A set of instructions or sequence of steps that tells a computer how to perform a task or calculation. In some AI applications, algorithms tell computers how to adapt and refine processes in response to data, without a human supplying new instructions.
Artificial intelligence describes an application or machine that mimics human intelligence.
A system in which machines execute repeated tasks based on a fixed set of human-supplied instructions.
A system in which a machine makes independent, real-time decisions based on human-supplied rules and goals.
The massive amounts of data that are coming in quickly and from a variety of sources, such as internet-connected devices, sensors, and social platforms. In some cases, using or learning from big data requires AI methods. Big data also can enhance the ability to create new AI applications.
An AI system that mimics human conversation. While some simple chatbots rely on pre-programmed text, more sophisticated systems, trained on large data sets, are able to convincingly replicate human interaction.
A subset of machine learning. Deep learning uses machine learning algorithms but structures the algorithms in layers to create “artificial neural networks.” These networks are modeled after the human brain and are most likely to provide the experience of interacting with a real human.
An approach that includes human feedback and oversight in machine learning systems. Including humans in the loop may improve accuracy and guard against bias and unintended outcomes of AI.
A computer-generated simplification of something that exists in the real world, such as climate change, disease spread, or earthquakes. Machine learning systems develop models by analyzing patterns in large data sets. Models can be used to simulate natural processes and make predictions.
Interconnected sets of processing units, or nodes, modeled on the human brain, that are used in deep learning to identify patterns in data and, on the basis of those patterns, make predictions in response to new data. Neural networks are used in facial recognition systems, digital marketing, and other applications.
A hypothetical scenario in which an AI system develops agency and grows beyond human ability to control it.
The data used to “teach” a machine learning system to recognize patterns and features. Typically, continual training results in more accurate machine learning systems. Likewise, biased or incomplete datasets can lead to imprecise or unintended outcomes.
An interview-based method proposed by computer pioneer Alan Turing to assess whether a machine can think.