What do I do with artificial intelligence, machine learning, data science, and progress through digitization as a controller? – More than you think!

Companies worldwide are increasingly feeling the need to integrate new, data-based technologies to remain competitive. The use of these technologies implies far-reaching changes in the company’s internal handling of data, affecting control. You can check ProjectPro Machine Learning Projects to learn what kind of machine learning project is used by some biggest companies.

Do not be afraid of these changes, but seize the opportunity and make yourself indispensable for the upcoming transformation. Innovations in the use of data are difficult to implement without support from the specialist area. It is not uncommon for projects to fail due to a lack of a common basis for communication.

If not you as a controller, who is better suited to act as an interface between the department and data science? Their technical expertise is more in demand than ever because they are familiar with business practice and company data.

Actively Helping To Shape Progress

Prepare yourself in good time for future requirements and actively shape your company’s future! A first step in the right direction is to get a realistic picture of the job of a data scientist.

Build Up Knowledge – Assess Benefits

Brush up on your basic statistical knowledge from your school and university days! You can use various options for this:

Print And Online Media To Build Up Basic Knowledge

Numerous print and online media entertainingly convey the basics and largely do without mathematical jargon and complicated formulas. Familiarize yourself with how basic statistical techniques work. So you can have a say when it comes to correlations, regressions, classifications, and clustering methods.

Once you have established a basic understanding, you will soon understand machine learning, neural networks, and artificial intelligence (AI) principles. You will find that this is not rocket science or sheer magic.

Online Courses For Deeper Insights Into Practice

To delve deeper into practice, the Internet has a variety of free or inexpensive online courses available. These offer an easy introduction to coding with Python or R and other data science applications.

You do not have to complete retraining to become a data scientist, and a rough understanding of the instruments and the possibilities is sufficient. In this way, you reduce reservations and better assess the added value of data science. 

Promotion By The Employer

Coping with such a build-up of knowledge in addition to professional and private obligations is undoubtedly a challenge. Here, the employer must be made aware of further training measures. Actively claim your funding. Do not wait until the topic has taken you by surprise and suddenly you are confronted with data scientists as work colleagues.

If this is already the case, treat them with suspicion and interest. They can learn a lot from each other and benefit from them. If your employer offers further training on its initiative, you should take advantage of them. In this way, you do not get sidelined with new developments in the company.

How AI and Machine Learning Can Be Used In Controlling

As soon as you have recognized the potential of data science, you can actively help shape innovations and act as the linchpin for new projects. Machine learning and deep learning in controlling make everyday work easier and relieve you of annoying repetitive tasks.

Time-consuming activities that follow fixed procedures and rules and require a great deal of attention can often be automated relatively easily. Machine learning and AI have proven themselves many times in the finance and accounting departments and the creation of reports and dashboards.

As a controller, you do not have to fear a loss of importance in your job. As an expert, you have an exclusive understanding of the business processes based on the numbers. Combined with your acquired basic understanding of data science, you make yourself indispensable for your company. Only you can deliver solutions where algorithms fail.

In the meantime, you can concentrate on your core task as a controller and provide important impulses for planning and controlling company processes. In this way, you can locate the control part more strongly in control.

It is all the more important to drive change in your own company in these dynamic times. Therefore, the focus of our online conference Digital Finance & Controlling this year is on the successful digitization of the finance sector.

Get to know the DNA of a digital finance area and find out which software can support you in your processes. The event is now available on-demand.
Although algorithms are superior to people when it comes to the systematic processing of large amounts of data, they can only produce meaningful results based on fixed rules and unambiguous data. They are good at recognizing patterns of relationships and deriving rules from them but fail in unforeseen events that do not follow any structure.

The correct classification of such events and the corresponding reaction can only be mastered by actual intelligence. This is where you come into play as a “human in the loop.” Only you have a feel for when algorithms are wrong.

With your knowledge of the limits of technology, you protect your company from consequential decisions made due to blind trust in algorithms. Here, too, control by capable controllers is required.

How Does Machine Learning Work?

Gain a realistic idea of ​​machine learning and its possibilities! Free yourself from exaggerated expectations and gloomy future scenarios from science fiction!

Machine learning is currently the most prominent aspect of the sub-area of ​​computer science dedicated to imitating human behavior: artificial intelligence.

