As artificial intelligence polarises, the media attention around the topic increases. This is indeed very positive, as the public discourse leads to a wider and more even distribution of knowledge. But at the same time, it increases the diversity of interpretations and opinions, often resulting in the circulation of half-truths. As a result, uncertainty and fear of new technologies are on the rise due to lacking or inconsistent information.
For that reason, first and foremost: AI will not replace humankind – not today, not tomorrow, never.
But artificial intelligence will impact our everyday (working) lives in the long run, just as the entire digital transformation has done so far and will continue to do, resulting in new ways of work, the creation of new professions and a rising demand for lifelong learning.
A changing world of work lies in the nature of any transformation – whether industrial or digital – but often, it makes us doubt and condemn the innovation itself. These doubts are neither new nor justified – why?
Reason 1: “Artificial intelligences are nerdy”
Yes, artificial intelligence is more efficient than humans – at least in the specific field for which it was developed. AI is not an all-round solution, but a collective term for different intelligent applications which focus on one specific problem each. Individual abilities are simulated efficiently, but not the entirety of our human intelligence.
Also, certain processes or characteristics cannot be automated at all. For instance, automatic text generation is primarily suitable for high-volume, standardised use cases, such as product descriptions for online shops or data-based reports of football matches. And although natural language generation can be used to create texts in different tones, there are certain types of text that AI can’t serve. Creativity, empathy and humour will remain human, non-automatable qualities – meaning only human journalists can write captivating reports, sensitive portraits or amusing interviews.
Reason 2: “Artificial intelligence can’t just be plugged in”
It’s not as simple as it sounds: AI can’t be developed just like that. The right algorithm must be written for the right problem. This may sound quite simple and obvious; however, it requires a deep understanding of the (almost always) very complex problem.
Yet the development alone is not enough: implementing applications based on artificial intelligence in a company involves rethinking existing processes from the ground up. This includes a well-thought-out strategy and the correct and consistent use of the system in order to benefit as much as possible from the integration.
Reason 3: “No data, no AI”
One would think that in the digitalised world there is a vast amount of data available for every problem. That is true, but at the same time it’s not. In order to train an AI algorithm for its specific application, it takes an incredibly large amount of training data as the algorithm has to learn a suitable reaction to every potential situation.
To avoid formally correct, but practically inappropriate or potentially dangerous conclusions, the data must not only be of high quantity, but of high quality. While we humans can evaluate how to react in each situation we’re faced with individually, the machines must be adequately “prepared” for each situation by the training data already. A data basis that covers every single situation that might occur is therefore a fundamental prerequisite, but quite often not sufficiently available.
Man and machine: the right way to deal with artificial intelligence
The fear of an “AI Invasion” is understandable but misplaced. There are various reasons why AI neither can nor will replace humans; three of the most basic aspects have been dealt with here.
Instead of putting all of our energy into protesting against something that is, after all, unstoppable, we should focus on finding appropriate uses for the intelligent assistants. Our everyday and our working lives will continue to change. While some tasks no longer need to be done by us humans, new professions and exciting challenges are emerging. And one of these challenges certainly lies in using these developments actively and constructively.
We can’t stop the future, but we can certainly shape it. We’re entering an exciting phase in which a lot of new potential is opening up. Now is the time to create exciting ideas and flexible models for our future. At Retresco, we’re looking forward to exchanging exciting ideas and developing new projects together with you.
About Retresco | @retresco
Founded in Berlin in 2008, Retresco has become one of the leading companies in the field of natural language processing (NLP) and machine learning. Retresco develops semantic applications in the areas of content classification, recommendation, as well as highly innovative technology for natural language generation (NLG) . Through nearly a decade of deep industry experience, Retresco helps accelerate its digital transformation, increase operational efficiencies, and enhance customer engagement.