Artificial intelligence and Machine-learning technologies have evolved greatly over the past decade and have been useful to many people and businesses, especially in the fields of finance, banking, investment and business.
In these industries, there are many activities that machines can perform better and faster than humans, such as computation and financial reporting, until the machines are given complete data.
Today, AI tools made by humans are becoming one more level of strength in predicting trends, providing complex analysis, and the ability to perform automation more quickly and cheaply than humans. However, an AI-powered machine has not yet been built that can do business on its own.
There are many activities that machines can perform better and faster than humans, such as computation and financial reporting, until the machines are given complete data.
Even if it were possible to train an arrangement that could alter human judgment, there would still be a margin of error, as well as some things only understood by humans. Humans are still responsible for the design of AI-based prediction machines, and progress can only occur with their input.
Data is the backbone of any prediction machine
The creation of an AI-based prediction machine requires an understanding of the problem being solved initially and user requirements. After that, it is important to select the machine-learning technique that will be implemented based on what the machine will do.
There are three techniques: supervised learning (learning from examples), unprocessed learning (identifying common patterns) and reinforcement learning (learning based on the concept of compecification).
Once the technology is identified, it is time to implement the machine-learning model. For “time series forecasting” – which involves making predictions about the future – long-term memory (LSTM) that can be used to model Sequence to Sequence (Seq2Seq).
LSTM networks are particularly suited to make predictions based on a series of data points indexed in time order. Even simple convoluted neural networks, image and video recognition, or recurrent neural networks, can be applied to handwriting and speech recognition.