The initial attempt to achieve the set goal by programming complex rules soon reached its limits, as social behavior can only be mapped to a limited extent by static rules. Machine learning takes an innovative way to solve this problem.

With the help of special algorithms, this approach automatically derives rules from data for which results are already available. These rules can, in turn, be used to forecast potential results for data for which they are not yet available (predictive analytics).

Machine learning can therefore be understood as the automated programming of software solutions for data processing:

Also Read: What are Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

What Is Deep Learning?

Deep learning works on the same principle as machine learning, with the difference that data is processed with so-called artificial neural networks. These neural networks extract and compress data into a form that makes it easier and faster for computers to access the information it contains.

The use of neural networks has proven itself in the processing of audiovisual data (speech, image, document, and video recognition) but is not limited to these types of data.

The idea for artificial neural networks for information processing was formulated as early as the late 1940s. Still, it has only been relatively recently that technological progress and the lower prices for high-performance computer processors have made it possible to use this technology cost-effectively.

Neural networks consist of layers of simple, functional units, so-called perceptrons, which receive signals and send out signals when threshold values ​​are exceeded.

To use a neural network to be referred to with the media-relevant term deep learning, there must be at least one additional layer (hidden layer) between an input and an output layer.

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A professor in the area of ​​Computer Science and Artificial Intelligence could contribute to public confidence in AI. In the first part of the interview, he tells us it is essential to have explainable algorithms and how they are technically developed.

What is Reliable and Explainable Artificial Intelligence?

Today, Artificial Intelligence is a technology that is practically in any area, helping us make decisions and obtain patterns, usually using large amounts of data.

So it is essential, for learning and this information to be helpful, that humans can understand it. The European Union has opted for this ethical, reliable, responsible, and explainable Artificial Intelligence.

How Can We Get Fair and explainable Algorithms?

There are several cases that we can investigate. Still, the general idea is that the algorithms stop being as a black box as they are today and allow some explanation, auditability, maintenance of privacy, or guarantee of sustainability. Efforts are being made on many models, and we need more research. It is a boiling field.
In my research group, we work in different areas. For example, we have developed algorithms that work in distributed environments, and what they try is to guarantee the privacy of the data of each node. So we can learn from the data of all the nodes simultaneously, but without sharing it and without sending it over the network to gather it in the cloud or any central node. What is communicated are the parameters of the algorithm.

We have also worked on the explanatory part of the algorithms. We have introduced in the evaluation metric of the algorithm the number of variables that we are using to develop the explanation. So we try to maintain the performance of complex algorithms but having an understandable algorithm above them so that a human can interpret that result.

What Profiles are Needed to Address The Explainability of AI?

We would need to be able to work in more diverse teams. this is not very common. Still, it is interesting that we learn to work with personnel from other areas, such as Sociology of Law, to address more ethical aspects or those that have to do with the development of suitable algorithms but with the developing algorithms that are good for people. They can help us integrate this change from a more social perspective.

How Does Explainable Artificial Intelligence Benefit Us?

There are areas where most of the algorithms we are using, such as deep learning ones, are pretty opaque. Very powerful from an accuracy point of view, but not very playable. I believe that the subject of the explicability of Artificial Intelligence is a particular term that perhaps for some areas is not necessary, while in others it is.
We must learn to develop more transparent algorithms, or at least that they can be auditable. From knowing if the data we are entering is partial to understanding the entire algorithm or the algorithm’s output.

Transparency can have different degrees. The explicability of the algorithms would be the highest degree, in which we would need that algorithm or its results to be easy for a person on the street to see.

It is one of the issues included in the General Data Protection and Regulation Law. It is said that a person has the right to receive an understandable explanation when they are affected by a decision of an Artificial Intelligence algorithm.

In Which Areas is Explainable AI Most Necessary?

We speak of sensitive or high-risk areas, and this has yet to be defined precisely because we do not want to put restrictions on the research and development of algorithms that can be very exact. I think the issue is not that we are going to sacrifice accuracy for the sake of explainability but rather in trying to balance.
A sensitive area can be healthy if a person is affected by a decision determining what treatment they obtain or what diagnosis they face. Also, areas that have to do with Fintech, insurance issues, loan concessions, legal issues, etc.

