Extensive use of Artificial Intelligence (AI) today emanates from the colossal data that churns out every day, advanced algorithms, and improved computing and storage powers. AI has defined uses that make it possible for machines to learn from experience and adjust their future activities according to new inputs to perform tasks much like humans do. In short, AI helps computers train to carry out specific tasks by processing large chunks of data and recognizing patterns in them.
With the help of AI, machines and products imbibe greater intelligence, letting humans use automation, bots and conversational platforms among others. These in fact, combine and recognize large amounts of data to produce smarter technologies used to upgrade homes, workplaces, better healthcare systems, and security and investment analysis among the myriad of other applications.
Intelligent machines learn to adapt using progressive learning algorithms finding structure and regularities. Its deep neural networks with their hidden layers enable smooth fraud detection in no time using Big Data. Incredibly accurate results are possible when these deep neural networks train on object recognition and deep learning models using huge amount of data. Data is at the heart of all AI and its ensuing activities letting machines learn and work as humans do. When machines learn, they automate repetitive, high-volume tasks quickly and without fatigue giving businesses smarter solutions and the competitive advantage.
Artificial Intelligence has brought together humans and machine where machines are able to mimic human actions intelligently. They understand human requests, analyze and connect data and draw apt conclusions. Machines automate analytical model using methods from statistics, neural networks, physics, and operation research to extract data insight without being programmed.
Let’s understand how machines are able to carry our these tasks with their distinctive AI capabilities.
5 distinct capabilities of AI
Machine Learning
Machine Learning is among the first subsets of AI that lets applications learn from data using mathematics and statistics. Algorithms of ML are not hardcoded to give out particular output. Rather, they are coded in a way that ingest data with labels and subsequently use the statistical models to find relationships within the large data that humans would find difficult to conceptualize. The relationships that the machines discover represent their learning. This explains how it is data and not codes that drive Machine Learning to produce optimal results. Generalized data models built for certain tasks are at the base of all ML activities where the models separate data into groups based on learnt features.
Neural Network
Neural Network is the next in line which is a kind of Machine Learning but inspired by the workings of the human brain. It is a network of interconnected units much like the human neurons that process information based on external inputs, and relay them between the units. Computing systems resulting from a Neural Network needs several passes to the data to be able to find the right connection and derive meaning from them. Use of drones in industrial disaster relief and aerial surveillance and improved guidance systems in the automotive industry are 2 well-known uses of Artificial Intelligence Neural Network capabilities.
Deep Learning
Deep Learning is the next advanced stage of Artificial Intelligence which makes use of huge Neural Networks with multiple layers of processing units. These advanced computing systems achieve improved training techniques using these networks and their layers to learn from huge and complex data patterns. Output activities of machines such as speech recognition and image recognition are some common Deep Learning results.
Computer Vision
is an AI capability that is based on Deep Learning and pattern recognition of picture and video data. These intelligent computing systems process, analyze and understand images by capturing real-time images or videos around them and interpreting their surroundings. Next time you use Augmented Reality features while shopping, ride a self-driving vehicle or get access to a secured area due to facial recognition security screening, you know it’s because of the use of Computer Vision.
Natural Language Processing (NLP)
Natural Language Processing is among the most advanced use of Artificial Intelligence that lets machines analyze, understand and ultimately converse in human language. The email filters that you use every day is among the most basic and initial applications of NLP. Predictive text, search results, language translation, text analytics are some other uses of this subset of AI. The next target stage of NLP is to let gadgets communicate with humans using normal everyday language to perform tasks.
Concluding thoughts
Artificial Intelligence refers to the broad spectrum of capabilities that machines are armed with to be able to perform tasks. Machine Learning is the defined subset of AI that lets machines learn by sifting and analyzing data. Machines get smarter as they achieve better capabilities using Neural Network, Deep Learning, Computer Vision and Natural Language Processing. Each of these capabilities are being used to solve actual problems in all aspects of human life and industry verticals. Preventing financial fraud, providing better, quicker, and accurate healthcare, optimized retail business services, better government systems, smoother transportation, and fruitful oil and gas endeavors best summarize the use of Artificial Intelligence and its capabilities.
