The US military is chasing a ‘third wave’ of artificial intelligence (AI) that will see robots endowed with the basic common sense of a 10-year-old child.
Its research branch the Defense Advanced Research Projects Agency, or DARPA, is calling for researchers to breed a new type of AI that can solve complex problems.
The goal is to build AI that can ‘communicate more effectively with people’ and ‘understand new situations’ better than any previous machines.
The project, called The Machine Common Sense Program, is part of a $2 billion investment in AI by Darpa – the military research branch that pioneered the internet.
The US military is chasing a ‘third wave’ of artificial intelligence that will see robots endowed with common sense. Its research branch the Defense Advanced Research Projects Agency is calling for researchers to breed a new type of AI that can solve complex problems (stock)
It aims to build AI that operates outside of the hyper-specific niches where it works well today – such as categorising photos, or playing chess.
‘The absence of common sense prevents an intelligent system from understanding its world, communicating naturally with people, behaving reasonably in unforeseen situations, and learning from new experiences,’ a Darpa spokesperson said.
‘This absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general AI applications we would like to create in the future.’
Darpa is aiming to help develop an AI with the common sense of a ten-year-old child.
In other words, something that can draw on a multitude of facts and observations to find its own solutions to complex problems.
AI submitted to the program will undergo rigorous intelligence testing courtesy of the Allen Institute for AI – a lab funded by Microsoft co-founder Paul Allen.
Machines will answer more than 100,000 questions designed by the institute to test for the presence of common sense.
HOW DOES ARTIFICIAL INTELLIGENCE LEARN?
AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.
ANNs can be trained to recognise patterns in information – including speech, text data, or visual images – and are the basis for a large number of the developments in AI over recent years.
Conventional AI uses input to ‘teach’ an algorithm about a particular subject by feeding it massive amounts of information.
AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn. ANNs can be trained to recognise patterns in information – including speech, text data, or visual images
Practical applications include Google’s language translation services, Facebook’s facial recognition software and Snapchat’s image altering live filters.
The process of inputting this data can be extremely time consuming, and is limited to one type of knowledge.
A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other.
This approach is designed to speed up the process of learning, as well as refining the output created by AI systems.
One example reads: On stage, a woman takes a seat at the piano. She a) sits on a bench as her sister plays with the doll; b) smiles with someone as the music plays; c) is in the crowd, watching the dancers; d) nervously sets her fingers on the keys.
While to a human the correct answer is clearly d, even top AIs only get this question right about 60 per cent of the time.
Darpa believe the solution to this problem lies in mimicking the learning processes used by young children.
‘During the first few years of life, humans acquire the fundamental building blocks of intelligence and common sense,’ Mr Gunning said.
‘Developmental psychologists have founds ways to map these cognitive capabilities across the developmental stages of a human’s early life, providing researchers with a set of targets and a strategy to mimic.’