Artificial intelligence is already in significantly more everyday objects than we think. Intelligent algorithms filter and control our purchasing behavior – and also make machines fail-safe. But what exactly is artificial intelligence? Why is this trend of technology booming now of all time? And how does it simplify our everyday work? When Amazon recommends suitable books, clothing, or household items, the shopping platform analyzes our buying behavior beforehand.
The image analysis from Google and Facebook recognizes and manages faces – regardless of age, situation, and image composition. Spam filters in PCs and laptops are also AI-controlled, as are techniques that prevent credit card fraud. So we already encounter artificial intelligence in many everyday situations. And it is also crucial for the production of the future.
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Artificial Intelligence Is Picking Up Speed
Artificial intelligence (AI) is not new. For centuries, authors have been dealing with future scenarios in which machines act intelligently and independently – sometimes more, sometimes less threatening to humanity. And the dawn of the scientific AI discourse – the Dartmouth Conference of 1956 – was a little bit long ago. But now the topic is picking up speed because the technology has developed rapidly in recent years:
- Computing and data storage capacities: The computing power of computers has multiplied, cloud solutions offer enormous data storage capacities across company boundaries.
- Companies can implement their own AI applications: With TensorFlow, Google provided a mature open-source platform with a machine learning framework in 2015, which users have been using since then to implement their own AI applications. In the meantime, other companies and universities also offer ML frameworks and open-source software.
- SaaS, PaaS, IaaS: IT applications are so uncomplicated today, and the costs of necessary resources have fallen so far that complex algorithms can not only be used by large corporations but also by medium-sized companies. Everyone can now obtain software, platforms, or infrastructures as a service based on their needs.
Many Advantages When Machines Learn
When we speak of artificial intelligence in general, we usually mean machine learning (ML) – a sub-discipline of AI. With artificial intelligence, the software can solve problems independently.
In machine learning, algorithms first train to differentiate correctly and incorrectly based on large amounts of data and, on this basis, can make independent decisions about predefined scenarios.
This relieves employees in their day-to-day work or assists people in different situations:
- Routine services in financial accounting or the post office are automated
- Customer communication and the customer experience can be improved
- Cars drive autonomously
- Medical assistance systems support doctors in tumor diagnostics.
As in real life, machines are dependent on extensive training data and numerous repetitions to learn to recognize relationships and to be able to make the right decision later, even with unknown data input. Using the trial-and-error principle, they know to assign data or tones to the given training goal correctly. The advantage: One machine works 24/7, 365 days a year. In contrast to humans, machines do not tire and require a break or food to recharge the batteries. The only food they need is amounts of data.
Use Of AI And Machine Learning Tools In Medium-Sized Companies
Data is the basis for business success, whether it is customer-related knowledge or detailed knowledge of internal company processes. Thanks to the technical development and the lower costs, medium-sized companies also benefit from ML, analyze and combine their data knowledge, optimize their business processes and increase their competitiveness. QSC AG accompanies medium-sized companies in their digitization processes and supports the introduction of intelligent technologies in various business areas:
Automate finance, sales, and procurement: With SAP Cash Applications, the QSC experts use an ML tool, for example, with which companies automate manual processes in the areas of finance, sales, and procurement.
Improve complaint management: With the help of ML models from QSC, the degree of automation in e-mail routing is increased. This improves complaint management and relieves employees.
Optimizing processes holistically through process mining: Operating methods are very complex and sometimes very confusing. This results in errors or long processing times. Because every computer-based work step leaves a digital footprint, Celonis uses so-called process mining to digitally map each business process and identify relationships between the process steps in real-time.
In this way, all deviations from optimal strategies can be analyzed, and inefficiencies in purchasing, loops in logistics, or bottlenecks in production can be identified. QSC offers this technology to its customers – and thus supports them in identifying sources of error and removing process obstacles.
Digitize everyday objects with the Internet of Things: The Q-Freezer ice chest shows how QSC, in cooperation with its subsidiary Q-loud, also makes simple everyday objects intelligent through machine learning and SAP Leonardo. The freezer, networked via IoT sensors from Q-loud, independently reports temperature, power consumption, and fill level.
A specially trained ML program differentiates between different types of ice cream, for example, and predicts when visitors will take a specific kind of ice cream out of the chest. Retailers from kiosks to supermarkets always know the current inventory and can automatically generate ERP processes through the interface in their SAP landscape.
ML Is An Essential Driver For Industry 4.0
Improving business processes, automating work steps, reducing costs: there are many reasons for companies to use intelligent algorithms. Last but not least, they make comprehensive digitalization of industrial production – i.eIndustry 4.0 – possible in the first place. For example, predictive maintenance: The predictive maintenance of production machines allows failures, error patterns, or material damage to be forecast even before a standstill occurs due to material wear or material defects. Historical machine data are the basis for ML models to predict expected failures.
In the training phase, the ML system learns to derive typical relationships from an existing database. For example, Turbines, escalators, escalators, normal record parameters such as vibrations, voltage curves, temperature, and pressure. Information about previous functional failures supplements the database. ML models then categorize and evaluate individual data sets based on learned relationships to find solutions to new and unknown problems in the application phase.
SAP Predictive Analytics Provides Forecast Models
One software solution that can be used to create quickly, train, and implement such forecast models is SAP Predictive Analytics. SAP customers benefit from an interface into existing system processes: The SAP Cloud Platform provides technology building blocks in the form of cloud services, supplements, or the SAP ERP and SAP S / 4hana as software-as-a-service, such as SAP SuccessFactors used can be.
The SAP Cloud Platform is based on the in-memory database from SAP HANA. Data is no longer stored on conventional hard disk storage but instead uses the main memory directly. Due to the significantly higher access speeds, ML models can be easily trained and operated in-house. QSC AG also supports its customers in using these solutions.
Plan Production And Resources, Tailor Marketing To Customer Needs
Once an ML model has internalized historical machines and associated them with defined parameters such as vibrations, voltage curves, or noise development, it knows about project-related patterns and regularities. Because the program predicts the probability of specific outcomes occurring, industrial companies can optimize their production and resource planning.
The retail sector also benefits from intelligent algorithms: companies can, for example, tailor their marketing activities to individual customer wishes and needs. ML models create purchase forecasts for each customer based on historical customer data on purchasing behavior and online searches. For example, ML models know about buying decisions related to the relevant seasons and life events such as trips, weddings, or relocations – and sometimes they know the customers better than they do themselves.
