What challenges will the next generation of Artificial Intelligence innovations face?
Time has shown that Artificial intelligence (AI) is a concept that is here to stay. This emerging sector blossomed as an Enabling Technology at the beginning of the 21st century, but was first described by ‘The father of AI’ back in 1956: John McCarthy. Developments in the field of AI have followed each other in rapid succession and went hand in hand with the technological leaps that have made these innovations possible. However, the roller coaster of development today also brings new challenges to the forefront. You can read about four hurdles below:
The engine behind AI is of course the data with which it is trained. However, the collection and labelling of high-quality data is increasingly a limiting factor within the field. In addition, obtaining a large and comprehensive dataset is a disastrously difficult task. The lack of qualitative datasets is therefore one of the most common causes of poorly functioning AI. In particular, start-ups and new applications run the risk of stagnation in the growth of AI innovations. As a result, new applications may be slowed down even before they are launched.
Privacy and security
Once the data has been collected, the possession of this data also creates other difficulties. In particular, the safe use and anonymous storage of data comes under pressure. Some datasets used to train a neural network consist of millions of user details, and sometimes information is even used without permission. Think of the Cambridge Analytica and Facebook scandal. But what can we learn from this? Dealing with large amounts of data comes with a lot of responsibility and danger. By using better regulatory measures such as the GDPR (General Data Protection Regulation) and applying data anonymity and encryption, we ensure that we can continue to use machine learning and neural networks without the risk of data misuse.
If the data is the engine of an artificial intelligence system, then the algorithm is the driver of the car. The driver determines the direction of the system: the decisions and predictions made by the model. As algorithms increase in complexity to achieve the required accuracy, they become less and less easy to understand for humans. Especially unsupervised neural networks can achieve the desired results in incomprehensible ways. In order to trust artificial intelligence and integrate it into our society and legislation, we will have to prioritise transparent solutions.
In addition, transparent algorithms are not only important to be able to trust them, they are also necessary to counteract data bias and discrimination. As described earlier, the data is central to the operation of an AI system, and the algorithm determines the direction of the results. A model trained with data that contains insufficient ethnic, gender and racial diversity will undoubtedly develop different preferences than a neural network that has been designed completely without bias. The rules of the algorithm that define how the AI deals with data are also important here.
Artificial Intelligence raises some major issues
Artificial Intelligence has truly become a key technology, but the central role it is taking on in our society comes with it a number of social and ethical issues. How do we collect enough data in an appropriate way? How do we deal with this data in the right way? How do we keep an eye on the technology, and how can we learn to understand what our computer programmes are doing? How do we ensure that no one is left out?
These are quite the questions, and I would even like to add one more: How do we ensure that artificial intelligence remains a sustainable technology? Without billions of bytes of data, hours of computing power and heavily loaded networks. In my opinion, it is time to leave behind the adolescent phase and the wild west of ever more new innovations and solutions, and make AI a truly mature enabling technology. In this new generation of innovations, socially, ethically and sustainably responsible AI will play a crucial role, and these themes will undoubtedly be reflected in grant programmes and successful applications in the highly competitive world of funding.
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