What is the principle of artificial intelligence?

This article provides a comprehensive overview of how artificial intelligence (AI) works, including its definition, its components, and how to build and apply AI systems step by step. This article elaborates on the workflow of AI from data collection, preprocessing, model selection, training, testing and evaluation, optimization, deployment, and continuous learning, aiming to help readers better understand AI technology and its applications, and hope to help you.

Artificial intelligence (AI) is an evolving technology designed to mimic human intelligence. It helps computers learn how to reason, learn, and solve problems like the human brain.

Industries ranging from healthcare to finance are implementing AI technologies that are making a meaningful positive impact on our lives. AI has the potential to advance in areas such as self-driving cars and personal assistants, potentially driving scientific breakthroughs, enhancing medical scanning capabilities, and enabling accurate facial recognition.

As AI research accelerates and its applications play an increasing role in business and personal life, it's more important than ever to understand how AI works and how to use it.

This article provides a comprehensive overview of artificial intelligence, including its components and a step-by-step introduction to how it works!

What is Artificial Intelligence?

Artificial intelligence is a field of computer science that attempts to simulate the way humans think. You feed the information from your data source to an AI system, let the AI process it, and create a trained model that uses the input data as a reference.

The more data you have, the better the AI system can learn.

However, not all AI systems require big data sources. You can train some models with smaller datasets using different techniques, such as reinforcement learning (a machine learning technique that we'll discuss next).

Once you're done, you can ask the AI a question and let it estimate and act on what you've learned. But the extent and accuracy of the AI's response depends mainly on the quality of the training data and the algorithm.

You can use AI solutions in a variety of ways, including:

• Chatbots. AI bots are trained with business data to chat with humans and answer questions in real-time using human language.
• 虚拟助理。 Amazon Alexa、Apple Siri 和 Google Assistant 等人工智能工具在日常生活中为消费者提供帮助。
• Generative AI. Writing AI tools (e.g., OpenAI's ChatGPT), image tools (e.g., Midjourney), as well as voice tools (e.g., ElevenLabs) can generate different forms of media based on input.
• Speech recognition. Speech recognition tools determine who the speaker is and what they are saying based on audio input.
• Search engine. Search tools improve the information-gathering process by creating a better user experience and generating results in real-time.

Machine Learning: The Basis of Artificial Intelligence

Machine learning (ML) is the foundation of AI system learning. The data you provide to machine learning tools helps AI create datasets to learn how to make decisions and predictions without having to be programmed to perform specific tasks.

However, while machine learning allows AI systems to learn from data, they still require programming and algorithms to process the data and generate meaningful insights.

Machine learning works by feeding tools with large amounts of data. You can then process that data to create a mathematical model that can be used to handle AI tasks. Essentially, it allows AI applications to perform tasks like humans.

Image classification is a good example of this. Let's say you want to train an artificial intelligence to recognize cats.

You can provide images of cats to a machine learning system and label them as cats. The system then learns from what you provide and recognizes any cat pictures you provide after the training is complete.

Neural networks: the building blocks of artificial intelligence

A neural network is a machine learning algorithm that provides the tools to process information created based on artificial intelligence models. They consist of interconnected nodes (or artificial neurons).

These nodes adjust based on the information that goes into the neural network. This allows neural networks to discover relationships and patterns in data.

Nodes are divided into layers, each with its own function:

Deep learning is a type of neural network with multiple hidden layers, so it can learn more complex relationships in data. Data scientists can then optimize these layers with different formats (text, audio, video, and images) to improve accuracy, but they also need more training to work. Data: The Fuel of Artificial Intelligence

Data is the "fuel" of AI systems. Without a large dataset to train an AI model, AI wouldn't have any capabilities.

Good AI training data has several characteristics, including:

• The information is complete and there are no omissions
• Consistent with AI system functionality
• Accurate, error-free data
• Up-to-date, no outdated information

You use many types of data to train AI systems, which fall into three categories: structured, unstructured, and semi-structured.

Structured data has a predefined format. Think of dates, addresses, credit card numbers, numeric series, and other standard entry methods. There will be a standard format for every piece of data that is fed into the AI system.

Unstructured data lacks any specific information. Input unstructured text, images, videos, and images and let AI find patterns in your data. AI can process information using natural language processing (NLP), computer vision, and other methods.

If you don't have a predefined model, you can use semi-structured data. This data uses file formats such as JSON, XML, and CSV. Going this route will give you the benefits of an unstructured data source as well as the ability to easily store your training data

Algorithms: Problem solvers for artificial intelligence

Algorithms are the backbone of artificial intelligence. They are mathematical programs that tell AI how to learn, improve decision-making, and solve problems. Algorithms turn raw data into insights that you can use every day.

Popular algorithms used in AI programs include:

• Linear regression. Predictions are made based on the mathematical relationship between inputs and outputs.
• Decision tree. Model decision-making based on data attributes.
• K-means clustering. Create data clusters and find the hub of each cluster to identify patterns based on inputs.

These algorithms work by taking the data you enter and feeding it into the algorithm. The more high-quality data you provide, the easier it will be for algorithms to find patterns and turn them into actionable insights!

How artificial intelligence works step by step

Now that you understand what AI is, you might be wondering how you can use it in practice. This section will walk you step-by-step through the process of building an AI system.

