Artificial Intelligence and Automation

Artificial Intelligence (AI) and Machine Learning have evolved from buzzwords to mainstream technologies, transforming several industries. In the near future, we can expect AI and ML to play an even more significant role as they integrate with various applications and systems. Some of the trends to watch include AI-powered natural language processing for improved human-computer interaction, deep reinforcement learning enabling AI systems to learn from trial and error, improving decision-making processes, and AI-driven cybersecurity advancements to protect against hacking and data breaches.

Robotic Process Automation, or RPA, is a technology that uses software robots to automate repetitive, rule-based tasks in organizations. The RPA market is expected to grow and transform various business processes. Some of the anticipated trends include the integration of AI and Machine Learning into RPA systems to enhance decision-making capabilities, increased adoption of RPA in industries such as banking, insurance, healthcare, and retail for process optimization and cost reduction, and the development of user-friendly and easily customizable RPA tools, enabling wider adoption among small and medium-sized businesses.

Extended Reality and Internet of Things

Extended Reality (XR) is an umbrella term that covers Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies. As XR gains increased adoption, we’ll see the rise of new interactive and immersive experiences in the gaming, entertainment, and education sectors, as well as wearables and smart glasses, which are becoming more mainstream, offering a hands-free MR experience. Despite its slow adoption so far, Apple’s potential entry into the AR/VR market could also spur rapid growth of the XR industry.

The Internet of Things (IoT) will continue to change how we interact with the world around us, with an increasing number of devices being connected to the Internet. In the near future, we can witness IoT device security improvements, addressing vulnerabilities in connected devices, and applications expanding into healthcare, agriculture, and urban planning sectors. Edge computing may also bolster the performance of IoT devices by moving computation closer to the data source.

Voice Tech and Blockchain

Voice technology is becoming increasingly popular, and its adoption will continue to rise. As advancements in natural language processing and machine learning are made, voice assistants will become even more capable and user-friendly. Key developments in voice technology may encompass increased integration of voice assistants into everyday devices, such as televisions, automobiles, and household appliances, voice-driven customer service systems, providing seamless and personalized customer experiences, and the expansion of voice technology across multiple languages and accents, making it more accessible to a global audience.

Blockchain technology is gradually moving beyond its initial association with cryptocurrencies and finding new applications. Watch out for decentralized finance (DeFi) platforms making traditional financial services accessible to a broader audience, blockchain-based systems being used in supply chain management and logistics for increased transparency and traceability, and non-fungible tokens (NFTs) and their continuing impact on the art, gaming, and entertainment industries.

5G and Quantum Computing

Quantum computing has the potential to revolutionize industries, thanks to its ability to solve complex problems in a fraction of the time that traditional computers take. Developments in the quantum realm may include advancements in quantum cryptography to ensure secure data transfer, the collaboration between academia, governments, and the private sector to accelerate quantum research and development, and the successful implementation of hybrid quantum-classical computing systems for solving optimization tasks.

The rollout of 5G has begun, promising higher speeds, lower latency, and improved reliability. The impact of the wider adoption of 5G might lead to remote work and learning capabilities being more effective, thanks to improved video streaming and collaboration tools. New IoT applications may also be able to leverage 5G infrastructure to enhance data transfer speeds and device responsiveness, and gaming experiences could be enhanced as cloud gaming platforms become more mainstream.

Also Read: Benefits of Machine Learning and AI

Green Technologies

As concerns about climate change and environmental issues grow, technology will play a critical role in addressing these challenges. The IT industry will likely continue to align with sustainable practices and develop eco-friendly solutions. Many businesses will focus on energy-efficient data centers and network infrastructure to minimize carbon footprints and reduce energy consumption. The adoption of circular economy principles, including designing for longevity, repairability, and recyclability of electronic devices will also be a focus, along with the development of smart cities and green technologies, improving overall environmental performance and resource management.

The world of technology is constantly evolving, and the future will see the IT industry reaching new heights. Keeping up with these emerging trends, innovations, and potential game-changers is crucial for both businesses and consumers alike. By staying informed and prepared, we can collectively harness the opportunities of these breakthrough technologies to shape a brighter future.

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This success represents a boost in the digitization of organizations and marks the future roadmap. Technologies that manage to provide greater flexibility will enable companies to adapt to changes quickly and efficiently.

What Technologies Lead Companies To Invest?

The four main strategic pillars attracting the most technological investments from companies for 2022 are digital transformation, operational efficiency, customer-centric innovation and cybersecurity, the first two being the highest priority.

