Artificial intelligence (AI) and related technologies are rapidly developing and penetrating all spheres of lifemdash; from mobile applications to global business systems. A whole world of terms and concepts has formed around AI and machine learning (Machine Learning), in which it is easy to get confused. In this article, we have prepared a reference of 50 key terms necessary for understanding modern approaches to artificial intelligence.
The article will be useful:
- for beginners and students studying the basics of AI;
- for business and managers who want to competently implement AI technologies;
- for everyone interested in trends and prospects of machine learning.
At the end of the material, we will tell you how EasyByte uses these technologies in practice and how it can help your business.
1. Artificial Intelligence (AI)
Artificial intelligencemdash; is a field of computer science aimed at creating systems capable of performing tasks requiring «human» intelligence, such as speech understanding, learning, decision-making and problem solving.
2. Machine Learning (ML)
Machine learningmdash; is a subset of AI where models learn to find patterns in data without explicit programming. Instead of writing clear instructions, the developer provides the algorithm with a set of examples, on which it «learns» and then makes predictions.
3. Deep Learning
Deep learningmdash; is a direction of machine learning based on multi-layered neural networks. Such networks are able to detect more complex and abstract patterns, thanks to which impressive results are achieved in computer vision, text processing, etc.
4. Neural Network
Neural networkmdash; is a mathematical model inspired by biological neurons. It consists of layers of neurons, each of which receives and transmits signals. Adapting the weights of connections, the network is able to «learn» to reproduce the desired output signals.
5. Perceptron
Perceptron — the simplest model of an artificial neuron, proposed in 1957 by Frank Rosenblatt. It serves as the historical basis for modern multi-layer neural networks.
6. Recurrent Neural Network (RNN)
Recurrent neural networks remember the result of previous steps and use this information when processing subsequent elements of a sequence. They are used for analyzing time series, texts, audio (e.g., speech recognition).
7. LSTM (Long Short-Term Memory)
LSTM — this is a type of recurrent neural network that solves the "long memory" problem. The LSTM architecture includes special "memory cells" that allow it to retain context over longer periods of time.
8. Generative Adversarial Networks (GAN)
GAN (Generative Adversarial Networks) — a pair of models (generator and discriminator) that learn through a competitive process. The generator creates "fake" data, and the discriminator tries to recognize fakes. As a result, the generator becomes capable of generating realistic content.
9. Autoencoder
Autoencoders — models that learn to compress data into a more compact internal representation (encoder) and then reconstruct the original data (decoder). They are often used for dimensionality reduction and feature extraction.
10. Natural Language Processing (NLP)
NLP — this is a set of methods and algorithms that allow computers to understand, interpret, and generate human speech and texts. Bright examples: chatbots, machine translation, emotion recognition in text.
11. Transformer
Transformer — a neural network architecture proposed in 2017 (paper "Attention Is All You Need"). It uses the "attention" mechanism for processing sequences. It is the basis for models such as GPT, BERT and other modern language models.
12. GPT (Generative Pre-trained Transformer)
GPT — is a family of language models based on the Transformer architecture. Developed by OpenAI. GPT models are capable of generating texts, answering questions, translating, writing code and much more.
13. BERT (Bidirectional Encoder Representations from Transformers)
BERT — is a language model from Google that analyzes text bidirectionally, taking into account the context of words to the left and right. It is widely used for sentiment analysis, question-answering systems and other NLP tasks.
14. Embedding
Embedding — is a way to represent objects (e.g., words) as vectors of numbers. It allows encoding the semantic similarity of objects: similar words will have close vector representations.
15. Data Labeling
Data Labeling — is the process of assigning labels to samples in a dataset (e.g., "cats" and "dogs" in images). This is a key stage in creating datasets for training machine learning algorithms.
16. Dataset
Dataset — is a structured set of data used for training, validation and testing machine learning models. The quality and diversity of the dataset significantly affect the accuracy of the model.
17. Supervised Learning
Supervised Learning — is a machine learning method where the model is trained on labeled data (input + desired output). Examples of tasks: classification, regression.
18. Unsupervised Learning
Unsupervised Learning — is a method where the model is trained on unlabeled data and tries to identify structures, clusters or patterns without prior known labels. For example, customer clustering.
19. Reinforcement Learning
RL (Reinforcement Learning) — is a method where an agent learns to act in an environment, receiving rewards or penalties for its actions. It is used in robotics, games and the management of complex systems.
20. Classification
Classification — is a task of assigning one object from a set of categories. For example, «spam / not spam» in an email client or «sick / healthy» in medical diagnostics.
21. Regression
Regression — is a task of predicting a continuous variable (e.g., predict the price of a house, demand for goods, stock prices). The model outputs a numerical value.
22. Clustering
Clustering — is an unsupervised learning method aimed at grouping objects in such a way that they are as similar as possible within a cluster, and differ between clusters. Example: market segmentation by consumer groups.
23. Metric
Metric — is an indicator that allows you to assess the quality of the model's work. Examples: Accuracy (accuracy), Precision (precision in determining the positive class), Recall (completeness), F1-score.
24. Overfitting
Overfitting — is a situation where the model memorizes the training data too well and loses the ability to generalize to new data. It manifests itself in high quality on the training dataset and low — on the test set.
25. Underfitting
Underfitting — is the reverse problem: the model does not learn enough and does not capture even simple patterns. It gives low accuracy on both the training and test sets.
