artificial neural networks
What It Means
Artificial neural networks are computer systems designed to mimic how the human brain processes information, using interconnected nodes that learn patterns from data. Instead of following pre-programmed rules, these systems adjust their internal connections based on examples they're shown, allowing them to recognize patterns, make predictions, or classify information automatically.
Why Chief AI Officers Care
Neural networks are the foundation of most modern AI capabilities your organization likely depends on, from chatbots and recommendation engines to fraud detection and predictive analytics. Understanding their limitations is crucial for managing AI risk - they can be unpredictable, require massive amounts of data to work well, and their decision-making process is often impossible to explain to regulators or customers.
Real-World Example
A bank uses neural networks to detect credit card fraud by training the system on millions of transaction records labeled as fraudulent or legitimate. The network learns to identify suspicious patterns like unusual spending locations or amounts, automatically flagging potentially fraudulent transactions in real-time without human programmers having to write specific rules for every possible fraud scenario.
Common Confusion
People often think neural networks are mysterious black boxes that always work like magic, when in reality they're sophisticated pattern-matching tools that can fail dramatically when encountering data different from what they were trained on. They're also frequently confused with general AI or consciousness, when they're actually narrow mathematical models that excel at specific tasks.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, artificial neural networks power diagnostic imaging systems that can detect cancers, analyze medical scan...
Finance: In finance, artificial neural networks are extensively used for algorithmic trading, credit risk assessment, fraud detec...
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Technical Definitions
NISTNational Institute of Standards and Technology
"A computing system, made up of a number of simple, highly interconnected processing elements, which processes information by its dynamic state response to external inputs."Source: Reznik,_Leon
"Definition 1. A directed graph is called an Artificial Neural Network (ANN) if it has x at least one start node (or Start Element; SE), x at least one end node (or End Element; EE), x at least one Processing Element (PE), x all the nodes used must be Processing Elements (PEs), except start nodes and end nodes, x a state variable ni associated with each node i, x a real valued weight wki associated with each link (ki) from node k to node i, x a real valued bias bi associated with each node i, x at least two of the multiple PEs connected in parallel, x a learning algorithm that helps to model the desired output for given input. x a flow on each link (ki) from node k to node i, that carries exactly the same flow which equals to nk caused by the output of node k , x each start node is connected to at least one end node, and each end node is connected to at least one start node, x no parallel edges (each link (ki) from node k to node i is unique)."Source:
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