As for more specific technological trends, up to ten types are distinguished: virtual and augmented reality, blockchain, big data, cybersecurity, RPA, machine learning, cloud, digital channels, internet of things and others. Among them, the most relevant are: big data, cloud and digital channels, to which the participants contributed their visions in this meeting.

The Cloud Revolution

This great growth is explained by the investments of large companies and the incentives they provide to smaller ones to facilitate their transition to this technology, which offers interesting opportunities:

• Innovation: the products are available to everyone, thus facilitating the production of new advances.
• Flexibility: the cloud allows you to manage resources comfortably.
• Low cost: it is a cheaper system than the traditional ones.

On the other hand, there are several risks associated with this technology that must also be taken into account:

• Security: This content’s heterogeneity and great accessibility make it more insecure.
• Cost – If not properly optimized, the cloud can become more expensive than others.

A company’s transition to the cloud must be carried out efficiently to take full advantage of its benefits. The first step to achieving this is to have an initial plan analyzing the intended starting point and the intended objective. In addition, there are four other main challenges to consider:

• Expense control: although this technology represents cost savings, it is necessary to adjust them properly within the budget.
• Team Management – ​​This technology is most beneficial with flat organizational models and an agile structure.
• Security: the increase above in risks that the cloud implies a new security policy with more complex and effective systems.
• Maximize the value of information: Cloud allows data to be handled quickly and easily, so you should take full advantage of this visibility.

The cloud is also bringing about a remarkable change in the human factor because the information is no longer exclusively in the hands of the main person in charge of an organization. Still, rather anyone can self-manage their environment more independently. This supposes a more open environment, where the best solution is to commit to specialized talent training.

How To Transform Towards Customer-Centric?

Innovation will be one of the priorities in companies. According to the survey above, 65% of them will have a group dedicated to that field and 56% of those who invest in digital channels put customer-centric innovation first.

• Marketing automation + CRM: these two solutions allow you to choose the right channel, time and content to offer each customer what they need.
• Artificial intelligence: this technology enables, from big data, the creation of algorithms to offer each person the products that best align with their interests.
• Marketplace: digitization makes it easier to sell your products and reach agreements with third parties to increase the offer.
• Digital transformation in the BackOffice: the company no longer operates around a physical catalog but has adapted its resources and processes to the online context.

The Benefits Of RPA

Another great technological trend is machine learning, which focuses on facilitating operations, and, related to it, RPA (Robotic process Automation), which has a top 3 priority in technological investment for 16% of companies by 2021.

This technology emulates the tasks of a human through robotics and offers important advantages for organizations, making them more productive and competitive. Among them, six mainly stand out:

• Error minimization
• Greater speed and productivity in tasks
• Cost reduction and better process traceability
• Elimination of processes that do not provide added value so that people can focus on relevant tasks
• easily scalable
• Greater regulatory compliance

Big Data – The Engine of Innovations

Technological advances make digital processes increasingly complex, and it is essential to analyze them. For this reason, big data is tremendously useful for companies, and two out of three indicate it as one of the three priorities for technological investment.
The pandemic has accelerated the trend of remote work and, with it, the conversion of the digital home user to a corporate digital user. Therefore, companies seek to know what the experience of their remote employees is like.

This monitoring of the digital experience allows organizations to improve operational efficiency, digital infrastructures, employee satisfaction and security. This monitoring should be expanded in the future with the notable increase in the level of devices and connectivity that the development of the Internet of Things implies.

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More and more companies are using IoT to network their products to establish new digital offers and services on the market or to increase the attractiveness of their product portfolio. Also, and especially in medium-sized companies, the realization has prevailed that this approach is a critical success factor for their future viability. There are many concepts and ideas for promoting digital offerings, and the companies that use the IoT for their solutions are correspondingly numerous. But long-term success also requires concrete monetization approaches.

Direct Monetization Approaches At a Glance.

A basic distinction can be made between direct and indirect monetization approaches. Direct approaches generate deposits through the IoT solution, and indirect approaches enable sales of other products to increase. Direct approaches include:

Selling The IoT Device.

The most obvious way to generate sales with the Internet of Things is to sell the connected products themselves. Smart home gadgets such as digital kitchen appliances, language assistants, and sports and fitness trackers are among the best-known examples. In addition, manufacturers may also offer usage licenses to use associated apps or platforms.

Charging a Setup

Smart and connected products often need to be installed and set up before customers can use them. Especially for less technically savvy users, it can make sense to offer the initial setup as a service for a fee – for example, activating a product on an IoT platform. The best-known example of such a service is probably the setting up of Internet access by the respective provider.

Freemium and Premium Services

The freemium model adds fee-based extensions to a free basic product. Customers have the advantage that they can first convince themselves of the benefits of the product before they spend any money. Companies can address a wide range of potentially paying customers with this approach. The freemium model for the appropriate Software for an IoT device is often encountered. For example, an app for condition monitoring can be available free of charge and expanded with paid features that can analyze the monitoring data.

Transaction

Fees Transaction fees are common in financial applications. In this case, it is not (or not exclusively) the purchase or general use of an IoT product subject to a fee, but rather individual transactions or the amount of data generated by use. The advantage for companies is that the transaction fees are directly proportional to the operating and hosting costs of the required software infrastructure.

Licensing And Subscription, Usage Models

Licensing and Subscription

Companies can offer licenses and subscriptions for their IoT solutions to cover the development effort and ongoing costs of operating the underlying infrastructure. This includes IT infrastructures in your data center or rented cloud resources and Time-limited offers to achieve an endless cash flow. For example, license fees can be monthly or per user and are often found with Software as a Service (SaaS) offerings.

Compared to one-time license costs, the subscription model allows companies to regularly contact their customers and address customer needs with tailor-made offers. Customers can use the solution as needed and can adapt the subscription to their needs. In addition, similar to classic transaction fees, monthly pricing can scale with resource requirements.

Sharing, Renting, Leasing

With shared use (sharing), several users can share products and services to minimize the costs for everyone involved compared to a new purchase. The usage costs are calculated based on actual use. The IoT enables offers, processing, and billing here. For customers, this means only bearing a portion of the cost instead of the total cost. The provider can optimize and maximize the utilization of the product. An example of such models is car-sharing offers.

Renting and leasing do not focus on purchasing a product but rather on satisfying the actual need. Slogans like “hole instead of the drill” and “mobility instead of the vehicle” illustrate this approach. Here, too, the IoT enables billing, for example, by documenting the use of a solution.

Pay-per-use models align the price even more closely with the actual use of a product – for example, according to time units, the number of uses, or quantities used. Here, too, the advantage is that it is not the product itself that is paid for, but the use and thus the added value.

Indirect Approaches

In addition to these six direct monetization opportunities, there are three indirect approaches companies can use IoT solutions to increase their revenue:

IoT-Based Servitization

Servitization describes the business models of manufacturing companies that build new and innovative services on networked products. These new services in the IoT environment are created through knowledge and insights into the product portfolio. For example, the data generated can be used to maintain and optimize existing products. In addition, companies can forward the data from their IoT solutions to their (end) customers – as raw data or already filtered – to offer them completely new user experiences or added value. Manufacturers of swimming pool technology can, for example, use networked IoT data to improve water quality by adding chlorine and permanently reducing the environmental pollution. The advantage for the manufacturer of the IoT solution:

Cross-Selling

Cross-selling aims to facilitate or initiate the sale of other products or services through the IoT product. The sale of the IoT solution itself is not the focus of the monetization process – the solution may even be completely free for users. Such cross-selling approaches, such as printer cartridges and razor blades, are common for consumer goods in the B2B and B2C sectors. A product is automatically reordered with a simple interaction with the IoT solution or when the minimum stock is reached. Users save themselves the entire process of an online purchase and ideally always have a well-stocked inventory.

Advertising

Companies can also use data generated by using an IoT solution for marketing. In this way, conclusions can be drawn from the information collected about user behavior and their interests to be able to place targeted advertising in a second step. This is common practice in the B2C sector, for example, to use data from fitness trackers profitably.

Also Read: Big Data – The Strategic Ally Of Electronic Commerce

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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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Advantages Provided by AI and Machine Learning

You can add to this list more and more areas that change thanks to AI and made a huge spurt because of its influence. We believe that it is only for the best in general. For the modern person, it is hard to imagine his life without a smartphone or internet connection. They appeared before AI and machine learning, but all the devices became quality assistants and indispensable parts of our lives thanks to them.

