Impact of ITS advances on the industry Chapter | 9 107of all publications behind the United States (41%) and before China (10%) to
only 13% and third place only 5 years later. Over these 5 years, China more than
doubled its contributions (from 10% to 23%) to this main research venue in AI.
The entire European research output published in the prime conference on AI
has by now reached an alarmingly low level.
The investments made in China in AI over the last 5 years have put them into
a position rivaling the United States, leaving Europe far behind. Besides the aca-
demic dimension, there are many indicators that there is an arms race for domi-
nance in AI, with China investing heavily and continuously. Other countries
including Russia, Japan, India, and South Korea have realized the importance
of the field and have prioritized research moving AI forward.
In Europe, individual states and regions are investing in AI and are setting up
programs to support research and development efforts. Many realize that an ini-
tiative is required. Many companies and institutions try to set up AI research and
competence centers. However, in order to be competitive on a global scale, Eu-
ropean effort is required. The regional and national efforts are generally targeted
at enabling local companies to use AI, rather than moving AI itself forward.
In contrast, China is pushing for leadership in this area by 2030, with the
Chinese government to announce the first major investment plan on an AI indus-
trial park that will cost 2.1 billion USD (Cyranoski, 2018). Companies provid-
ing solutions in the area of AI are currently mostly found in the United States.
Companies like Google, IBM, and Microsoft are offering cloud-based solutions
for machine learning and AI. Such services include image recognition, text un-
derstanding, conversational systems, emotion detection, and more general big
data analytics. Such services are extensions to conventional cloud services and
are provided such that little experience is required to use them. This makes the
application of AI simple, but at the same time requires to collaborate on nearly
any modern and meaningful product with US companies and to share revenue
with them.
Also commonly used libraries are developed and provided by companies
from the United States. One prime example is TensorFlow, the open-source ma-
chine learning and AI library, which has been developed by Google’s ITS Team.
The efforts put in by Google, Microsoft, and IBM (and many others) and the re-
sults that are achieved are just amazing, for example, from an AI that plays GO
better than any human to systems that have machine perception on a stunning
level. Without a large and coordinated effort, it seems impossible to imagine to
rival these technologies or to even just create technologies that are comparable
in quality. The big software companies and also Amazon have moved into the AI
field to extend their business models and to force a tighter integration with their
customers and their other products. European companies make use of these AI
services provided by US companies, as there is a clear need for these technolo-
gies and as they are not able to develop them in-house or to get them in Europe.
It is foreseeable that for many companies operating in more traditional busi-
ness models, for example the automotive industry, logistics, manufacturing, and