The Bot Revolution Is Changing Content Marketing—Algorithms and AI for Generating and Distributing Content

The subject of AI has become increasingly popular in companies ever since

the beginning of 2017. It is co-responsible for the search results on Google

or Bing. In addition, some of our digital assistants on our smartphone as

well as some messenger bots are based on (simple) AI.

At the end of 2015, Google extended its algorithm by AI: Google

RankBrain. Behind it is a system that learns little by little more about the

semantics of user queries and which increasingly improves with this knowledge. The aim: RankBrain is meant to fulfil the users’ needs in an increasingly better way. And with it, Google has taken the first step towards

self-learning algorithms. Many upgrades will be possible in the future with-

out any human assistance, because the systems will learn something new all

on their own.

AI will also play a significant role in content marketing when it comes to

combining contents with each other and promoting them. What still sounds

like dreams of the future will be totally normal in a few years. The abilities

of artificial AI are said to go to such lengths that it can automatically publish

and distribute content on various platforms.

AI already offers useful features for companies that would like to operate

on an international basis. With the help of algorithms, Facebook is able to translate a post into the user’s respective mother tongue. This depends on

the given location, the preferred language and the language in which the

user normally writes posts. The cumbersome multi-posting of contributions

can thus be avoided.

AI is used for, among other things, optimising the targeting of adverts

and search engines. As well as that, information can be tailored to the users’

needs more efficiently in the bot economy.

Comments

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- ...

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...

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...

Tech & Fun

Search This Blog