• Education. A lot of innovations start from the higher education system. Students are the most open and ready people for all kinds of challenges. So they took all the aspects of machine learning and AI into their daily life. A lot of online education programs are built with the help of AI that analyzes the involvement of the group and creates a special approach to the students.    
• Space exploring. The magic place that every dreamer wants to enter is space. Technologies that are used in this area are the most innovative and modern. The common person is hardly understandable, but an obvious fact is that space tourism is closer than we thought. Thanks to AI, we can predict and calculate the behavior of the spaceships and avoid a lot of visible and hidden problems. Making decisions for the better functioning of all spaceship systems is a strong point and the obvious ability of AI that is necessary for the whole space industry.     
• Medicine. It might seem that medicine is about humans and the qualification of doctors. It is indeed impossible to help a patient without the caring hands of nurses and doctors. Nevertheless, they can’t be successful without using medicines that were created thanks to the technologies in general and AI in particular. The biggest part of medicine development is analyzing the combination of components. Let’s not forget about high-tech devices that help surgeons with the most painstaking operations on the brain or heart. 
• Connections and communications. Have you heard about clever automatic systems that distribute data streams and regulate network load? There are billions of users on the open internet who send, buy, order, book, make payments, watch movies, and do other things at the same time. This is possible thanks to machine learning and AI that are responsible for the whole system. It’s security and working capacity. 
• Construction. You can say that we built pyramids without AI, and they are still standing. There are only two aspects we want to mention. The first one is the number of injuries of workers. Another one is creating infrastructure around. Predicting the number of citizens, their needs in roads, banks, shops, and restaurants distinguish good constructions from bad. To do it perfectly, you need to find out a lot of information about customers and make a deep analysis. Only based on the data that you receive can you start smart construction. As you see, AI helps to avoid mistakes again and builds more quality systems.
• Creating machines and mechanisms. It seems that Boston Dynamics is a synonym of AI. Their creatures study and create their logic according to personal experience. Today they jump and dance. Tomorrow they will do a much harder job. They are frightening, a little but magical. This show is only one side of the coin. Only imagine how deep they can use machine learning for the huge mechanisms for all kinds of production. Looking forward, the future when robots will take part in society doesn’t seem so impossible.  
• Finance and banking. Two centuries ago, gold and paper banknotes were money. Nowadays, you can use a lot of instruments to pay, sometimes even hardly understandable like cryptocurrency. Machine learning is important for this area in questions of security and protection of personal data. Online bots and clever, sensitive interfaces use AI and use it successfully. Systems of money exchange, trading business, IPO, and international financial system, in general, can’t exist without technologies anymore. 

How to Stay on the Top of the Tech Wave

When it comes to standard trends, you don’t need some specific education to keep abreast. You need to read about innovations and feel like you know enough. This level will actually be enough for conversational and general knowledge purposes. However, if you want to work in the field where Machine learning and Ai are implemented, you need to do much more than that. There are long, short, official, and commercial edu programs you can choose from. The only problem you can actually face is the complexity of these studies. Don’t give up! If you feel like you get stuck with doing your assignments, address a STEM-oriented student homework assistance service, such as MyAssignmentLab.com, and share your tasks with them. You will receive instant help with your homework and also understand how to work on similar assignments better and faster. Here, you have direct access to an assigned writer and can ask questions to get to the bottom of the problem. If you have decided to make Machine Learning and AI your profession, use all the means available, as these fields are the future. 

Also Read: Application of Machine Learning in The Company

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For example, decision-making is a critical point in the medical field since a decision can directly influence people’s health. Therefore, if these AI methods aid in decision-making, it is necessary to know more about how each variable affects the prediction emitted by the model.

It is also helpful to know if our model is biased when making predictions or when an AI model deviates from the standard criteria in certain decisions. After all, when we are training an Artificial Intelligence model, we are trying to discover the patterns that the data follows. If our data has biases caused mainly by people entering the information, our AI model will also learn according to those biases.
How is it possible to know the decisions our model is making if it is a black box? How can we prevent our model from making biased decisions? To resolve these questions, the explicability and interpretability of the models arise. The local and global explicability techniques try to extract information about the decisions made by the Artificial Intelligence models.

Explanatory Methods of Artificial Intelligence

Some models of Artificial Intelligence are interpretable per se. Simple models such as regressions, which in themselves offer us the importance of each variable in the decisions that are made, or decision trees, which by their structure indicate the path of decisions on the different variables that lead to the prediction or decision. Final.
However, in most cases, we will need to use more complex algorithms that are not as transparent as to why a specific conclusion has been reached. These algorithms are called black-box algorithms since their interpretability is practically nil.

Having to use interpretable models can lead to a loss of flexibility when troubleshooting machine learning problems. For this reason, for the explicability of the black box models, the so-called agnostic explanatory methods arise.

These interpretability techniques are alien to the learning model that is being used. Although they do not give us a clear vision of the decisions made by the black-box algorithms, they do provide us with an approach that helps us better understand the problem we are facing. Trying. In turn, the agnostic models of interpretability can be classified into global explicability models and local explicability models.

