1. What is Artificial Intelligence?

Do you know that artificial intelligence is everywhere around—on your smartphone, in automobiles, hospitals, and many other aspects of your life? Artificial intelligence records and analyzes things you do every day, and humans make decisions based on those things.

Artificial Intelligence:

·       Recommends merchandise you might like to buy on the internet

·       Alerts you if your smartwatch or fitness band detects low oxygen in your bloodstream, inflammation, or an unhealthy spike in blood sugar

·       Scans your social media posts to learn more about what you are thinking

·       Helps banks invest money in your family’s bank accounts to keep the economy around you growing

Welcome to the Introduction to Artificial Intelligence course! In this course, you’ll become familiar with fundamental artificial intelligence (AI) concepts, such as types of AI, methods that AI uses to find connections and make predictions, and ways that humans can interact with AI systems.

Learning objectives

After completing this course, you should be able to: 

·       Define artificial intelligence

·       Differentiate between AI and augmented intelligence

·       Describe three levels of artificial intelligence

·       Describe the history of AI, from the past to the possible future

·       Define and describe machine learning

·       Differentiate between structured, unstructured, and semi-structured data

·       Describe how machine learning structures unstructured data

·       Describe how machine learning uses probabilistic calculation to solve problems

·       Describe three methods by which machine learning analyzes data

·       Describe an ideal relationship between humans and machine learning

Artificial intelligence (AI) refers to the ability of a machine to learn patterns and make predictions. AI does not replace human decisions; instead, AI adds value to human judgment. 

In its simplest form, artificial intelligence is a field that combines computer science and robust datasets to enable problem-solving.

What is the difference between AI and augmented intelligence?

When learning about artificial intelligence, you’ll come across the term augmented intelligence. Both terms share the same objective, but have different approaches. Augmented intelligence has a modest goal of helping humans with tasks that are not practical to do. For example, “reading” 1000 pages in an hour. In contrast, artificial intelligence has a lofty goal of mimicking human thinking and processes. However, it’s important to note that AI today is not mature enough to perform independent tasks such as diagnosing cancer.

So, what continues to drive the development of AI? 

As computing power and algorithms become more powerful and data volumes increase, companies will adopt new use cases for AI technologies. Companies will embed smart systems into their applications to drive innovation and efficiencies, enhance employee experience, automate tasks, decrease costs, and improve revenue.

Artificial intelligence machines (researchers call them “AI services”) don’t think. They calculate. They represent some of the newest, most sophisticated calculating machines in human history. Some can perform what’s called machine learning as they acquire new data. Others, using calculations arranged in ways inspired by neurons in the human brain, can even perform deep learning with multiple levels of calculations.

 

Imagine you are given the job to sort items in the meat department at a grocery store. You realize that there are dozens of products and very little time to sort them manually. How could you use artificial intelligence, machine learning, and deep learning to help with your work?

ARTIFICIAL INTELLIGENCE

To separate the chicken, beef, and pork, you could create a programmed rule in the format of if-else statements. This allows the machine to recognize what is on the label and route it to the correct basket. 

A programmed rule might look something like this:

if beef_is_on_label:
    route_items_to_center_basket()

else:
    redirect_item_to_main_basket()

 

Artificial intelligence makes this process more efficient.

MACHINE LEARNING

To improve the performance of the machine, you expose it to more data to ensure that the machine is trained on numerous characteristics of each type of meat, such as size, shape, and color. The more data you provide for the algorithm, the better the model gets. By providing more data and adjusting parameters, the machine minimizes errors by repetitive guess work.

DEEP LEARNING

The grocery store has expanded its selection to include more products such as chicken tenders, ground beef, and wild boar. In addition, the products now come in different sizes, shapes, and seasonings. What makes deep learning different?

Deep learning models eliminate the need for feature extractions. For your work in the meat department, you decide to use algorithms based on deep learning to sort meat by removing the need to define what each product looks like. Feature extraction is built into the process without human input. Once you have provided the deep learning model with dozens of meat pictures, it processes the images through different layers of neural networks. The layers can then learn an implicit representation of the raw data on their own.

How do AI services calculate? And, what do they do with those calculations? Let’s break this down into two parts.

ANALYSIS

AI services can take in (or “ingest”) enormous amounts of data. They can apply mathematical calculations in order to analyze data, sorting and organizing it in ways that would have been considered impossible only a few years ago.

PREDICTION

AI services can use their data analysis to make predictions. They can, in effect, say, “Based on this information, a certain thing will probably happen.” 

This is what AI services do! Based on data analysis, they make predictions. It might not seem like much, but that analysis and those predictions can have an enormous impact on human life.

Most people have a love-hate relationship with the autocorrect feature on phones or computers. What’s happening when you enter a misspelled word? And how does the machine know to suggest a better spelling?

Simply put, the software analyzes what you’ve typed so far and predicts a likely correction. Your phone or computer (or its online service) has more than just a dictionary of correct spellings. It has a huge library of phrases that humans use in certain contexts on many subjects. So, when you enter a word that’s not in its dictionary, it begins analyzing and predicting and suggests the word you need. Predictions aren’t always accurate. But if they’re correct often enough, they’re useful and can save you time.

Here are more ways that AI uses data to make predictions.

Human language

Online chatbots use natural language processing (NLP) to analyze poorly typed or spoken questions, then predict which answers to give on topics ranging from shipping or business hours to merchandise and sizes.

Vision recognition

AI helps doctors identify serious diseases based on unusual symptoms and early-warning signs, and it reads speed limit and stop signs as it guides cars through traffic.

 

Fraud detection

AI analyzes patterns created when thousands of bank customers make credit card purchases, then predicts which charges might be the result of identity theft.

Today’s AI has gone beyond creating driving directions, vacuuming floors, or recommending new fashions. Now it really can mimic the capabilities of the human mind. AI can learn from examples and experience, recognize objects, understand and respond to language, and solve problems. Even more exciting are its future possibilities.

How is AI Evolving?

Computer scientists have identified three levels of AI based on predicted growth in its ability to analyze data and make predictions. They call these levels:

·       Narrow AI

·       Broad AI

·       General AI

As shown in the following graphic, Narrow AI, and Broad AI are available today. In fact, most enterprises use Broad AI. General AI won’t come online until sometime in the future.

 Narrow AI

• Narrow AI is focused on addressing a single task such as predicting your next purchase or planning your day. 

• Narrow AI is scaling very quickly in the consumer world, in which there are a lot of common tasks and data to train AI systems. For example, you can buy a book with a voice-based device. 

• Narrow AI also enables robust applications, such as using Siri on an iPhone, the Amazon recommendation engine, autonomous vehicles, and more. Narrow AI systems like Siri have conversational capabilities, but only if you stick to the script.

Broad AI

• Broad AI is a midpoint between Narrow and General AI. 
• Rather than being limited to a single task, Broad AI systems are more versatile and can handle a wider range of related tasks. 
• Broad AI is focused on integrating AI within a specific business process where companies need business- and enterprise-specific knowledge and data to train this type of system. 
• Newer Broad AI systems predict global weather, trace pandemics, and help businesses predict future trends.

General AI

• General AI refers to machines that can perform any intellectual task that a human can. 
• Currently, AI does not have the ability to think abstractly, strategize, and use previous experiences to come up with new, creative ideas as humans do, such as inventing a new product or responding to people with appropriate emotions. And don't worry, AI is nowhere near this point.

There might be another level, known as artificial superintelligence (ASI) that could appear near the end of this century. Then machines might become self-aware! Even then, no levels of AI are expected to replace or dominate you. Instead, scientists hope AI will extend humans’ ability to lead richer lives.