Artificial
Artificial Intelligence Overview
Artificial Intelligence though having become a common term in today’s time, not just to the technologically aware citizens of the world, but even among regular people has the potential to drive humanity forward in an exponential impact index that hasn’t surfaced yet. The untapped potential of AI will take years and if not many more decades to come to fruition before its growth comes to a halt. In this article, we talk about Artificial Intelligence and its key elements and the services provided by Microsoft Azure to help innovators build AI Intelligent Systems.
What does AI stand for?
AI stands for Artificial Intelligence; henceforth: AI.
What is Artificial Intelligence?
Artificial Intelligence (AI) is the branch of computer science with multiple inter-relations to various domains which refers to the creation of intelligence forms that imitate human capabilities and behavior. Artificial intelligence was first ever coined in 1955 and was envisioned for general artificial intelligence during the initial inception but later, progressed into domain-specific and task-based artificial intelligence.
Key Elements of artificial intelligence includes,
Machine learning
Machine Learning refers to the process by which machines can be taught to learn from data. It is the approach of teaching computer models to learn from data to make predictions and draw out conclusions. Usually, huge data is needed to create these models and to train them to design an effective and accurate system.
Learn about the mathematics used in Machine Learning
Anomaly detection
Anomaly detection helps in finding out anomaly ie. Usually detection of errors and unusual activities.
Computer vision
Computer Vision is synonymous with its name. This branch of AI aids by supporting computers to analyze data from images, cameras, videos, and other visual content. Check out object detection implemented with a hands-on example on this previous article Object Detection with ImageAI.
Natural language processing
Natural Language Processing (NLP) refers to the ability of computers to understand the natural languages of humans through audio or text means. Microsoft’s Cortana Assistant is an example of NLP.
Conversational AI
Conversational AI refers to the ability of computers to engage and participate in natural conversation in spoken or written language.
Machine Learning
Machine Learning is a subject of AI and it is an approach to solve numerous problems. From Computer Vision to Natural Language Processing to Analysis in Stock Market and Healthcare, Machine Learning is everywhere. It is tremendously powerful and has a subset under it to solve even exponentially difficult problems.
Use Case Scenarios
Machine Learning has magnitudes of multiple use cases. Some of the most evident areas where machine learning has enriched our life is as follows,
- Email Filtering and Spam Detection
- Fraud Detection in Credit Systems
- Recommendation Systems (in eCommerce, social media, content consumptions and more)
- Personalized Voice Assistants
- Image Processing (in Cameras, Televisions)
- Scientific Research
- Space Exploration
One of the easily understood examples of Machine Learning can be applied to identify and catalog different species of Wildflowers. With a similar analogy, we could use ML to detect multiple objects, face detection and recognition, and numerous other applications.
Microsoft AI
Microsoft AI is a powerful framework that enables organizations, researchers, and non-profits to use AI technologies with its powerful framework which offers services and features across domains of Machine Learning, Robotics, Data Science, IoT, and many more. To read the full article, check it out at Microsoft Azure AI Fundamentals.
Machine Learning in Microsoft Azure
Automated Machine Learning
Automated Machine Learning (Auto ML) refers to automating the machine learning model development process which is mostly iterative and extremely time-consuming which enables developers, analysts, and data scientists to build highly scalable, efficient, and productive Machine Learning Models. Read the in-depth article on AutoML.
Anomaly Detection
Anomaly basically means something beyond normal. It is commonly used in the field of Data Mining. Machine Learning can be implemented to detect cases of an anomaly in multitudes of scenarios through supervised and unsupervised methods. It is vigorously used to detect errors and failures, thus help to prevent crisis scenarios.
Natural Language Processing (NLP)
Natural Language Processing (NLP) refers to the ability of computers to understand the natural languages of humans through audio or text means.
Natural Language Processing in Microsoft Azure
Various cognitive services are supported by Microsoft Azure to build solutions using NLP.
Speech
The Speech service of Microsoft Azure enables developers to create applications with the feature to synthesize and recognize speech and to translate verbal languages.
Language Understanding Intelligent Service (LUIS)
Text-based commands and spoken language can be trained using the LUIS feature provided by Microsoft Azure.
Text Analytics
Text Analysis help to analyze text documents and to extract required key phrases, perform sentiment analysis in term of positive of negative and supports the detection of entities, for instance, location, people and date.
Translator Text
The Translator Text feature provided by Microsoft Azure helps translate text in over sixty different languages.
Computer Vision
Computer Vision mainly deals with the processing of visual data such as images or videos. Multitudes of Machine Learning Models can be implemented using various Algorithms to perform different tasks. Some of the key tasks are performed in Computer Vision are listed below.
Image Classification
Image Classification is the process of using machine learning algorithms to classify images and their content. The models are trained and thus are able to classify images. Differentiating between Dogs and Cats, Different types of fruits are enabled by image classification.
Object Detection
Object Detection is performed by training models to classify individual objects within the frame of the image or video. With object detection, we can detect various images within an image, for what the model is trained for.
Semantic Segmentation
Semantic Segmentation is the linkage of each individual pixel in an image to a specific class label. This is also commonly known as a dense prediction. This uses the process of clustering parts of the image together for a similar object class. It is a pixel-level prediction approach to classify data based on the category.
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