Models of Global Interpretability of Algorithms

One of the objectives of the interpretability of the models is to explain which variables an algorithm uses to make a decision. The technique called Permutation Importances can be used for this problem. The goal is simple: measure the prediction error of a model before and after permuting the values ​​of each variable. In this way, we can calculate which variables have the most influence on the predictions made by the model. The problem with this method is that we are assuming that there is no dependency between the variables.

A similar method is that of the Partial Dependence Plots. This method of explicability consists of choosing a series of values ​​to evaluate the behavior of a specific variable in the data set. The way to calculate this explicability metric is by setting the selected variable, for all the instances of the set, to each value of a list that we have previously decided and calculating the error difference that we obtain with each one.

With the Partial Dependence Plots model, we can measure the importance of the different values ​​of a variable for the algorithm’s predictions. In this method, we are also assuming that there is no dependency between the variables.

Models of Local Interpretability of Algorithms

Thanks to global explicability methods, we can know how our model behaves in a general way. That is, knowing what variables you are taking into account to make decisions and how the values ​​of those variables affect the predictions in general. However, we frequently want to know what is happening with each prediction that the model is generating and how each variable’s values affect each specific prognosis. Local explanatory models help us solve this problem.

The most widespread local explicability model is that of Shapley values. Shapley values ​​are based on game theory, how much weight each player brings to the entire game. In our case, we try to know how much weight each variable contributes to the prediction made. However, the calculation of Shapley values ​​is computationally costly, although several properties always comply and help optimize the algorithms. These properties are:

• Efficiency: the sum of the Shapley values ​​is the total value of the game.
• Symmetry: If two players are equal, their Shapley values ​​are similar.
• Additivity: If a game can be split in two, the Shapley components can also be broken down.
• Null player: if a player does not add value to the game, its value is 0.

For example, with the property of additivity, you can decompose a set of classifiers, calculate the Shapley values ​​for each classifier, and, adding the Shapley values ​​obtained for each classifier, receive the final discounts.

Although the Shapley values ​​give us a local view of each prediction, by grouping them, we can observe the global behavior of the model. Grouping the Shapley values ​​obtained in each prediction, we will see the model’s behavior with the different values ​​that each variable has.

In a health case, for example, we have a simple model that predicts whether a person will have a heart attack based on the medical tests that have been carried out. We will indeed observe that, for optimistic predictions (the patient has suffered a cardiac arrest), the Shapley values for high cholesterol values ​​are also very high, and they are low for low cholesterol values.

This situation indicates that the higher the cholesterol values, the more they influence the prediction that the patient will suffer a cardiac arrest. And in the same way, the low Shapley values ​​for the common cholesterol values ​​indicate that, although they also influence the prediction, they have a negative influence. That is, they lower the probability that the patient suffers a cardiac arrest.

Restrictions of Algorithm Interpretability Models

A series of restrictions tend to indicate that the interpretability models are not always optimal and that it will depend on the problem we want to solve. The most notable limitations within the explicability are the explicability-precision balance and the computational cost.

As we have already mentioned, there are more interpretable models, such as regression algorithms or decision trees, but these, in turn, do not have to obtain good results in terms of the predictions they make. It depends on the problem we are dealing with.

If we have the opportunity to use this type of simpler model and obtain good results, we will also be able to have models that, per se, are explainable. However, we want to use more complex and, therefore, much less solvable models in most Artificial Intelligence problems.

This is when the agnostic interpretability models come into play, which, we already know, have certain disadvantages or negative features that we must assume, such as the independence of variables or the computational cost involved in using them.

Agnostic interpretability algorithms are computationally expensive in themselves and also work on previously trained models. We have to add the computational cost involved in preparing a model to the computational cost involved using the explicability algorithms.

It depends on the problem we are dealing with, and this situation will be more or less viable. If, for example, we are dealing with thousands of predictions per minute, or even less, it is practically impossible to obtain the explicability for each of the predictions.

Cases of Explicability of Algorithms in Health

Artificial Intelligence algorithms are based on the knowledge contained in the data to make predictions. For various reasons, generally social, los data may be biased, and therefore AI algorithms learn from these biases. With the explanatory algorithms, we can detect if our models are limited and try to solve them so that these biases are not considered.