Probably in other areas much less sensitive for people and in which we use Artificial Intelligence every day, as it is not so important. As I said, I think that we will try to achieve a balance and try to explain the explanatory nature of Artificial Intelligence. as much as possible in susceptible areas.

Would These Measures Help Society To Accept and Incorporate AI?

It is essential to build trust. Sometimes the media or movies are broadcasting Artificial Intelligence topics that make citizens distrust them. Some of the actions that we have seen, mostly related to data privacy, have created confusion and a specific need to protect themselves. And so we see that some tools, such as Radar COVID, are not being adopted by the population, perhaps a little because of that mistrust.

Citizens must understand that Artificial Intelligence is at their service, and for that, it is essential that it be. So, we need to modernize the Public Administration and convey this idea of ​​a much more reliable AI, and I think this is catching on in Europe little by little. And probably in other areas such as the US, where we have witnessed scandals that have to do with the transfer of data, privacy, companies that backtrack on a project, etc.

I think it is essential that we create a citizen conscience. The more educated we are about the capabilities and limitations of current Artificial Intelligence, the more we will trust technology, and I believe that we can offer better tools.

Also Read: Does A Chatbot Need Artificial Intelligence?

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Without a doubt, the most cutting-edge technological trend of recent years is everything related to artificial intelligence and data analysis. And it is that relatively recently there have been great advances in this field, which together with the availability of enormous amounts of data and increasing computing power are giving rise to all kinds of very interesting practical applications.

The problem comes when the terms related to the field become marketing empty words that in many cases are outright lies. It is very common to talk that this or that product uses artificial intelligence to achieve something and, sometimes, they are conventional algorithms making predictable decisions.

What is Artificial Intelligence?

Artificial intelligence (AI) was born as a science many years ago when the possibilities of computers were really limited, and it refers to making machines simulate the functions of the human brain.

AI is classified into two categories based on its capabilities:

• General (or strong) AI: that tries to achieve machines/software capable of having intelligence in the broadest sense of the word, in activities that involve understanding, thinking, and reasoning on general issues, on things that any human being can do.
• Narrow (or weak) AI: which focuses on providing intelligence to a machine/software within a very specific and closed area or for a very specific task.

Thus, for example, a strong AI would be able to learn by itself and without external intervention to play any board game that we “put before it”, while a weak AI would learn to play a specific game like chess or chess. Go. What’s more, a hypothetical strong AI would understand what the game is, what the objective is, and how to play it, while the weak AI, although it plays Go better than anyone else (a tremendously complicated game), will not really have a clue what it is doing.

One of the crucial questions when it comes to distinguishing an artificial intelligence system from mere traditional software (complex as it may be, which brings us to the jokes above) is that AI “programs” itself. That is, it does not consist of a series of predictable logical sequences, but rather they have the ability to generate logical reasoning, learning, and self-correction on their own.

The field has come a long way in these years and we have weak AIs capable of doing incredible things. Strong AIs remain a researcher’s dream and the basis of the scripts for many science fiction novels and films.

What is Machine Learning?

Machine Learning (ML) or machine learning is considered a subset of artificial intelligence. This is one of the ways we have to make machines learn and “think” like humans. As its name suggests, ML techniques are used when we want machines to learn from the information we provide them. It is analogous to how human babies learn: based on observation, trial, and error. They are provided with enough data so that they can learn a certain and limited task (remember: weak AI), and then they are able to apply that knowledge to new data, correcting themselves and learning more over time.

There are many ways to teach a machine to “learn”: supervised, unsupervised, semi-supervised, and reinforcement learning techniques, depending on whether the correct solution is given to the algorithm while it is learning, it is not given the solution, it is Sometimes you give or are only scored based on how well or poorly you do, respectively. And there are many algorithms that can be used for different types of problems: prediction, classification, regression, etc …

You may have heard of algorithms such as simple or polynomial linear regression, support vector machines, decision trees, Random Forest, K nearest neighbors … These are just some of the common algorithms used in ML. But there are many more.

But knowing these algorithms and what they are for (to train the model) is just one of the things that need to be known. Before it is also very important to learn how to obtain and load the data, do an exploratory analysis of the same, clean the information … The quality of the learning depends on the quality of the data, or as they say in ML: “Garbage enters, garbage comes out”.

