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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What are Intelligent document processing software solutions?

IDP tools are robust solutions to ensure resource efficiency by focusing on data management, its handling & hygiene too. So, what is intelligent document processing, then? IDP is a culmination of digital technologies (including AI & ML) and deep learning with Natural Language Processing (NLP). Intelligent document processing solutions rely upon these underlying principles to process documents & extract required or pertinent information.

AI-powered Intelligent Document Processing software is the answer to many organizational woes in implementing AI & IDP within a connected environment. This drives efficient frameworks, ensuring continuity of information at all levels. In addition, multilevel navigability promotes ergonomic data management & accessibility. 

How does intelligent document processing operate?

Intelligent document processing utilizes intelligent document technology solutions to classify documents, then process the required data with pattern recognition technology. Following the processing of these documents, intelligent document processing software extracts the important data and assembles them into an accessible format.

Difference between intelligent document processing and automated document processing

Some assume that IDP and automated document processing are the same. However, there is a slight difference in the documents they act upon. Automated document processing is applied mostly for digitizing paper documents while intelligent document processing fully processes many distinct types of documents and consolidates their relevant data, thus nullifying the requirement for human data processors.

How do I implement intelligent document processing software?

Employing intelligent document software solutions starts with the assessment of document processing workflows to identify the ones that can be automated. Once the documents that need to be automated are identified, then it becomes mandatory for companies to find out the workflows that need the ultimate level of accuracy to initiate the automation process accordingly.

Intelligent document processing use cases

Just before exploring this, let us have a brief overview of the entire IDP process:

Input > Priming (pre-processing) > Detection cum data segmentation (classification) > Extraction (via OCR, deep ML & NLP) > Post processing (RPA, commercial logic & ERP integration)

Now, these are some popular applications of IDP:

• Banking, credit, finance & insurance: whether it is loan application processing, customer satisfaction, claims, data management, document security, KYC, mortgaging, or account opening, IDP caters for all – in terms of extraction & processing, as well as analysis.
• Healthcare: be it admission documents, clerical services, patient data handling, record management, health monitoring, medication information & clinical history
• Transport & logistics: supply chain forms, inventory management, demand sensing, invoice & contract verification, ERP integration, data validation plus delivery confirmation
• Legal: ranging from insurance claims eligibility to contractual authentication or even underwriting processes themselves, IDP serves to analyze, scrutinize & verify documentation
• RPA & robotic intelligence integration for enterprises, propelling automation with data points extracted from information volumes. This drives interconnectivity & efficiency.

Following this flow:

DIGITIZE

Absorb & Extract intricate document orientation, written & visual items.

ENRICH

Hone extracted data with detailed context.

ANALYZE

Extract actionable data insights.

CONSUME

Use analyzed information through downstream integration & query.

What does the future hold for IDP?

In addition to addressing automating & streamlining existing conventions for documentation, IDP will bring a host of other evolving advantages. Its holistic approach drives many industries beyond their current capabilities. For instance, the ability to search for specific keywords or terms via a phrase query search (by applying intelligent filters) is one such application. This can be extended beyond the existing realms of the education, financial, retail & leisure industries with integrated solutions. These could include the ability to revise a current contract, update or even overhaul a press release – the possibilities are endless.

What is intriguing about IDP is its ability to learn continually deep, process, redefine & analyze information efficiently. Moreover, driving new processes with streamlining in mind can further enhance efficiency: predictive pipelines, deep learning, flexible information management & data handling. 

Edge Verve’s XtractEdge platform does just that by offering the following salient features:

• Integrated ERP connectors 
• Automated document categorization as per written text content or visual orientation
• Detailed & specific multiformat document data extraction, which is template training agnostic
• Object detection via Deep Learning Computer Vision & NLP models to extract intent & entity
• Cognitive Search queries for NLP & keywords
• Intuitive GUI orchestration Workbench guides manual review with a feedback loop with personalized & flexible post-processing workflows
• Performance analytics dashboard drives stellar Quality Control

IDP will always dominate the business automation industry with its AI, ML & NLP capabilities – all unified in one package. This underpins the principle of data intelligence, autonomy, security & scalability. The day that most organizations (if not all) realize & can implement IDP, the better equipped our industries will become to deal with data volumes at scale. This is irrespective of the infrastructure available (bar interconnected server networks & computing hardware), industry & document type.

The quicker we can achieve a more extensive adoption of this data enablement, the greater our global ability to identify & exchange data at speed will become. This is imperative for the upcoming web3 interface, where information will become ever more decentralized theoretically. It is only feasible to operate in such environments with interconnected pathways. Thankfully, IDP’s integration abilities live up to this necessary expectation.

