AIP Institute definitions
Skills – general cognitive and non-cognitive abilities or specific knowledge and skills necessary to perform a specific task.
Cognitive skills are conscious intellectual efforts, such as logical thinking, reasoning, imagination or memorization, used, for example, when reading, writing texts and solving mathematical problems.
Non-cognitive skills – the ability of a person to recognize emotions and use them to stimulate thinking, as well as to understand the intentions, motivation and desires of both other people and their own, in order to solve practical problems. An example of the use of cognitive abilities is the ability to work in a team, establish social contacts and work under stress, controlling your emotions.
AIP Institute definitions
Adapting Information Potential (AIP – /ˈeɪ ˈaɪ pɪː/ n [C]) is a piece of information containing a semantic core that is presented in several contexts and scenarios. It is evaluated using a binary assessment of the feasibility of its application (“carried out” or “not carried out”, “can do” or “cannot do”).Here, “semantic core” can be understood to mean the designation of a retained piece of information for a specific use.
Cognigraph – is an individual’s personal dynamic potential map, created using binary scores for theoretical and practical knowledge and psychomotor, cognitive, and other physiological capabilities, as well as other human activities. Each user’s cognigraph is populated with cognigraphic data compiled by experts in various fields of general ontology.
The AIP Qualification Framework – is a tool for building a binary assessment system for an employee’s work activities (a personal potential assessment) and organizing isolated personal training tailored to business goals and objectives, including training for specific objectives to optimize execution timeframes.
Cognigraphic data (cognitive + graphics) consist of user information on cognitive and learning abilities, preferences, intelligence, style and actual knowledge, comprising a personalized dynamic map in the form of a graph.
Cognigraphic data may include:
- Categorical indications (users’ psychological types, comprehension profile, etc.) determined through testing or through other methods;
- A data set indicating the user’s experience, development history, previously acquired knowledge and skills, and other parameters that are useful for the profiling methods and algorithms that adapt content to the learner’s characteristics.
The cognigraphic data ontology – is a dynamic ontological system that evolves to suit users’ needs and goals. It organizes information multi-dimensionally and conceptually. The ontology comprises minimal elements, or cognigraphic units (CU), identified by their names and parameters that indicate the levels of and relationships between units representing a person’s activity.
AIP Human-Centered Automation – is an approach to the development of balanced automation systems between a person and the software and technical environment, based on the methods and tools of the AIP Institute to maintain the skills of users in any conditions and extreme situations, and the development of a human cognitive functions through continuous training.
Hardware Automation Scenarios Framework – is a tool for creating a software solution to be integrated into robotic technologies for monitoring and control through new generation of multi-modal interfaces.
Modern trends and technologies
Autopilot – a system of devices, a software and hardware complex for automatic control of a vehicle along a certain trajectory given to it.
Hyperautomation -it is a combination of machine learning, batch software and automation tools for solving automation tasks. This applies not only to the variety of tools, but also to all stages of automation. This trend started with Robotic Process Automation (RPA). But RPA alone is not hyperautomatics. Hyperautomatization requires a combination of tools that help to reproduce fragments of where a person participates in a task.
Autonomous things – this is about how we look at autonomous things, including robots, drones and vehicles, and how they become more autonomous and how they share many basic underlying technologies. As technological capabilities improve, regulation is allowed and public acceptance grows, more autonomous things will unfold in uncontrolled public places.
Human augmentation – this is how we use technology to improve people both physically and cognitively. When physically enlarged, it changes their inherent physical capabilities by implanting or placing a technological element on their bodies, for example, a wearable device. Cognitive expansion can mean access to information and the use of applications in traditional computer systems and the emerging multi-experimental interface in intelligent spaces.
Расширенные возможности (The empowered edge) – In the field of smart spaces, we have advanced capabilities. Advanced capabilities are actually the transfer of computing power to peripherals for greater centralization, and this allows us to do several things. This reduces latency and provides some level of autonomy on these peripherals.
Multiexperience deals with how we move from this kind of two-dimensional screen and keyboard interface to a much more dynamic, multi-modal interface world in which we are immersed in technology and it surrounds us. By 2028, the user experience will undergo significant changes in how users perceive the digital world and how they interact with it. Communication platforms are changing the way people interact with the digital world, and virtual reality( VR), augmented reality (AR) and mixed reality (MR) are changing the way people perceive the digital world. This combined shift in perception and interaction will lead to future multisensory and multimodal experiences.
Artificial intelligence (AI) – is an extensive domain of computer science related to the creation of intelligent machines capable of performing tasks that usually require human intelligence. AI is an interdisciplinary science with many approaches, but advances in machine learning and deep learning are changing the paradigm in almost all sectors of the technology industry.