Human–computer interfaces, a long sought-after goal, would open new worlds. Disabled people could regain autonomy, people could access information and operate seamlessly in a digital world. This goal is yet to be realized because training machines to follow our mental commands, such as move a cursor across the screen, is a complicated and tedious process. Now, by approaching the problem of this machine learning from a brand-new angle, researchers from the University of Helsinki are drastically improving how we can interface with machines. Rather than teaching the computer to do something when we ask it, the machine is now capable of learning what we want it to do without being told.

When it comes to earning social currency, being attractive is as good as gold. A team of scientists from Finland has now designed a machine learning algorithm that can plumb the depths of these subjective judgments better than we can and can accurately predict who we find attractive via our unique brainwaves -- and even generate a unique portrait that captures these qualities -- with 83 percent accuracy. Far beyond just the laws of attraction, this novel brain-computer interface (BCI) could push wide-open a new era of BCI that can bring our unvoiced desires to life. The research was published this February in the journal IEEE Transactions on Affective Computing. The hallmarks of attraction may change over time (from twisted mustaches and monocles to a clean shave and aviators), but regardless, top-tier social status can not only give your love life a boost but can even help you score the big promotion or easily slide into the good graces of the powerful elite. But while the societal effects of being deemed attractive are numerous, the mechanism behind these personal preferences are still often shrouded in shadow.

Summary: Combining EEG brain function data, brain-computer interface technology, and artificial intelligence, researchers have created a system that can generate an image of what a person is thinking. Researchers at the University of Helsinki have developed a technique in which a computer models visual perception by monitoring human brain signals. In a way, it is as if the computer tries to imagine what a human is thinking about. As a result of this imagining, the computer is able to produce entirely new information, such as fictional images that were never before seen. The technique is based on a novel brain-computer interface.

At each identical desk, there is a computer with a person sitting in front of it playing a simple identification game. The game asks the user to complete an assortment of basic recognition tasks, such as choosing which photo out of a series that shows someone smiling or depicts a person with dark hair or wearing glasses. The player must make their decision before moving onto the next picture. Only they don't do it by clicking with their mouse or tapping a touchscreen. Instead, they select the right answer simply by thinking it.

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.