Abstract
In recent years, artificial intelligence (AI) has gained attention from policymakers, universities, researchers, companies and businesses, media, and the wide public. The growing importance and relevance of artificial intelligence (AI) to humanity is undisputed: AI assistants and recommendations, for instance, are increasingly embedded in our daily lives. The chapter starts with a critical review on AI definitions since terms such as “artificial intelligence,” “machine learning,” and “data science” are often used interchangeably, yet they are not the same. The first section begins with AI capabilities and AI research clusters. Basic categorisation of AI is presented as well. The increasing societal relevance of AI and its rising inburst in our daily lives though sometimes controversial are discussed in second section. The chapter ends with conclusions and recommendations aimed at future development of AI in a responsible manner.
TopIntroduction
Artificial Intelligence is a powerful and transformative technology. On one hand, AI embraces huge potential to provide real social, economic and environmental benefits. It is considered as an area of critical importance for national competitiveness. McKinsey Global Research, for instance, estimates that the use of AI will add as much as $13 trillion to global GDP by 2030 (Bughin et al., 2018). On the other hand, the growing use of algorithms raises potential ethical concerns toward the societal relevance of AI.
There are many inconsistencies and confronting points of view when it comes to AI. Artificial intelligence quite often is compared and even is defined related or opposed to human intelligence. Regarding its application AI is divided into “strong” (AI for general applications) and “weak” AI (AI for practical applications) thus conradicting cognitive science versus engineering.
The chapter starts with a critical review on AI definitions since terms such as “artificial intelligence,” “machine learning,” and “data science” are often used interchangeably, yet they are not the same. The first section begins with AI capabilities and AI impact. Basic categorisation of AI is presented as well. The increasing societal relevance of AI and its rising penetration in our daily lives though sometimes controversial are discussed in second section. The chapter ends with conclusions and recommendations aimed at future development of AI in a responsible manner.
Key Terms in this Chapter
SHAP (SHapley Additive exPlanations): SHAP is a method to explain individual predictions. It is based on the game theoretically optimal Shapley Values. The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition (Sources: Lundberg, Scott M., and Su-In Lee. (2017) A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems; Molnar, C. (2019). Interpretable machine learning. A Guide for Making Black Box Models Explainable. https://christophm.github.io/interpretable-ml-book/ ).
Machine Learning: Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time (Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai ).
ICTAIs: International Centres for Transformational AI. The priority domains of ICTAIs include agriculture, health, education, smart cities and infrastructure, smart mobility and transportation (Source: http://niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf ).
LIME (Local Interpretable Model-Agnostic Explanations): LIME is short for Local Interpretable Model-Agnostic Explanations. Each part of the name reflects something that characterizes the model. Local refers to local fidelity - i.e., we want the explanation to really reflect the behaviour of the classifier “around” the instance being predicted. This explanation is useless unless it is interpretable - that is, unless a human can make sense of it. LIME is able to explain any model without needing to 'peak' into it, so it is model-agnostic. LIME is a technique to explain the predictions of any machine learning classifier, and evaluate its usefulness in various tasks related to trust (Source: https://bit.ly/36SmFbz ).
Deep Learning: Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches. In deep learning, interconnected layers of software-based calculators known as “neurons” form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer. The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image (Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai ).