Skip to main content

AI Eats the World

 Artificial intelligence (AI) has catered for an immense leap in development

in business practice. AI is also increasingly addressing administrative, dispos-

itive and planning processes in marketing, sales and management on the way

to the holistic algorithmic enterprise. This introductory chapter deals with

the motivation for and background behind the book: It is meant to build a

bridge from AI technology and methodology to clear business scenarios and

added values. It is to be considered as a transmission belt that translates the

informatics into business language in the spirit of potentials and limitations.

At the same time, technologies and methods in the scope of the chapters


on the basics are explained in such a way that they are accessible even with-

out having studied informatics—the book is regarded as a book for business

practice.

AI and the Fourth Industrial Revolution

If big data is the new oil, analytics is the combustion engine (Gartner 2015).

Data is only of benefit to business if it is used accordingly and capitalised.

Analytics and AI increasingly enable the smart use of data and the associated

automation and optimisation of functions and processes to gain advantages

in efficiency and competition.

AI is not another industrial revolution. This is a new step on the path of

the universe. The last time we had a step of that significance was 3.5 billion

years ago with the invention of life.

Comments

Popular posts from this blog

Possible Limitations of AI-Based Bots

 The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...

What is Machine Learning

 The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

A Bluffer’s Guide to AI, Algorithmics and Big Data

 Big Data—More Than “Big” A few years ago, the keyword big data resounded throughout the land. What is meant is the emergence and the analysis of huge amounts of data that is generated by the spreading of the Internet, social media, the increasing number of built-in sensors and the Internet of Things, etc. The phenomenon of large amounts of data is not new. Customer and credit card sensors at the point of sale, product identification via barcodes or RFID as well as the GPS positioning system have been producing large amounts of data for a long time. Likewise, the analysis of unstructured data, in the shape of business reports, e-mails, web form free texts or customer surveys, for example, is frequently part of internal analyses. Yet, what is new about the amounts of data falling under the term “big data” that has attracted so much attention recently? Of course, the amount of data avail- able through the Internet of Things (Industry 4.0), through mobile devices and social media has ...