An international team of researchers recently unveiled an algorithm that can be scaled to simulate the human brain’s entire neural network. But there’s a slight catch.
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Category: information science – Page 293
The renowned physicist Dr. Richard Feynman once said: “What I cannot create, I do not understand. Know how to solve every problem that has been solved.”
An increasingly influential subfield of neuroscience has taken Feynman’s words to heart. To theoretical neuroscientists, the key to understanding how intelligence works is to recreate it inside a computer. Neuron by neuron, these whizzes hope to reconstruct the neural processes that lead to a thought, a memory, or a feeling.
With a digital brain in place, scientists can test out current theories of cognition or explore the parameters that lead to a malfunctioning mind. As philosopher Dr. Nick Bostrom at the University of Oxford argues, simulating the human mind is perhaps one of the most promising (if laborious) ways to recreate—and surpass—human-level ingenuity.
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Amazing.
An international team of scientists has developed an algorithm that represents a major step toward simulating neural connections in the entire human brain.
The new algorithm, described in an open-access paper published in Frontiers in Neuroinformatics, is intended to allow simulation of the human brain’s 100 billion interconnected neurons on supercomputers. The work involves researchers at the Jülich Research Centre, Norwegian University of Life Sciences, Aachen University, RIKEN, KTH Royal Institute of Technology, and KTH Royal Institute of Technology.
An open-source neural simulation tool. The algorithm was developed using NEST (“neural simulation tool”) — open-source simulation software in widespread use by the neuroscientific community and a core simulator of the European Human Brain Project. With NEST, the behavior of each neuron in the network is represented by a small number of mathematical equations, the researchers explain in an announcement.
Scientists at Imperial College London are attempting to use powerful lasers turn light into matter, potentially proving the 84-year-old theory known as the Breit-Wheeler process. According to this theory, it is technically possible to turn light into matter by smashing two photons to create a positron and an electron. While previous efforts to achieve this feat have required added high-energy particles, the Imperial scientists believe they have discovered a method that does not need additional energy to function. “This would be a pure demonstration of Einstein’s famous equation that relates energy and mass: E=mc2, which tells us how much energy is produced when matter is turned to energy,” explained Imperial Professor Steven Rose. “What we are doing is the same but backwards: turning photon energy into mass, i.e. m=E/c2.”
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You may have heard of Numerai — the unorthodox hedge fund that crowdsources predictive stock market models from data scientists around the world. It is now seeking more brain power and announced today that it is giving away $1 million worth of cryptocurrency to Kaggle users who sign up. The San Francisco-based hedge fund incentivizes its community members by giving them digital tokens they can stake during tournaments to express confidence in their predictions. The best trading algorithms are then selected based on how they perform on the live market, and their creators are rewarded with more tokens.
Looking at most Wall Street hedge funds’ models, it’s fair to say open, collaborative efforts aren’t at their core. Movies like Wall Street, which portrays a greedy Gordon Gekko, and The Wolf of Wall Street, which highlights the derailing decadence of power and money, paint a rather unflattering picture of egocentric traders and financiers. Numerai founder and CEO Richard Craib is looking to change that.
The 30-year-old South African wants to create a more open and decentralized ecosystem for hedge funds. Rather than restricting access to trading data, Craib encrypts it before sharing it with his global network of data scientists, which effectively prevents them stealing and replicating the trades on their own. They can, however, use the shared information to build predictive models for the hedge fund.
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Hey, remember that dog-like robot, SpotMini, that Boston Dynamics showed off last week, the one that opened a door for its robot friend? Well, the company just dropped a new video starring the canine contraption. In this week’s episode, a human with a hockey stick does everything in his power to stop the robot from opening the door, including tugging on the machine, which struggles in an … unsettling manner. But the ambush doesn’t work. The dogbot wins and gets through the door anyway.
The most subtle detail here is also the most impressive: The robot is doing almost all of this autonomously, at least according to the video’s description. Boston Dynamics is a notoriously tight-lipped company, so just the few sentences it provided with this clip is a relative gold mine. That information describes how a human handler drove the bot up to the door, then commanded it to proceed. The rest you can see for yourself. As SpotMini grips the handle and the human tries to shut the door, it braces itself and tugs harder—all on its own. As the human grabs a tether on its back and pulls it back violently, the robot stammers and wobbles and breaks free—still, of its own algorithmic volition.
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We all know that physics and maths can be pretty weird, but these three books tackle their mind-bending subjects in markedly contrasting ways. Clifford V. Johnson’s The Dialogues is a graphic novel, seeking to visualise cosmic ideas in comic-book style. Darling and Banerjee’s Weird Maths is a miscellany of fun oddities, ranging from chess-playing computers to prime-counting insects. Philip Ball’s Beyond Weird argues that we’ve got quantum mechanics all wrong: it’s not so weird actually, but quite sensible. All three books do a fine job for their respective audiences. Just make sure you know which target group you’re in.
The Dialogues is a sequence of illustrated conversations, often between pairs of youthful and attractive characters, scrupulously diverse in race and gender, who happen to meet in a café, gallery or train carriage, and find themselves talking about physics. Perhaps ‘The Lectures’ would be a better title, since one interlocutor is the expert, while the other is an interested lay person whose role is to feed questions at appropriate intervals.
The author shows himself to be a highly talented graphic artist as well as being a distinguished theoretician, and while the ping-pong chats may be somewhat lacking in narrative drive, they do provide a platform for some admirably lucid explanations of topics such as Maxwell’s equations or Einstein’s cosmological constant. Not the kind of comic book you roll up in your pocket, but a weighty hardback that would grace any coffee table.
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Like every other major tech company, Google has designs on being the first to achieve quantum supremacy — the point where a quantum computer could run particular algorithms faster than a classical computer. Today it’s announced that it believes its latest research, Bristlecone, is going to be the processor to help it achieve that. According to the Google Quantum AI Lab, it could provide “a compelling proof-of-principle for building larger scale quantum computers.”
One of the biggest obstacles to quantum supremacy is error rates and subsequent scalability. Qubits (the quantum version of traditional bits) are very unstable and can be adversely affected by noise, and most of these systems can only hold a state for less than 100 microseconds. Google believes that quantum supremacy can be “comfortably demonstrated” with 49 qubits and a two-qubit error below 0.5 percent. Previous quantum systems by Google have given two-qubit errors of 0.6 percent, which in theory sounds like a miniscule difference, but in the world of quantum computing remains significant.
However, each Bristlecone chip features 72 qubits, which may help mitigate some of this error, but as Google says, quantum computing isn’t just about qubits. “Operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself,” the team writes in a blog post. “Getting this right requires careful systems engineering over several iterations.”
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