What Is Artificial Intelligence?
Artificial Intelligence (AI) is a field of computer science focused on creating machines capable of performing tasks that usually require human intelligence. This is done through learning, reasoning, understanding, and adapting. AI has been around for many years and has been used for specific, often narrow applications such as recommendation engines in online searches.
Narrow AI and General AI
With the launch of large language models (LLMs) and generative AI (GenAI) tools such as ChatGPT, AI has become more prevalent and useful in daily life. This has also given rise to a new categorization of AI that anticipates future use cases:
- Narrow AI or weak AI: This type of AI is designed to perform specific tasks, such as facial recognition or driving a car. Most current AI applications fall into this category.
- General AI or strong AI: This type of AI is designed to use a broader range of cognitive abilities to perform any reasoning task that a human being can. General AI does not exist yet but is considered a long-term goal for AI research.
Benefits of Artificial Intelligence
AI offers numerous advantages for end users and businesses across every industry. Depending on the use cases, AI is capable of identifying patterns and forecasting events, automating complex processes, and tailoring workflows to meet the individual needs of a project or person. AI can also play a critical role in monitoring and optimizing resource management. With advances such as natural language processing (NLP), AI can also power chatbots and interfaces to provide personalized interactions to users, helping make information more accessible. AI can have a transformational impact on the way people and organizations work, make decisions, and express creativity.
Challenges of Artificial Intelligence
There are some barriers to entry to deploying AI, including a high initial cost associated with setting up AI infrastructure and hiring skilled professionals to develop and maintain these systems. Business leaders may also find that integrating AI technologies into their existing workflows can be time-consuming and disruptive, as well as requiring continuous monitoring, analysis, and refinement of AI models for best results. Furthermore, organizations must be proactive about counteracting the potential bias in AI models when using AI to inform their decision-making.
Fortunately, there may be ways for organizations to leverage existing IT infrastructure for their AI processes, in addition to using hybrid cloud resources, to help offset initial costs. Gradual implementation and pilot programs can help ease the transition to AI prior to full-scale rollout, and continuous refinement of AI models can result in more-efficient AI over time. To help mitigate bias in AI models, organizations can regularly audit data and inference results for greater transparency, use diverse sets of training data, and facilitate diversity and equity within teams that design and develop AI systems.
How Does Artificial Intelligence Work?
At the core of AI technology lies the ability of AI models to analyze data, recognize patterns, and make decisions with minimal human intervention. On a technical level, AI models operate through sophisticated algorithms that enable machines to process data, learn from it, and make informed decisions. AI models are essentially software that data scientists and AI developers code and train using vast quantities of data.
The AI Development Workflow
The AI development workflow involves three main stages. The first stage is data preprocessing in which the data that will be analyzed is cleaned and formatted. Next is AI modeling, where algorithms and frameworks are selected to build the model, and the model learns from the preprocessed data. Finally, the trained model is deployed and used for inferencing, making predictions or decisions based on new data to demonstrate its real-world applicability. From end to end, this entire process is often referred to as an AI pipeline.
Why Is Artificial Intelligence Important?
AI is already making a profound impact on society, from aiding clinicians in medical diagnoses to helping businesses design improved, more sophisticated products. Wherever knowledge and data are present, AI offers new ways to understand and interact with that data to produce new outcomes.
How Is Artificial Intelligence Used?
The application of AI can vary greatly in terms of its complexity and capabilities. Here are four common types of AI deployment:
Machine Learning
Machine learning uses multiple algorithms—sets of logical instructions—to recognize and learn from patterns in data. The more data machine learning acts on, the more accurate it gets.
Deep Learning
Deep learning is a multilayered version of machine learning built to act on vast amounts of data. Unlike machine learning, deep learning is designed to work on raw data and requires less or no human intervention to improve accuracy.
Neural Networks
Neural networks are the building blocks of machine and deep learning systems, consisting of interconnected nodes that emulate the structure of the human brain. Each node performs a computation and passes its result onto subsequent nodes.
Computer Vision
Computer vision is a type of AI that allows computers to understand and act on visual inputs. Generally, computer vision helps machines recognize specific objects in the physical world.
Artificial Intelligence Industry Applications
Owing to its adaptability and potential for yet-to-be-envisioned applications, AI is maturing as a fundamental component of digital transformation across several industries. Here are a few highlighted examples:
- AI in Automotive: AI is helping driverless vehicles become a reality, using computer vision to enable driver and passenger monitoring and bringing Gen AI assistants and AI-enabled gaming to vehicles.
- AI in Banking, AI in Financial Services: AI chatbots are personalizing customer interactions, while on the back end, AI is helping to detect and prevent fraud, automate risk assessment, and facilitate algorithmic stock trading.
- AI in Cybersecurity: AI supports defense-in-depth strategies by automating threat detection and response. As the digital footprint of businesses expands, SecOps and IT teams are increasingly relying on AI to scale operations beyond human limitations.
- AI in Education: AI tools are helping teachers and students personalize lessons as well as drive administrative efficiency in assignment scoring or attendance taking.
- AI in Healthcare: AI is being leveraged by healthcare practitioners to improve diagnostic speed and accuracy. In medical research, AI’s pattern recognition abilities are helping to accelerate drug discovery.
