Time to read: 15 minutes | Published: Feb, 2025
Artificial intelligence What is artificial intelligence (AI)?
Artificial intelligence (AI) broadly refers to any human-like behavior displayed by a machine or system. In AI’s most basic form, computers are programmed to “mimic” human behavior using extensive data from past examples of similar behavior. This can range from recognizing differences between a cat and a bird to performing complex activities in a manufacturing facility. This also extends to other areas like Machine learning, large language models (LLMs) and generative AI as these all fall under the umbrella of AI.
- History of AI
- Types of artificial intelligence
- Artificial intelligence training models
- Artificial neural networks
- Benefits of AI in business and lifestyle
- Partner with HPE
A brief history of artificial intelligence
Before 1949, computers could execute commands, but they could not remember what they did as they were not able to store these commands. In 1950, Alan Turing discussed how to build intelligent machines and test this intelligence in his paper “Computing Machinery and Intelligence.” Five years later, the first AI program was presented at the Dartmouth Summer Research Project on artificial intelligence (DSPRAI). This event catalyzed AI research for the next few decades.
Computers became faster, cheaper, and more accessible between 1957 and 1974. Machine learning algorithms improved and, in 1970, one of the hosts of DSPRAI told Life Magazine that there would be a machine with the general intelligence of an average human being in three to eight years. Despite their success, computers’ inability to efficiently store or quickly process information created obstacles in the pursuit of artificial intelligence for the next ten years.
AI was revived in the 1980’s with the expansion of the algorithmic toolkit and more dedicated funds. John Hopefield and David Rumelhart introduced “deep learning” techniques that allowed computers to learn through experience. Edward Feigenbaum introduced “expert systems” that mimicked human decision-making. Despite a lack of government funding and public hype, AI thrived and many landmark goals were achieved in the next two decades. In 1997, reigning chess World Champion and Grandmaster Gary Kasparov was defeated by IBM’s Deep Blue, a chess-playing computer program. The same year, speech recognition software developed by Dragon Systems was implemented on Windows. Cynthia Breazeal also developed Kismet, a robot who could recognize and display emotions.
In 2016, Google’s AlphaGo program beat Go master Lee Se-dol and in 2017, Libratus, a poker-playing supercomputer beat the best human players.
Types of artificial intelligence
Artificial intelligence is classified into two main categories: AI that’s based on functionality and AI that’s based on capabilities.
Based on Functionality
Reactive Machine–This AI has no memory power and does not have the ability to learn from past actions. IBM’s Deep Blue is in this category.
Limited Theory–With the addition of memory, this AI uses past information to make better decisions. Common applications like GPS location apps fall into this category.
Theory of Mind–This AI is still being developed, with the goal of its having a very deep understanding of human minds.
Self-Aware AI–This AI, which could understand and evoke human emotions as well as have its own, is still only hypothetical.
Based on Capabilities
Artificial Narrow Intelligence (ANI)–A system that performs narrowly defined programmed tasks. This AI has a combination of reactive and limited memory. Most of today’s AI applications are in this category.
Artificial General Intelligence (AGI)–This AI is capable of training, learning, understanding, and performing like a human.
Artificial Super Intelligence (ASI)–This AI performs tasks better than humans due to its superior data processing, memory, and decision-making abilities. No real-world examples exist today.
The relationship between artificial intelligence, machine learning, and deep learning
Artificial intelligence is a branch of computer science that seeks to simulate human intelligence in a machine. AI systems are powered by algorithms, using techniques such as machine learning and deep learning to demonstrate “intelligent” behavior.
Machine Learning
A computer “learns” when its software is able to successfully predict and react to unfolding scenarios based on previous outcomes. Machine learning refers to the process by which computers develop pattern recognition, or the ability to continuously learn from and make predictions based on data, and can make adjustments without being specifically programmed to do so. A form of artificial intelligence, machine learning effectively automates the process of analytical model-building and allows machines to adapt to new scenarios independently.
The four steps for building a machine learning model are:
1. Select and prepare a training data set necessary to solving the problem. This data can be labeled or unlabeled.
2. Choose an algorithm to run on the training data.
If the data is labeled, the algorithm could be regression, decision trees, or instance-based.
If the data is unlabeled, the algorithm could be a clustering algorithm, an association algorithm, or a neural network.
3. Train the algorithm to create the model.
4. Use and improve the model.
There are three methods of machine learning: “Supervised” learning works with labeled data and requires less training. “Unsupervised” learning is used to classify unlabeled data by identifying patterns and relationships. “Semi-supervised” learning uses a small labeled data set to guide classification of a larger unlabeled data set.
Deep Learning
Deep learning is a subset of machine learning that has demonstrated significantly superior performance to some traditional machine learning approaches. Deep learning utilizes a combination of multi-layer artificial neural networks and data- and compute-intensive training, inspired by our latest understanding of human brain behavior. This approach has become so effective it’s even begun to surpass human abilities in many areas, such as image and speech recognition and natural language processing.
Deep learning models process large amounts of data and are typically unsupervised or semi-supervised.
Generative AI
Generative AI, often known as Gen AI, is a specialized field within artificial intelligence that focuses on producing a wide range of material in many areas, such as text, photographs, music, audio, movies, and 3D representations. These models acquire complex patterns and structures from their training data, allowing them to generate new material with comparable properties independently. DALL-E is a generative model that can produce visuals by utilizing textual descriptions. Gen AI can perform the opposite action by generating textual descriptions for provided photos. Generative AI uses advanced algorithms and neural networks to enhance creativity, expanding the capabilities of robots in content production. AI applications encompass several fields, such as creative arts, design, entertainment, and others.
