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Artificial intelligence (AI) is widely considered as a kind of a machine system that adopts human-like behavior and provides functions attributable to human intelligence (HI). This is similar to the human brain’s ability the knowledge development is carried out by fusing the information obtained from the environment with the already accumulated knowledge (Sumari & Ahmad, 2016). Particular examples are the ability to learn, reasoning, planning, decision-making and acting (Russel & Norvig, 2009). Modern AI methods employ ambient intelligence (AmI) environments (Sadri, 2011). Such an environment is digitalized using many embedded, mobile, and multimedia devices. They (in cooperation) construct services to support the people. This form of digital support is especially needed for elderly and disabled people (Bravo, Cook, & Riva, 2016).
The emerging Information-Communication Technology (ICT) with its growing multitude of everyday computing devices (smartphones, tablets, interactive screens, surveillance camera, at-hand medical sensors, etc.) can be used for AMI environments which enables AI to be implemented as a part of the human brain rather than a separate entity (Meigal, Korzun, Gerasimova-Meigal, Borodin, & Zavyalova, 2018). Moreover, AMI environments become personalized and support everyday use by being deployed at home and other settings (Korzun, Nikolaevskiy, & Gurtov, 2016; Korzun, 2017). In this article, we extend our previous work (Meigal, Korzun, Gerasimova-Meigal, Borodin, & Zavyalova, 2018), which considered AI as a part of continued human brain evolution. We analyze existing opportunities of AMI environments for creating the personalized support as AmI At-Home Laboratory (AHL) in everyday human life (Meigal, Prokhorov, Bazhenov, Gerasimova-Meigal, & Korzun, 2017; Zavyalova, Korzun, Meigal, & Borodin, 2017). We argue that AHL supports the HI function evolving and developing along with its user. The feasibility of this AHL approach follows from our previous development in a narrower healthcare domain-mobile personalized recommendation services for patients with cardiovascular diseases (Orlov, Platov, Shevtsova, Fokin, & Zavyalova, 2017; Zavyalova, Kuznetsova, Korzun, Borodin, & Meigal, 2018).
Our research focus is on the connection between HI and AI. So far, the understanding of AI and its relation to HI are not uniform, and emerging AI methods are centered around qualitatively different aspects. For example, such areas as machine learning algorithms, deep learning, neurolinguistic programming, connectionist networks, generative adversarial networks, mechatronics, and robotics are rapidly converging, often interrelated, and connected or fully integrated (Bini 2018). In that respect, two ways of connecting AI and HI are possible, see Figure 1. One way is chimerical (syncretic) combination of a computer-like “exogenous/alien” AI with HI. Another way is more natural (synthetic) combination of more “endogenous/human-like” AI and HI. In general, we propose to consider AI as a continued (and even predetermined) form of the HI evolution. Based on this consideration, the contribution of this article consists of the following three parts.
Figure 1. AI combination with human brain: chimerical (left) and AmI-based (right)