AI is about building intelligent computer programs that carry out tasks like:
- Visual perception
- Speech recognition
- Sentiment analysis
- The process of using algorithms to tell you something interesting about your data without writing code specific to the problem
- Instead of writing code, you feed a machine data and it builds its own logical function based on this data.
- The more data you feed your model, the stronger it gets.
- The better the quality of your data, the better your model.
- The algorithm can only be as good as the data that goes into training it.
- Performance of machine learning algorithms can weaken when key information is buried in unstructured data.
- Deep learning is great at automatically learning the best features from noisy data
- Deep learning uses complex algorithms to perform tasks in domains where it actually learns the domain with little or no human supervision.
- It learns how to learn. For example, consumer apps like Google use deep learning to power facial recognition in photos.
Natural Language Processing
Natural Language Processing (NLP) is a form of machine learning that recognizes language, its many usage and grammar rules by finding patterns within large data sets.
- NLP can perform sentiment analysis, where algorithms look for patterns in social media postings to understand how customers feel about a specific brand or product.
- NLP handles speech recognition, providing a text summary derived from “listening” to an audio clip of a human speaking.
- NLP conducts question answering, typically handling those questions with a specific answer (for example, What is the square root of 4?), but also exploring how to handle more complex and open-ended questions.