TechyInformers

Home
artificial intelligence

Fundamentals of Artificial Intelligence - Part 2

Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language.

Understand natural language processing

Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language.



NLP enables you to create software that can:

  • Analyze and interpret the text in documents, email messages and other sources.
  • Interpret spoken language and synthesize speech responses.
  • Automatically translate spoken or written phrases between languages.
  • Interpret commands and determine appropriate actions.
For example, Starship Commander, is a virtual reality (VR) game from Human Interact, that takes place in a science fiction world. The game uses natural language processing to enable players to control the narrative and interact with in-game characters and starship systems.

Natural language processing in Microsoft Azure

In Microsoft Azure, you can use the following cognitive services to build natural language processing solutions:
ServiceCapabilities
Text AnalyticsUse this service to analyze text documents and extract key phrases, detect entities (such as places, dates, and people), and evaluate sentiment (how positive or negative a document is).
Translator TextUse this service to translate text between more than 60 languages.
SpeechUse this service to recognize and synthesize speech, and to translate spoken languages.
Language Understanding Intelligent Service (LUIS)Use this service to train a language model that can understand spoken or text-based commands.

Try this

To see an example of a how you can use natural language to interact with an AI system, follow these steps:
  • Open another browser tab and go to https://aidemos.microsoft.com/luis/demo.
  • Use the demo interface to control the lighting in the virtual home. You can type instructions, use the microphone button to speak commands, or select any of the suggested phrases to see how the system responds.

Understand conversational AI

Conversational AI is the term used to describe solutions where AI agents participate in conversations with humans. Most commonly, conversational AI solutions use bots to manage dialogs with users. These dialogs can take place through web site interfaces, email, social media platforms, messaging systems, phone calls, and other channels.



Bots can be the basis of AI solutions for:

  • Costumer support for product or services.
  • Reservation systems for restaurants, airlines, cinemas and other appointment based businesses.
  • Health care cosultations and self-diagnosis.
  • Home automation and personal digital assistents.

Conversational AI in Microsoft Azure

To create conversational AI solutions on Microsoft Azure, you can use the following services:
ServiceCapabilities
QnA MakerThis cognitive service enables you to quickly build a knowledge base of questions and answers that can form the basis of a dialog between a human and an AI agent.
Azure Bot ServiceThis service provides a platform for creating, publishing, and managing bots. Developers can use the Bot Framework to create a bot and manage it with Azure Bot Service - integrating back-end services like QnA Maker and LUIS, and connecting to channels for web chat, email, Microsoft Teams, and others.

Try this

The Microsoft healthcare bot is built on Azure Bot Service and enables developers to quickly create conversational AI solutions for health care. To see an example of the healthcare bot:
  1. Open another browser tab and go to https://www.microsoft.com/research/project/health-bot/.
  2. Select the option and Try a demo of an example end-user experience.
  3. Use the web chat interface to interact with the bot.

Understand responsible AI

Artificial Intelligence is a powerful tool that can be used to greatly benefit the world. However, like any tool, it must be used responsibly.

At Microsoft, AI software development is guided by a set of six principles, designed to ensure that AI applications provide amazing solutions to difficult problems without any unintended negative consequences.

Fairness

AI systems should treat all people fairly. For example, suppose you create a machine learning model to support a loan approval application for a bank. The model should make predictions of whether or not the loan should be approved without incorporating any bias based on gender, ethnicity, or other factors that might result in an unfair advantage or disadvantage to specific groups of applicants.

Azure Machine Learning includes the capability to interpret models and quantify the extent to which each feature of the data influences the model's prediction. This capability helps data scientists and developers identify and mitigate bias in the model.

Reliability and safety

AI systems should perform reliably and safely. For example, consider an AI-based software system for an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and recommends prescriptions. Unreliability in these kinds of system can result in substantial risk to human life.

AI-based software application development must be subjected to rigorous testing and deployment management processes to ensure that they work as expected before release.

Privacy and security

AI systems should be secure and respect privacy. The machine learning models on which AI systems are based rely on large volumes of data, which may contain personal details that must be kept private. Even after the models are trained and the system is in production, it uses new data to make predictions or take action that may be subject to privacy or security concerns.

Inclusiveness

AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.

Transparency

AI systems should be understandable. Users should be made fully aware of the purpose of the system, how it works, and what limitations may be expected.

Accountability

People should be accountable for AI systems. Designers and developers of AI-based solution should work within a framework of governance and organizational principles that ensure the solution meets ethical and legal standards that are clearly defined.

Explore responsible AI in practice


The principles of responsible AI can help you understand some of the challenges facing developers as they try to create ethical AI solutions. It's easy to read about the principles, or watch a video; but it's much harder to put the principles into practice.

Try this

Here's an activity to help you learn more about how principles apply to an AI system that interacts with human users.
  • Open another browser tab and go to https://aidemos.microsoft.com/guidelines-for-human-ai-interaction/demo.
  • Select cards from each of the decks, which represent four phases in a Human-AI interaction:
    • Initially
    • During the interaction
    • When something goes wrong
    • Over time
  • For each card, view the examples provided.
  • Try to identify which of the six principles the examples reflect (an example might reflect more than one principle).

Further resources

For more resources to help you put the responsible AI principles into practice, see https://www.microsoft.com/ai/responsible-ai-resources.

Hello! Myself Tejas Mahajan. I am an Android developer, Programmer, UI/UX designer, Student and Navodayan.