Artificial consciousness (AC), also known as machine consciousness (MC), synthetic consciousness or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience. The same terminology can be used with the term "sentience" instead of "consciousness" when specifically designating phenomenal consciousness.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.
Anticipatory democracy is a theory of civics relying on democratic decision making that takes into account predictions of future events that have some credibility with the electorate. The phrase was coined by Alvin Toffler in his book Future Shock and was expanded on in the 1978 book Anticipatory Democracy, edited by Clement Bezold.
A cognitive model is an approximation of one or more cognitive processes in humans or other animals for the purposes of comprehension and prediction. There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks. In terms of information processing, cognitive modeling is modeling of human perception, reasoning, memory and action.
Andy Clark, is a British philosopher who is Professor of Cognitive Philosophy at the University of Sussex. Prior to this, he was a professor of philosophy and Chair in Logic and Metaphysics at the University of Edinburgh in Scotland, director of the Cognitive Science Program at Indiana University in Bloomington, Indiana and previously taught at Washington University in St. Louis, Missouri. Clark is one of the founding members of the CONTACT collaborative research project whose aim is to investigate the role environment plays in shaping the nature of conscious experience. Clark's papers and books deal with the philosophy of mind and he is considered a leading scholar on the subject of mind extension. He has also written extensively on connectionism, robotics and the role and nature of mental representation.
On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines is a 2004 book by Jeff Hawkins and Sandra Blakeslee. The book explains Hawkins' memory-prediction framework theory of the brain and describes some of its consequences.
The memory-prediction framework is a theory of brain function created by Jeff Hawkins and described in his 2004 book On Intelligence. This theory concerns the role of the mammalian neocortex and its associations with the hippocampi and the thalamus in matching sensory inputs to stored memory patterns and how this process leads to predictions of what will happen in the future.
Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory.
Computational cognition is the study of the computational basis of learning and inference by mathematical modeling, computer simulation, and behavioral experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of processing of information. Early on computational cognitive scientists sought to bring back and create a scientific form of Brentano's psychology.
In intelligence and artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
Cognitive Robotics or Cognitive Technology is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition, consisting of Robotic Process Automation, Artificial Intelligence, Machine Learning, Deep Learning, Optical Character Recognition, Image Processing, Process Mining, Analytics, Software Development and System Integration.
Flood forecasting is the process of predicting the occurrence, magnitude, timing, and duration of floods in a specific area, often by analysing various hydrological, meteorological, and environmental factors. The primary goal of flood forecasting is to deliver timely and accurate information to decision-makers, empowering them to take appropriate actions to mitigate the potential consequences of flooding on human lives, property, and the environment. By accounting for the various dimensions of a flood event, such as occurrence, magnitude, duration, and spatial extent, flood forecasting models can offer a more holistic and detailed representation of the impending risks and facilitate more effective response strategies.
Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.
In psychology, a dual process theory provides an account of how thought can arise in two different ways, or as a result of two different processes. Often, the two processes consist of an implicit (automatic), unconscious process and an explicit (controlled), conscious process. Verbalized explicit processes or attitudes and actions may change with persuasion or education; though implicit process or attitudes usually take a long amount of time to change with the forming of new habits. Dual process theories can be found in social, personality, cognitive, and clinical psychology. It has also been linked with economics via prospect theory and behavioral economics, and increasingly in sociology through cultural analysis.
The LIDA cognitive architecture is an integrated artificial cognitive system that attempts to model a broad spectrum of cognition in biological systems, from low-level perception/action to high-level reasoning. Developed primarily by Stan Franklin and colleagues at the University of Memphis, the LIDA architecture is empirically grounded in cognitive science and cognitive neuroscience. In addition to providing hypotheses to guide further research, the architecture can support control structures for software agents and robots. Providing plausible explanations for many cognitive processes, the LIDA conceptual model is also intended as a tool with which to think about how minds work.
In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills.
MANIC, formerly known as PMML.1, is a cognitive architecture developed by the predictive modeling and machine learning laboratory at University of Arkansas. It differs from other cognitive architectures in that it tries to "minimize novelty". That is, it attempts to organize well-established techniques in computer science, rather than propose any new methods for achieving cognition. While most other cognitive architectures are inspired by some neurological observation, and are subsequently developed in a top-down manner to behave in some manner like a brain, MANIC is inspired only by common practices in computer science, and was developed in a bottom-up manner for the purpose of unifying various methods in machine learning and artificial intelligence.
Cognitive assets are tangible and intangible organizational assets that constitute sources of the cognition that is necessary for action coordination. These assets allow for the integrity and efficiency of the multiple conversions of individual knowledge into organizational knowledge.
Anticipatory profiling, is the practice used by the state to collect information and data about its citizens in order to assess events or behaviours in a predictable manner based on the knowledge gathered. Anticipatory governance is also a method of decision making that uses predictive measures to anticipate possible outcomes to then make decisions based on the data provided. Anticipatory governance is a system of governing that is made up of processes and institutions that rely on foresight and predictions to decrease risk and develop efficient methods to address events in their early conception or prevent them altogether.
In neuroscience, predictive coding is a theory of brain function which postulates that the brain is constantly generating and updating a "mental model" of the environment. According to the theory, such a mental model is used to predict input signals from the senses that are then compared with the actual input signals from those senses. With the rising popularity of representation learning, the theory is being actively pursued and applied in machine learning and related fields.
