Tag Archives: Computers

How To Merge Your Brain With A.I.

Elon Musk said startup Neuralink, which aims to build a scalable implant to connect human brains with computers, has already implanted chips in rats and plans to test its brain-machine interface in humans within two years, with a long-term goal of peoplemerging with AI.” Brain-machine interfaces have been around for awhile. Some of the earliest success with the technology include Brown University’s BrainGate, which first enabled a paralyzed person to control a computer cursor in 2006. Since then a variety of research groups and companies, including the University of Pittsburgh Medical Center and DARPA-backed Synchron, have been working on similar devices. There are two basic approaches: You can do it invasively, creating an interface with an implant that directly touches the brain, or you can do it non-invasively, usually by electrodes placed near the skin. (The latter is the approach used by startup CTRL-Labs, for example.)

Neuralink, says Musk, is going to go the invasive route. It’s developed a chip containing an array of up to 96 small, polymer threads, each with up to 32 electrodes that can be implanted into the brain via robot and a 2 millimeter incision. The threads are small — less than 6 micrometers — because, as Musk noted in remarks delivered Tuesday night and webcast, Once implanted, according to Musk, the chip would connect wirelessly to devices. “It basically Bluetooths to your phone,” he said. “We’ll have to watch the App Store updates to that one,” he added (the audience laughed).

Musk cofounded Neuralink in 2017 and serves as the company’s CEO, though it’s unclear how much involvement he has given that he’s also serving as CEO for SpaceX and Tesla. Company cofounder and president, Max Hodak, has a biomedical engineering degree from Duke and has cofounded two other companies, MyFit and Transcriptic. Neuralink has raised $66.27 million in venture funding so far, according to Pitchbook, which estimates the startup’s valuation at $509.3 million. Both Musk and Hodak spoke about the potential for its company’s neural implants to improve the lives of people with brain damage and other brain disabilities. Its first goal, based on its discussions with such patients, is the ability to control a mobile device.

The company’s long-term goal is a bit more fantastical, and relates to Musk’s oft-repeated concerns over the dangers of advanced artificial intelligence. That goal is to use the company’s chips to create a “tertiary level” of the brain that would be linked to artificial intelligence.We can effectively have the option of merging with AI,” he said. “After solving a bunch of brain related diseases there is the mitigation of the existential threat of AI,” he continued.

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In terms of progress, the company says that it has built a chip and a robot to implant it, which it has implanted into rats. According to the whitepaper the company has published (which has not yet undergone any peer review), it was able to record rat brain activity from its chips, and with many more channels than exist on current systems in use with humans. The first human clinical trials are expected for next year, though Hodak mentioned that the company has not yet begun to the FDA processes needed to conduct those tests.

Source: https://www.forbes.com/

AI Closer To The Efficiency Of The Brain

Computers and artificial intelligence continue to usher in major changes in the way people shop. It is relatively easy to train a robot’s brain to create a shopping list, but what about ensuring that the robotic shopper can easily tell the difference between the thousands of products in the store?

Purdue University researchers and experts in brain-inspired computing think part of the answer may be found in magnets. The researchers have developed a process to use magnetics with brain-like networks to program and teach devices such as personal robots, self-driving cars and drones to better generalize about different objects.

Our stochastic neural networks try to mimic certain activities of the human brain and compute through a connection of neurons and synapses,” said Kaushik Roy, Purdue’s Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering. “This allows the computer brain to not only store information but also to generalize well about objects and then make inferences to perform better at distinguishing between objects.

The stochastic switching behavior is representative of a sigmoid switching behavior of a neuron. Such magnetic tunnel junctions can be also used to store synaptic weights. Roy presented the technology during the annual German Physical Sciences Conference earlier this month in Germany. The work also appeared in the Frontiers in Neuroscience.

The switching dynamics of a nano-magnet are similar to the electrical dynamics of neurons. Magnetic tunnel junction devices show switching behavior, which is stochastic in nature.  The Purdue group proposed a new stochastic training algorithm for synapses using spike timing dependent plasticity (STDP), termed Stochastic-STDP, which has been experimentally observed in the rat’s hippocampus. The inherent stochastic behavior of the magnet was used to switch the magnetization states stochastically based on the proposed algorithm for learning different object representations. “The big advantage with the magnet technology we have developed is that it is very energy-efficient,” said Roy, who leads Purdue’s Center for Brain-inspired Computing Enabling Autonomous Intelligence. “We have created a simpler network that represents the neurons and synapses while compressing the amount of memory and energy needed to perform functions similar to brain computations.

Source: https://www.purdue.edu/

First Woman To Win Mathematics’ Prestigious Abel Prize

This year, another glass ceiling broke when Karen Uhlenbeck became the first woman to win mathematics‘ prestigious Abel Prize. Her achievement no doubt inspired legions of young girls already passionate about STEM—science, technology, engineering, and mathematics—and served as a salute to the woman mathematicians who came before.

One such woman is NASA mathematician Katherine Johnson, who once said of her love of math, “I counted everything. I counted the steps to the road, the steps up to church, the number of dishes and silverware I washed … anything that could be counted, I did.”

Here, for National Women’s History Month, we honor some coding pioneers whose careful calculations led to many of the world’s greatest technological advances, from programming the first computers to successfully putting humans on the moon.

Source: https://www.nationalgeographic.com/science/

Artificial Synapses Made from Nanowires

Scientists from Jülich together with colleagues from Aachen and Turin have produced a memristive element made from nanowires that functions in much the same way as a biological nerve cell. The component is able to both save and process information, as well as receive numerous signals in parallel. The resistive switching cell made from oxide crystal nanowires is thus proving to be the ideal candidate for use in building bioinspired “neuromorphic” processors, able to take over the diverse functions of biological synapses and neurons.

Image captured by an electron microscope of a single nanowire memristor (highlighted in colour to distinguish it from other nanowires in the background image). Blue: silver electrode, orange: nanowire, yellow: platinum electrode. Blue bubbles are dispersed over the nanowire. They are made up of silver ions and form a bridge between the electrodes which increases the resistance.

Computers have learned a lot in recent years. Thanks to rapid progress in artificial intelligence they are now able to drive cars, translate texts, defeat world champions at chess, and much more besides. In doing so, one of the greatest challenges lies in the attempt to artificially reproduce the signal processing in the human brain. In neural networks, data are stored and processed to a high degree in parallel. Traditional computers on the other hand rapidly work through tasks in succession and clearly distinguish between the storing and processing of information. As a rule, neural networks can only be simulated in a very cumbersome and inefficient way using conventional hardware.

Systems with neuromorphic chips that imitate the way the human brain works offer significant advantages. Experts in the field describe this type of bioinspired computer as being able to work in a decentralised way, having at its disposal a multitude of processors, which, like neurons in the brain, are connected to each other by networks. If a processor breaks down, another can take over its function. What is more, just like in the brain, where practice leads to improved signal transfer, a bioinspired processor should have the capacity to learn.

With today’s semiconductor technology, these functions are to some extent already achievable. These systems are however suitable for particular applications and require a lot of space and energy,” says Dr. Ilia Valov from Forschungszentrum Jülich. “Our nanowire devices made from zinc oxide crystals can inherently process and even store information, as well as being extremely small and energy efficient,” explains the researcher from Jülich’s Peter Grünberg Institute.

Source: http://www.fz-juelich.de/