f. Environments, systems, and concepts for the evaluation of machine intelligence.
g. Signal processing techniques for the decoding of neural signals in real time, specifically those associated with operationally relevant cognitive events.
h. Quantitatively describe and understand complex human neuroscience. [DSO BAA-10-55]
Создание абсолютного аналога мыслящего мозга, а затем по возможности системы абсолютного электронного стратега — мыслящей машины, способной полностью заменить людей в сфере управления войсками в оперативно-тактическом и даже стратегическом масштабах. [254,250]
Машины обладающие хотя бы десятой долей гибкости и многосторонности человека стали бы постине сказочными. [254,250]
Создание субъективной реальности. Конспект Д. И. Дубровского
Несмотря на растущий объём знаний о психике человека — его языке, семиотических возможностях и способности к формированию концептов и на данные о сопоставлении этих функций с высшими проявлениями психических способностей других биологических видов, мы тем не менее очень плохо представляем себе, что такое Сознание — главная наша характеристика как вида (наряду с языком) — и как оно обеспечивается мозговой активностью. В этой связи стоит вспомнить дискуссию «The Self and Its Brain», происходившую почти 30 лет назад между нейрофизиологом Джоном Экклзом и философом науки Карлом Поппером и признать, что всё нарастающая лавина надёжных данных функционального картирования мозга и некоторый прогресс в теоретических знаниях тем не менее не привели за это время к значимому прорыву в осмыслении проблемы. Вероятно, следует возлагать надежды не на ещё большее усложнение разрешающей способности техники, а на методологический и даже философский прорыв.
То, что заключено в механизме, не может выйти из него и спросить, как стало возможным это целое; именно
Так появляется интеллект и автономность, ИИ начинает
Я
Aa interconnected fabric of theories, models, empirical methods and findings, and educational approaches, opening new opportunities to understand complex aspects of neural and cognitive systems through integrative multidisciplinary approaches.
Neuroengineering and Brain-Inspired Concepts and Designs:
Merging insights gained from neuroscience and cognitive science with those from rapidly changing technologies will lead to significant innovations that are inspired by or directed toward the brain. These may include technologies for imaging, sensing, recording, or affecting real-time brain activity and behavior; brain-inspired computing paradigms; brain-computer interfaces; augmented and adaptive systems (e.g., for communication, prosthetics, learning, education, or performance); functional neurotechnologies; and other computational and bioengineered systems. Proposals advancing this theme must show how computational and/or engineering principles are advanced synergistically with neural and cognitive investigations.
Individuality and Variation:
Neural and cognitive processes at all levels, from synapses to societies, display functionally important variability across time, context, individual units of analysis (e.g., neurons, nodes, persons), and populations. Explaining this variation, including the role of noise, in biological and machine systems, signaling and communication at all levels, representations, learning and adaptation, development, resilience, ability, cultural and social processes, and group differences, will have far-reaching consequences in many scientific domains. Proposals advancing this theme must consider these domain-specific issues alongside statistical and modeling challenges to explore, describe, and understand the role of naturally occurring variability.
Cognitive and Neural Processes in Realistic, Complex Environments:
Understanding the brain in action and in context requires moving beyond static, artificial experimental settings that minimize naturally occurring complexity and interactions. This theme includes, but is not limited to: adaptive processes during complex physical, social, and educational interactions; flexibility and contextual aspects of cognitive, biological, and machine learning; experimental paradigms leveraging immersive environments (e.g., virtual reality) or other simulation or synthesis methods; mobile technologies for cognitive and neural processing and data gathering; and cyber-human interactions such as human-robot symbiosis. Proposals advancing this theme must push present boundaries of scientific understanding of cognitive and neural processes beyond standard experimental settings.
Data-Intensive Neuroscience and Cognitive Science:
New methods and technologies for gathering and analyzing vast amounts of data are rapidly changing how neural and cognitive processes can be explored, modeled, and understood. Neural and cognitive data pose specific challenges with respect to complexities of scale, heterogeneity, throughput requirements, experimental limitations, and behavioral, cognitive, and biological richness; and may involve acquisition by multiple instruments, investigators, or communities in a wide variety of contexts. Proposed research and innovation to enable large-scale analysis, modeling, aggregation, sharing, and open science must confront these complexities, while being driven by integrative neural and cognitive discovery goals that require data-intensive approaches to succeed. Proposals advancing this theme must develop innovative approaches to addressing the unique problems associated with data-intensive research. [Integrative Strategies for Understanding Neural and Cognitive Systems (NSF)]
Robots — smart electro-mechanical devices that sense and operate within the environment of their surroundings — have the potential to transform our lives for the better. Specialized collaborative robots (co-robots) will safely assist people in their work and daily activities, while other robots will perform jobs too dangerous for people. We envision a future in which co-robots will no longer be expensive novelties, but rather ubiquitous technologies that significantly enrich the quality of life and quality of work for each of us. [National Robotics Initiative 2.0: Ubiquitous Collaborative Robots]