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经济学人_报告:人工智能在现实世界(英文)2018.8_28页

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文本描述
1 The Economist Intelligence Unit Limited 2016
Artifcial intelligence in the real world:
The business case takes shape
Contents
About the report 2
Executive summary 3
Introduction 5
1. Testing the waters 7
The AI business index 8
Gathering momentum 8
Case study 1: Algorithms for your evening wear2. Hopes and expectations 10
The shape of returns to come 11
Artificially intelligent decisions 11
Case study 2: Creating a new healthcare market with AICase study 3: AI in financial markets: Risk agent or risk minimiserCase study 4: Ocado’s “flying swarms of intelligent robots”3. Industry perspectives on the AI impact 17
4. Rising to the challenges 19
Cost and data 19
Culture skirmishes 20
Case study 5: AI visions for manufacturing
22
5. A question of disruption 23
Happier employees 24
6. Conclusion: Embracing the unknown 26
2 The Economist Intelligence Unit Limited 2016
Artifcial intelligence in the real world:
The business case takes shape
Artifcial intelligence in the real world: The business
case takes shape
is a report from The Economist
Intelligence Unit (EIU) sponsored by Wipro Limited.
The report was written by Denis McCauley and edited
by Charles Ross. It draws upon a survey conducted
in the second half of 2016 of 203 executives around
the globe. Respondents were evenly split among
the fnancial services, manufacturing, retailing, as
well as the health and life sciences industries. Just
less than half (48%) had an annual global revenue
of greater than US$1bn. C-level executives formed
50% of the sample, and those located in Asia-Pacifc
(35%) and North America (36%) formed the majority
of respondents.
To complement the survey, The Economist Intelligence
Unit conducted in-depth interviews with the following
executives and AI experts from the industries under
investigation (listed alphabetically by surname):
Matteo Berlucchi, chief executive offcer,
Your.MD (UK)
Paul Clarke, chief technology offcer, Ocado (UK)
Eric Colson, chief algorithms offcer, Stitch Fix (USA)
Chris Gelvin, chief operating offcer, Group COO
functions, UBS (Switzerland)
Ben Goertzel, chief scientist, Aidyia (Hong Kong)
James Hendler, director, Institute for Data
Exploration and Applications, Rensselaer Polytechnic
Institute (USA)
Ralf Herbrich, director of machine learning, Amazon
(Germany)
Matthew Howard, European lead, IBM Watson Health
(UK)
Jerry Kaplan, visiting lecturer, Stanford University
(USA)
Frank Kirchner, head, Robotics Innovation Center,
German Research Center for Artifcial Intelligence
(Germany)
Yann LeCun, director, AI research, Facebook (USA)
Markus Lorenz, partner and managing director,
Boston Consulting Group (Germany)
Per Vegard Nerseth, managing director, Business
Unit Robotics, ABB (Switzerland)
John Straw, AI venture capitalist (UK)
Jared Teo, senior program offcer, Health Innovation
Fund, California Health Care Foundation (USA)
Gerrit van Wingerden, managing director, Tora
Trading Services (Japan)
The EIU bears sole responsibility for the editorial
content of this report. The fndings do not necessarily
refect the views of the sponsor
Note that not all answers add up to 100%, either
because of rounding or because respondents were
able to provide multiple answers to some questions.
All monetary amounts are in US dollars. n
About the report
3 The Economist Intelligence Unit Limited 2016
Artifcial intelligence in the real world:
The business case takes shape
Artifcial intelligence (AI) is no longer the future.
For businesses, it is the here and now, and this study
conducted by The Economist Intelligence Unit makes
clear that executive suites and boardrooms around
the world see it as such. They might be expected to
be wary, given that much is unknown, even amongst
scientists, about how AI capabilities might develop in
the coming years. Or that policymakers and regulators
have barely begun to study its potential implications
for markets and workforces.
Many business leaders certainly expect AI to be
disruptive. More than 40% of those surveyed for the
study anticipate that AI will start displacing humans
from some jobs in their industry within the next
fve years. Slightly more think their own role will be
changed by AI in the same time frame. But they see
this more as augmentation than marginalisation. An
overwhelming majority believe AI will make their job
easier and help improve their own performance. They
clearly believe it will do the same for the businesses
they manage.
The purpose of this study has been to gauge corporate
attitudes toward AI in different regions and different
industries. Based on a global survey of 203 senior
executives, it fnds that, especially in North America,
companies in health and life sciences, in retail,
in manufacturing and in fnancial services are
actively testing the waters. Amongst this group, AI
technologies and applications are in the exploratory
phase at around one-third of companies, but another
third have moved on to experimentation, and one-
tenth have begun to utilise AI in limited areas. A small
handful (2.5%) have even deployed it widely.
Following are other key fndings from the research:
n The pace of adoption is quickening. AI will be
“actively implemented” in their companies within
the next three years, according to 75% of surveyed
executives. Another 3% say this is already the
case. The pace will remain the quickest in North
America (active implementation in 84% of frms
there) and, in industry terms, in retail (also 84%).
nNorth America and the health sector lead the
way. Converting the survey results into an index,
the AI implementation score is 2.40 on a 1-5 scale,
where 1=nascent, 2=exploratory, 3=experimental,
4=applied and 5=deployed. North American
companies in the study have advanced furthest
Defning AI
The term artificial intelligence (AI) refers to
a set of computer science techniques that
enable systems to perform tasks normally
requiring human intelligence, such as visual
perception, speech recognition, decision-
making and language translation. Machine
learning and deep learning are branches of
AI which, based on algorithms and powerful
data analysis, enable computers to learn and
adapt independently. For ease of reference
we will use “artificial intelligence”, or AI,
throughout this report to refer to machine
learning, deep learning and other related
techniques and technologies.
Executive summary。