Artificial Intelligence Overview

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 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.

To learn more about Artificial Intelligence, check out this video.

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.

Image Analysis

Image Analysis can be understood as the approach to analyze the image in a way that descriptive captions can be generated to better explain the image with not only the detection of the objects in the image but the overall understanding of the image such as by including tags and cataloging image to summarize the scene.

Face Detection and Recognition

Face Detection refers to a specialized form of object detection which is used to locate faces of humanity in an image or video. Such a process is done with algorithms and models such as Haar Cascade Classifier. Face Recognition is being able to recognize individual faces and differentiating between one them. Face Recognition is widely used today for security purposes in smartphones.

Optical Character Recognition

Optical Character Recognition (OCR) is a technique that enables the reading and detection of text. It can be used to read characters from images, photographs, scanned documents, currency, and more.

Conversational AI

Conversational AI can be understood as this specific type of AI which is capable of having a to and fro conversation with a human entity. It is prevalent in social media messaging platforms, phone calls, and web interfaces to use this technology.

Microsoft Azure has services catered to conversational AI.

Azure Bot Service

Azure Bot Service enables developers with the bot framework to create, publish and manage bots service such that back end services such as LUIS and QnA Maker can be integrated into the system and be connected to various channels such as emails, Microsoft Teams, and web chats.

QnA Maker

QnA Maker is a cognitive service provided by Microsoft AI to build a knowledge-based question-answer system, which makes it capable for the AI agent to have a decent conversation with the human agent.

 

Responsible AI

Artificial Intelligence has the potential to create a huge impact in almost every field of society. It can transform industries just like electricity. From manufacturing, security, communication, healthcare, agriculture – it can touch the lives of people across the globe. With great power, comes great responsibility. Thus, it is crucial, Artificial Intelligence that we create as developers are crafted thoughtfully and responsibly. Microsoft has pointed our six different principles to abide by as the guidelines while designing our Artificial Intelligent Systems.

Principles

Fairness

AI system should be fair to every entity. It cannot prefer one entity over the other based on ethnicity, gender, nationality, or other superficial factors. This fairness if not taken into care, can create havoc in judicial decision-making scenarios such as law, justice, and criminal case analysis among others. Unfair advantages and disadvantages must both be taken care of while developing an AI system.

Reliability and Safety

In critical use case scenarios such as healthcare, autonomous driving – the reliability and safety of AI is paramount. The AI system must go through rigorous testing and approval before it can be in use.

Privacy and Security

AI system must be secure and it must respect the privacy of individuals. Data leaks cannot occur in the AI system. Since AI models heavily rely on data to better themselves, it is important that security and privacy concerns are taken care of. Some of the points to note during designing AI with privacy and security in mind are,

  • Data Origin and Lineage
  • Data Use – Internal vs External
  • Data Corruption Considerations

Inclusiveness

AI should be inclusive. It must engage and empower people from wide aspects of society. After all, AI is here to help and give better lives to humans. Multitudes of demographics must be considered while developing an AI system such that no single group is left out or is made to feel disempowered.

Transparency

The AI systems that we developed should be transparent. The users of the system should be enabled and made aware of the purpose of the system, its working protocols, and processes, and its expected limitations. This will give a transparent outlook to the user side so that their data are not manipulated and the decision-makings supported by the AI system in different use cases are indeed transparent.

Accountability

AI systems should be designed with Accountability in mind. The developers and architects of the AI system need to work within the framework of governance and organizational principles such that the system can be relied upon by every user. The solutions that are provided must be ensured to be ethical and up-handle the legal code of standards.

Human – AI Interaction

It cannot be stated enough how important it is to develop a responsible Artificial Intelligence practice. Artificial Intelligence has the power to solve some of the most complex and critical problems of the world which was next to impossible some time back. AI has become so powerful over the years with the development and growth of Hardware necessary to run them. The predictions and inferences AI can perform at this age surpass human capabilities in multitudes of domains and it's been proved now and then by super-specialized AI system that had beat Grandmasters of Chess, Go Word Champion, and even in Jeopardy. In recent times, every technology giant has adopted Artificial Intelligence for the scale of impact it can provide. Hence, it is crucial as designers, developers, and architects to ensure that we create AI systems that only provide benefits to people without any disadvantage to any specific groups of individuals or sections of our community. This is possible with the principles described above. Moreover, we can adopt these principles to create an equalized playground where everyone is empowered and can grow.

Thus, in this article, we learned briefly overall about Artificial Intelligence. The various aspects of Artificial Intelligence were discussed ranging from the basics of Machine Learning, Computer Vision, Natural Language Processing, Conversational AI, and ethics and principles to abide by to design inclusive, fair, and transparent AI systems. The need for AI that can empower AI is paramount and the wide applications it can have in our lives will only be realized with the passage of time as AI adoption grows across the world. In the meantime, this amazing feat of engineering and approach to solving problems still has many eyes to shock with its potential.

References

  • https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/configure-qna-maker-resources
  • https://azure.microsoft.com/en-us/services/bot-services/
  • https://aidemos.microsoft.com/computer-vision