Kismet, a robot head which was
made in the 1990s; it is a machine
that can recognize and simulate
emotions.[64]
that runs inputs through biologically inspired artificial neural networks for all of these types of
learning.[48]
Computational learning theory can assess learners by computational complexity, by sample complexity
(how much data is required), or by other notions of optimization.[49]
Natural language processing (NLP)[50] allows programs to read, write and communicate in human
languages such as English. Specific problems include speech recognition, speech synthesis, machine
translation, information extraction, information retrieval and question answering.[51]
Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with
word-sense disambiguation[f] unless restricted to small domains called "micro-worlds" (due to the
common sense knowledge problem[29]). Margaret Masterman believed that it was meaning and not
grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the
basis of computational language structure.
Modern deep learning techniques for NLP include word embedding (representing words, typically as
vectors encoding their meaning),[52] transformers (a deep learning architecture using an attention
mechanism),[53] and others.[54] In 2019, generative pre-trained transformer (or "GPT") language models
began to generate coherent text,[55][56] and by 2023, these models were able to get human-level scores on
the bar exam, SAT test, GRE test, and many other real-world applications.[57]
Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless
signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is
the ability to analyze visual input.[58]
The field includes speech recognition,[59] image classification,[60] facial recognition, object
recognition,[61]object tracking,[62] and robotic perception.[63]
Affective computing is a field that comprises systems that
recognize, interpret, process, or simulate human feeling, emotion,
and mood.[65] For example, some virtual assistants are
programmed to speak conversationally or even to banter
humorously; it makes them appear more sensitive to the emotional
dynamics of human interaction, or to otherwise facilitate human–
computer interaction.
However, this tends to give naïve users an unrealistic conception
of the intelligence of existing computer agents.[66] Moderate
successes related to affective computing include textual sentiment
Natural language processing
Perception
Social intelligence