People with paralysis can now have their thoughts turned into speech just by imagining talking in their heads. While brain-computer interfaces can already decode the neural activity of people with paralysis when they physically attempt speaking, this can require a fair amount of effort. So Benyamin Meschede-Krasa at Stanford University and his colleagues sought a less energy-intensive approach. Take control of your brain's master switch to optimise how you think "We wanted to see whether there were similar patterns when someone was simply imagining speaking in their head," he says. "And we found that this could be an alternative, and indeed, a more comfortable way for people with paralysis to use that kind of system to restore their communication."

Using only a sensor-filled helmet combined with artificial intelligence, a team of scientists has announced they can turn a person's thoughts into written words. In the study, participants read passages of text while wearing a cap that recorded electrical brain activity through their scalp. These electroencephalogram (EEG) recordings were then converted into text using an AI model called DeWave. Chin-Teng Lin at the University of Technology Sydney, Australia, says the technology is non-invasive, relatively inexpensive and easily transportable. While the system is far from perfect, with an accuracy of approximately 40 per cent, Lin says more recent data currently being peer-reviewed shows an improved accuracy exceeding 60 per cent.

A computer that can read the thoughts of many people at once would make normal human life impossible, the Slovenian cultural philosopher told RT in the wake of the World Artificial Intelligence (AI) Conference in Shanghai, which saw Alibaba's chairman Jack Ma and Tesla CEO Elon Musk clashing over the future of AI. While the two technopreneurs engaged in a heated discussion over the possibility of humans being controlled by machines in the future, the senior researcher at the Institute for Sociology and Philosophy at the University of Ljubljana shared his thoughts on the issue with RT. What I am studying now is the so-called phenomenon of wired brains, a possibility of our brains being connected with strong digital machines. And that is not a utopia. In the media lab at MIT, Massachusetts, they already have simple machines like that.

A computer that can read the thoughts of many people at once would make normal human life impossible, the Slovenian cultural philosopher told RT in the wake of the World Artificial Intelligence (AI) Conference in Shanghai, which saw Alibaba's chairman Jack Ma and Tesla CEO Elon Musk clashing over the future of AI. While the two technopreneurs engaged in a heated discussion over the possibility of humans being controlled by machines in the future, the senior researcher at the Institute for Sociology and Philosophy at the University of Ljubljana shared his thoughts on the issue with RT. What I am studying now is the so-called phenomenon of wired brains, a possibility of our brains being connected with strong digital machines. And that is not a utopia. In the media lab at MIT, Massachusetts, they already have simple machines like that.

As computer scientists attempt to make machines think and learn like humans, the middle ground is being taken up by researchers attempting to use AI to read our minds. In the latest breakthrough, scientists at Kyoto University, Japan, have studied deep neural networks (AI) and discovered that computers wield the capacity to at least visualise what humans are thinking. Before we get ahead of ourselves, it's worth noting that the technology is nascent, and applies in only optimal conditions. If you recoil at someone's dubious new choice of profile picture on Facebook, your laptop isn't going to start registering your distaste and broadcasting it to the world. That being said, the new technology certainly has seemingly impressive – if ominous – potential applications.

Scientists at the ATR Computational Neuroscience Labs in Japan have created an AI-based system that's capable of performing deep image reconstruction from human brain activity. Now, what that means is the AI system can't see inside our brain or the things we're picturing. It takes help of the brain waves (MRI data) to guess what we are thinking and draws an image out of it. To train their AI, the researchers fed it with recorded brainwaves of human subjects after showing them images. Over the course of 10 weeks, they collected brain activity data in real-time and also by making the human subjects visualize what they had seen in the past. There is still scope for improvement for the AI which lacks perfection when it comes to reading our minds.

The first "this could change everything" AI story of the year comes to us in the form of (yet another) AI that's supposed to read minds. We're one step closer to being able to broadcast our thoughts to a screen, thanks to artificial intelligence. Japanese scientists have created AI capable of reading a person's brainwaves and displaying an image based on what they're looking at. If a person is staring at a picture of the letter "A" the AI will successfully create an image that resembles a fuzzy version of that. The scientists published their paper "Deep image reconstruction from human brain activity" wherein they state: Here, we present a novel approach, named deep image reconstruction, to visualize perceptual content from human brain activity.

Researchers from the Kuhl Lab at the University of Oregon explored how faces could be decoded from neural activity in the study Reconstructing Perceived and Retrieved Faces from Activity Patterns in Lateral Parietal Cortex, published in the Journal of Neuroscience. Hongmi Lee and Brice A. Kuhl tested whether faces could be reconstructed from the'angular gyrus' (ANG) located in the upper back area of the brain through functional magnetic resonance imaging (fMRI) activity patterns. They conducted the experiment by making facial reconstructions based on brainwave patterns from participants, initially during their perception of faces and later just from memory. Participants were shown more than 1,000 color photos of different faces, one after another, while an fMRI scan recorded their neural responses. The researchers then applied principal component analysis (PCA) to generate 300 'eigenfaces' - a set of vectors used in human face recognition.