AI inspired by the evil HAL-9000 successfully keeps astronauts alive for four hours

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The supercomputer HAL 9000, which is best remembered for killing astronauts in the ‘2001: A Space Odyssey’ has been recreated by scientists to aid astronauts in space.

The new prototype, which has been developed by Texan company TRACLabs Inc, has successfully controlled a simulated planetary base for hours and can display information such as life support and robot status.

In the movie, HAL 9000 is the artificial intelligence controlling the nuclear-powered Discovery One spaceship.

In the novel by science-fiction legend Arthur C. Clarke written alongside the film, the computer is described as capable of talking with astronauts ‘in the perfect idiomatic English he had learned during the fleeting weeks of his electronic childhood.’

In the movie, HAL 9000 is the artificial intelligence controlling the nuclear-powered Discovery One spaceship

In the movie, HAL 9000 is the artificial intelligence controlling the nuclear-powered Discovery One spaceship

Artificial intelligence researcher Pete Bonasso at TRACLabs had first saw ‘2001: A Space Odyssey’ in his senior year at West Point university, where he programmed the academy’s lone computer to play a virtual version of pool, and since then has set about trying to create something just like it.

In a study in the journal Science Robotics Bonasso said: ‘When I saw ‘2001,’ I knew I had to make the computer into another being, a being like HAL 9000.’

The new technology can visually display information such as people who need life support and robot status, but can also converse with people so they can ask questions, send commands and be warned about any problems.

Instead of murdering astronauts the software is designed to carry out plans only after sharing them with people and getting consent for action.

The new software that Bonasso and his colleagues have designed, branded the ‘cognitive architecture for space agents,’ or CASE, is composed of three key layers.

The new software has three layers and was developed by researchers and scientists in Texas

The new software has three layers and was developed by researchers and scientists in Texas

The first layer is a continuously running control that connects to and runs hardware such as robotic hands and eyes, as well as controlling a simulation of a planetary base.

The second layer focuses on what some would call the mundane and carries out procedures underlying routine activities such as connecting power to batteries, controlling oxygen-generation and carbon-dioxide-removal systems, and charging and sending rovers to retrieve samples of planetary rock.

The third layer consists of automatic planning software that decides how to achieve CASE’s programmed goals for the day and the order in which to perform them.

 2001 - A Space Odyssey as originally released in 1968 by director Stanley Kubrick

 2001 – A Space Odyssey as originally released in 1968 by director Stanley Kubrick

It can also automatically reschedule activities when problems arise, such as gas leaks, broken motors or planetary dust storms, Bonasso said.

The layers are linked to an ontology server a database that can reason about its data.

If someone moves a toolbox from the equipment locker to the crew quarters, the ontology server reasons that all of the tools in the box will change location as well, the researchers said.

Speaking to Space.com Bonasso said: ‘Our colleagues and NASA counterparts are not concerned that our HAL might get out of control.

‘That’s because it can’t do anything it’s not programmed to do.’

The group have conducted trials and CASE managed a simulated planetary base for about four hours. However, the researchers stressed more work is needed before it can run an actual base.

‘Though CASE is impressive, it’s not the fully realized HAL from ‘2001: A Space Odyssey,’ nor is it Lt. Commander Data from ‘Star Trek: The Next Generation,’

‘Its capability is very narrow, focused on events occurring on a planetary base. While it can keep the life support systems running, it has no idea who won the last presidential election.’

The group is now working with what NASA calls analogs — places where volunteers pretend they are living on a distant world.

The aim is to incorporate CASE into the analogs to see how the software can help improve future space expeditions.

Bonasso added he sometimes loses track of how this work aims to create a real-life version of HAL for distant astronauts.

‘When you’re deep in the workings of software, you forget sometimes that you’re actually imagining what it would be like to live on Mars or the moon.

‘Sometimes we have to step back and say, ‘Hey! This is pretty cool.” 

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

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. 

 



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