Artificial intelligence is often referred to as the supreme discipline of digital transformation. And rightly so. Because AI is only really picking up speed, it is becoming more and more mainstream in the sense of predictive models. And NLP, Natural Language Processing, is the booster.
Digital transformation is a process, a journey in which the direction is changed again and again and the destination is open. However, many people mistakenly understand this to mean that once this or that has been achieved, a tick can be put in the box. But that is not the case. This is the topic of the next years and decades. NLP has the enormous potential to give the digital transformation a decisive push. Therefore, here is first a definition, then a status report and finally an outlook.
By definition, digital transformation refers to an ongoing process of change based on digital technologies - mainly in an economic context, i.e. what this means for companies. Then there is also the administrative and, of course, the educational sector. How it stands, well, that remains to be seen. A striking example: equipping all schools with computers for all pupils and then saying "done", that's not it. The keyword here is "ongoing process of change".
New operating systems, new software, new application possibilities - if you don't understand digital transformation as a process, you quickly find yourself back at square one. Why this gloomy picture now? Can't we be more optimistic? Yes, of course. Developments in the field of AI, above all NLP, are opening up ever greater dimensions of applicability at ever shorter intervals. Models in Natural Language Processing are coming closer and closer to human understanding, and in some cases they are even better. This is already having a major impact on the economy, or could have a major impact.
This depends on the extent to which companies understand, accept and use AI and NLP as beneficial technology. But it's not a question of if, but when? It will happen, I am sure of it. A common cliché is that AI is only for science or large corporations - rocket science, in other words. That may have been true at one time. Maybe that's why some companies are hesitant to approach this field. But companies of all sizes can already profit from NLP, and blue chips no longer have a monopoly on it. But in the case of domain knowledge, i.e. proprietary knowledge and data of a company, NLP has so far been used rather rarely. Yet productive use is possible in weeks to a few months - and the potential for improvement is enormous. Because:
More and more companies are discovering the many possibilities of NLP, which are continuously becoming more diverse. One of the reasons for this is that NLP makes the boundary between structured data and text more and more fluid, i.e. text can be treated like structured data. A simple example of structured data: Excel and address databases... This data can be analysed in a fraction of a second (most of the time). Text is text - also available in various forms, in documents, but also in audio files. And this is where NLP comes into play as a joker:
NLP, as a reminder: Natural Language Processing, makes it possible to process information in a very short time, similar to structured data. Does all this sound very abstract? Then here is something more vivid: to extract data from an Excel spreadsheet or to carry out a duplicate check in an address database, it only takes one click (at the right place) even in standard Office programmes. However, if you want to extract information from a text document, this is not done with one click - at least in standard programmes without NLP... Information is not initially marked as information. And as such, it is not classified or analysed in a context. With NLP:
My favorite example - a text document with many, many pages, you use the "search" function for "bank". Then hits are displayed. But only those. Whether it's a bank for sitting meditatively in the park or a bank to park one's money for safety's sake, that's not apparent - unless a trained AI carries out this search... To visualize it:
This Strategic Alliance Agreement is made and entered into this 9th day of September 2005, by and between UTEK Corporation ("UTK "), 202 South Wheeler Street, Plant City, Florida 33566 a Delaware corporation, and World Energy Solutions , ("AVDU "), 3900A 31st Street North, St. Petersburg, Florida, a Florida corporation.
Another tangible example - an extensive contract archive with hundreds of documents, each several hundred or thousands of pages long. Without NLP solutions, this means that it has to be evaluated individually, by hand, by a human being. That means reading, searching, evaluating, typing. That can take time. With an NLP solution, it can be done in seconds. Concrete examples of use? For example, extracting relevant information from data room documents (legal liabilities, supplier service level agreements, notice periods, etc.) for legal and commercial due diligence. There are many other examples and possible applications and uses of NLP, and many more will definitely be added.
NLP has also long since arrived in everyday life. Just recently, it was announced that BERT, an NLP solution, has significantly improved the Google Assistant (https://mixed.de/google-assistant-wird-durch-bert-ki-verbessert/). In other words, the continuous development of NLP is the next rocket stage that will be - or already is - ignited in AI. And this is what I mean by true digital transformation...