New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
No two neurons are alike. What does that mean for brain function? Brain cells may be as unique as the people to which they belong. This genetic, molecular, and morphological diversity of the brain leads to the functional variation that is likely necessary for the higher-order cognitive processes that are unique to humans. As researchers continue to probe the enormous complexity of the human brain at the single-cell level, they will likely begin to uncover the answers to these questions--as well as those we haven't even thought to ask yet.
Your brain is also vastly more energy efficient at interpreting the world visually or understanding speech than any computer system. That's why academic and corporate labs have been experimenting with "neuromorphic" chips modeled on features seen in brains. These chips have networks of "neurons" that communicate in spikes of electricity (see "Thinking in Silicon"). They can be significantly more energy-efficient than conventional chips, and some can even automatically reprogram themselves to learn new skills. Now a neuromorphic chip has been untethered from the lab bench, and tested in a tiny drone aircraft that weighs less than 100 grams.
Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases (Miyawaki et al., 2008; Wen et al., 2016) or to the matching to exemplars (Naselaris et al., 2009; Nishimoto et al., 2011). Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features (Horikawa & Kamitani, 2017). Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery.
In 1739, Parisians flocked to see an exhibition of automata by the French inventor Jacques de Vaucanson performing feats assumed impossible by machines. In addition to human-like flute and drum players, the collection contained a golden duck, standing on a pedestal, quacking and defecating. It was, in fact, a digesting duck. When offered pellets by the exhibitor, it would pick them out of his hand and consume them with a gulp.
Hacking is what happens when nefarious outside sources try to break into computer systems to get to sensitive data or to plant malware. It costs governments and corporates billions of dollars each year in damages and reputation management. But scientists have now developed technology that could see people hacking the human brain. Humans have been trying to hack the human body and mind with the likes of drugs, smokes, alcohol, and coffee, since the dawn of time. The only difference now is that we have empirical evidence to know which techniques work and which don't. What does all this mean for the way we live our lives?
Today, global technology company Huawei launched a study on the similarities between the human brain and Artificial Intelligence, which revealed that the average UK resident is unaware of 99.68% of the actual decisions they make every day, showing how hard our brain works without us having to consciously engage it.
The human brain is responsible for making us adaptable and widespread -- a singularly adept instrument to help humans survive and thrive. Even as artificial intelligence quickly progresses, when it comes to military conflicts, people still outpace robots in crucial split-second decision-making. Slowly but surely, though, the gap is lessening, and training robots' targeting capabilities using human brain responses may help close it.