The New Scientist Book Club read Silvia Park's near-future sci-fi novel Luminous in May, and had lots of good things to say (along with a few complaints) The New Scientist Book Club read Silvia Park's Luminous in May The New Scientist Book Club had quite a change of science-fictional pace in May, moving from the wilds of space in our April read, Kim Stanley Robinson's, to a much closer-to-home future in Silvia Park's . Like another of our reads this year, Sierra Greer's, this imagines a world where robots are integrated into society - and explores how we might deal with this on many different levels: emotionally, spiritually, practically, sexually. Set in a reunified Korea, it's a compelling blend of three storylines: a police procedural, in which detective Jun is out to discover what might have become of a robot girl who has gone missing; a ragtag bunch of kids on an adventure, in which Ruijie and her schoolmates find an abandoned robot boy in a scrapyard; and a tale of a dysfunctional family. Jun and his younger sister Morgan grew up with a third sibling, a robot who disappeared when they were young, fracturing their family. Author Silvia Park: 'No one is your enemy, not even death' Silvia Park, author of the May read for the New Scientist Book Club, 'Luminous' on emotional artificial intelligence, our inevitable love for robots and coping with grief.

The late Ian Watson's sci-fi The Embedding is intriguing - but dated Watson's death last month prompted sci-fi columnist Emily H. Wilson to read his acclaimed 1973 debut and find out what she'd been missing. The acclaimed British science-fiction writer Ian Watson, author of more than two dozen novels, died this April. His fame may have faded over the decades, but his debut novel The Embedding was greeted with acclaim when it was published in 1973. The Spectator declared it "the most spectacular thing in science fiction since the outstanding Solaris by Stanisław Lem". Watson's later work, both sci-fi and fantasy, included novels relating to Warhammer 40,000 games and a stint developing the script of A.I. Artificial Intelligence with Stanley Kubrick.

What do researchers of artificial intelligence, medicine and climate change have in common? They could all learn from the story of Rumpelstiltskin. As the fairy tale teaches us, knowing something's "true name", an ancient concept in folklore, gives us power over it. While this may not seem very scientific, psychologists have repeatedly found that your name changes how people perceive you . The same may be true for scientific terms. Take "artificial intelligence": while the technology is undeniably impressive, much of the drama around AI might have been avoided if we used the less grandiose name "machine learning".

Simone Stolzoff: 'My intolerance of uncertainty was causing so much angst.' Simone Stolzoff: 'My intolerance of uncertainty was causing so much angst.' S imone Stolzoff describes himself as "naturally an uncertain person" inclined to rumination and self-doubt. This tendency benefits him in his work as a journalist, but can otherwise be a double-edged sword. While working for a magazine in New York, Stolzoff was approached about a job at a design firm in San Francisco.

Uta Frith seems remarkably cheerful and content for someone who's spent six decades trying and failing to get to grips with her life's obsession. "Very little has stood the test of time," she tells me as we sit down in her living room in a leafy estate in Harrow-on-the-Hill, London. Around us, high-ceilinged walls papered in a luxurious red print are barely visible between rammed bookshelves, several model brains and a collection of abstract art. Frith has been searching for the mechanisms that underpin the enigmatic condition of autism ever since she first met profoundly autistic children in the late 1960s. "We could identify them intuitively, but not really scientifically - and I have to say that this is, unfortunately, still the case." Still, Frith's influence on our ever-shifting understanding of autism has been monumental.

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Color doesn't exist--at least not how you think That's why it's impossible to describe the color red. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Our eyes know the color purple when we see it, but we'd find it really hard to describe it to someone who's never seen it. Breakthroughs, discoveries, and DIY tips sent six days a week. Red means Red means Red means The color conjures up a whole range of emotions and associations.

Overall, our work offers a novel perspective on the cerebellum as a brainneuronal observations while making several testable predictions across multiple mental observations. Moreover, our model also explains recent behavioural and learning while reducing ataxia-like behaviours, consistent with classical experishown to be cerebellar-dependent. In all tasks, we observe that ccRNNs facilitates and cognitive tasks (pattern recognition and caption generation) that have been network (ccRNN) model on a number of sensorimotor (line and digit drawing) tions from a cerebellar module. We test this cortico-cerebellar recurrent neural in which a recurrent cortical network receives online temporal feedback predicdemonstrate the potential of this framework we introduce a systems-level model lum, helps the cerebral cortex solve similar locking problems akin to DNIs.

How do brain circuits learn to generate behaviour? While significant strides have been made in understanding learning in artificial neural networks, applying this knowledge to biological networks remains challenging. For instance, while backpropagation is known to perform accurate credit assignment of error in artificial neural networks, how a similarly powerful process can be realized within the constraints of biological circuits remains largely unclear. One of the major challenges is that the brain's extensive recurrent connectivity requires the propagation of error through both space and time, a problem that is notoriously difficult to solve in vanilla recurrent neural networks. Moreover, the extensive feedback connections in the brain are known to influence forward network activity, but the interaction between feedback-driven activity changes and local, synaptic plasticity-based learning is not fully understood. Building on our previous work modelling motor learning, this work investigates the mechanistic properties of pre-trained networks with feedback control on a standard motor task. We show that feedback control of the ongoing recurrent network dynamics approximates the optimal first-order gradient with respect to the network activities, allowing for rapid, ongoing movement correction. Moreover, we show that trial-by-trial adaptation to a persistent perturbation using a local, biologically plausible learning rule that integrates recent activity and error feedback is both more accurate and more efficient with feedback control during learning, due to the decoupling of the recurrent network dynamics and the injection of an adaptive, second-order gradient into the network dynamics. Thus, our results suggest that feedback control may guide credit assignment in biological recurrent neural networks, enabling both rapid and efficient learning in the brain.