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This book is about innovation, big data, and data science seen from
a business perspective. Big data is a buzzword nowadays, and there
is a growing necessity within practitioners to understand better
the phenomenon, starting from a clear stated definition. This book
aims to be a starting reading for executives who want (and need) to
keep the pace with the technological breakthrough introduced by new
analytical techniques and piles of data. Common myths about big
data will be explained, and a series of different strategic
approaches will be provided. By browsing the book, it will be
possible to learn how to implement a big data strategy and how to
use a maturity framework to monitor the progress of the data
science team, as well as how to move forward from one stage to the
next. Crucial challenges related to big data will be discussed,
where some of them are more general - such as ethics, privacy, and
ownership - while others concern more specific business situations
(e.g., initial public offering, growth strategies, etc.). The
important matter of selecting the right skills and people for an
effective team will be extensively explained, and practical ways to
recognize them and understanding their personalities will be
provided. Finally, few relevant technological future trends will be
acknowledged (i.e., IoT, Artificial intelligence, blockchain,
etc.), especially for their close relation with the increasing
amount of data and our ability to analyse them faster and more
effectively.
This book reflects the author's years of hands-on experience as an
academic and practitioner. It is primarily intended for executives,
managers and practitioners who want to redefine the way they think
about artificial intelligence (AI) and other exponential
technologies. Accordingly the book, which is structured as a
collection of largely self-contained articles, includes both
general strategic reflections and detailed sector-specific
information. More concretely, it shares insights into what it means
to work with AI and how to do it more efficiently; what it means to
hire a data scientist and what new roles there are in the field;
how to use AI in specific industries such as finance or insurance;
how AI interacts with other technologies such as blockchain; and,
in closing, a review of the use of AI in venture capital, as well
as a snapshot of acceleration programs for AI companies.
This book is about innovation, big data, and data science seen from
a business perspective. Big data is a buzzword nowadays, and there
is a growing necessity within practitioners to understand better
the phenomenon, starting from a clear stated definition. This book
aims to be a starting reading for executives who want (and need) to
keep the pace with the technological breakthrough introduced by new
analytical techniques and piles of data. Common myths about big
data will be explained, and a series of different strategic
approaches will be provided. By browsing the book, it will be
possible to learn how to implement a big data strategy and how to
use a maturity framework to monitor the progress of the data
science team, as well as how to move forward from one stage to the
next. Crucial challenges related to big data will be discussed,
where some of them are more general - such as ethics, privacy, and
ownership - while others concern more specific business situations
(e.g., initial public offering, growth strategies, etc.). The
important matter of selecting the right skills and people for an
effective team will be extensively explained, and practical ways to
recognize them and understanding their personalities will be
provided. Finally, few relevant technological future trends will be
acknowledged (i.e., IoT, Artificial intelligence, blockchain,
etc.), especially for their close relation with the increasing
amount of data and our ability to analyse them faster and more
effectively.
This book deals with artificial intelligence (AI) and its several
applications. It is not an organic text that should be read from
the first page onwards, but rather a collection of articles that
can be read at will (or at need). The idea of this work is indeed
to provide some food for thoughts on how AI is impacting few
verticals (insurance and financial services), affecting horizontal
and technical applications (speech recognition and blockchain), and
changing organizational structures (introducing new figures or
dealing with ethical issues). The structure of the chapter is very
similar, so I hope the reader won't find difficulties in
establishing comparisons or understanding the differences between
specific problems AI is being used for. The first chapter of the
book is indeed showing the potential and the achievements of new AI
techniques in the speech recognition domain, touching upon the
topics of bots and conversational interfaces. The second and thirds
chapter tackle instead verticals that are historically
data-intensive but not data-driven, i.e., the financial sector and
the insurance one. The following part of the book is the more
technical one (and probably the most innovative), because looks at
AI and its intersection with another exponential technology, namely
the blockchain. Finally, the last chapters are instead more
operative, because they concern new figures to be hired regardless
of the organization or the sector, and ethical and moral issues
related to the creation and implementation of new type of
algorithms.
Artificial Intelligence is a huge breakthrough technology that is
changing our world. It requires some degrees of technical skills to
be developed and understood, so in this book we are going to first
of all define AI and categorize it with a non-technical language.
We will explain how we reached this phase and what historically
happened to artificial intelligence in the last century. Recent
advancements in machine learning, neuroscience, and artificial
intelligence technology will be addressed, and new business models
introduced for and by artificial intelligence research will be
analyzed. Finally, we will describe the investment landscape,
through the quite comprehensive study of almost 14,000 AI companies
and we will discuss important features and characteristics of both
AI investors as well as investments. This is the "Internet of
Thinks" era. AI is revolutionizing the world we live in. It is
augmenting the human experiences, and it targets to amplify human
intelligence in a future not so distant from today. Although AI can
change our lives, it comes also with some responsibilities. We need
to start thinking about how to properly design an AI engine for
specific purposes, as well as how to control it (and perhaps switch
it off if needed). And above all, we need to start trusting our
technology, and its ability to reach an effective and smart
decision.
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