Chuyển đổi mô hình: Khảo sát về AI toàn cầu trong dịch vụ tài chính

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Chapter 1: Introduction

Chapter 1: Introduction 1.1 A Brief Juxtaposition of AI and Machine Learning Artificial Intelligence

• Pattern detection by recognising (ir) regularities in data

Artificial Intelligence (AI) is a term shaped by socio-behavioural rationales of human capabilities – essentially, expectations that machines could emulate human cognition and behaviour. Expectations of AI are derived and often benchmarked against human intelligence. The corollary is understanding that AI may be approached by attempting to understand human intelligence itself. While various definitions of intelligence have been proposed, Gottfredson notes in his editorial Mainstream science on intelligence that intelligence may be defined as:

• Foresight by extrapolating learned patterns in the presence of uncertainty • Customisation by generating rules from specific profiles and applying general data to optimise outcomes • Decision-making by generating rules from general data and apply specific profiles against those rules • Interaction by communicating with humans through digital or analogue mediums

“A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—‘catching on’, ‘making sense’ of things, or ‘figuring out’ what to do” (Gottfredson, 1997)

Machine Learning While underlying concepts of AI and machine learning suggest significant overlaps, the term ‘machine learning’ is more distinctly derived from existing frameworks in neuroscience, computer science, statistics, and mathematics. According to a definition which was originally coined by Mendel and McLaren (1970) and refined by Haykin (1994), machine learning describes the change of a system resulting from an interaction with its environment, as shown in Figure 1.1 below. A system interacts with its environment in such a way that the structure of the system changes, in turn transforming its interaction with its environment, creating an iterative process.

Extrapolating these traits to a set of distinct machine capabilities, this report follows the definition adopted by previous World Economic Forum reports2 in characterising AI as a suite of technologies, exhibiting some degree of autonomous learning and enabling:

2

The New Physics of Financial Services (McWaters et al., 2018)

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