Extending Information and Communications Technologies Impact on Knowledge Based Society through Artificial and Collective Intelligence -Part1-

The paper approaches the analysis of the role of collective intelligence in the context of Information and Communications Technologies (ICT) exponential evolution, which tends to have an important impact on the progress of the Information society (IS) toward Knowledge Based Society (KBS). Premises for this impact refer to the most prominent advances and challenges of ICT, including Cloud, Big Data, IoT, green ICT, artificial intelligence (AI) and Digital Disruption (DD), which have to be approached in a responsible and systemic way, as their interdependent consequences reached unprecedented complexity and crucial importance for World economy, humankind life and future of Earth ecosystem. It is enough to recall the pace of ICT s carbon footprint growing versus the totality of airplanes. The Collective intelligence main concrete components, crowdsensing (CSENS), crowdsourcing (CSOURS) and generally crowd intelligence (CI), enable AI to create more knowledge from the real time data deluge that is generated at Earth scale, impacting IS/KBS, people s life and Earth environment. The functional structures of crowdsourcing are analysed and classified by two main criteria, referring to the degree individuals are implied and respectively the way crowd intelligence is collected. Machine learning (ML) is pointed as the most important, performant and productive subfield of AI due to its unprecedented capacity of self-learning algorithms, which make the difference between ML and the prior advances on AI. Going further and deeper into ML, the AI most prominent performant applications will be based on Deep Learning (DL). On the other hand, the paper point out the fact that the further AI advances, the complexity of its jobs and challenges is increasing and this way new kind and complicate issues are raising. IoT and other emerging sensing systems could be more performant using highest technologies as AI - HI combinations, but this way they become more complex, especially by volunteer and generally human participation and eventually progressively changing from sensing to sourcing or including both CSENS and CSOURS. As a consequence, combining HI and AI deep learning is an emerging trend in Big Data and Collective intelligence, based on the power of AI to offer performant solutions of processing the overwhelming data volumes, along with the HI s deepness of imagination, capacity of generalize and superiority of processing sparse data. Practically AI could further improve its performance when approaching difficult problems for machines, which could be solved by addressing CSENS and CSOURS systems and then discovering information/patterns in collected data, but we have to watch every time to reasonably keep the control of this complexity increasing, as remarkably also argued the genial and regretted Stephen Hawking. Consequently, we have to watch this HI/AI combination as the natural trend of high technologies exponential progress and keep it in the secure area of KBS development. We have to notice the need for human intervention to resolve issues and improve algorithms, but we would add the imagination and responsibility of human to design safe AI (including algorithms). A paper s conclusion is that AI needs high value data in order to generate information and eventually knowledge, but the data value is depending on ... crowd wisdom. In order to assure an efficient contribution of crowd, we have to carefully manage the combination of AI-HI in all typical scenarios, i.e. providing expertise for reference design tools, improve algorithms, supervising ML/DL processes and ...contributing in large volumes of data/information/knowledge by CSENS and CSOURS systems. We have to conclude that responsibly analyzing people and information quality or refining knowledge are among the most complex, complicate and time sensitive problems to be solved, perhaps perpetually.