Document AI is a technology that automates tasks such as document scanning, decoding, and extracting information from documents. It reduces the time and labor costs of data processing and enables businesses to focus on their core business priorities.

In this paper, we investigate the current state of documentation for AI from a practitioner’s point of view. Our interviews with bankers, consultants, and employees of software companies reveal how they document their AI applications.

1. Machine Learning

Machine Learning is a way to use algorithms to learn from data. This can help solve problems like speech and language recognition, image processing, and prediction.

Many businesses have been utilizing Machine Learning to make their products better and to help them understand their customers. This includes companies such as Google, Uber, and Amazon.

While the advantages of machine learning are many, there are also disadvantages to using it. First, the algorithms need a lot of data to work properly and can take a long time to get results.

Second, if the algorithm does not have an objective, it can be hard to optimize. A good machine learning model should have one or more metrics that it’s trying to optimize.

As with any other software engineering task, documenting the pipeline is a critical step to success in ML. It reduces onboarding time for new hires and project members, helps give people a sense of direction, and can greatly improve performance.

2. Deep Learning

Deep Learning is a type of machine learning that combines algorithms and computing units–or neurons–into what’s called an artificial neural network. It takes inspiration from the structure of the human brain and uses layers of processing to recognize patterns in data.

To train a machine learning model, you need to tell it what unique features are important for identifying a specific object. For example, if you want to know if an image shows a car, you’d have to give it information about its shape, size and windows.

With deep learning, you don’t need to provide the information yourself because the algorithm will do it for you automatically. It will use the data it gets from training on thousands of images to identify the unique characteristics of a car and make predictions without the help of a human programmer.

Deep learning is already being used in a number of industries, from automated driving to medical research. It’s also helping to improve customer experience, for example by giving relevant recommendations based on a person’s browsing history and preferences.

3. Natural Language Processing (NLP)

Natural language processing (NLP) is a field of data science and artificial intelligence that helps computers understand and interpret human speech and text. This includes chatbots, smartphone personal assistants, search engines, translation software and many other business applications.

NLP models break down recorded voice and written text into smaller semantic units, tagging specific parts of speech like nouns, verbs and adjectives. They also standardize individual words by reducing them to their root forms.

Once enough labeled data is collected, deep learning for NLP takes over, interpreting the labeled data to make predictions or generate speech. This type of machine learning requires massive data sets.

Fortunately, NLP libraries and toolkits are widely available in Python. These are generally free, flexible and easy to customize.

4. Computer Vision

Computer vision is a technology that uses artificial intelligence to process visual inputs such as images and videos. It provides insights to computers and allows them to perform automated actions such as classifying, detecting, and tracking objects.

The applications of computer vision are wide and varied. They include a variety of tasks that are required in industries like transportation, retail, security and healthcare.

For example, a self-driving car uses computer vision to recognize pedestrians and traffic lights and act accordingly. Similarly, medical imaging systems use computer vision to detect diseases.

In the military, a lot of computer vision is used for missile guidance. These systems scan a battlefield for enemy soldiers or vehicles, and make the necessary decisions about targeting or avoiding them.

To recognize objects, a computer vision algorithm first translates an image to a set of numbers. It then uses a neural network to execute convolutions. This method identifies rudimentary shapes and hard edges in an image. Then, it patches gaps and executes iterations to produce an output that accurately ‘predicts’ the object it has seen.