Zuckerberg, Musk, and Kutcher Want to Build You a New Brain | Enterprise | WIRED

Zuckerberg, Musk, and Kutcher Want to Build You a New Brain | Enterprise | WIRED: ...a $40 million investment in a new kind of artificial intelligence called Vicarious...

Vicarious co-founder Dileep George previously built a similar company called Numenta...

This is the second big funding round for Vicarious, and it was lead by venture capital outfit Formation 8. The first $15 million round included investments by Facebook and Asana co-founder Dustin Moskovitz; former Facebook CTO and Quora founder Adam D’Angelo; PayPal and Palantir co-founder Peter Thiel; and Palantir co-founder Joe Lonsdale.

Robot elephant trunk learns motor skills like a baby - tech - 13 March 2014 - New Scientist

Robot elephant trunk learns motor skills like a baby - tech - 13 March 2014 - New Scientist: The design showed that a trunk formed of 3D-printed segments can be controlled by an array of pneumatic artificial muscles...

They used a process called "goal babbling"... the robot remembers what happens to the trunk's position when tiny changes are made to the pressure in the thin pneumatic tubes feeding the artificial muscles. This creates a map that relates the trunk's precise position to the pressures in each tube.

The trunk can now be manually forced into a series of positions and learn to adopt them on command...

IBM unveils concept for a future brain-inspired 3D computer | KurzweilAI

IBM unveils concept for a future brain-inspired 3D computer | KurzweilAI: IBM has unveiled a prototype of a new brain-inspired computer powered by what it calls “electronic blood,” BBC News reports.

The firm says it is learning from nature by building computers fueled and cooled by a liquid, like our minds...

Its new “redox flow” system pumps an electrolyte “blood” through a computer, carrying power in and taking heat out.

IBM Scientists Show Blueprints for Brainlike Computing | MIT Technology Review

IBM Scientists Show Blueprints for Brainlike Computing | MIT Technology Review: Modha’s team has also developed software that runs on a conventional supercomputer but simulates the functioning of a massive network of neurosynaptic cores—with 100 trillion virtual synapses and two billion neurosynaptic cores.

Each core of the simulated neurosynaptic computer contains its own network of 256 “neurons,” which operate using a new mathematical model. In this model, the digital neurons mimic the independent nature of biological neurons, developing different response times and firing patterns in response to input from neighboring neurons.

“Programs” are written using special blueprints called corelets. Each corelet specifies the basic functioning of a network of neurosynaptic cores. Individual corelets can be linked into more and more complex structures—nested, Modha says, “like Russian dolls.”

“Simplified” brain lets the iCub robot learn language

“Simplified” brain lets the iCub robot learn language: Thanks to so-called recurrent construction (with connections that create locally recurring loops) this artificial brain system can understand new sentences having a new grammatical structure. It is capable of linking two sentences and can even predict the end of a sentence before it is provided.

To put this advance into a real-life situation, the Inserm researchers incorporated this new brain into the iCub humanoid robot.

Google Puts Its Virtual Brain Technology to Work

Google Puts Its Virtual Brain Technology to Work: ...Google engineers published results of an experiment that threw 10 million images grabbed from YouTube videos at their simulated brain cells, running 16,000 processors across a thousand computers for 10 days without pause.

"Most people keep their model in a single machine, but we wanted to experiment with very large neural networks," says Jeff Dean, an engineer helping lead the research at Google. "If you scale up both the size of the model and the amount of data you train it with, you can learn finer distinctions or more complex features."

‘Green Brain’ project to create an autonomous flying robot with a honey bee brain - News releases - News - The University of Sheffield

‘Green Brain’ project to create an autonomous flying robot with a honey bee brain - News releases - News - The University of Sheffield: The team will build models of the systems in the brain that govern a honey bee's vision and sense of smell. Using this information, the researchers aim to create the first flying robot able to sense and act as autonomously as a bee, rather than just carry out a pre-programmed set of instructions.

A Startup Tries to Make a Better Artificial Brain - Technology Review

A Startup Tries to Make a Better Artificial Brain - Technology Review: Vicarious, George says, is using a more sophisticated architecture and training its system with a video stream that varies over time...

Amoeboid Robot Navigates Without a Brain - Technology Review

Amoeboid Robot Navigates Without a Brain - Technology Review: Umedachi's goal isn't simply to create a new kind of locomotion, however. He's exploring the way in which robots that lack a centralized command center -- i.e. a brain -- can accomplish things anyway. Slime molds are a perfect model for this sort of thing, because they don't even have the primitive neural nets that characterize the coordinated swimming and feeding actions in jellyfish...

A fully decentralized control using coupled oscillators with a completely local sensory feedback mechanism is realized by exploiting the global physical interaction between the body parts stemming from the fluid circuit. The experimental results show that this robot exhibits adaptive locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on the design scheme for autonomous decentralized control systems.

AI designs its own video game

AI designs its own video game: Angelina creates games using a technique known as cooperative co-evolution. The system separately designs different aspects, or species, of the game. In Space Station Invaders - in which players control a scientist who must fend off rogue robots and invading aliens to escape a space station - the species include the layout of each different level, enemy behaviour and the power-ups that give a player extra abilities. Angelina creates a level by randomly selecting from a list, then scattering enemies and power-ups throughout the level. Enemy movements and combat behaviours are also randomly selected from a list, while the effects of the power-ups are also random.