Today, the Machine Learning libraries for Python and R have evolved a lot, so even a developer with no knowledge of mathematics or statistics beyond that of the institute, can build, train, test, deploy and use ML models for applications of the real world. Although it is very important to know all the processes well and understand how all these algorithms work to make good decisions when selecting the most appropriate for each problem.

What is Deep Learning?

Within Machine Learning there is a branch called Deep Learning (DL) that has a different approach when creating machine learning. Their techniques are based on the use of what are called artificial neural networks. The “deep” refers to the fact that current techniques are capable of creating networks of many neural layers deep, achieving unthinkable results a little more than a decade ago, since great advances have been made since 2010, together with large improvements in computing power.

In recent years Deep Learning has been applied with overwhelming success to activities related to speech recognition, language processing, computer vision, machine translation, content filtering, medical image analysis, bioinformatics, drug design … obtaining results equal to or better than those of human experts in the field of application. Although you don’t have to go to such specialized things to see it in action: from Netflix recommendations to your interactions with your voice assistant (Alexa, Siri, or Google assistant) to mobile applications that change your face … They all use Deep Learning to function.

In general, it is often said (take it with a grain of salt) that if the information you have is relatively little and the number of variables that come into play is relatively small, general ML techniques are best suited to solve the problem. But if you have huge amounts of data to train the network and there are thousands of variables involved, then Deep Learning is the way to go. Now, you must bear in mind that the DL is more difficult to implement, it takes more time to train the models and it needs much more computing power (they usually “pull” GPUs, graphics processors optimized for this task), but the problems are usually more complex as well.

What is Big Data?

The concept of Big data is much easier to understand. In simple words, this discipline groups the techniques necessary to capture, store, homogenize, transfer, consult, visualize, and analyze data on a large scale and in a systematic way.

Think, for example, of the data from thousands of sensors in a country’s electrical network that send data every second to be analyzed, or the information generated by a social network such as Facebook or Twitter with hundreds (or thousands) of millions of users. We are talking about huge and continuous volumes that are not suitable for use with traditional data processing systems, such as SQL databases or SPSS-style statistics packages.

Big Data is traditionally characterized by 3 V:

• The high volume of information. For example, Facebook has 2 billion users and Twitter about 400 million, who are constantly providing information to these social networks in very high volumes, and it is necessary to store and manage it.
• Speed: following the example of social networks, every day Facebook collects around 1 billion photos and Twitter manages more than 500 million tweets, not counting likes and many other data. Big Data deals with that speed data receiving and processing so that it can flow and be processed properly without bottlenecks.
• Variety: the infinity of different types of data can be received, some structured (such as a sensor reading, or alike ) and others unstructured (such as an image, the content of a tweet, or a voice recording). Big Data techniques must deal with all of them, manage, classify, and homogenize them.

Another of the great challenges associated with the collection of this type of massive information has to do with the privacy and security of said information, as well as the quality of the data to avoid biases of all kinds.

As you can see, the techniques and knowledge necessary to do Big Data have nothing to do with those required for AI, ML, or DL, although the term is often used very lightly.

These data can feed the algorithms used in the previous techniques, that is, they can be the source of information from which specialized models of Machine Learning or Deep Learning are fed. But they can also be used in other ways, which leads us to …

What is Data Science?

When we talk about data science, we refer in many cases to the extraction of relevant information from data sets, also called KDD ( Knowledge Discovery in Databases, knowledge discovery in databases). It uses various techniques from many fields: mathematics, programming, statistical modeling, data visualization, pattern recognition, and learning, uncertainty modeling, data storage, and cloud computing.

Data science can also refer, more broadly,  to the methods, processes, and systems that involve data processing for this extraction of knowledge. It can include statistical techniques and data analysis to intelligent models that learn “by themselves” (unsupervised), which would also be part of Machine Learning. In fact, this term can be confused with data mining  (more fashionable a few years ago) or with Machine Learning itself.

Data science experts (often called data scientists ) focus on solving problems involving complex data, looking for patterns in the information, relevant correlations, and ultimately, gaining insight from the data. They are usually experts in math, statistics, and programming (although they don’t have to be experts in all three).

Unlike experts in Artificial Intelligence (or Machine Learning or Deep Learning ), who seek to generalize the solution to problems through machine learning, data scientists generate particular and specific knowledge from the data from which they start. Which is a substantial difference in approach, and in the knowledge and techniques required for each specialization.

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