Module 1

Fundamentals of AI

Introduction

Welcome to Module 1: Fundamentals of AI!

This module is designed to introduce you to artificial intelligence (AI), which is fast becoming one of the most important genres of technology in the world.

By the end of this module, you will have a solid understanding of what AI is, how it works, and some of its potential applications. We’ll review a brief history of AI, so you know where it comes from, as well as some key components common to AI like machine learning, neural networks, and natural language processing.

From there, you will also learn about the process of creating AI systems, including data collection, algorithm design, and model training. This is important for understanding future concepts such as data privacy and bias.

Lastly, to connect all of this to current events, we’ll review some of the key organizations at the center of contributing to the development of AI, such as OpenAI, DeepMind, Google Brain, Microsoft Research, and IBM Watson.

Learning objectives

Define artificial intelligence

Define artificial intelligence (AI) and differentiate it from other kinds of software

Review the origins of AI

Understand the basic history of the origins of AI and its evolution

Understand core components of AI

Identify key components of AI, including machine learning, neural networks, and natural language processing

Learn how AI systems are created

Understand the process of creating AI systems, including data collection, algorithm design, and model training.

Survey the landscape of AI

Understand key organizations that have contributed to the development of AI, such as OpenAI, DeepMind, Google Brain, Microsoft Research, and IBM Watson.

Terminology

Use this section like flashcards
Algorithm
A set of instructions for a computer to follow in order to solve a problem or perform a task
Data mining
The process of discovering patterns in large datasets
GPT models
Generative Pre-trained Transformer models, one of the most advanced language models in the world, trained on massive amounts of text data scraped from the internet
Natural language processing (NLP)
A type of AI that enables machines to understand, interpret, and generate human language
Model training
The process of using data to train an AI model to perform a specific task
Artificial intelligence (AI)
The ability of machines to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation
Deep learning
A subset of machine learning that involves the use of neural networks with many layers to enable machines to learn from complex data such as images, speech, and natural language
Machine learning
A subset of AI that involves the use of statistical models and algorithms to enable machines to improve their performance on a task as they are exposed to more data
Neural networks
A type of machine learning algorithm inspired by the structure of the human brain, consisting of interconnected nodes that process and transmit information
Prompt engineering
Prompt engineering is the process of carefully crafting input instructions or queries for natural language processing models to generate desired responses or behaviors

Have you encountered a term you'd like to see here?

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black computer keyboardby Fotis Fotopoulos

How is AI different from other kinds of software?

Artificial intelligence (AI) is a type of software that differs from other kinds of software in several key ways. Unlike traditional software, which has a predetermined set of instructions, AI can learn and adapt to new situations, making it more flexible and versatile.

For example, when you think about a calculator app on your phone, it can perform calculations, but it can't do anything else. It can't learn to do something new, like recognize your handwriting or understand spoken commands. AI, on the other hand, can be trained to recognize images, understand human speech, and even play games like chess or Go.

Another key difference between AI and other kinds of software is that AI can make decisions based on incomplete or ambiguous information. Traditional software requires clear instructions and fixed rules, but AI can make educated guesses and predictions based on patterns in data. This makes AI useful for tasks like fraud detection, where it can identify suspicious behavior even when the evidence is not straightforward.

Finally, AI is designed to be self-improving. As it is exposed to more data and experiences, it can learn from its mistakes and improve its performance. Traditional software requires updates and patches to fix bugs and add new features, but AI can improve on its own, making it a powerful tool for a wide range of applications.

CSIRAC on displayby Museums Victoria

A brief history of AI systems

In the mid-twentieth century, computer scientists started exploring the idea of creating machines that could simulate human intelligence. At that time, computers were still relatively new and could only perform simple calculations and store data.

One of the earliest examples of AI was expert systems, which were designed to mimic the decision-making abilities of human experts in specific domains. They used a set of rules and heuristics to make decisions based on a given set of inputs.

Expert AI Systems: These systems use a set of predetermined rules and heuristics to make decisions based on a given set of inputs. These systems do not learn or adapt to new situations, and their performance is limited to the rules and heuristics that have been programmed into them. They are still referred to under the category of AI, but differ significantly from Generative AI Systems.

Deep Blue, developed by IBM in the mid-1990s, is a noteworthy example of an expert AI system. It was a chess-playing computer that defeated a reigning world chess champion, Garry Kasparov, in a six-game match in 1997. Deep Blue used a brute-force approach to analyze millions of possible moves per second, along with a database of past games to make decisions. Its victory over Kasparov marked a major milestone in the field of AI.

