Artificial Intelligence Building blocks
Artificial Intelligence Building blocks
16 February 2021
Artificial intelligence refers to software that performs tasks in a similar way to the human brain, often detecting and responding to a characteristic of its environment. It may mean learning to solve problems in unexpected ways, to recognize nuances of speech, or to exhibit some form of human creativity. Artificial intelligence (AI) enables machines to learn from experience, adapt to new inputs, and perform human-like tasks. Some of the AI examples are from computers playing chess to driverless cars, relying heavily on deep learning and natural language processing.
Some applications of Artificial Intelligence
There are endless applications of Artificial intelligence and can be applied to many different sectors. AI is tested and used in the healthcare industry for dosing drugs and different treatments in patients, also for surgical procedures in the operating room.
Some other examples of Artificial intelligence include computers playing chess and driverless cars. Each of these machines must consider the consequences of any action they take, because every action will affect profit. In the game of chess, the end result is to win. In the case of an autonomous vehicle, the IT system must take all external data into account and compute it to act in a collision avoidance manner.
Also Artificial intelligence is used to detect and flag activities, such as unusual use of debit cards and large account deposits in the financial industry, which provides help to the bank fraud departments. Applications for AI are also used to help streamline and facilitate commerce. This is done by facilitating the estimation of supply, demand and the price of the security.
To build a smart machine or system, a few basic building blocks are required. Here are some important AI building blocks.
Machine learning is the field of study that gives computers the capability to learn on its own without being explicitly programmed. It is among one of the most exciting technologies that one would have ever come across. Machine learning programs adjust their algorithms in response to new information. If data mining algorithms leave the findings to humans for further work, machine learning can act on its own.
Types of Machine Learning:
Classical machine learning is generally categorized by how an algorithm learns to become more accurate in its predictions. There are different types of ML and the type of algorithm a data scientist chooses to use depends on what type of data they want to predict.
Here are the four basic approaches:
supervised learning, unsupervised learning, semi-supervised learning and the last one reinforcement learning.
Deep learning is a subfield of machine learning which deals with algorithms inspired by the structure and function of the brain called artificial neural networks. For example, they can collect information about customers and their behavior from social networks and from there infer their tastes and preferences. Deep learning is basically behind the driverless cars, helps them to recognize a stop sign or distinguish a pedestrian from a lamppost.
Computer models can learn to perform classification tasks directly from images, sound or text with Deep Learning. Deep learning models can achieve peak accuracy, sometimes outperforming human-level performance. Models use a large set of labeled data and neural network architectures that contain many layers to get trained.
Natural Language Processing:
Natural Language Processing is an important building block that helps computers learn, analyze, and understand human language. NLP can be used to organize and structure knowledge in order to answer queries, translate content from one language to another, recognize individual people by their speech., mine text, and perform sentiment analysis. Apart from improving customer service, natural language processing capable systems will, over time, learn to resolve issues automatically.
Natural Language Generation:
Natural Language Generation (NLG) is also a basic AI technology. Where NLG will help computers analyze, understand and understand human language, NLG will help them to communicate and interact intelligently with humans. Banks primarily use NLG for purposes that require combining data from multiple sources to produce information in an easy to understand format. NLG can also embed raw data into a story, which banks like Credit Suisse use to generate portfolio reviews.
Visual recognition is also an important building block of AI that recognizes images and their content. It uses deep learning to play its role in finding faces, tagging images, identifying components of visual elements, and selecting similar images in large ensembles. Visual recognition uses large amounts of data and requires open source software libraries and frameworks to function properly. The primary application of visual recognition technology in the banking industry will be similar to speech recognition – enabling a seamless customer experience.