For the first time, scientists John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in Physics this year for pioneering work in machine learning.
This paper forms the foundations of all artificial neural network applications, declared by the Royal Swedish Academy of Sciences. Their findings have changed how machines make sense of and learn from data, really forming the underpinnings of artificial intelligence as we know it today.
John Hopfield's Associative Memory Pioneering
John Hopfield, a researcher at Princeton University, made a current trend in scientific research by inventing a model for associative memory. For instance, machines can retain and retrieve patterns of information, such as images, with increasingly high precision due to associative memory. It has opened up various innovations in artificial intelligence because models of machines are well afforded to decode complicated data sets in order to have an understanding and re-produce information, TechCrunch states.
It is revolutionary when talking about associative memory in the realm of neural networks since it has altered the way machines interact with data, thus providing powerful new means to recognize and process patterns. Now, that impact can be seen in applications ranging from recognizing images to predictive algorithms; therefore, what he has done is crucial in the further development of AI.
Geoffrey Hinton's Revolutionary Neural Networks
At the University of Toronto, Geoffrey Hinton successfully developed a technique that lets machines automatically discover features from data.
A breakthrough technique developed by Hinton enables AI systems to perform tasks on images and objects without human intervention. His development improved efficiency and capabilities by huge margins through machine learning systems, on which research in this line leads to further innovation in AI technologies worldwide.
Hinton's work is the backbone of different AI-driven applications, ranging from facial recognition software to self-driving cars, allowing computers to process vast amounts of information with minimal guidance.
Indeed, he continues to be instrumental, and his influence on the industry cannot be measured in determining what AI-powered technology will look like.
The Nobel Prize Award Made People Question AI
As more recognition and praise are bestowed on these scientists, so has the question of ethics in its use of AI come to the fore. The Nobel committee recognized this by asserting that although this technology holds much promise, it needs caution in development owing to responsible issues on machine learning technologies.
In its press statement, the Nobel committee argued that it is mankind's responsibility to safely and ethically use AI.
"The laureates' work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties," Nobel Committee for Physics chair Ellen Moons said.
One particular scientist who has warned about AI's dangers includes Geoffrey Hinton. Hinton resigned from his job at Google to speak openly about the risks the continued development of AI would bring.
Following his award on Tuesday, Oct. 8, he repeated his concerns: His worst fear is when machines get out of control. Even with those fears, though, Hinton said he would do it all over again if he had the chance.
