- AI-powered robots can help children learn technical skills, but they could also be good tools for encouraging soft-skills development, including empathy and attentiveness. In what ways could AI robots achieve that?
In the future, AI could potentially be educated with different information on how to cultivate emotional intelligence on a large scale. This would enable them to provide personalized solutions for children. Additionally, we may encounter a situation – which, by the way, needs to be managed very carefully – where AI starts to develop emotional intelligence. How would we then manage and personalize the learning process? Would you want an AI that has developed its own emotional intelligence or feelings to educate your own children? Do you see what I’m saying?
We need to have better guidance, to clearly lay down potential benefits, as well as potential risks. If there are any risks, we need to minimize them and if there are any benefits, we need to maximize them.
Christina Yan Zhang, Source: WebMind
- AI is trained on a significant amount of data, which can potentially be outdated, biased, or flawed in various ways. Isn’t that a cause for concern, especially when considering the introduction of AI to children? How can we address this issue and ensure the responsible use of AI in education?
Just like any national curriculum, governments will establish clear guidelines on what should be included, ensuring the inclusion of ethical, responsible, and accurate information. I think once we try to have different AI companies joining the race to create education – a system of the future- maybe we need to follow a similar principle.
The purpose of these companies would be to really provide information which is carefully selected – it’s almost like a rating system of different movies. So, you need to have a little bit more categories to ensure online safety and AI safety for children especially when the cause is their education moving forward.
- There is a demand for high-quality data. Do you think new markets for training data might emerge in the future?
We need to clearly define what is called high-quality data because, at this moment, we’re generating too much data, which has never happened before. The issue is that only about 20% of the data is analyzed to create value, while the rest 80% of the data is not analyzed. It’s sitting there quietly, consuming huge amounts of energy without adding any value.
Some people will say that data is the new oil, which is true. But data will only become the new oil when it’s analyzed to assist evidence-based decision making. That’s where we’re really adding value. But worldwide, only 20% of data of any organization is analyzed to bring value for their future developments. That’s just not good enough.
The issue at this moment is mainly because the standardization of the data is not really efficient enough, so we end up having different definitions of the data. Different departments sometimes also have their own data management systems, meaning they aren’t talking to each other. That’s the issue that needs to be addressed when we talk about high-quality data.
All of the different departments, including finance, HR, procurement, and transport, need to standardize some kind of data interoperability to ensure the data is of the same format when you analyze it. We don’t need to go into each department to get the data, we could just have all the data in one place and in one kind of format and that makes it much easier to create high-quality data.
