Artificial intelligence (AI) is a growing technology that aims to mimic human intelligence. It helps computers learn how to reason, learn, and solve problems as a human brain would do.
Industries from health care to finance are implementing AI technology, making a meaningful positive impact on our lives. AI holds potential for advancements in fields like self-driving cars and personal assistants and may drive scientific breakthroughs, enhance medical scanning capabilities, and enable accurate facial recognition.
With AI research accelerating and the applications of AI playing an even greater role in both business and personal lives, understanding how artificial intelligence works and how to use it is more important than ever.
This article provides a comprehensive overview of AI, including its components and a step-by-step run-through of how it works.
What is artificial intelligence?
AI is a field of computer science that tries to simulate how humans think. You feed information from data sources to an AI system, let the AI process it, and create trained models that use the input data as a reference.
The more data they have, the better AI systems can learn.
However, not all AI systems require big data sources. You can train some models with smaller data sets using different techniques, like reinforcement learning (a type of machine learning technique, which we discuss next).
Once complete, you can ask your AI questions and have it make estimations and take action based on what it learned. But the extent and accuracy of the AI’s response primarily depend on the training data quality and algorithms.
You can use AI solutions in a variety of ways, including:
- Chatbots. AI bots train on business data to chat with humans and answer questions in real time using human language.
- Virtual assistants. AI tools like Amazon Alexa, Apple Siri, and Google Assistant help consumers in everyday life.
- Generative AI. Writing AI tools (like ChatGPT by OpenAI), image tools like Midjourney, and speech tools like ElevenLabs can generate different forms of media based on input.
- Speech recognition. From audio inputs, speech recognition tools determine who the speaker is and what they said.
- Search engines. Search tools improve the information gathering process by creating a better user experience and generating results in real time.
Machine learning: the foundation of AI
Machine learning (ML) is the foundation of how AI systems learn. The data you give machine learning tools help AI create data sets to learn how to make decisions and predictions without being programmed to perform specific tasks.
However, while machine learning allows AI systems to learn from data, they still need programming and algorithms to process that data and generate meaningful insights.
Machine learning works by giving a tool a large amount of data. You then process that data to create a mathematical model usable for handling AI tasks. Essentially, it allows AI apps to perform tasks as human beings do.
Image classification is an excellent example. Let’s say you want to train an AI to recognize cats.
You’d provide cat images to the machine learning system and tag them as cats. The system then learns from what you provide and should recognize any cat pictures you give it after training is complete.
Neural networks: building blocks of AI
Neural networks are a type of machine learning algorithm that provides the tools to process the information you create based on AI models. They’re made up of nodes (or artificial neurons) connected to each other.
These nodes adapt based on the information coming into the neural network. This gives neural networks the ability to find relationships and patterns in data.
The nodes are arranged in several layers, each with its own function:
- The input layer receives the data.
- The hidden layer processes the data.
- The output layer produces the results.
Deep learning is a type of neural network with multiple hidden layers, so it can learn more complex relationships in the data. Data scientists can then optimize those layers using different formats (text, audio, video, and images) to improve accuracy—but they also need much more training to work.
Data: fuel for AI
Data is the “fuel” for AI systems. Artificial intelligence wouldn’t have any functionality without great data sets to train AI models.
Good AI training data has several characteristics, including being:
- Complete, with no missing data
- Consistent with the AI system’s function
- Accurate, with no incorrect data
- Up to date, with no outdated information
You use several types of data to train AI systems, put into three categories: structured, unstructured, and semistructured.
Structured data has a predefined format. Think of dates, addresses, credit card numbers, number series, and other standard input methods. You’ll have a standard format for every piece of data entered into an AI system.
Unstructured data lacks any specific information. Input unstructured text, images, video, and images to allow AI to find patterns in the data. The AI can use natural language processing (NLP), computer vision, and other methods to process the information.
You can use semistructured data if you don’t have a predefined model. This data uses file formats like JSON, XML, and CSV. Going this route will give you the benefits of unstructured data sources and the ability to store your training data easily.
Algorithms: AI’s problem-solvers
Algorithms are the backbone of AI. They are mathematical procedures that tell AI how to learn, improve decision-making, and handle problem-solving. Algorithms turn raw data into insights you can use every day.
- Linear regression. Make predictions based on the mathematical relationship of input and output.
- Decision tree. Model decisions based on data attributes.
- K-means clustering. Create data clusters and find each cluster's center to identify patterns based on the input.
These algorithms work by taking the data you input and feeding it into the algorithm. The more high-quality data you provide, the easier for algorithms to find patterns and transform them into actionable insights.
How AI works, step by step
Now that you understand what AI is, you probably want to know how to use it in practice. This section takes you through the step-by-step process of building an AI system.
- Data collection
- Data preprocessing
- Model selection
- Training the model
- Testing and evaluation
- Model optimization
- Continuous learning
1. Data collection
Data collection is one of the most critical parts of developing an AI system. It’s the process of collecting vast amounts of data to train AI systems.
Your training data can be in any format: text, numbers, images, video, or audio. The format you put your data in depends on whether you use structured or unstructured data sets.
Let’s take the example of looking at the sentiment of social media posts for a brand. Collect large data sets from social media and classify the sentiment of those posts. Are they positive, negative, or neutral?
