Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading, and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. Given China's large population and business sector, both of which use digitized platforms and tools to an unparalleled extent, it may enjoy an advantage in AI. In addition, it has fewer constraints on the use of information gathered through the digital footprint left by people and companies. India has also taken a series of similar steps to digitize its economy, including biometric identity tokens, demonetization and an integrated goods and services tax.
AI traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language . Several issues must be considered when addressing AI, including, socio-economic impacts; issues of transparency, bias, and accountability; new uses for data, considerations of security and safety, ethical issues; and, how AI facilitates the creation of new ecosystems. At the same time, in this complex field, there are specific challenges facing AI, which include: a lack of transparency and interpretability in decision-making; issues of data quality and potential bias; safety and security implications; considerations regarding accountability; and, its potentially disruptive impacts on social and economic structures. Artificial intelligence (AI) traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language.
As seen in Part 1 and Part 2 of this series, it is hard not to feel excited about machine learning. First, it empowers machines to teach themselves the tasks that humans can perform but find difficult to "teach" a computer via conventional coding (e.g. Secondly, it enables computers to perform tasks that far exceed human abilities, like analysing terabytes of data at lightning speed to unearth hidden patterns and make sense of them. But it is also hard not to feel some unease about the prospect of self-improving computer systems with increasingly human-like and super-human aptitudes, whether it is the threat of mass unemployment, the erosion of privacy, or simply the inability to understand, validate and trust the technologies that will increasingly impact our lives. These problems that artificial intelligence (AI) is throwing back at us are complex and multifaceted, and to tackle them requires concerted endeavours by our technologists, entrepreneurs, lawmakers and thinkers from all fields and walks of life.
Gómez, Emilia, Castillo, Carlos, Charisi, Vicky, Dahl, Verónica, Deco, Gustavo, Delipetrev, Blagoj, Dewandre, Nicole, González-Ballester, Miguel Ángel, Gouyon, Fabien, Hernández-Orallo, José, Herrera, Perfecto, Jonsson, Anders, Koene, Ansgar, Larson, Martha, de Mántaras, Ramón López, Martens, Bertin, Miron, Marius, Moreno-Bote, Rubén, Oliver, Nuria, Gallardo, Antonio Puertas, Schweitzer, Heike, Sebastian, Nuria, Serra, Xavier, Serrà, Joan, Tolan, Songül, Vold, Karina
This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.
The Artificial Intelligence ("AI") vs Intelligence Augmentation ("IA") debate has been around for over half a century. IA or Intelligence Augmentation classically refers to the effective use of information technology in augmenting human capabilities, and the idea has been around since the 1950s. AI is increasingly being used today to broadly describe machines that can mimic human functions such as learning and problem solving, but was originally founded on the premise that human intelligence can be precisely described, and machines made to simulate it. The term Artificial General Intelligence (AGI) is often used to represent only the latter, stricter definition. There is unprecedented hype today around AI, its incredible recent growth trajectory, myriad potential applications, and its potential emergent threats to society. The broader definition of AI creates confusion, especially for those that may not be closely following the technology.