Final words

One of the best features of IDP is that it can adapt itself to diverse domains across industries. Hence, it is fair to acknowledge that IDP analyze virtually any document type at a much higher speed & scale. The only hindering factor is organizational adoption & active deployment of such tools. With these in order, it is only a matter of time before we witness a revolution in how we input, view & retrieve almost any kind of information. The future is mysteriously approaching fast: are you ready to embrace this massive change?

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Cobots can work alongside humans, doing the same activities and helping them complete their tasks. This article will look at the positives and negatives of the interaction between cobots and humans.

Interaction Between Humans And Robots: How It Works

Technology is advancing faster, leading to more significant interaction between humans and machines. The so-called collaborative robots or cobots are more and more widespread in our lives at home and professional levels.

In the home, cobots can be used for different activities, such as cleaning the house or preparing meals. In the professional field, on the other hand, cobots are used to carry out repetitive and sometimes even dangerous tasks, such as handling heavy materials or industrial machinery.

The Interaction Between Cobots And Humans Can Have Both Positive And Negative Sides.

Let’s see what the disadvantages of using collaborative robots are:

• From the point of view of the human worker in collaboration with the cobot, psychological risks must be addressed.

• The cost of cobots is relatively high (not accessible to any company). Their price can be a deterrent for many companies, tiny and medium-sized ones.

• Cobots can be dangerous if not used correctly. If placed in the wrong positions or handled incorrectly, they can almost prove to be a weapon for company employees.

Cobots also need maintenance to be carried out meticulously and on schedule to prevent accidents at work.

The negativity of the aspects can be quickly neutralized by the many positive aspects regarding using these very high-efficiency machines.

The positives mainly concern:

• Increase the efficiency of the production process.

• Reduce production times and increase the quality of the final product.

• By being used in dangerous or difficult areas for humans, the risk of injury is minimized.

• Cobots can be programmed to perform different tasks, which allows workers to focus on other tasks.

From whatever perspective you look at it, integrating manufacturing force with collaborative robots is a boon to business productivity. Knowing more details is easy; just contact Homberger Robotics and explain your needs.

These third-generation robots are on the rise. We believe that in a short time, many Italian companies (many more than expected) can choose to buy cobots, giving life to a real turning point in the industrial market and beyond.

The greater the market demand, the more extensive the offer will be; consequently, significant changes will be integrated, improving safety and versatility of use.

Furthermore, the interaction between humans and cobots is increasingly innovative and insightful. Collaborative robots are equipped with artificial intelligence capable of automating the production process in the workplace.

Thanks to the uninterrupted exchange of information, cobots are increasingly capable of overcoming obstacles and choosing alternative routes to routine ones.

The ultimate goal will be to make the cobots completely autonomous. In the not-too-distant future, the development of more robust processors and a broader vision will allow cobots to roam factories without necessarily having to interface with humans.

Also Read: Robots – History, Types & Application

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While some companies are seizing the opportunities that artificial intelligence offers them and using this technology to improve their business processes, others are missing out on this enormous value creation potential. But where exactly does this potential lie?

AI Creates Added Value.

• New, improved analysis: Machine vision allows machines to simulate how the human brain processes information in a fraction of a second. If, for example, components in industrial plants are broken, AI can detect this at an early stage before a costly failure occurs.
• Optimized pattern recognition Allows the evaluation of very, very large data sets.
• Precise forecasts for the future: Predictive analytics, i.e. forecasts based on data, are a challenge on the one hand but are of immense interest for companies that want to become market leaders.
• Natural Language Processing: Describes the interaction of intelligent machines with human language. This clever technology behind chatbots has made progress in recent years due to the development of deep learning.
• Speech recognition: AI understands spoken language and converts it into written text. With natural language understanding and intent analysis to determine what someone wants, speech recognition is the driving force behind Alexa, Siri and Co. But it can also be used for therapeutic purposes.

AI Predicts The Future.

Predictive Analytics

Artificial intelligence can make predictions: They use machine vision and learning to calculate the probabilities of an event occurring based on what they have learned from the past and the associated data. This strategy is used in marketing to make forecasts about customer decisions. For example, AI identifies patterns in user behaviour and increases the relevance of certain ads for certain users accordingly. On Netflix, Amazon Prime and Co., they create personalized series suggestions and thus give customer recommendations. This strategy can also help small companies to recommend customized services or products. On the other hand, if companies want to automate repetitive tasks, machine learning is used in various ways.

However, because AI uses computer vision to recognize, understand and classify images and videos in seconds, it is also used in financial monitoring and network security. AI identifies suspicious behaviour by examining data patterns, which is how they catch scammers.

Predictive Maintenance

Predictive maintenance, in turn, is an application area of ​​machine learning derived from predictive analytics. While predictive analytics describes a forward-looking analysis method based on data and statistics that develops, tests and applies prediction models, predictive maintenance already describes the next step. This form of application gives recommendations on how to react to an occurring event. They are used in particular in industry.

