Can machines think?, asked the famous mathematician, code breaker and computer scientistAlan Turingalmost 70 years ago.
But others argue that machines will never measure up.
Although AI can already outperform humans on certain tasks just like calculators they cant be taught human creativity.
Surely an AI wont ever be able to compete?
Well, it turns out they might.
Children learn by interacting with the surrounding world via trial and error.
Learning how to ride a bicycle often involves a few bumps and falls.
In other words, we make mistakes and we learn from them.
This is precisely the waymachine learning operates, sometimes with some extra educational input (supervised machine learning).
It’s free, every week, in your inbox.
Here, a human must show it images containing the object or not.
Here, AI automatically starts being able to detect patterns in data.
Suppose we want to understand some properties related to a specific material.
The obvious step is to search for information from books, web pages and any other appropriate resources.
NLP can, however, help us.
In the new study, an AI learned to retrieve information from scientific literature via unsupervised learning.
This has remarkable implications.
So far, most of the existing automated NLP-based methods are supervised, requiring input from humans.
Despite being an improvement compared to a purely manual approach, this is still a labour intensive job.
This was based on about 1.5m abstracts of scientific papers on material science.
For example, heat was classified as part of energetics, and gas as elements.
It provided insights well in advance compared to what scientists can predict at the moment.
In fact, the AI could recommend materials for functional applications several years before their actual discovery.
There were five such predictions, all based on papers published before the year 2009.
So if the AI had been around in 2009, it could have speeded up the discovery.
Thermoelectric seebeck power module.
Many thermoelectric materials share such properties, and the AI was quick to show that.
This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications.
AI systems are becoming more and more independent.
And there is nothing to fear.
Despite concerns related to privacy and security, AI is changing our societies.