In addition, for people outside the field of AI, it can be complex to understand how Artificial Intelligence algorithms work and wonder why they make the decisions they are making or how each variable affects the decision made by the model. In the area of Big Data and AI in Health at the Institute of Knowledge Engineering (IIC), we have found ourselves faced with these situations. In numerous projects, it has been necessary to apply these AI explanatory techniques.

Explanation of The Acceptance of Health Budgets

For example, in the Health area, we have used these techniques to explain to experts in a clinic which variables influence the acceptance of medical treatment budgets. Our objective was to build a model that was capable of predicting whether a budget, made up of demographic variables and a series of medical treatments, will be accepted by the patient or not. One of the most critical aspects of this project was that the reasons why a budget is going to be accepted or rejected could be known. This is where explicability comes in.

The procedure that has been carried out is as follows: first,

• Build an AI model that fits the problem at hand. One of the most restrictive features of explicability is the effectiveness of the model. If the model does not achieve the expected results, no matter how much we manage to interpret it, we would be analyzing a model that is not robust.
• After evaluating and checking the validity of the built model, using the Shapley values ​​technique, we can obtain the importance of each variable concerning the acceptance of the budget.

Also Read: How Artificial intelligence Helps In Recruiting

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Industrialization, Traceability and Verifiability In Machine Learning

In the first place, it started from the premise that, when implementing Machine Learning models, especially in the banking sector, “we need the models to be traceable, reproducible and verifiable”, as well as industrialized.

This industrialization makes it possible to standardize the processes that usually occur in all Machine Learning projects, to be agile while guaranteeing the three aspects mentioned above and reducing the cost of maintenance of the models.

The expert gave an example: “at the bank, we have to be able to answer why a person was denied a loan, tracing the path from the data to the score issued by the model.” To do this, it is necessary to know which version of the model is in production and what data was used or where the predictions were stored. Several versions of data are usually saved, associated with the models to cover the traceability and reproducibility part. Those are in production at all times.

On the other hand, verifiability is handled by a committee in which different bank areas intervene ( model owner, risks, legal, etc.). The Machine Learning model cannot go into production if the committee does not approve it. In addition, other business decisions are made: decision thresholds, when to launch or when to retrain the model. Check out this Best Machine Learning Course, taught by industry experts who have mastered this domain and have many years of experience in the industry.

Analysis and Design of The Machine Learning Model

As Experts explained, the design and development of a Machine Learning model are governed by a series of requirements: that it be simple, monitorable, interpretable, that it is not biased, that the input variables comply with the regulation and that it is adjusted to the case usage and operational restrictions.

All this means taking into account some aspects and addressing some challenges in the different phases of the process:

• Definition of the use case in which different areas are involved. Several fundamental questions are answered for the development of the model: what variables and what samples can be used, if there are legal restrictions that limit the use of the model, if the model is going to work in batch mode or real-time, as well as the technology necessary for it.
• According to the expert, the analysis of the target population is one of the phases that takes the longest. First, it is necessary to decide on which population the model is going to train and which one will be applied, with the possibility that it has not been historically dealt with. Then the availability of variables is studied, and the target is defined, which must be aligned with business and risks in terms of criteria, among other things.
• Data splitting or data division in the train, test and validation sets. It is decided how to make the cuts (temporarily, grouped or stratified), always keeping in mind that they are compatible.
• Possible preselection of variables. Although the selection of variables is still made on the training data, it is possible to make a distributed preselection to reduce the volume of data.
• Model training and predictions. Openbank has its flexible Auto-ML tool to adapt to the variety of use cases that are addressed. Here you have to know how to adjust the parameters to ensure traceability and reproducibility and avoid black boxes.
• Interpretability, for which they also have their tool. Once the model has been trained, an attempt is made to answer and explain, for example, why a particular score has been assigned to a client. In addition, this same tool can be applied to models that have not been implemented.
• Monitoring, of two types: the classic one that does business with its KPIs to make a standard follow-up of the improvements in the industry or, from a more technical point of view, aimed at measuring the so-called data shift.
• Possible biases. According to the expert, they can no longer afford to develop biased models, and she believes that it is necessary to define, from company policy, what type of fairness is to be achieved, using various strategies to maximize profit with restrictions.

As we can see, a Machine Learning project in the company cannot be limited to developing and training a helpful model. It is necessary to attend to a series of considerations before and during the process: for example, that the models fit the objective, but that they can also be generalized to be more efficient or not lose sight of legal or ethical issues.

