Autonomous systems in the real world
Prejudices in facial recognition and recruitment systems. Accidents involving self-driving cars. This type of failure shows that much remains to be done in the development of AI. The fastest way of moving that development forward is for AI and people to work closely together.
When self-driving cars were first introduced, we were all very impressed by what was possible at the time. And rightly so! Since then, the AI technology for autonomous systems has made huge progress. But a vehicle that is able to find its way and transport its passengers safely from A to B, without any human input? We’ve not yet reached that stage. Current AI technology is still too limited for that. Let’s not forget the accidents involving cars that have partly taken on the responsibility of driving from people.
Artificial intelligence and moral decisions
At an ethical level, too, AI still has a long way to go. When a person is behind the wheel in a dangerous situation, they don’t just look at what is the safest option is for them. They will think about other road users too. In some cases, they will have to make a moral decision. AI is not yet capable of that. The big question is how to program moral decisions into the software of a self-driving car. And is that actually a desirable thing to do? This is the kind of ethical dilemma facing developers. Not just in the case of self-driving cars, but also with other AI-controlled systems, such as robots.
Four areas of focus for autonomous AI
At TNO, we are currently developing hybrid AI algorithms (a combination of machine learning, symbolic reasoning, and domain knowledge) and software with which autonomous systems are able to operate safely and effectively in an open environment, without any direct human intervention. In doing so, we are focusing on four aspects:
- Environmental awareness – recognising previously unidentified objects and situations.
- Self-awareness – reliably assessing one’s own competencies.
- Decision making – planning actions that are safe and effective.
- System integration – implementing real-time hybrid AI algorithms for environmental awareness, self-awareness, and decision-making for commercially available robots.
In depth with use cases
It’s perhaps also useful to know that, in addition to this area of research, we are also working on responsible decision making between people and machines. In other words, we are working on possible solutions to the current challenges facing AI from a variety of angles. But in this area of research, however, our focus is very much on ‘safe autonomous systems in an open world’. And to gain a closer feel for this, we have written several use cases:
Judith DijkFunctie:senior research scientist
Judith is specialised in extracting information from camera images. She now applies the subject of her PhD thesis in Physics, which she obtained 18 years ago, to her work as a research scientist at TNO, including in a research programme on camera systems for the Dutch Ministry of Defence.
Christopher BrewsterFunctie:Senior scientist
Christopher Brewster is a Senior Scientist in the Data Science group and Professor of the Application of Emerging Technologies in the Institute of Data Science, Maastricht University. His research has focussed on the application of Semantic Technologies, Open and Linked Data, interoperability architectures and Data Governance, mostly to the food and agriculture domains.
Daniël WormFunctie:Senior consultant
Jok TangFunctie:Deputy Research Manager Data Science
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AI Systems Engineering & Lifecycle Management
The AI system for the future. At TNO, we work on AI systems that remain reliable and can handle new functions in the future.
You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?
Innovation with AI
What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.
Innovating with innovation AI
How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?
David Deutsch on the development and application of AI
Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?