Machine learning and artificial intelligence(AI) were still dreamed of the future for many companies. But the change is already underway, as small data and comprehensive data allow more and more companies to use the advanced technologies. Machine learning techniques such as transfer learning are enjoying growing popularity.
Artificial intelligence and the sub-area of machine learning (ML) have developed into essential and indispensable helpers in many areas. The application areas now go far beyond technical gimmicks such as Alexa or Siri. Whether in the form of chatbots on websites, as the brain for intelligent search engines, or under the hood in daily data and document processing: artificial intelligence has become a potent tool for analyzing and creating value from data.
Thanks to their data power, large corporations have benefited from the use of AI to date. However, this previously indispensable paradigm is slowly breaking: the entire industry is becoming aware that AI is less and less an excellent addition to the portfolio but a necessary business strategy.
Small Data And Comprehensive Data Abolish AI Monopolies
The amount of data available for AI training has divided the AI world into two areas: on the one hand, the platform giants, whose unimaginable mass of data made them appear almost omnipotent in the machine learning area. On the other hand, there are smaller companies that, in practice, do not have access to sufficient amounts of data. Google, Amazon, and Co. make their AI methods generally available for generalized use cases, which seems helpful at first glance. But big data was essential for a long time and the measure of all things when training artificial intelligence.
Smaller companies can only use the generalized models of the platform giants to a limited extent in the specific context of use since they do not have the opportunity to collect and use gigantic amounts of data or the general models do not always fit the project-specific use cases that are required in the companies. The result: a large part of the economy is largely excluded from the technologies of tomorrow. For this reason, for the optimal adjustment, they have to train their AI with the actual data sets that are only available in small quantities and about the respective, usually extraordinary context.
With the Small Data approach, companies use innovative analysis techniques to create value from smaller data pools with optimized machine learning processes. Another new method is Wide Data. This approach is about creating synergies from a wide range of different data sources and types to improve the context for AI applications.
With these approaches, more and more companies can use their data treasure effectively and profitably. Small and comprehensive data enable more robust analytics, reduce reliance on big data, and help organizations gain a 360-degree view of their data wealth.
Transfer Learning Trains AI In Practical Use
Another valuable tool, especially when dealing with small data, is transfer learning. This is a unique method of deep understanding, a machine learning discipline. Transfer Learning can reuse pre-trained models for evaluating data that the developers did not use for the initial training. Especially when tiny amounts of data are available, it is usually not possible to use them for extensive training of the AI. But even with the correspondingly smaller training data set, the pre-trained model can be fine-tuned. For example, a machine learning model designed for image classification of cats, dogs, or vehicles might be fine-tuned to detect carcinoma with a more minor data set of MRI images and then applied. Transfer learning, which initially comes from image processing, can also be used for language models and thus for analyzing text documents.
The intelligent analysis of documents enables completely new working methods, as companies can digitize processes and partially or fully automate them. In this way, processes can be optimized and implemented much more efficiently. Authorities and large companies, in particular, have vast amounts of data, and new ones are added every day. Several employees are often tasked with filtering the relevant information from documents required for further processing. This takes a lot of time, and the human factor makes for a comparatively high error rate.
Intelligent document processing, i.e., the use of AI-based software for processing documents, is becoming increasingly important and, at the same time, enables the automation of workflows based on modern AI methods. Above all, application checks, order acceptance, and updating customer and payment data are prominent areas of application for this technology. In addition, IDP software helps with regulatory compliance or product tracking through retail supply chain systems. The areas of application ultimately include all text-based work processes.
Conversational AI Improves The User Experience
Anthropomorphism, the humanization of technology, has always been an essential topic in artificial intelligence. This phenomenon has established itself in everyday life at the latest with Siri on the iPhone and Alexa on the television. Conversational AI is a real asset, especially in customer service. For chatbots and question-answering systems as virtual assistants to be of real help to customers and thus also to companies, several challenges need to be mastered. The AI must correctly interpret, “understand,” and provide answers to customer inquiries and as little as possible resort to a human expert. Natural Language Processing (NLP) is used to make this experience realistic. The better the conversational AI system works, the more customer inquiries companies can process automatically. This saves employee resources and makes customers less dependent on business hours.
New methods such as small data, comprehensive data, transfer learning, and the growing spread of existing technologies are making AI more democratized. More and more companies can use the innovative solutions profitably, for example, to automate processes or create value from existing amounts and vast amounts of data. Customers also benefit from this: Intelligent searches and AI-based dialogue systems improve the user experience fundamentally.
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