How Twisted Graphene Became the Big Thing in Physics

The lab produced dozens of twisted bilayer graphene “Devices,” as researchers call them, but none of them showed significant evidence of electron correlation.

The sudden jumps in twisted bilayer graphene – from conducting to insulating to superconducting – with just a tweak of an external electric field indicate that free electrons are slowing to a virtual halt, notes physicist Dmitri Efetov of the Institute of Photonic Sciences in Barcelona, Spain.

Said MacDonald, is the small number of electrons that seem to be doing the heavy lifting in magic-angle twisted bilayer graphene – about one for every 100,000 carbon atoms.

MacDonald points out, for example, that some of the insulating states in twisted bilayer graphene appear to be accompanied by magnetism that arises not from the quantum spin states of the electrons, as is typically the case, but entirely from their orbital angular momentum – a theorized but never-before-observed type of magnetism.

Semiconductors and transitional metals can be deposited in twisted layers and are seen as good candidates for correlated physics – perhaps better than twisted bilayer graphene.

Having burst far out into the lead of the twisted bilayer graphene field in stunning fashion, Jarillo-Herrero isn’t sitting back and waiting for others to catch up.

Such hopes ultimately pan out, for now the excitement in twisted bilayer graphene seems only to be building.

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For better deep neural network vision, just add feedback (loops)

While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child’s play for primates such as humans.

In findings published in Nature Neuroscience, McGovern Institute investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

Deep convolutional neural networks are currently the most successful models for accurately recognizing objects on a fast timescale and have a general architecture inspired by the primate ventral visual stream, cortical regions that progressively build an accessible and refined representation of viewed objects.

Rather than trying to guess why deep learning was having problems recognizing an object, the authors took an unbiased approach that turned out to be critical.

Instead, the authors presented the deep learning system, as well as monkeys and humans, with images, homing in on “Challenge images” where the primates could easily recognize the objects in those images, but a feedforward DCNN ran into problems.

“What the computer vision community has recently achieved by stacking more and more layers onto artificial neural networks, evolution has achieved through a brain architecture with recurrent connections,” says Kar.

“Since entirely feedforward deep convolutional nets are now remarkably good at predicting primate brain activity, it raised questions about the role of feedback connections in the primate brain. This study shows that, yes, feedback connections are very likely playing a role in object recognition after all.”

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How AI Will Enhance Human Capabilities

Whether you lean more toward marketing or sales, revenue roles are beginning to closely resemble a hybrid mix between data scientist and creative.

First, we cannot ingest large data sets and are completely dependent on accurate visualizations of data.

This is where AI can be leveraged to enhance human behavior.

AI allows us to take our attention off of data interpretation and focus on what we do best: create.

Our product roadmap is driven by the belief that AI will never be able to truly coach and train sales, success and support reps, but it can empower managers to coach their reps better.

AI can undoubtedly show an individual where they need to focus to improve, but it takes more than data to drive behavior change.

For marketers, AI will be leveraged to create optimal lead nurturing campaigns in marketing automation platforms like Marketo, Eloqua, Pardot and Mautic.

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