- AI in Manufacturing: AI is driving robotics on the factory and warehouse floor, automating situational awareness with digital twins, helping reduce downtime with predictive maintenance, and helping improve output with automated defect detection.
- AI in Sustainability, AI in Energy: AI enhances smart grids that efficiently integrate renewable energy, enables predictive maintenance for energy infrastructure like power lines, helps optimize energy use in buildings, and analyzes environmental and emissions data to help combat climate change.
History of Artificial Intelligence
AI has a rich, complex history full of many key figures, innovations, and institutions. Here are a few milestones to help illustrate how far AI has come to shape the present moment.
1945 |
John von Neumann proposes a computer architecture scheme that will become foundational to modern digital computers.1 |
1950 |
Alan Turing proposes the Turing Test to determine if a computer can successfully imitate human responses.2 |
1956 |
Researchers create the first AI computer program, Logic Theorist, which proves theorems using symbolic logic.3 |
1956 |
The Dartmouth Summer Research Project on Artificial Intelligence workshop establishes AI as a formal field of study.4 |
1956–1974 |
Leaps in AI progress spark interest and funding from government agencies such as the Defense Advanced Research Projects Agency (DARPA).5 |
1959 |
Arthur Samuel coins the term “machine learning” to describe self-teaching computers.6 |
1966 |
The Stanford Research Institute creates Shakey, the first mobile robot with computer vision-based navigation and the ability to process complex commands.7 |
1973 |
In the UK, the Lighthill report criticizes AI’s failure to produce major impacts, resulting in a period of governmental funding cuts—dubbed “AI winter”—that were subsequently mirrored in the US.8 |
1980 |
Backpropagation—a more efficient way to calculate how changes in variables impact machine learning’s accuracy—becomes foundational to training neural networks.9 |
1981 |
The first IBM PC launches, leading to a shift away from AI-based expert systems to a client-server model in business settings. This culminates in another AI winter through the 1990s.10 |
1997 |
The IBM supercomputer Deep Blue wins a chess rematch against world champion Garry Kasparov.11 |
2004 |
The DARPA Grand Challenge begins awarding cash prizes for groundbreaking developments in autonomous driving, with successive challenges in the following years.12 |
2014 |
Google subsidiary DeepMind begins developing AlphaGo, an AI that plays the game Go, considered to be more complex than chess. The effort culminates in AlphaGo defeating legendary player Lee Sedol in 2016.13 |
2018 |
Stanford’s Artificial Intelligence Index reports a surge of new AI research efforts worldwide, suggesting a new AI boom.14 |
2021 |
UNESCO publishes the first global standard on AI ethics to address concerns about AI’s impact on human rights and climate change.15 |
2023 |
OpenAI’s ChatGPT, an AI adept at simulating human conversation, reaches 100 million users.16 |
The Four Types of Artificial Intelligence
Researchers have identified four types of AI. These types reflect the current state of AI and what it might look like when fully realized.
Reactive Machines
AI that is task-specific and retains no memory of past events is known as a reactive machine. This type of AI works on repeatable data inputs and provides predictable outputs. An example of a reactive machine is a visual inspection appliance on an assembly line.
Limited Memory
Limited memory refers to AI processes that learn from additional data inputs. They apply deep learning to continually adjust and improve their accuracy. Examples of limited memory AI include self-driving cars and LLMs.
Theory of Mind
Theory of mind describes a type of AI that can understand and interpret the emotions, beliefs, and intentions of other beings. This type of AI does not currently exist.
Self-Awareness
AI with self-awareness can comprehend its own existence and possess a sense of self. For now, this type of AI remains in the purview of theory and science fiction.
Artificial Intelligence Solutions
Most AI deployments consist of AI software running on AI hardware—which may include devices and/or servers—and always some type of AI processor.
AI Hardware
AI hardware encompasses the general purpose and specialized computer parts and components used to support AI workloads across devices, servers, or cloud environments. Generally, AI hardware refers to systems built for postdeployment inference, but it can also refer to systems used in developing and training AI models.
AI Processors
AI processors typically refer to central processing units (CPUs) designed for AI workloads in addition to AI accelerators such as graphics processing units (GPUs), neural processing units (NPUs), or field-programmable gate arrays (FPGAs).
AI Servers
AI servers refer to any server configuration including processors, accelerators, memory, storage, or networking designed specifically to support AI workloads.
AI Software
AI software is a broad topic covering many types of programs. It can refer to AI applications or AI models users interface with directly, as with AI chatbots, or to AI programs that run as background processes without user prompting. AI software can also refer to the programs or tools used by developers to prepare datasets and develop, deploy, and optimize AI models.
Future of Artificial Intelligence
AI is evolving quickly, demonstrating remarkable progress that suggests a future brimming with potential. Advances in the already-established fields of machine learning and deep learning, combined with the ingenuity of LLMs, could potentially reshape industries, enhance efficiency, and unlock new realms of creativity.
Responsible AI
As more businesses and members of the public embrace AI, the use of responsible AI can help curb potentially negative impacts. Responsible AI describes AI processes that are transparent, fair, and accountable. Integrating these practices into the development and implementation of AI can help mitigate the effects of bias and help ensure that AI works to uplift communities.