All posts by Daniel Davies

Stanford scientists discover humans get aroused by touching robots’ butts

Scientists studying how humans interact with robots have discovered that touching a robot in intimate areas elicited the same physiological arousal that humans evoke in each other.

In the Stanford University study a robot –which is described as appearing somewhere between C-3PO and Wall-E – was programmed to ask participants to touch 13 parts of its body, which included its ears and butt.

Participants were fitted with an sensor on their fingers that measured skin conductance – a measure of physiological arousal – and reaction time of the participant.

The scientists found that when participants were instructed to touch the robot in areas that people usually do not touch, like the butt, they were more emotionally aroused than they were when touching more accessible body parts like the hands and neck.

“Our work shows that robots are a new form of media that is particularly powerful. It shows that people respond to robots in a primitive, social way,” said Stanford University’s Jamy Li. “Social conventions regarding touching someone else’s private parts apply to a robot’s body parts as well. This research has implications for both robot design and theory of artificial systems.”

Touch has been an underexamined aspect of human-to-robot relationships.

However, a large body of research in communication shows how touch builds relationships and influences trust in people. Until now not as much has been known about touch between a person and a robot.

Image courtesy of Bondara.

Image courtesy of Bondara. Featured image and video courtesy of Jamy Li

Prior to the study, the researchers believed that humans’ response to robots would be to look at them as a friendly, non-threatening computers, but the findings show that humans respond to robots in the same way they do to other humans.

The study’s findings contradict Dr Ian Perason’s claims that sex with robots will become commonplace by 2050 as “love and the act of sex” become separated.

In the Stanford study, the researchers noted that “physiological arousal was inversely related to body accessibility” as it is in humans.

Scientists are using machine learning to interpret “dark matter” DNA

Scientists at Gladstone Institutes are using machine learning to target genetic disorders in so-called genomic “dark matter”.

The computational method being used, called TargetFinder, predicts where non-coding DNA – the DNA that does not code for proteins – interacts with genes. By analysing big data, researchers are abble to connect mutations in genomic “dark matter” with the genes they affect, potentially revealing new targets for genetic disorders.

In the study, published in Nature Genetics, the team from Gladstone Institutes looked at fragments of non-coding DNA called enhancers which act like an instruction manual for a gene, dictating when and where a gene is turned on.

“Most genetic mutations that are associated with disease occur in enhancers, making them an incredibly important area of study,” said the study’s senior author, Katherine Pollard. “Before now, we struggled to understand how enhancers find the distant genes they act upon.”


The new study revealed that, on a strand of DNA, enhancers can be millions of letters away from the gene they influence.

However, using machine learning technology, the researchers were able to analyse hundreds of existing datasets to look for patterns in the genome and identify where a gene and enhancer interact.

They discovered that when an enhancer is far away from the gene it affects, the two connect by forming a three-dimensional loop, like a bow on the genome.

“It’s remarkable that we can predict complex three-dimensional interactions from relatively simple data,” said biostatistician at Gladstone, Sean Whalen. “No one had looked at the information stored on loops before, and we were surprised to discover how important that information is.”


The new computational approach is a much cheaper and a less time-consuming way to identify gene-enhancer connections in the genome as performing experiments in the can take millions of dollars and years of research.

The technology also gives an insight into how DNA loops form and how they might break in disease.

“Our ability to predict the gene targets of enhancers so accurately enables us to link mutations in enhancers to the genes they target,” said Pollard. “Having that link is the first step towards using these connections to treat diseases.”

Gladstone is set to offer all of the code and data from TargetFinder online for free.