal a problem that needs attention.
Simple speech recognition: In smaller devices with limited computing power, RNNs can still be useful for basic voice commands.
Time series forecasting: When you need to predict what happens next based on what happened before (like weather patterns or website traffic), RNNs can be very helpful.
Think of RNNs as one of the important stepping stones that helped scientists understand how to build better AI systems. While newer architectures like Transformers have taken over for many tasks (especially those involving very long texts), RNNs taught us valuable lessons about how machines can learn to remember and use past information to make better predictions. Just like how the invention of the bicycle helped us eventually build cars, RNNs helped us build the amazing AI systems we have today!