AI Basics: Artificial Intelligence 101

Discussions around artificial intelligence (AI) don’t seem to be slowing down anytime soon. And with so many terms and concepts — models, algorithms, networks, etc. — it can be hard to keep up.

To help you out, we put together the guide below. It covers some AI basics, with the goal of providing a high-level understanding of what artificial intelligence is all about — how it works, and how it shows up in our everyday lives.

Basic AI Concepts to Know

Artificial Intelligence

Artificial intelligence, broadly speaking, refers to a field in computer science concerned with machines that simulate human learning, reasoning, problem solving, decision making and creativity.

AI systems learn from large amounts of data, identifying patterns and relationships that help them make predictions and recommendations. And they improve over time as they get exposed to new information. The goal is to match (or even surpass) the capabilities of the human mind in an effort to automate work and solve problems more efficiently.

Machine Learning

Machine learning is a branch of artificial intelligence that gives computers the ability to learn and adapt without fixed instructions. Through a combination of arithmetic, statistics and trial-and-error, machine learning systems identify relationships and patterns within large datasets, enabling them to draw conclusions about new data — whether that be for predictions, decisions or classifications. As they get exposed to more data over time, these systems refine their abilities and learn to perform new tasks without ever having to be explicitly programmed to do so.

Machine learning is generally categorized into four main training methods: 

1. Supervised learning relies on labeled datasets that contain both input and output information, enabling machines to make predictions and classify new data.
2. Unsupervised learning analyzes unlabeled data, allowing machines to discover patterns and anomalies in large, unstructured datasets that may have otherwise gone undetected by humans
3. Semi-supervised learning is a mix of both supervised and unsupervised learning, where small amounts of labeled data are processed alongside larger chunks of raw data.
4. Reinforcement learning teaches computer programs to complete tasks through a process of trial and error, where desired behavior is rewarded and undesired behavior is punished. 

Deep Learning 

Deep learning is a subfield of machine learning that teaches computers to process large quantities of unstructured data in a way that mimics the human brain. Using multi-layered structures of artificial “neurons” called neural networks, deep learning systems can recognize complex patterns in text, images, audio and other forms of data to produce accurate predictions and insights.

The primary benefit of deep learning is its ability to learn what features are important in the data on its own, with no human involvement. For example, if an image recognition system is attempting to locate a car, deep learning models can automatically identify relevant characteristics — wheels, windows, body shape, etc. — without the need for programmers to define those characteristics manually. And as the system processes more images, it refines its understanding, growing increasingly accurate at recognizing cars based on these learned patterns.

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Neural Networks

Neural networks are computational systems that make decisions and predictions in a way that’s similar to the human brain, processing information through a series of interconnected nodes called “neurons.” These neurons are organized in layers: (1) An input layer that receives raw data, (2) one or more hidden layers where information is processed and patterns are learned and (3) an output layer that produces a final result. 

Every neuron in each layer connects to the next, and has its own associated weights and thresholds. If the output of an individual node is above a specific threshold value, it is activated, sending data to the next layer in the network. So when data (like images or text) enters a network, it passes through each layer — with neurons activating based on the strength of their input — enabling the network to gradually improve its accuracy over time.

AI Models

AI models are trained computer programs that learn to perform artificial intelligence tasks by analyzing patterns in data — and without explicit programming. This is done with algorithms, which are step-by-step rules that process input data into outputs automatically using arithmetic and repetition and decision-making logic. A model that can translate text prompts, for example, is trained on thousands of paired sentences in different languages so it can learn to generate accurate translations based on patterns in vocabulary, grammar and sentence structure.

While some models are built for set tasks tailored to the industry they are in or the company using them, others are more general-purpose, serving as a flexible base for a variety of AI products. Known as foundation models, these systems are trained on large, diverse datasets to handle a wide range of tasks. And they can be fine-tuned with additional data for specific use cases, making the AI development process faster and more cost-effective. Some examples of foundation models include OpenAI’s GPT-4, Anthropic’s Claude 3 models and Stability AI’s Stable Diffusion model.

Natural Language Processing

Natural language processing (NLP) enables machines to understand, interpret and respond to human language. It works by breaking down text and speech into elements that a computer can analyze — words, grammar, context — and identifying patterns like sentence structures and word associations. This allows models to not only comprehend written and spoken language, but also generate it, making conversations between humans and computers more natural and intuitive.

NLP is a driving force behind many AI applications we see today, including voice assistants like Amazon Alexa and Google Home. As these systems continue to learn from larger and more diverse datasets, they get better at understanding and mimicking the nuances of human communication.

Generative AI

Generative AI is an application of artificial intelligence you’re probably most familiar with. It refers to a model that creates new content by identifying patterns within massive amounts of annotated data. Then, given a prompt or input, the model is able to draw upon what it has learned to generate relevant, original works in real time. Unlike traditional AI, which focuses on classifying or predicting things based on existing data, generative AI produces completely new outputs that resemble the examples it was trained on, predicting the next word in a sentence or the next pixel in an image.

Common types of generative AI tools include:

• Text generators, which produce fluent and human-like written copy.
• Image generators, which create visuals ranging from photorealistic portraits to surreal landscapes.
• Video generators, which produce video clips from scratch.
• Audio generators, which compose original music in a variety of styles, as well as unique voices.
• Code generators, which write their own code, as well as fix bugs in existing code and translate between programming languages.

