AI the Eternal Talent Is Growing Up

The subject of AI is nothing new—it has been discussed since the 1960s.

The great breakthrough in the business world has failed to appear, but for a

few exceptions. Thanks to the immensely increased computing power, the

methods can now be massively parallelised and intensified. Innovative deep

learning and predictive analytics methods paired with big data technology

facilitate a quantum leap of AI potential benefits for business applications

and problems. In the last ten years, the breakthrough with regard to the

applicability in business practice has succeeded due to this further devel-

opment. At present, the discussion is, on the one hand, shaped by hardly

realistic science fiction scenarios that postulate computers taking over man-

kind. On the other hand, there is a strongly informatics-/technology-laden

discourse. In addition to that, there are singular popular science publications

as well as articles in the daily press. The latter adhere to the exemplary level

without holistic context. A systematic overview of the AI relevant for busi-

ness, a reference model for classification for the respective business functions

and problems, a maturity model for the classification and evaluation of the

respective phases and a process model including an economic cost-benefit

analysis are all lacking.

Comments

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 ...

Tech & Fun

Search This Blog