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Literature Review: Artificial Intelligence Problems for the 2020s
The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence . The 2021 report shows the effects of COVID-19 on AI development from multiple perspectives. The Technical Performance chapter discusses how an AI startup used machine-learning-based techniques to accelerate COVID-related drug discovery during the pandemic, and our Economy chapter suggests that AI hiring and private investment were not significantly adversely influenced by the pandemic, as both grew during 2020.
The United Nations has called upon governments to develop national strategies for sustainable development, incorporating policy measures to achieve the 2030 Agenda for Sustainable Development. While AI technologies may support breakthroughs in achieving the Sustainable Development Goals, they may also have unanticipated consequences that will exacerbate inequalities and negatively impact individuals, societies, economies and the environment. The Resource Guide on Artificial Intelligence Strategies is a UN publication laying out existing resources on artificial intelligence ethics, policies and strategies on national, regional and international level .
To help humanity solve fundamental problems of cooperation, scientists need to reconceive artificial intelligence as deeply social . The state of AI applications reflects that of the research field. It has long been steeped in a kind of methodological individualism. As is evident from introductory textbooks, the canonical AI problem is that of a solitary machine confronting a non-social environment. AI needs social understanding and cooperative intelligence to integrate well into society.
Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application . Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more.
For the purpose of entrusting all sentient beings with powerful AI tools to learn, deploy and scale AI in order to enhance their prosperity, to settle planetary-scale problems and to inspire those who, with AI, will shape the 21st Century, Montreal.ai introduces this VIP AI 101 CheatSheet for All . Montreal.ai is preparing a global network of education centers to pioneer an impactful understanding of AI and to foster a vector for safe humanitarian Artificial General Intelligence (AGI).
Starting from such general references, this booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the “Ten Problems for Artificial Intelligence in the 2020s” that we are going to introduce in this booklet:
- computer games,
- intelligent optimization,
- human intervention,
- ethical issues,
- software development,
- open source,
- operations research.
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
GENERAL REFERENCES CITED
 D. Zhang et al., “The AI Index 2021 Annual Report,” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, March 2021, online at https://aiindex.stanford.edu/wp-content/uploads/2021/03/2021-AI-Index-Report_Master.pdf
 W. Liu et al., “Resource Guide ON ARTIFICIAL INTELLIGENCE (AI) STRATEGIES”, United Nations, June 2021, online at https://sdgs.un.org/sites/default/files/2021-06/Resource%20Guide%20on%20AI%20Strategies_June%202021.pdf
 A. Dafoe et al., “Cooperative AI: machines must learn to find common ground”, Nature, Vol 593, 6 May 2021, online at https://media.nature.com/original/magazine-assets/d41586-021-01170-0/d41586-021-01170-0.pdf
 I.H. Sarker, “Machine Learning: Algorithms, Real‑World Applications and Research Directions”, SN Computer Science (2021) 2:160, online at https://link.springer.com/content/pdf/10.1007/s42979-021-00592-x.pdf
 V. Boucher, “VIP AI 101 Cheatsheet”, Montreal.ai Academy, preprint 8 Jun 2021, online at http://www.montreal.ai/ai4all.pdf
booklet updated on 19 Jun 2021, now on sale as version 1.2