One important milestone in the history of AI was the creation of neural networks, which were inspired by the structure and function of the human brain. Neural networks consist of interconnected nodes that process and transmit information, and they can learn from experience to improve their performance on a given task. So, if you want to create an AI system, it's important to understand how neural networks work and how they can be used to improve the system's performance.

Generative AI Systems: These are AI systems capable of generating new content, such as text, images, or music, based on a set of training data. They are able to learn and adapt to new situations, and their performance is not limited to the rules and heuristics that have been programmed into them. They are still referred to under the category of AI, but differ significantly from Expert AI Systems.

In the past few years, AI has become incredibly popular thanks to advancements in machine learning and natural language processing. These breakthroughs have led to the development of powerful new applications like speech recognition, image recognition, and self-driving cars. But as AI continues to evolve and become more sophisticated, it also raises important ethical questions about the role of machines in our lives and society. It's important to consider the impact that AI may have on us as we move forward.

Software Comparison
Traditional Software
AI Systems
Predetermined set of instructions
Yes
No
Learn and adapt to new situations
No
Yes
Make decisions based on incomplete or ambiguous information
No
Yes
Self-improving
No
Yes
Performance
Limited
Not Limited
Based on rules
Yes
No
Creativity
No
Yes

Key concept

It's really important to understand the difference between expert and generative AI systems, and if you don't understand this distinction, you could run into some serious problems. Here’s a real life example of this happening to a nonprofit organization, which I wrote about in this article.
Expert AI Systems
Generative AI Systems
Learning
No
Yes
Adaptability
No
Yes
Performance
Limited
Not limited
Based on rules
Yes
No
Creativity
No
Yes
Examples of usage
Decision-making
Content generation
Examples of systems
AI chess opponent
ChatGPT
Here’s a real life example of this happening to a nonprofit organization, which I wrote about in this article.
Instructor
Philip Deng
Co-founder & CEO
Grantable

Philip Deng is cofounder and CEO of Grantable. He has worked in the nonprofit sector for more than 15 years on three continents and in three languages, with experience as a development manager, executive director, and professional grants consultant.

Philip is a regular speaker in front of nonprofit audiences, on topics concerning the intersection of technology and philanthropic work.

He is a member of the OpenAI Forum, his work has been featured in the Chronicle of Philanthropy and Stanford Social Innovation Review, and he publishes a regular newsletter called, The Process.

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What goes into AI systems?

The process of creating AI systems involves several key steps, including data collection, algorithm design, and model training.

Data collection

To create an AI system, you need data. Lots of data. This data can come from a variety of sources, such as sensors that collect information about the environment, social media feeds that capture human behavior, or scientific studies that provide insights into how the world works.

It is important to ensure that the data you collect is representative of the problem you are trying to solve. If your data is biased or incomplete, your AI system may not perform well or may even make harmful decisions.

A wood carving of Al-Khwarizmi - Source: https://en.wikipedia.org/wiki/Al-Khwarizmi
The word "algorithm" comes from the name of the Persian mathematician Al-Khwarizmi, who was one of the first scholars to write about algebraic methods.

The algorithm can be designed using a variety of techniques, including rule-based programming, statistical models, and machine learning algorithms. Rule-based programming involves creating a set of if-then statements that the AI system can use to make decisions based on the input data. Statistical models involve using mathematical models to analyze the data and make predictions based on patterns in the data. Machine learning algorithms involve training the AI system on a large amount of data and adjusting the algorithm's parameters until it can accurately predict the correct answers.

Overall, the algorithm is the heart of any AI system, and the design of the algorithm is a critical step in the process of creating AI systems that can learn from data and make predictions based on that learning.

A common real-world algorithm is the recommendation algorithm used by streaming services like Netflix and Spotify. This algorithm suggests new content based on a user's past viewing or listening habits, taking into account factors like genres, artists, and ratings. It learns and adapts based on the user's feedback, and has changed how people discover and consume media.
Model training
Model training - Source: Zoolander

Once you have your data and your algorithm, you need to train your model. Model training involves feeding your algorithm with labeled data (data that has been manually annotated with the correct answers) and adjusting the algorithm's parameters until it can accurately predict the correct answers.

Training an AI model is similar to teaching a child to identify, say, animals. With enough examples and guidance, the child gains the ability to differentiate between a cat and a dog. Likewise, an AI model is trained with labeled data, allowing it to improve its prediction accuracy. The more labeled data the model sees, the better it gets at recognizing patterns and making predictions. Like a child, the AI model needs practice and feedback to improve its performance.

Once your model is trained, you can use it to make predictions on new, unlabeled data. This is the essence of AI: creating systems that can learn from data and make predictions based on that learning.

Self assessment

This short quiz is only meant to help you check your understanding of these materials. Your score is not recorded, so please write it down if you want to keep track for your own records.