26. Batch
Batch or «packet» — is a part of the training sample that is processed by the model in one pass (iteration). The use of mini-batches helps to speed up training and stabilize the optimization process.
27. Epoch
Epoch — is a complete pass (iteration) over the entire training dataset. Usually, several (sometimes dozens or hundreds) epochs of training are required to achieve good accuracy.
28. Loss Function
Loss function — a criterion showing how much the model's predictions differ from the actual values. The training algorithm tries to minimize this function by adjusting weights.
29. Gradient Descent
Gradient descent — an optimization method used to tune the weights of a neural network. In each iteration, the weights are adjusted in the direction opposite to the gradient of the loss function.
30. Local Minimum
Local minimum — a point where the loss function has a smaller value than in the nearby points, but is not necessarily a global minimum. Neural network models can «get stuck» in local minima.
31. Regularization
Regularization — a set of techniques (L1, L2 regularization, Dropout) to combat overfitting. Adding a penalty to the loss function helps the model generalize better without memorizing «noise» from the training dataset.
32. Dropout
Dropout — a regularization method where part of the neurons are randomly «turned off» during training. This allows the network not to rely on the operation of individual neurons, which reduces the risk of overfitting.
33. Batch Normalization
Batch Normalization — a normalization method for the inputs of each layer of a neural network within a mini-batch. It speeds up training and helps stabilize gradients.
34. Computer Vision
Computer vision — an area of AI dealing with the analysis of images and videos. Tasks: object recognition, face detection, scene segmentation and much more.
35. Convolutional Neural Network
CNN — a neural network architecture particularly effective in computer vision tasks. It uses convolutional operations to extract features from images.
36. Object Detection
Object Detection — is a task where the algorithm not only indicates the object class (e.g., «cat» or «car»), but also determines the coordinates of the boundaries (bounding box) in an image or video.
37. Transfer Learning
Transfer Learning — is an approach where a model already trained on a large dataset is used in a new task, usually with additional fine-tuning. It helps save resources and accelerate development.
38. Fine-tuning
Fine-tuning — is the process of adapting a pre-trained model to a specific task or dataset by continuing training on new data.
39. Edge AI
Edge AI — is the execution of AI computations on local devices (smartphones, IoT devices, cameras), rather than in the cloud. It reduces latency, reduces network load, and improves data security.
40. Federated Learning
Federated Learning — is a method where a model is trained on multiple distributed nodes (devices) without transferring the data itself to a central server. Only the model weights are updated. It is used for privacy purposes.
41. Explainable AI (XAI)
Explainable AI — is a direction that focuses on creating models whose decisions can be interpreted and explained to users. This is important for regulatory requirements and customer trust.
42. Robotic Process Automation (RPA)
RPA — is a technology for automating business processes using software «robots» that mimic human actions in the user interface (filling forms, processing applications, etc.). ML is sometimes additionally used for more «smart» scenarios.
43. Sentiment Analysis
Sentiment Analysis — is an NLP task where the algorithm determines the emotional coloring of text (positive, negative, neutral). It is important in marketing research, comment moderation, and social networks.
44. Tokenization
Tokenization — splitting text into minimal semantic units (tokens), which can be words, parts of words or even individual characters. This is the first step in most NLP pipelines.
45. Word2Vec
Word2Vec — a popular method for training word vector representations (embeddings) proposed by the Google team. It allows you to «know» that vector (king) − (man) + (woman) ≈ (queen).
46. One-Hot Encoding
One-Hot Encoding — a way to encode categorical features where each category is represented by a vector in which all elements are 0, except for one, equal to 1. It is used for features that have no numerical meaning.
47. Hyperparameters
Hyperparameters — settings that affect the model training process (learning rate, number of layers, mini-batch size, etc.). You cannot learn them directly — they are chosen using optimization methods or manually.
48. Ensemble Methods
Ensemble Methods — a technique where multiple models (decision trees, neural networks, etc.) are combined to improve the final result. Examples: Random Forest, Gradient Boosting.
49. Data Augmentation
Data Augmentation — artificial increase of the training dataset by transforming original samples (rotation, reflection, color change, etc. for images). It helps to improve the generalization ability of the model.
50. Pipeline
Pipeline — an end-to-end process of working with data and a model, including collection, preprocessing, training, evaluation and deployment. Often automated using specialized frameworks (Airflow, Kubeflow, MLflow, etc.).
How EasyByte uses these technologies in projects
At EasyByte we use the most modern approaches and tools in the field of artificial intelligence to solve customer problems quickly and efficiently. Our strengths:
- Comprehensive approach. We assist at all stages: from data collection and annotation to implementation and subsequent support of ready-made solutions.
- Expertise in various fields. Among our projects are generative models for texts and images, computer vision systems, NLP-based chatbots, and much more.
If you want to find out, approximately how much it will cost to develop your neural network, use the convenient cost calculator on our website. The tool will calculate approximate costs based on the type of project, data volume and required functionality.
Conclusion
A glossary of terms on artificial intelligence will be useful to anyone who wants to delve deeper into the topic and understand the latest trends. Although we have described only 50 basic concepts, there are actually many more, and the AI field continues to develop rapidly.
If you are looking for a team of professionals ready to take on the development, training and implementation of artificial intelligence turnkey, contact us at EasyByte. We will help you create a reliable and scalable solution that will bring real benefits to your business.
Contact EasyByte today to stay ahead of the competition and take advantage of all the benefits of modern AI technologies!