Also Read: Clarifying The Concepts Of Various Technology Terms – Artificial Intelligence, Deep Learning, Machine Learning, Big Data, and Data Science

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In this way, RPA can reproduce human actions and integrate them into digital systems. This technology can be implemented in different departments within a company, drastically changing its operation, with the sole objective of eliminating the time invested in repetitive tasks and reducing errors.

The RPA is made up of an automation system that reproduces the activities of a professional. Thus, the company has the opportunity to eliminate work overload and overstaffing.

How does RPA work?

RPAs use bots, that is, software solutions controlled by computer code capable of executing programmed actions and learning new functions. These bots are capable of imitating almost all human actions, such as:

• Login to mobile applications;

• Move files and folders;

• Copy and paste data;

• Complete forms;

• Multi-system connection;

• Extract data from documents;

• Read and write to databases;

• Automatic replies to emails;

• Perform calculations;

• Collection of data from the web;

• Compilation of social media statistics.

RPA bots use the user interface to collect data and manipulate applications, just like humans. They interpret, respond, and communicate with other systems to perform various simple and repetitive tasks.

However, the goal of RPA is not to replace information systems but to improve them. The great advantage of this technology is the possibility of adapting the software to changing situations. This allows the bot to take new actions and communicate automatically.

Also Read: RPA For Financial Management Of The Company

What Are The Advantages Of RPA?

In addition to automating routine processes, one of the greatest benefits of RPA is that it offers greater efficiency and eliminates the margin for error, thus reducing operating costs.

Here are some other advantages of RPA technology:

Greater Precision

Manual and repetitive tasks have a higher probability of error, but with task automation, the risk decreases, and the precision increases.

Optimization of Processes

The use of RPA streamlines and simplifies processes. Repetitive tasks, such as filling out forms, collecting data or statistics, or even submitting automatic responses, can be optimized using RPA. As a result, interactions become simpler and faster, and the service is much more efficient.

More Productive Employees

The automation of tasks also reduces the workload of employees, optimizing time and resources. In addition, by simplifying processes, the work routine becomes much more efficient and more active. The fewer processes employees have to worry about, the more time they have to focus on more complex tasks with greater value.

Technology On The Rise

According to Gartner, RPA is growing rapidly – 20-30% every quarter. For this reason, companies must be aware of the importance of this technology for their growth and know-how to get the most out of it to face the digital transformation.

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Even though computers became widespread and it was possible to put them at the service of business processes, it took time and refinement to achieve the desired analysis and storage capacity.

Today, big data and powerful BI ( Business Intelligence) tools accompany businesses throughout their business management. In this article, we will tell you the advantages of carrying out correct information management.

What is Business Intelligence, And How Does It Work?

The concept of business intelligence or Business intelligence (BI) contemplates the set of efforts that organizations allocate to improve their business processes to make better decisions that allow them to grow.

These efforts consider:

• the analysis of historical data (previously captured and stored thanks to big data )
• the optimization of analytical tools
• the use of technologies.

Business Intelligence: Tools & Advantages For The Company

From the analysis of a database, there are several advantages that business analytics and, more specifically, business intelligence tools bring to the business world.

In what processes can BI intervene in the company or apply it so that you can benefit from it?

Information Gathering

Every day, each company department generates a whole series of data, which needs to be ordered, analyzed, and validated. Converting a set of data into complete business reports is one of the advantages offered by analytical tools.

For example, performs advanced data analytics by centralizing all functions of a business in one place. Thanks to the power of this tool, you get dashboards, reports, and embedded analytics in real-time, easily.

In this way, problems, trends,, and opportunities are identified in time, making better decisions.

Process Optimization

The workflow in the company can be optimized by using BI solutions in the different internal processes. Some examples of the application of business intelligence are:

• Financial control → optimization of financial management thanks to a digital Chief Financial Officer ( CFO ).
• The commercial processes → customer acquisition and loyalty through a CRM system.
• Inventory management → redesign of the storage logistics process, thanks to a Decision Support System (DSS).
• The production process → global vision of the business performance indicators with the implementation of an Executive Information System (EIS).
• The supply chain → guarantees the quality of the data to be analyzed using follow-up and monitoring techniques and tools that ensure continuous improvement.
• Also Read: How Business Intelligence Can Transform Your Business

Generation of New and Better Opportunities

Creating and implementing balanced scorecards to monitor performance indicators allows companies to increase their sales force and, therefore, the business’s profitability.

Determining the performance of your sales department, thanks to the knowledge of data in real-time, guarantees you the speed of response and the ability to adapt to market needs.

