Skip to main content

AI as a Game Changer

 In the early phases of the industrial revolutions, technological innovations

replaced or relieved human muscle power. In the era of AI, our intellectual

powers are now being simulated, multiplied and partially even substituted

by digitalisation and AI. This results in fully new scaling and multiplication

effects for companies and economies.

Companies are developing increasingly strongly towards algorithmic

enterprises in the digital ecosystems. And it is not about a technocratic or

mechanistic understanding of algorithms, but about the design and optimi-

sation of the digital and analytical value added chain to achieve sustainable

competitive advantages. Smart computer systems, on the one hand, can

support decision-making processes in real time, but furthermore, big data

and AI are capable of making decisions that today already exceed the quality

of human decisions.

The evolution towards the algorithmic enterprise in the spirit of the

data- and analytics-driven design of business processes and models directly

correlates with the development of the Internet. However, we will have to

progressively bid farewell to the narrow paradigm of usage of the user sit-

ting in front of the computer accessing a website. “Mobile” has already

changed digital business significantly. Thanks to the development of the IoT,

all devices and equipment are progressively becoming smart and proactively

communicate with each other. Conversational interfaces will equally change 

human-to-machine communication dramatically—from the use of a text-

based Internet browser down to natural language dialogue with everybody

and everything (Internet of Everything).                                                             


Machines are increasingly creating new scopes for development and

possibilities. The collection, preparation and analysis of large amounts of

data eats up time and resources. The work that many human workers used

to perform in companies and agencies is now automated by algorithms.

Thanks to new algorithmics, these processes can be automated so that

employees have more time for the interpretation and implementation of the

analytical results.

In addition, it is impossible for humans to tap the 70 trillion data points

available on the Internet or unstructured interconnectedness of companies

and economic actors without suitable tools. AI can, for example, automate

the process of customer acquisition and the observation of competition so

that the employees can concentrate on contacting identified new customers

and on deriving competitive strategies.

Recommendations and standard operation procedures based on AI and

automated evaluation are often eyed critically by companies. It surely feels

strange at the beginning to follow these automated recommendations that

are created from algorithms and not from internal corporate consideration.

However, the results show that it is worthwhile because we are already sur-

rounded by these algorithms today. The “big players” (GAFA = Google,Apple, Facebook, Amazon) are mainly to solely relying on algorithms that

are classified in the category “artificial intelligence” for good reason. The

advantage: These recommendations are free of subjective influences They are

topical, fast and take all available factors into consideration.

Even at this stage, the various successful use and business cases for the

AI-driven optimisation and design of business processes and models can

be illustrated

Comments

Post a Comment

Popular posts from this blog

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- ...
Post a Comment
Read more

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...
Post a Comment
Read more

Random Forest Classifier

  The algorithm gradient boosted regression trees, also called random forests, belong to the ensemble learning methods This classifier uses an ensemble of weak regression trees that have a low hit quota when considered in isola- tion. The quality of the prediction can be improved significantly when vari- ous trees are trained with different parameters or samples. The results of the individual trees are aggregated to a total result which then enables a more balanced and high-quality prediction. The so-called bagging triggered a boom of the traditional regression trees. As aggregation, either a majority vote or a probability function is chosen (Fig. 5.5). The lead prediction generates high-conversion leads because • The entire spectrum of information available about a company is inte- grated into the decision-making; • The data is highly topical and without bias; • The random forest is capable of abstracting complex correlations in the data; and • The method learns iteratively from t...
Post a Comment
Read more