Place those results into a CSV file for training. Once complete, you can determine what your brand’s sentiment is like online.
2. Data preprocessing
You shouldn’t just input data as you find it. AI systems need accurate, up-to-date, and relevant information for the best results. Without preprocessing your data, there’s no guarantee of that happening—especially if you have a large amount of data.
Noise removal (also known as data smoothing) is one essential process. This means finding and removing any data that hurts the learning process and fixing the formatting of any structured data.
Take an AI model being trained to perform financial analysis, for instance. Look through your training data, like stock prices and interest rates, to find any incorrectly formatted values. Include or remove dollar signs, ensure the decimal is in the right place, and remove any other abnormalities.
3. Model selection
Model selection is the step of the AI development process where you choose the AI model most suited to the current problem. Many AI models are available—including machine learning algorithms, deep neural networks, or hybrid models using various techniques.
In addition to different types of AI algorithms, several types of machine learning are available:
- Supervised learning. Relies on human-labeled data to learn and gain knowledge.
- Unsupervised learning. Relies on unlabeled data and learning patterns to gain knowledge.
- Reinforcement learning. Relies on the AI’s interactions with the environment to learn from mistakes and gain knowledge.
Deep learning models can transform data through multiple layers. It’s suitable for more complex tasks.
Your chosen model will depend on several factors, including:
- The amount of data you have
- The time you have to wait for training
- Your total resources
- The type of data you have
- Your total budget
4. Training the model
The training stage comes when you have preprocessed the data and chosen your model.
During this phase, you’ll split your data into two sets: a training set and a validation set. The training set is what you use to train the model, and the validation (test) set helps you see how well-trained the model is.
Your chosen model will begin reading your data set, using mathematical and computational models to look at data patterns and create an output model to help it make future predictions.
The time this takes depends on the amount of training data you have and how large a model you plan to train. The more layers you have, the longer it will take and the more resources you’ll use.
5. Testing and evaluation
You shouldn’t just count on your AI model to be in a production state after it finishes training. Depending on the data set’s quality and how good a job you did at preprocessing, the final model may not give great results.
This is where the separate validation data set you created helps. Your validation data set contains input and expected output after it’s put into your AI application.
You’ll want to take several measurements when validating an AI model. Accuracy (percentage of predictions that are correct), precision (percentage of predictions that are actually positive), and recall (percentage of cases correctly identified) are the most common.
Problems can arise from a few scenarios:
- Poor data. Inaccurate data means your model can’t produce good results.
- Underfitting. The AI model is too simple and can’t capture data patterns.
- Biases. The data leans in one direction and trends toward the same biases humans have.
6. Model optimization
Model optimization is the process you go through to improve an AI model’s performance. It can mean fine-tuning or modifying your model parameters and using regularization techniques.
Fine-tuning means optimizing your model’s parameters. You can change the neural network’s weights or the AI algorithm used to tune the model.
Adjusting the model’s architecture means adding and removing layers from the neural network to change the connections between the layers and better capture data complexities.
Regularization techniques help prevent overfitting, which is useful when models perform well on trained data, not unseen data. Regularization makes it easier for the AI to generalize and offers more accurate results.
Deployment is the final stage of the model development life cycle after you finish training and optimizing your AI model. It’s the process of integrating your model into your existing systems or building new computer programs to use your model.
For instance, let’s say you have a new AI model you want to use for financial forecasting. You have a product business and want to understand how many sales you’ll have in the future.
You’ll tie your model into your current computer systems to take in sales data, financials, and other relevant information. In return, the model produces reports estimating how many sales and how much revenue you can expect down the line.
8. Continuous learning
AI models aren’t something you train one time. You must regularly train your models on new information to continue seeing accurate output.
You can do this in a couple of ways. The first is to fine-tune your base models. You can generate base models based on initial training data and fine-tune that model based on new data. This gives your AI models updated data to make more accurate predictions.
Another way to update AI models is through reinforcement learning human feedback (RLHF). Through this process, you’ll monitor and grade the feedback of your AI systems. The system then learns what it did wrong and uses that feedback to give better results in the future.
As you can see, training an AI system requires several steps.
- Data collection. Gather the relevant data for your use and pass it to a training program to inform the AI.
- Data preprocessing. Look through your data set to remove bad data, fix formatting, and ensure information stays updated.
- Model selection. Pick the AI model that best suits your needs.
- Model training. Give your training data to the AI model for training.
- Model testing. Use a test data set to ensure your model produces accurate results.
- Model optimization. Make changes to your model to improve the results and performance.
- Deployment. Integrate your new AI model with your current systems.
- Continuous learning. Keep updating your AI model on new information to keep it relevant and producing great results.
However, this is just a sample process. Not all AI systems are the same, so you may need to change this process to account for your unique needs.
Integrate AI into your workflow
AI is useful for anyone who needs to get work done—from businesses needing better insights into the market and their operations to freelancers needing productivity tools to get more done in less time.
If you’re a business owner wanting to increase your use of AI, start by using Upwork to find AI engineers. Post your requirements and search the Talent Marketplace™ for those with the skills you need.
And if you’re a freelancer with experience in AI and looking for businesses to help, browse the AI jobs available on Upwork to get started.
Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.
Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
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