Artificial Intelligence 101: Explaining basic AI concepts you need to know

In May 2017, AlphaGo, an AI product of then British startup DeepMind, defeated the world’s best player, Ke Jie, at the popular Asian mind game, Go. DeepMind was acquired by the world’s top technology company, Google, for  $400 million.

Go is a deceptively complex game that was invented over 2500 years ago. It is played on a nineteen-by-nineteen lined board populated by little black and white stones. Similarly to playing chess, two players alternate placing stones on the board, attempting to encircle the opponent’s stones.

In ancient China, Go represented one of the four art forms any Chinese scholar was expected to master. The game was believed to imbue its players with a Zen-like intellectual refinement and wisdom.

The rules of the game are so simple that they can be laid out in just nine sentences, but the number of possible movements on a Go board exceeds the number of atoms in the known universe. With infinitely more possible positions during play, many believe that Go is more complex than chess.

This possibly explains why the creators of AlphaGo decided to test the competence of this newfound computer feature using the most complex game available. The engineers may have thought that the board offered too many possibilities for a computer to evaluate.

But, on this day, the AI not only defeated the world champion. It dismantled him.

Fun fact: That wasn't AlphaGo's first victory against human champions. It scored its first high-profile victory in March 2016 during a five-game series against the legendary Korean player Lee Sedol, winning four to one. While barely noticed by most Americans, the five games drew more than 280 million Chinese viewers.

In fact, the earliest computer versus man intellect contest happened in 1997 when IBM’s Deep Blue defeated world chess champion Garry Kasparov in a match dubbed “The Brain’s Last Stand.”

That event had little impact on IBM’s stock price, but it spawned anxiety about the possibility of computers conquering mankind someday and becoming our overlords. The movies and news channels made sure the lessons of fear were driven home in the years that followed.

Today, Artificial Intelligence (AI) has become the dominant factor with countries racing to lead in theory, technology development and application. Global tech giants are ramping up AI investment and research, and unveiling products on a historic scale. And, funds for AI startups are pouring in from venture capitalists and the government.

In this article, I explained concepts that are crucial to understanding the field of AI. I will define the key concepts of machine learning, deep learning, and generative AI, and share a few positives that you need to know.

Perhaps, if you are a newbie like me, this will prepare you to understand the concept and strategically position yourself for leverage.

Artificial Intelligence

Of all the definitions of Artificial Intelligence that I have seen, my most preferred is by Kai-Fu Lee, in his 2021 book, “AI 2041“:

“Artificial intelligence (AI) is smart software and hardware capable of performing tasks that typically require human intelligence. AI is the elucidation of the human learning process, the quantification of the human thinking process, the explication of human behavior, and the understanding of what makes intelligence possible.”

Back in the mid-1950s, the pioneers of AI set themselves an impossibly lofty but well-defined mission: to recreate human intelligence in a machine.

Today, AI-enabled devices can emulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Applications and devices equipped with AI can recognise and identify objects. They can understand and respond to human language.

Fun fact: Computer scientist John McCarthy coined the term “artificial intelligence" at the Dartmouth Summer Research Project in the summer of 1956.

In the past five years, AI has beaten human champions in Go, poker, and the video game Dota 2, and has become so powerful that it learns chess in four hours and plays invincibly against humans.

But it’s not just games that it excels at.

In 2020, AI solved a fifty-year-old riddle of biology called protein folding. The technology has surpassed humans in speech and object recognition, served up “digital humans” with uncanny realism in appearance and speech, and earned passing marks on college entrance and medical licensing exams.

AI is outperforming judges in fair and consistent sentencing, and radiologists in diagnosing lung cancer, as well as powering drones that will change the future of delivery, agriculture, and warfare.

Eventually, I believe, AI will become an omni-use technology that will penetrate virtually all industries.

Machine Learning

Machine learning is simply the process of creating AI models by training an algorithm to make predictions or decisions based on a given dataset.

Imagine that we have an application that you want to be able to recognise the faces of everyone living in Lagos and match the faces with their names. The first thing to do is to input images of the faces with their appropriate names (data). Then, you will include a set of instructions (let us call that algorithms) to help the application identify and match the faces with the names.

And, because the application will make some unavoidable errors, you will need to update the data and add new instructions to make the application more efficient. The process (not as simple as described) of training the system is machine learning.

It is a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.

There are many types of machine learning techniques or algorithms. These include linear regression, logistic regression, decision trees, random forest, support vector machines (SVMs), k-nearest neighbor (KNN), clustering and more.

Each of these approaches is suited to different kinds of problems and data.

However, one of the most popular types of machine learning algorithms is the neural network. A neural network consists of interconnected layers of nodes (analogous to neurons) that work together to process and analyze complex data. Simply put, it is modeled after the human brain’s structure and function.

The simplest form of machine learning is called supervised learning, which involves the use of labeled data sets to train algorithms to classify data or predict outcomes accurately.

Deep learning

Deep learning is a subset of machine learning.

Basically, algorithms (remember that?) require large amounts of data to make a decision. Our Lagos faces application will not be able to recognise faces efficiently if there aren’t enough angles of one face or faces of many people. What is more? As more citizens sign up on the app, more faces and names will be registered.

Hence, to be consistently accurate, AI trains itself to recognize deeply buried patterns and correlations continuously. It can then draw on its extensive knowledge of these correlations— many of which are invisible or irrelevant to human observers—to make better decisions.

Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

Its most natural application is in fields like insurance and making loans. Relevant data on borrowers is abundant (credit score, income, recent credit-card usage), and the goal to optimize for is clear (minimize default rates).

Taken one step further, deep learning will power self-driving cars by helping them to “see” the world around them—recognize patterns and use that information to make decisions (apply pressure to the brake to slowly stop) that optimize for your desired outcome (deliver me safely home in minimal time).

Generative AI

Generative AI (also called gen AI) refers to deep learning models that can create original content such as long-form text, high-quality images, realistic video or audio and more; in response to a user’s prompt or request.

Now, imagine that you ask our Lagos app to generate a carousel of the faces of people living in Yaba and put a piece of energetic music in its background. You intend to use the video for a mental health campaign. That there is generative AI.

Generative models can encode a simplified representation of their training data and draw from that representation to create new, similar work, but different from the original data.

Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. Some examples of popular Generative AI models include Meta’s Llama, Google’s Gemini, OpenAI’s ChatGPT, Zoho’s Zia and the latest wonder piece, DeepSeek.

In general, generative AI operates in three phases:

• Generation, evaluation and more tuning, to improve accuracy.
• Training, to create a foundation model.
• Tuning, to adapt the model to a specific application.

Benefits of AI

AI offers numerous benefits across various industries and applications. Some of the most commonly cited benefits include:

1. Reduced physical risks: AI can eliminate the need to put human workers at risk of injury or worse by automating dangerous work such as animal control, handling explosives, deep ocean, high altitudes or outer space jobs.
2. Automation of repetitive tasks: AI can automate routine, repetitive and often tedious tasks such as data collection, entering and preprocessing, as well as physical tasks such as warehouse stock-picking and manufacturing processes. This automation frees us to work on higher-value, more creative work.
3. Increased faster insight from data: Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support.
4. Enhanced decision-making: AI enables businesses to act on opportunities and respond to crises.
5. Reduced human errors: AI reduces human errors by guiding people through the proper steps of a process and flagging potential errors before they occur.
6. 24×7 availability: Chatbots and virtual assistants enable always-on support and provide faster answers to frequently asked questions (FAQs). It can also free human agents to focus on higher-level tasks, and give customers faster, more consistent service.