While useful, generative AI poses several unique risks. It can be used to create false, misleading or otherwise harmful content on a large scale. It also raises some legal complications regarding copyright infringement and fair use, as these models are often trained on copyrighted material, yet the content they generate cannot be copyrighted in its own right.

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Large Language Models

Large language models (LLMs) are AI models that have been trained on vast amounts of text data to understand and generate written language. With the help of deep learning, these models can grasp the nuanced meaning and context of words, allowing them to perform tasks like text generation, language translation, and content summarization.

LLMs work by taking a text prompt or a question and using neural networks to predict the next logical word, ultimately generating coherent and contextually relevant responses. These models rely on petabytes of data (including books, websites and other written content) and often contain at least a billion parameters, with more parameters indicating a deeper understanding of the relationships between words. This broad training is what enables LLMs to generate natural, human-like outputs across a wide range of topics.

Generative Adversarial Networks

Generative adversarial networks (GANs) are AI models that generate images, videos and audio by using two competing neural networks: the generator and the discriminator. The generator creates artificial outputs disguised as real data, and the discriminator evaluates them against real data to determine whether they are genuine or fake — creating a sort of competition that pushes both networks to improve.

During this training process, the generator learns to produce more realistic outputs, while the discriminator becomes better at distinguishing between real and AI-generated content. This exchange continues until the generator produces content that is so realistic that the discriminator can no longer reliably distinguish it from real data. GANs are used to generate realistic pictures, enhance image resolution and even produce deepfake videos and audio clips. 

Multimodal AI

Multimodal AI refers to a model that can process multiple types (or modalities) of data at once to form insights, make predictions and generate content. So, instead of relying on just one type of data, such as text, multimodal AI models can simultaneously analyze information from multiple sources — text, images, audio and video — providing the system with a deeper understanding of subjects. 

Multimodality in AI mimics the innately human approach to understanding the world, where we combine sensory inputs like sight, sound and touch with words to form a more nuanced perception of our reality. By integrating multiple data types into a single model, multimodal AI systems can achieve a more holistic and comprehensive understanding of its environment, leading to more accurate results and a more intuitive user experience.

AI in Practice

Artificial intelligence is woven into our everyday lives — often in ways we don’t even realize. AI-powered virtual assistants help us manage our calendars, navigate the world and control the lights and temperature in our homes. Recommendation engines on platforms like Netflix and Amazon help us decide what shows we should watch or what products we should buy. AI is even transforming the way we create art, allowing people to generate unique images, videos, music, poetry and prose with little more than a single text prompt.

AI is also shaking up the business world, enabling companies to strategize, streamline operations, reduce costs and make more informed decisions. This technology helps organizations to identify market trends faster, optimize their supply chains and enhance their customer service experience through chatbots, which can instantly answer questions and resolve issues with no human intervention. Plus, by analyzing vast amounts of data in real-time, AI can be used to detect and defend against cyberattacks that threaten the business.

In healthcare, AI helps diagnose illnesses and analyzes medical images. Banks and other financial institutions use it to detect fraudulent transactions, automate their lending processes and trade in the stock market. Manufacturing facilities employ AI-powered industrial robots and IoT sensors to increase productivity and precision. And retailers use AI to optimize their inventory management and enhance shoppers’ experience through personalized ad campaigns. Even lawyers use AI to speed up the legal research process, analyze documents and generate contracts, agreements and other legal documents of their own.

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Common AI Issues

AI can be incredibly helpful, but it also comes with some significant issues. For one, AI models often have societal stereotypes and historical inequalities baked into their training data, which can result in biased outputs that disproportionately favor or discriminate against certain groups. These systems also tend to get things wrong, presenting false, misleading or illogical information as fact in a phenomenon known as “hallucinating.” 

The “black box” nature of many AI models can make it difficult to correct these problems and others, as the internal decision-making processes are often unclear and not easily interpretable. As a result, users, companies and even developers themselves often struggle to fully trust the safety of AI-generated results, which can lead to a general reluctance in adopting this technology and may pose challenges in ensuring accountability and responsible use.

Training and running AI systems also requires significant computational resources, often resulting in a considerable carbon and water footprint. And the rapid growth of the artificial intelligence industry has driven an unsustainable demand for the hardware and raw materials, which negatively impacts the planet’s air and soil quality.

The Future of AI

The future of AI holds immense promise, with the potential to enhance human capabilities, solve complex challenges and revolutionize industries. One of the next big steps for the industry is to progress beyond the narrow (or weak) AI of today — where systems are only able to perform the specific tasks it was designed for — and achieve artificial general intelligence (AGI), where machines possess the ability to understand, learn and apply knowledge across a wide range of tasks with human-like flexibility and adaptability. 

Achieving AGI could pave the way for increased automation and problem-solving capabilities in sectors like medicine, transportation and more. Further down the line, it could even lead to the development of sentient AI, where artificial intelligence systems perceive, think — and perhaps even experience emotions — in ways similar to humans.

The growing sophistication of AI raises important questions regarding the very nature of intelligence, the moral implications of creating entities that can think and feel and the consequences of developing machines that could potentially rival human capabilities — particularly as it relates to job-loss, widespread disinformation and loss of privacy. What happens if AI outpaces human intelligence to the point where it is uncontrollable? The jury is still out.

As we navigate these advancements, AI researchers, legislators and other stakeholders are working to establish adequate ethical frameworks and regulations. The goal is to ensure that AI development benefits society while also addressing the risks associated with this revolutionary technology.

Frequently Asked Questions