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Super intelligent robots and other predictions
Published in Arkapravo Bhaumik, From AI to Robotics, 2018
One of the earliest proponents of this dystopia was author and futurist Vernor Vinge [338,339]. Vinge projected that in about the next thirty years, we would have created superhuman intelligence which will lead to the end of the human era. He suggested five possible paths by which we may reach this unsettling future. The AI Scenario: We create superhuman artificial intelligence (AI) — computers which are ‘awake’.The Digital Gaia Scenario: The network of embedded microprocessors becomes sufficiently effective to elicit superhuman intelligence — a ‘wake up’ call for the network.The Intelligence Amplification Scenario: We enhance human intelligence through smart human-to-computer interfaces, which melds together the human mind with the AI’s capabilities — that is, we achieve intelligence amplification.The Biomedical Scenario: We directly increase our intelligence by improving the neurological operation of our brains — the route via brain scanning and genetics.The Internet Scenario: Humanity, its networks, computers, and databases become sufficiently effective to be considered a superhuman being — the Internet of Things (IoT) coalesces to represent coherent intelligence. This is an extension of the Digital Gaia scenario, however over a network which covers the entire planet.
From blockchain Internet of Things (B-IoT) towards decentralising the Tactile Internet
Published in Muhammad Maaz Rehan, Mubashir Husain Rehmani, Blockchain-enabled Fog and Edge Computing, 2020
Abdeljalil Beniiche, Amin Ebrahimzadeh, Martin Maier
In [28], we touched on the importance of shifting the research focus from AI to intelligence amplification (IA) by using information technology to enhance human decisions. Note, however, that IA becomes difficult in dynamic task environments of increased uncertainty and real-world situations of great complexity. IA, also known as ‘cognitive augmentation’ or ‘machine-augmented intelligence’, will be instrumental in enhancing the creativity, understanding, efficiency, and intelligence of humans.
Ensuring that Superintelligence is Friendly (FAI)
Published in Chace Calum, Artificial Intelligence and the Two Singularities, 2018
Some people hope that instead of racing against the machines we can race with them: we can use AI to augment ourselves rather than having to compete with it. This is called intelligence augmentation, or IA, and is also known as intelligence amplification. Brain–computer interfaces have made impressive advances, and a world in which humans become the superintelligences is surely prefera ble to one in which humans might be enslaved or killed by machine superintelligences.
Intelligence in cyberspace: the road to cyber singularity
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2021
Ishaani Priyadarshini, Chase Cotton
According to (Vinge, 2008), it is quite likely that using technology, it is possible to create or become entities with more than human intelligence. Singularity may come in one of the following ways or as a combination of Artificial Intelligence Scenario: Creation of Superhuman Artificial Intelligence in computers.Intelligence Amplification Scenario: Enhancing human intelligence using human-computer interfaces.Biomedical Scenario: Improving neurological operations of the brain to enhance intelligence.Internet Scenario: Using networks, computers, and databases to create superbeings.Digital Gaia Scenario: Using embedded microprocessors to create superhuman beings.
Stream Reasoning to Improve Decision-Making in Cognitive Systems
Published in Cybernetics and Systems, 2020
Caterine Silva de Oliveira, Franco Giustozzi, Cecilia Zanni-Merk, Cesar Sanin, Edward Szczerbicki
In this scenario, the concept of Augmented Intelligence, also known as Cognitive Augmentation or Intelligence Amplification (IA) comes into play (Ashby 1961). For any specific application humans being and machines have both their own strengths and weaknesses. Machines are very efficient in numerical computation, information retrieval, statistical reasoning, with almost unlimited storage. Machines can capture many categories of information from the environment through various sensors, such as range sensors, visual sensors, vibration sensors, acoustic sensors, and location sensors (Yu et al. 2016). On the other hand, humans have their own cognitive capabilities which include consciousness, problem-solving, learning, planning, reasoning, creativity, and perception. These cognitive functions allow humans to learn from last experiences and use this experiential knowledge to adapt to new situations and to handle abstract ideas to change their environment. Therefore, the combination of both human experiential knowledge and information collected by a system can be used to enhance smartness of systems and for improved decision-making (Pathak 2017).