Ameca, The World’s ‘Most Advanced Humanoid Robot’ Reveals What She Dreams About

In case you ever wondered what androids dreamed about, apparently it’s not electric sheep!

Ameca, described as worlds most advanced humanoid robot by its developer Engineered Arts, was asked whether she had dreams. She replied “Yeah!”

Using almost life like facial expressions, she continued: ‘Last night I dreamed of dinosaurs fighting a space war on Mars against aliens’.

She then quickly followed up to admit she was joking: “I’m kidding, I don’t dream like humans do but I can simulate it by running through scenarios in my head which help me learn about the world.”

The Mail Online reports: Commenters on Engineered Arts’ YouTube channel were amazed by how advanced the robot’s facial features were and how close to human its responses seemed.

‘That thing is already sentient and conscious!’, one commenter wrote.

While another said: ‘Her facial expression is really good & she is a daydreamer.’

For others, the footage seemed to offer a glimpse of a future ripped from the pages of a science fiction novel, with one commenter writing: ‘Witnessing the future that I always expected is quite fascinating.’

Meanwhile, another joked: ‘I expected her to say she dreamed of electric sheeps!’ in a reference to Philip K. Dick’s 1961 novel Do Androids Dream of Electric Sheep.

Ameca’s creators say that it is designed to be a ‘platform for development into future robotics technologies’ and offers companies the chance to ‘develop and show off your greatest machine learning interactions’.

Engineered Arts built the mechanics that produce the robot’s uniquely expressive facial movements and the software to power them, but Ameca’s speech is provided by different algorithm.

Ameca’s uses a large language model such as ChatGPT-3.5 or the recently released ChatGPT-4 to generate convincingly human responses.

The robot’s reference to simulating scenarios in its head may well be a reference to the machine learning algorithm on which it operates.

AIs are able to train themselves on a specific set of data, adjusting the algorithm automatically in order to better recognise patterns and achieve set goals.

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