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Sundaram Subramanian
Sundaram Subramanian

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Microsoft’s Artificial Intelligence – An Overview

In this article, we will have a detailed listing at AI Services supported and provided by Microsoft.

What is Artificial Intelligence
Artificial Intelligence is a branch of computer science that aims to create intelligent machines, in simple terms, Human Intelligence processed by Machines, specifically computers.

AI Categories
AI can be categorized as either weak and strong. Weak AI is AI systems that are specifically designed and trained for a particular task. A good example is personal assistants, such as Apple’s Siri, Google Assistance, Microsoft Cortana, Amazon’s Alexa are a form of Weak AI.

Strong AI a system with generalized human cognitive abilities. These AI’s will find solutions without Human Intervention for Unfamiliar tasks.

AI Service providers
Artificial Intelligence as a Service (AIaaS) – Since the hardware and software cost are very expensive, many AI vendors are available in the industry, like Microsoft Cognitive Services, Amazon AI, IBM Watson, and Google AI to provide services to us.

Types of artificial intelligence

AI can be categorized into four types, as follows:

Type 1 Reactive machines. An example is Deep Blue, It can identify pieces on the chessboard and make predictions.

Type 2 Limited memory. These AI systems can use past experiences to inform future decisions. Eg: Self Driving Car.

Type 3 Theory of mind. This psychology term refers to the understanding that others have their own beliefs, desires, and intentions that impact the decisions they make. This kind of AI does not yet exist.

Type 4 Self-awareness. In this category, AI systems have a sense of self, have consciousness. This type of AI does not yet exist.

Examples of AI technology
AI is incorporated into a variety of different types of technology. Here are seven examples. Automation What makes a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repeatable tasks that humans normally performed. RPA is different from IT automation in that it can adapt to changing circumstances. Machine learning The science of getting a computer to act without programming. In very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms,

Supervised learning Data sets are labeled so that patterns can be detected and used to label new data sets

Unsupervised learning Data sets aren’t labeled and are sorted according to similarities or differences

Reinforcement learning Data sets aren’t labeled but, after performing an action or several actions, the AI system is given feedback

Machine vision
The science of allowing computers to see. This technology captures and analyzes visual information using a camera, analog-to-digital conversion, and digital signal processing. It is often compared to human eyesight, but machine vision isn’t bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.

Natural language processing (NLP)
The processing of human — and not a computer — language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and the text of an email and decides if it’s junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis, and speech recognition.

A field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings. Self-driving cars These use a combination of computer vision, image recognition, and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.

AI applications
Artificial intelligence has made its way into a number of areas. Here are six examples.

AI in healthcare
The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson.

AI in business
Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Automation of job positions has also become a talking point among academics and IT analysts.

AI in education
AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.

AI in finance
AI in personal finance applications, such as Mint or Turbo Tax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. AI in law The discovery process, sifting through documents, in law is often overwhelming for humans. Automating this process is a more efficient use of time.

AI in manufacturing
This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as technology advanced that changed.

Future of AI
Automated Transportation
Taking Over Dangerous Jobs
Solving Climate Changes
Robot as Friends
Improved ElderCare
Microsoft AI
Conversational AI
AI Services
Machine Learning
Intelligent Edge
Conversation Artificial Intelligence (AI)

Microsoft’s Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more.

Microsoft Bot Builder software development kit (SDK)
You can host your bot directly on Azure at scale using Azure Bot Service, or in your preferred hosting location.

Artificial Intelligence (AI) Services
AI Services, in other terms well known as Cognitive Services
Azure Cognitive Services are APIs, SDKs, and services available to help developers build intelligent applications without having direct AI or data science skills or knowledge.

Enable developers to easily add cognitive features such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications.

The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason.

Types of Cognitive Services
Vision API

Computer Vision – This API is an advanced algorithm for processing images & returning information.

Custom Vision Service – It allows us to build custom image classifiers. It’s in the preview stage.

Content Moderator – It provides monitoring for possible offensive, undesirable, and risky content in text, image & video

Face API – Face API provides access to advanced face algorithms, enabling face attribute detection and recognition.

Video Indexer – Video Indexer enables you to extract insights from your video.

Speech APIs

Speech Service – This API adds speech-enabled features to applications. It’s in the preview stage

Bing Speech API – This API provides you with an easy way to create speech-enabled features in your applications.

Translator Speech – Translator Speech is a machine translation service.

Speaker Recognition API – The Speaker Recognition API provides algorithms for speaker identification and verification. It’s in the preview stage

Language APIs

Bing Spell Check – Bing Spell Check allows you to perform contextual grammar and spell checking

Language Understanding LUIS – Language Understanding service (LUIS) allows your application to understand what a person wants in their own words.

Text Analytics – Text Analytics provides natural language processing over raw text for sentiment analysis, keyphrase extraction, and language detection.

Translator Text – Translator text provides for machine-based text translation in near real-time.

Search APIs

Bing News Search – Bing News Search returns a list of news articles determined to be relevant to the user’s query.

Bing Video Search – Bing Video Search returns a list of videos determined to be relevant to the user’s query.

Bing Web Search – Bing Web Search returns a list of search results determined to be relevant to the user’s query.

Bing Autosuggest – Bing Autosuggest allows you to send a partial search query term to Bing and get back a list of suggested queries.

Bing Custom Search – Bing Custom Search allows you to create tailored search experiences for topics that you care about.

Bing Entity Search – Bing Entity Search returns information about entities that Bing determines are relevant to a user’s query.

Bing Image Search – Bing Image Search returns a display of images determined to be relevant to the user’s query.

Bing Visual Search – Bing Visual Search provides returns insights about an image such as visually similar images, shopping sources for products found in the image, and related searches.

Bing Local Business Search – Bing Local Business Search API enables your applications to find contact and location information about local businesses based on search queries.

Knowledge APIs

QnA Maker – QnA Maker allows you to build a question and answer service from your semi-structured content.

Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Machine Learning evolved from the study of pattern recognition, which enables machines to learn from data to make predictions & progressively improve over time.

Azure Machine Learning Studio – cloud predictive analytics service to create & deploy predictive models as an analytics solution.
Module – a set of code that can run independently and perform a machine learning task, given the required inputs.

The module might contain a particular algorithm or perform a task.
Choosing ML algorithms

Training Time
Number of Parameters
Number of features
Special cases

Types of Machine Learning

Supervised Learning – Training Data is Labeled one

Unsupervised Learning – Training Data is not a Labeled one

Reinforcement Learning – Not explicitly supplied the data. It must interact with the environment to achieve the goal

Intelligent Edge AI
The intelligent edge is a continually expanding set of connected systems and devices that gather and analyze data
An intelligent edge is a place at which data is generated and analyzed, interpreted, and addressed.

Intelligent edge allows ML models to run on edge devices like cell phones, drones, robots, and other IoT dev

In this article, we have learned an overview of Microsoft’s Cognitive Services. Later on, we will learn more in detail in upcoming articles. Please share your feedback in the comment section.

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