Artificial intelligence training models
- Supervised Learning: AI models learn from labeled data in supervised learning. The algorithm adjusts predictions based on failures by pairing inputs with accurate outputs. Support vector machines, neural networks, and decision trees are common algorithms. It is commonly used in spam detection, image recognition, and language translation. Iterative learning enhances model correctness. Obtaining vast volumes of labeled data for supervised learning is expensive and time-consuming. Despite this drawback, it is one of the best predicting approaches.
- Unsupervised Learning: AI models trained on unlabeled data identify patterns, connections, and structures without specified classifications. Clustering and dimensionality reduction are common uses. Algorithms like k -means, hierarchical, and PCA algorithms provide insights from raw datasets. Unsupervised learning helps with consumer segmentation, anomaly detection, and recommendations. However, without specific instruction, evaluating model findings is difficult. When labeled data is limited or big datasets contain hidden patterns, it is helpful.
- Reinforcement Learning: An AI agent learns by interacting with an environment and obtaining incentives or penalties based on its behaviors in reinforcement learning (RL). Maximizing cumulative rewards requires optimum judgments over time. Robotics, gaming, and autonomous cars employ RL. Key techniques include Q-learning, policy gradient methods, and deep reinforcement learning. RL uses trial-and-error rather than labeled data. It performs well in dynamic contexts but is computationally costly and requires careful tweaking to balance exploration and exploitation.
What are Artificial neural networks?
Artificial Neural Networks (ANNs): ANNs are computational models based on brain anatomy and function. Artificial neurons process and transfer information in layers. Adjusting weights through training data helps ANNs discover patterns and forecast. Image recognition, audio processing, and decision-making systems employ them extensively. Different architectures of ANNs excel in sequential data analysis, image classification, and generative modeling. Here are some common neural networks and their uses:
- Feedforward Neural Networks (FF): In feedforward neural networks (FFNNs), the simplest form of ANN, data travels in one direction from the input layer through hidden layers to the output layer. No loops or cycles make them excellent for classification and regression. Training techniques include backpropagation and gradient descent. FFNNs are used for handwritten digit recognition, stock price prediction, and medical diagnosis. Although simple, they suffer with sequential data or memory-dependent tasks, which need RNNs or LSTMs.
- Recurrent Neural Networks (RNN): RNNs handle sequential data by remembering prior inputs. RNNs are good for speech recognition, machine translation, and time-series forecasting because they include loops that enable previous information to impact present predictions. The vanishing gradient problem reduces standard RNNs' long-term dependency memory. Advanced designs like LSTMs increase memory retention and decrease gradient difficulties due to this constraint.
- Long Short-Term Memory (LSTM): Special RNNs called Long Short-Term Memory (LSTM) networks manage long-range dependencies. Memory cells with gates control information flow, determining what to keep, update, or delete. Language modeling, speech synthesis, and predictive text input benefit from LSTMs' context preservation over extended sequences. They are better for real-time translation, chatbot discussions, and music production than regular RNNs since they avoid the vanishing gradient issue.
- Convolutional Neural Networks (CNN): CNNs analyze grid-like data like photos and movies. Edges, textures, and patterns are detected via convolutional layers with filters. CNNs' hierarchical visual data representations have revolutionized image classification, facial recognition, and medical imaging. Popular designs like LeNet, AlexNet, and ResNet have enhanced computer vision accuracy. CNNs use shared weights and pooling layers for large-scale image processing to simplify computation.
- Generative Adversarial Networks (GAN): Two competing neural networks—a generator and a discriminator—make up Generative Adversarial Networks (GANs). The generator generates synthetic data, and the discriminator verifies it, improving continuously. GANs are used for image synthesis, deepfake production, and data augmentation. They create lifelike faces, artwork, and super-resolution photos. GAN training is complicated and requires careful calibration to avoid mode collapse, when the generator creates limited variants. Despite these obstacles, GANs are one of the most inventive AI advances, driving generative modeling and creative applications.
Benefits of AI in business and lifestyle
- AI business and lifestyle benefits
AI is improving productivity, automating activities, and delivering intelligent insights in businesses and daily lives. Businesses use AI for better decision-making, cost savings, and customer experience, while people gain from ease, customization, and smarter technology. Below are major AI contributions to both fields.
- Business AI benefits
- Better decision-making: AI-driven data analysis improves business strategy, keeping organizations competitive.
- Automation & productivity: Repetitive processes are automated using AI, decreasing burden and operating expenses and enhancing efficiency.
- Enhanced customer experience: Customer happiness is improved with AI chatbots, tailored suggestions, and virtual assistants.
- Fraud detection and cybersecurity: AI detects suspicious activity to safeguard enterprises from cyberattacks and financial fraud.
- Optimized supply chain management: AI optimises inventory, logistics, and demand forecasts, eliminating delays and waste.
- Cost reduction & resource optimization: AI reduces human mistakes, optimizes resource allocation, and saves operational costs.
- AI benefits in daily life
- Smart assistants & automation: Siri and Alexa handle schedules, reminders, and smart home gadgets.
- Healthcare and wellness: AI helps detect early diseases, provide virtual consultations, and propose workout programs.
- Customized content & shopping: AI recommends movies, music, and items.
- Navigation & travel assistance: AI improves real-time navigation, route planning, and ride-hailing.
- AI-powered smart home devices: These regulate lighting, heating, and security for ease and energy savings.
- Fraud prevention & online security: AI identifies questionable transactions and improves internet security to prevent fraud.
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