Artificial intelligence

What is artificial intelligence (AI)?

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.

•  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.

Explore how HPE can help Accelerate your AI initiatives with our products and services

HPE AI Solutions - AI is everywhere, disrupting every industry and opening unlimited possibilities. AI can turn questions into discovery, insights into action, and imagination into reality.
 
Are you ready to advance and scale AI projects with confidence? Fuel your transformation to an AI-powered business—and be prepared to tackle complex problems and massive data sets with ease—with AI solutions from HPE.

HPE Private Cloud AI - Simplify accelerated infrastructure configuration and provide the speed and scale of public cloud—while keeping your data private and more secure—with an end-to-end lifecycle software platform. With a scalable, pretested, AI-optimized and fully accelerated private cloud, you can give your AI and IT teams the freedom to experiment and scale AI projects with a rich ecosystem of AI models and development tools, while maintaining control over costs and financial risks.

HPE AI Services - The emergence of generative AI and the widespread use of large language models mark major advancements in AI. Leverage the full potential of AI, with the right expertise, data, tools, technology and partnerships. Harness the power of AI to uncover valuable insights, create trusted transactions, and employ intelligence.