• Artificial Intelligence Process:
• Data acquisition
• Data preprocessing
• lectotype
• Train the model
• Testing & Evaluation
• Model optimization
• deploy
• Keep learning

1. Data Collection

Data collection is one of the most critical parts of developing an AI system. This is the process of collecting large amounts of data to train an AI system.

Your training data can be in any format: text, numbers, images, video, or audio. The format of the data depends on whether you're using a structured or unstructured dataset.

Let's take the example of looking at the sentiment of a social media post towards a brand. Collect large datasets from social media and categorize the sentiment of these posts. Are they positive, negative, or neutral?

Put these results into a CSV file for training. Once you're done, you can determine your brand's sentiment online.

2. Data preprocessing

You shouldn't just enter the data you find. AI systems need accurate, up-to-date, and relevant information to get the best results. There's no guarantee that this will happen without preprocessing the data, especially if you have a lot of data.

Noise removal, also known as data smoothing, is an important process. This means finding and removing any data that impairs the learning process and fixing any structured data formatting.

Take, for example, an AI model that is being trained in financial analysis. Review your training data, such as stock prices and interest rates, to look for any values that are incorrectly formatted. Include or remove dollar signs, make sure the decimal is in the correct place, and remove any other anomalies.

3. Model selection

Model selection is a step in the AI development process where you can choose the AI model that best suits the problem at hand. Many AI models are available, including machine learning algorithms, deep neural networks, or hybrid models that use a variety of techniques.

In addition to the different types of AI algorithms, there are several types of machine learning that can be used:

Supervised learning. Rely on human-labeled data to learn and acquire knowledge.

Unsupervised learning. Rely on unlabeled data and learning patterns to acquire knowledge.

Reinforcement learning. Rely on AI to interact with the environment to learn from mistakes and gain knowledge.

Deep learning models can transform data through multiple layers. It is suitable for more complex tasks.

The model you choose will depend on several factors, including:

• The amount of data you have
• Time to wait for training
• Your total resources
• The type of data you have
• Your total budget

4. Train the model

When you preprocess the data and select a model, you enter the training phase.

In this phase, you'll divide your data into two groups: a training set and a validation set. A training set is a dataset that you use to train your model, and a validation (test) set helps you understand how well your model is trained.

The model you choose will start reading your dataset, using mathematical and computational models to see patterns in the data and create output models to help it make future predictions.

The time it takes depends on the amount of training data you have and how big the model you plan to train is. The more layers, the longer it takes and the more resources are used.

5. Testing and Evaluation

You shouldn't just expect an AI model to be in production once it's finished training. Depending on the quality of the dataset and how well you do in terms of preprocessing, the final model may not give good results.

This is where the separate validation dataset you create can come into play. Your validation dataset contains the input and expected output once you put it into your AI application.

When validating an AI model, you need to take multiple measurements. Accuracy (the percentage of correctly predicted), precision (the percentage of predictions that were actually positive), and recall (the percentage of correctly identified cases) are the most common.

There are a few things that can be problematic:

Poor data. Inaccurate data means that your model is not producing good results.

Underfit. The AI model is too simple to capture patterns in the data.

Bias. The data leans in one direction and follows the same bias as humans.

6. Model optimization

Model optimization is the process of improving the performance of AI models. This could mean fine-tuning or modifying model parameters and using regularization techniques.

Fine-tuning means optimizing the parameters of the model. You can change the weights of the neural network or the AI algorithm used to adjust the model.

Adjusting the architecture of the model means adding and removing layers from the neural network to alter the connections between the layers and better capture the complexity of the data.

Regularization techniques help prevent overfitting, which is useful when the model performs well on trained data, rather than unseen data. Regularization makes it easier for AI to generalize and provide more accurate results.

7. Deployment

Deployment is the final stage of the model development lifecycle after AI model training and optimization. This is the process of integrating a model into an existing system or building a new computer program to use the model.

For example, let's say you have a new AI model that you want to use for financial forecasting. You own a product business and want to know about future sales.

You'll link the model to your current computer system to get sales data, financials, and other relevant information. In return, the model generates reports that estimate the sales and revenue you can expect in the future.

8. Keep learning

AI models are not something that can be trained all at once. You must regularly train the model based on new information to continue to see accurate output.

You can do this in a few ways. The first is to fine-tune your base model. You can build a base model based on the initial training data and fine-tune the model based on the new data. This provides your AI model with 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 the feedback from the AI system and score it. The system then learns what you're doing wrong and uses that feedback to deliver better results in the future. wraparound

As you can see, there are several steps required to train an AI system.

1. Data acquisition. Collect relevant data for your use and pass it on to the training program to inform the artificial intelligence.
2. Data preprocessing. Inspect datasets to remove erroneous data, fix formatting, and ensure that information is kept up to date.
3. Model selection. Choose the AI model that best suits your needs.
4. Model training. Feed your training data to AI models for training.
5. Model testing. Use test datasets to ensure that your model produces accurate results.
6. Model optimization. Make changes to the model to improve results and performance.
7. Deploy. Integrate new AI models with current systems.

Keep learning. Constantly update your AI model with new information to keep it relevant and produce good results.

However, this is just an example process. Not all AI systems are the same, so you may need to change this process to meet your unique needs.

This article is written by Everyone is a Product Manager Author [Cheng Yu Nian], WeChat public account: [Old Driver Chat Data], original / authorized Published in Everyone is a product manager, without permission, it is forbidden to reprint.

Image from Unsplash, based on the CC0 license.