Artificial intelligence (AI) represents one of the most transformational technological advances of the digital age. But for the average person, AI concepts remain complex and their inner workings opaque. This article aims to change that by clearly defining AI and offering an accessible overview of how artificial intelligence systems operate.  

Defining Artificial Intelligence (AI)

Artificial intelligence refers to computational systems that exhibit characteristics and behaviors commonly associated with human intelligence such as reasoning, learning, problem-solving, and planning. These systems take data as input, process it, and respond via outputs like recommendations or predictions.

The key differentiator from traditional computing software is the ability of AI systems to adaptively improve their functioning over time without being explicitly programmed to do so. By detecting patterns in data and experience, they streamline their operation, reduce errors, and expand their capabilities relative to initial algorithmic design.

AI has attained its most advanced current manifestations by focusing architectural components on specific use cases like playing chess or categorizing images. This contrasts with ambitions of developing artificial general intelligence where singular systems possess mental acuity to fluidly conduct all human cognitive functions.

How AI Systems Work

At an architectural level, AI systems comprise of databases supplying key information, algorithms enabling operational logic and capacities, and computing power to efficiently run the algorithms against available data.

Several core disciplines like machine learning, computer vision, natural language processing, and robotics provide frameworks to construct algorithms and data architectures tailored to classes of tasks ranging from object recognition to conversant voice assistants.

More specifically, important inner workings powering AI systems include:

1. Data Ingestion

• Structured and unstructured data gets captured via sensors, API feeds, and external sources. 
• Incoming data gets cleaned, tagged, and normalized into formats digestible by downstream processes.

2. Algorithm Training

• If leveraging machine learning, selected algorithms get trained on prepared data to create models that generate findings, classifications, predictions, or recommendations.
• Models iteratively calibrate their internal logic based on feedback signals that score effectiveness relative to desired objectives. 

3. Inference Execution 

• Trained models apply their learned logic and pattern recognition to interpret new real-world data previously unseen during training.
• For example, a model identifying credit risks assesses application data to infer a new applicant’s repayment probabilities.

4. Knowledge Retention

• Interactions generate additional data like new images or conversational histories.
• Accumulated data, contextual understandings, and any model enhancements flow back into the databases for further model retraining and perpetual knowledge expansion.

Architectural manifestations vary widely based on use cases – from robots physicalizing movements to software APIs classifying loan risk. But combinations of the above core functions enable diverse AI applications to intake stimuli, evolve understanding over time, and then respond intelligently relative to embedded objectives.

AI Classifications and Methods

Many specific approaches enable the achievement of intelligent functionalities:

Machine Learning 

The most pervasive current technique – systems self-calibrate logic per data patterns without explicit programming. Classification types include supervised, unsupervised, semi-supervised, and reinforcement learning.

Computer Vision

Processing and analyzing visual inputs like images or video for pattern recognition and contextual understanding tasks.

Natural Language Processing 

Systems enabling comprehension and analysis of spoken languages and texts for applications like translations or sentiment analyses. 

Robotics

Development of kinetic/mechanical synthetic systems capable of movements, object manipulations, semiautonomous functioning, and humanlike mobility.

Expert Systems

Software encoding domain-specific decision rules that emulate judgments of human specialists for domains like medical diagnostics.

While the above represent traditionally delineated categories, leading systems increasingly blend multifaceted approaches to attain more human-emulative general capabilities – touching into the concept of artificial general intelligence discussed earlier.

Business leaders mainly focus today on narrow AI use cases with clear financial incentives or operational efficiencies. But academics continue pushing boundaries toward more expansive realms like autonomous systems, creative output generation, and emotional intelligence which could transform society in previously unimaginable ways over coming decades.

Key Takeaways

• Artificial intelligence refers broadly to machines exhibiting human-associated mental capabilities like learning and problem-solving by processing data and experiences.
• Myriad computational methods power AI systems – most popularly machine learning, computer vision, and natural language processing. 
• Core functions enabling artificial intelligence include data ingestion and preparation, model training, inference execution based on new inputs, and knowledge retention for continual learning.
• While general AI replicating full human cognitive faculties remains aspirational, innovations across many AI disciplines will fundamentally reshape society and business in the coming years.

So while artificial intelligence retains an aura of mystery for many, its inner workings adhere to comprehensible, if sometimes complex, computational processes. And continuing demystification of these fascinating and hugely consequential technologies will ensure democratic understanding accompanies their societal proliferation.