Artificial Intelligence and Machine Learning

This is the Artificial Intelligence and Machine Learning tutorial offered by Simplilearn. The tutorial is part of the Digital Transformation course and will help understand the Basics of Artificial Intelligence and Machine Learning with examples and also learn its application in various domains.

Objectives

At the end of this tutorial, you will be able to:

  • Define Artificial Intelligence and machine learning
  • Explain the impact of machine learning in various industries
  • Discuss how to leverage machine learning

Overview

You have already learned how big data is exploding. Its analysis is becoming a tedious and time-consuming activity. There is a real-time flow of all types of structured and unstructured data from social media, communication, transportation, sensors, and devices.

Data science is moving toward a new paradigm where one can teach machines to learn from data and derive a variety of useful insights. This is known as Artificial Intelligence.

Training machines can help in reducing human effort and can decrease the time required for analysis.

What is Artificial Intelligence?

Artificial Intelligence refers to intelligence displayed by machines that simulate human intelligence. It is a broad term for smart machines that can perform human tasks that require cognitive, judgment-based decision making.

Artificial Intelligence in Practice

Artificial intelligence (AI) is live. With all the excitement and hype, you might not be aware that you are already using it in many ways. For instance:

Voice Assistants like Apple Siri, Amazon Alexa, Microsoft Cortana, and Google Assistant are trained to understand human speech and intent. Based on human interactions, these chatbots take appropriate action.

Gmail filters a new email into Inbox (normal) or Junk folder (Spam) based on past information about what you consider spam.

The predictions made by weather apps at a given time are based on some prior knowledge and analysis of how the weather has been over a period of time for a particular place.

In addition to this, you would have also come across Google’s AlphaGo AI, Amazon ECHO product (home control chatbot device), self-driving cars, etc., which are excellent examples of artificial intelligence.

Let’s look at some of the examples to understand the impact of Artificial Intelligence in our daily life.

Amazon’s Recommendations

Ever wondered how Amazon makes product recommendations?

Amazon pulls in data from its user database to recommend products to users. It uses AI algorithms to predict what items the user may like based on the purchase history of similar classes of users.

Also, more users generate more data, which helps enhance the recommendations even further.

AI in Healthcare

IBM Watson, a healthcare initiative by IBM, understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a scoring schema.

Companies are even applying AI to make better and faster diagnoses than humans with the objective to improve patient outcomes and reducing costs through different forms of AI.

Types of AI

Let us learn about different types of Artificial Intelligence now

  • Reactive Machines: The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. For example, IBM’s chess-playing supercomputer can identify pieces on a chess board to make predictions, but it has no memory and cannot use past experiences to inform future ones.
  • Limited Memory: These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in autonomous vehicles have been designed this way. Observations are used to inform actions happening in the not-so-distant future, such as a car has changed lanes. These observations are not stored permanently.
  • Theory of Mind: This is a Psychology term. It refers to the understanding that others have their own beliefs, desires, and intentions that impact the decisions they make. This is an important distinguishing factor between the machines now and the machines intended to be built in the future.
  • Self-awareness:  These AI systems have a sense of self and have consciousness. Machines with self-awareness understand their current state and can use the information to infer what others are feeling.

Artificial intelligence and Machine learning are often spoken of together. Let’s understand how they are related.

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Relationship between AI and Machine Learning

Machine learning (ML) 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 Techniques

Machine Learning uses a number of theories and techniques from Data Science. Some of them include:

  1. Classification
  2. Categorization
  3. Clustering
  4. Trend analysis
  5. Anomaly detection
  6. Visualization
  7. Decision Making

Artificial intelligence and Machine learning are being increasingly used in various industries.

Machine Learning: Impact on Industries

Let us learn about the impacts of Machine Learning in different industries one by one.

Travel: Expedia and other sites provide recommendations based on your search and booking history. They can even recommend alternative travel dates, destinations, and local sightseeing options based on your search queries. Expedia uses machine learning techniques and algorithms to make these recommendations.

Insurance: Allstate (one of the largest insurance companies in the US) partnered with EIS to develop a virtual assistant called ABle (the Allstate Business Insurance Expert) to assist Allstate agents who are seeking information on Allstate Business Insurance (ABI) commercial insurance products.

ABle, who appears as an avatar, provides agents with step-by-step guidance for “quoting and issuing ABI products” using a natural language.

Health Care: Apixio, a cognitive computing firm, aims at improving the accessibility of clinical knowledge from digitized medical records to improve healthcare decision making.

It has to deal with a huge amount of unstructured data, gaps in patient documentation, and inaccuracies in disease prevalence and treatment.

Apixio introduced machine learning techniques to aggregate data across the population and derive insights around disease prevalence and treatment patterns.

Banking: Bank of America Corporation recently made a bold push into AI technology with the debut of an intelligent virtual assistant named Erica.

Erica is a chatbot that leverages “predictive analytics and cognitive messaging” to provide financial guidance to the company’s over 45 million customers.

All these predictions and recommendations are based on various machine learning methods. Let’s learn them.

Machine Learning Methods

Let’s understand different Machine Learning methods below.

  1. Supervised Learning: Supervised Learning is a type of Machine Learning used to learn models from labeled training data. It allows us to predict the output for future or unseen data. For example, voice assistants like Apple Siri, Amazon Alexa, Microsoft Cortana, and Google Assistant are trained to understand human speech and intent. Based on human interactions, these chatbots take appropriate action.

  2. Unsupervised Learning: Unsupervised Learning is a subset of Machine Learning used to extract inferences from datasets that consist of input data without labeled responses. For example, an online news portal segments articles into various categories like business, technology, sports, etc. This is based on clustering, which is one of the most popular techniques of unsupervised learning.

  3. Reinforcement Learning: Reinforcement Learning is a type of Machine Learning that allows the learning system to observe the environment and learn the ideal behavior based on trying to maximize some notion of cumulative reward. For example, manufacturing units use robots to identify a device from one box and put it in a container. The robot learns this by means of a rewards-based learning system, which incentivizes it for the right action.

Now that you have learned about the various methods, let’s understand how machine learning can be leveraged to provide effective outcomes.

Leveraging Machine Learning

Leveraging machine data and combining it with existing enterprise data enables a new generation of applications that are able to analyze and gain insights from large volumes of multi-structured machine data.

The Insights from Analytics:

  • Empower the C-suite that acts as a reassurance to the decision makers
  • Improve Reliability and help identify failures
  • Speed Operations to improve the flow of operations by reducing bottlenecks and problems
  • Monitor and Visualize Data, help to monitor end-to-end infrastructure, and provide real times alerts

The strength of machine learning lies in the fact that humans do not have to feed the algorithm like in traditional programming. The computer learns by itself, which saves effort and brings more accuracy and transparency.

Now, let’s summarize what you have learned so far about AI and Machine Learning.

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Key Takeaways

Let’s have a quick look at what you have learned so far in this Artificial Intelligence and Machine Learning tutorial.

  • Artificial Intelligence refers to intelligence displayed by machines that simulate human and animal intelligence.
  • Machine Learning is an approach or subset of Artificial intelligence that is based on the idea that machines can be given access to data along with the ability to learn from it.
  • Leveraging Machine Data and combining it with existing enterprise data enables a new generation of applications that are able to analyze and gain insights from large volumes of multi-structured machine data. This improves results.  

Conclusion

With this, we come to an end to the ArtificiaI Intelligence and Machine Learning tutorial. In the next chapter, we will discuss Blockchain and IoT.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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