With an AI platform like IBM Analytics, you can visualize, analyze, and share your company’s performance, thanks to the fact that you can create attractive dashboards and reports, following a series of recommendations and sales standards. Sharing these dashboards with the entire team makes it easy for you to guide them toward their sales goals.

Centralization of Information

All operational data stored in the company’s relational database can be centralized and offer you a global vision of the business that allows you to draw conclusions and implement strategies based on them.

Increased Competitive Advantage

In front of the other players in the market, you must be able to stand out. This is achieved thanks to reliable tools that allow you to obtain better results than your competition.

The visualization of data through integrated AI functionalities makes you identify meaningful insights and can share them. For example, working collaboratively with the same data to create reports from popular applications such as Microsoft Office, Microsoft Teams, and Excel is possible with Power BI.

With this Microsoft BI tool, you make fast and controlled decisions that drive strategic actions and ensure your competitive advantage.

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One of its main advantages is real-time access to the degree of compliance with business objectives. We can consider it a ‘still photo’ that allows us to put it in context and speed up the analysis of complex situations. Always with the sole purpose of identifying the origin of negative or positive data that allow making decisions.

Business Intelligence technology is applied mainly in banking, pharmaceutical industry, logistics, e-commerce, fashion, education, and Retail. In this article, we will analyze its application in four of these sectors: logistics, ecommerce, Retail, and education.

Business Intelligence Application Sectors

Business Intelligence in Logistics

The automation of warehouse tasks, the optimization of communication with clients, and the use of Software have brought a significant number of advantages at the production and business level. But now there is another excellent derivative of all this new scenario, the massive amount of data that is generated within the organization, which has the potential to provide valuable information on the operation of the company such as fleet management, stock minimum, storage, and distribution costs, quality management or reverse logistics among others.

Benefits that Business Intelligence Brings to Logistics Companies

Decrease in Labor Costs

If this technology provides something, it is that a multitude of processes is automated. Now the time in which reports are made to suppliers, customers or other company departments is reduced.

Greater control in logistics management

Thanks to the automation functions, all the information is available, from freight management to costs.

Real-time access to information

Orders, load analysis, merchandise distribution, etc., favors better decision-making in each logistics process.

Improve customer service

Thanks to its technology, information can be given to the company’s clients, where their order is located in real-time, if there have been any incidents, and provide various solutions to the client.

E-commerce and Business Intelligence

Companies dedicated to online sales manage many orders, which translates into a large volume of data, and its treatment and security can be very complex. This is where Business Intelligence comes into play. This technology allows you to analyze, transform and store all that information and later develop a business or marketing strategy that enhances the business and increases sales.

 The advantages that Business Intelligence can bring to online sales are:

• Automate processes
• Decision making in real-time
• Inventory control reducing delivery processes and ensuring that there are no stock breaks
• Control real costs
• Know the buying habits of customers
• Build data-driven sales strategies

Also Read: Benefits Of Having Business Intelligence tools

The Importance Of Data In The Education Sector

In schools, universities, and training centers, they implement a Business Intelligence technology that allows managers to access reports based on accurate data. Thus, decision-makers, teachers, and planners can identify trends and comply more precisely with academic or organizational goals.

The Central Areas of Planning & Evaluation are:

Academic record

Analyze performance by subject, individual student performance, tests, and behavioral indicators such as attendance and incident reports.

Administrative data

Analyze student enrollment, classroom size, school location, and costs to establish budget deficits and surpluses that allow planning funding needs.

Personal

Analyze tradeoffs between teaching and non-teaching staff, assigned workloads, student-teacher ratio, etc.

The application of Business Intelligence technology in the education sector will ensure that educators and administrators have access to key indicators of operational and educational performance, will help them drive decision-making and long-term, fact-based strategic planning. Likewise, they will monitor the fulfillment of the defined objectives and make corrections when necessary to guarantee the desired results.

Decision Making in Retail

Business Intelligence technology tries to obtain a scorecard with the data that is decided and updated in real-time, organizing the leading indicators on the operation of the business, the target market, and its environment. Any of the trade decisions in the trade will be a more successful decision if data such as: what new products or services to add, eliminate the less profitable ones, organize staff shifts, segment communications and promotions to customers, etc…

The tools for obtaining store data are:

• POS-ERT
• CRM or Software to manage customer relationships
• Other technologies such as people counting cameras, intelligent changing rooms, etc.

As you can see after reading this article, whether you work or own a business in any of these sectors, you are interested in learning about Business Intelligence technology. It can help you jump-start your career by improving your company’s results and efficiency.

Also Read: What is Business Intelligence & Cloud Computing

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