With the help of predictive maintenance, the condition of a technical device or component can be transparently displayed and analyzed using the data from numerous built-in sensors. In this way, even the most minor deviating data can be recognized as a sign of an error, which can be addressed preventively. Preventive maintenance of worn parts and failure-sensitive machines makes it possible to predict whether and when preventative maintenance is required – before a time-consuming and costly failure occurs—using artificial intelligence benefits you from enormous future-oriented and competitive potential.

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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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This article tells you how you can make the most out of AI apps and use them to make your daily life more productive and streamlined. According to Gartner, the worldwide artificial intelligence software market will reach a valuation of $62 billion in 2022, which is why it is important to know how you can benefit from AI-powered apps.

Facial Recognition Technology

One of the ways in which you can embed artificial intelligence in your life is through the facial recognition technology that is present on your smartphones and other security devices. Thanks to this software, you can unlock your phone and also access sensitive apps through biometrics by simply bringing your face in front of the camera. The camera captures an image and uses machine learning to compare it with a stored image of your face. If it determines that your face matches the image, the phone will be unlocked.

Social Media

Social media apps have also come a long way, and there is no chance that you don’t check your Facebook, Twitter, Instagram, and other accounts several times throughout the day. In all of these apps, artificial intelligence is always working behind the scenes and provides you with a personalized experience. The social network picks up on the content that you like to view the most, and uses it to provide you with tailored posts, friend suggestions, filtering out inappropriate or fake content, and also preventing cyberbullying and other malicious actions.

Grammar Tools

If your job or academics require you to write several emails or draft various documents, you can benefit from grammar checking and correction tools like Grammarly and the spell check feature in Google Docs, which make use of artificial intelligence to check for spelling and grammar. Grammarly, and other similar apps, also look for sentence structure, tone, and other factors. You can also set the tone that you are aiming for, and the writing tool will offer personalized suggestions as to what you can change to achieve the desired style.

Virtual Assistants

Have you ever used Google Assistant or Siri on your Android or iOS smartphone? These virtual assistants are powered and driven by artificial intelligence, which allows them to provide you with a human-like experience. They use natural language processing and machine learning to receive and understand your voice or text input and provide you with the most suitable solution or response. You can instruct it to make a call, send a text message, set a reminder, book an appointment, tell the weather, book tickets for travel or the movies, and do much more.

Smart Home Devices

One of the best ways to leverage the wonders of artificial intelligence in your daily life is to make use of smart home devices, which are designed to automate most of the operations that go on daily inside your house. Examples of these devices include Google Home, Amazon Alexa, Amazon Echo, and also several types of smart thermostats, which can adjust the temperature inside the home according to certain factors. Plus, there are smart refrigerators that create shopping lists full of items that aren’t inside them, and also and also offer recommendations. By using these applications and appliances, your life becomes much more convenient.

Daily Commute

Artificial intelligence has also revolutionized the way people navigate and find their way around their city or state. Apps like Google Maps make use of artificial intelligence to provide you with real-time traffic conditions and updates, as well as personalized suggestions regarding the best route to your destination, the amount of time spent on each possible route, any roadblocks or hurdles that come in the way, and much more. You can also make use of the AI capabilities of Google Maps and other apps to help you avoid traffic or find the best route to work or school.

Banking

Apart from the other applications we have discussed, artificial intelligence has also penetrated the baking sector, and it is used to ensure the security of transactions, as well as to detect and prevent all kinds of fraud. You might have a banking app on your phone that allows you to deposit a cheque simply by scanning it or receive an alert every time your account balance goes below the threshold or do much more. All of these functions are performed with the help of AI, and it helps in validating each transaction as well, thus keeping your banking and financial information safe from cyber attackers.

Shopping Recommendations

Another way in which AI can help make things easier for you is through personalized shopping recommendations on e-commerce stores, such as Amazon, Alibaba, and several others. These platforms make use of AI and machine learning to understand your purchase history and search patterns and also learn what people like you like the most. Then, they provide tailored recommendations for what you might like to buy. These apps even comb through your search history on various platforms so that they can show you products you are most likely to buy.

Streaming Services

Be it YouTube, Netflix, Amazon Prime Video, or any other video streaming platform, none of them can function without artificial intelligence. These services offer you suitable recommendations for movies, TV series, and documentaries based on the type of content that you usually watch. It takes into account various factors to do so, including the genre, actors, time period, duration, and much more. Most of the stuff that you watch is recommended by the platform, instead of you searching for it.
This concludes our guide on how you can make the most out of AI apps. As you can see, artificial intelligence has made its way into nearly every industry, which is why it is hard to imagine spending even one day without our smartphones or AI-powered apps.

Also Read: Explainable And Reliable Artificial Intelligence

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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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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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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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