It then combines the species and simulates a human playing the game to see which designs lead to the most fun or interesting results. For example, levels that are initially hard to complete but get easier through clever use of power-ups are considered fun, while those that are impossible to complete are discarded. Angelina then cross-breeds and mutates the most successful members of each species to evolve a new generation, typically 400 times.

Squishybots: Soft, bendy and smarter than ever

Squishybots: Soft, bendy and smarter than ever: They were born thanks to a rethink of how we should design intelligent machines - an approach called "morphological computing". Its proponents argue that it is not only a robot's brain that can compute, but its body too. The way a limb, torso, or whisker interacts with its surroundings can be optimised to enhance its computational abilities - and therefore how smart the robot is...


Out of the water, the arm is floppy and helpless. But place it in its tank and something extraordinary happens. Its movement suddenly bears an uncanny resemblance to the reaching motion of an octopus. In fact, it almost looks alive.
And that's the trick. With morphological computing, it's not just the shape and substance of a body that's important, it's also the interaction with its environment that is crucial. It has the dexterity and grip to grab hold of all sorts of different objects placed into its tank. It can also push against the bed in the same way that octopuses use to "walk". And all with relatively little programming.

Robot biologist solves complex problem from scratch | KurzweilAI

Robot biologist solves complex problem from scratch | KurzweilAI: The biological system that the researchers used to test ABE is glycolysis, the primary process that produces energy in a living cell. They focused on how yeast cells control glycolytic oscillations  because it is one of the most extensively studied biological control systems. ABE derived the equations a priori. The only thing the software knew in advance was addition, subtraction, multiplication and division...

Lipson used genetic programming for the breeding process... However, this process also proved to be too slow.
So Lipson combined the breeding and the debugging processes in an approach he calls co-evolution.

IBM eyes brain-like computing - Hardware - Technology - News - iTnews.com.au

IBM eyes brain-like computing - Hardware - Technology - News - iTnews.com.au: IBM is building arrays of electronic, synapse-like devices under its SyNAPSE project that aim to physically mimic the human brain.

The project delivered prototype silicon chips in August that contained 256 neurons and either 262,144 programmable synapses or 65,536 “learning synapses”.

The next step, Kelly said, is to replace components of the chip with materials that support multiple states, and not just the traditional ones and zeros.

Software tricks people into thinking it is human - New Scientist - New Scientist

Software tricks people into thinking it is human: Both the participants and the audience then rated the humanness of all the responses, with Cleverbot voted 59.3 per cent human, while the humans themselves were rated just 63.3 per cent human. A total of 1334 votes were cast – many more than in any previous Turing test, says Cleverbot's developer and AI specialist Rollo Carpenter.

"The world had better decide rather than me – it's either passed or it's come very close in this particular test," says Carpenter.

Learning machines: The education of an animat - tech - 24 August 2011 - New Scientist

Learning machines: The education of an animat: He and the rest of the team seized on these advances to "cheat" at embodiment. Instead of toiling over a real body, they built a virtual one, whose synthetic sensors would interact with a painstakingly rendered virtual environment (IEEE Computer, vol 44, p 21). This way, they reasoned, they could reap all the advantages of embodied AI with none of the drawbacks. If it worked, they would be able to hit fast-forward on the evolution of an embodied intelligence.

Animat was born on 11 January. That was the day Versace's team hooked up its brain, which is made up of hundreds of neural models - for colour vision, motor function and anxiety, among others - all of which are faithful imitations of biology.

Inside IBM's cognitive chip : Nature News

Inside IBM's cognitive chip : Nature News: The theory is that the computational components act as 'neurons', while the RAM units act as the 'synapses'...

In the cognitive chip, a pattern of signals from the RAM can cause a computational element to carry out a simple operation. The result goes to another RAM synapse, which can send signals to other computational neurons...

New Computer Chip Modeled on a Living Brain Can Learn and Remember | Popular Science

New Computer Chip Modeled on a Living Brain Can Learn and Remember | Popular Science: Built on 45 nanometer silicon/metal oxide semiconductor platform, both chips have 256 neurons. One chip has 262,144 programmable synapses and the other contains 65,536 learning synapses — which can remember and learn from their own actions...
 IBM's chip doesn't use a memristor architecture, but it does integrate memory with computation power — and it uses computer neurons and axons to do it. The building blocks are simple, but the architecture is unique..
"When a neuron changes its state, the state it is modifying is its own state, not the state of something else. So you can physically co-locate the circuit to do the computation, and the circuit to store the state. They can be very close to each other, so that cooperation becomes very efficient," he said...
It integrates memory with processor capability on a typical SOI-CMOS platform, using traditional transistors in a new design. Along with integrated memory to stand in for synapses, the neurosynaptic “core” uses typical transistors for input-output capability, i.e. neurons.

Video: A Robot That Can Figure Out New Tasks Based On the Ones It Knows | Popular Science

Video: A Robot That Can Figure Out New Tasks Based On the Ones It Knows | Popular Science: To borrow an example from the video below, the robot can fill up a glass of water from a bottle via pre-programmed instructions. But if halfway through the task its overseer asks it to chill the water, the robot will actually stop and think about the next steps.

Figuring that it can’t grab an ice cube until it empties one of its two hands, it then reasons that the water bottle is more expendable than the glass of water and sets the bottle down. It then grabs the ice and drops the cube into the glass. Task completed, no extra programming necessary.

Artificial neural network created from DNA | KurzweilAI

Artificial neural network created from DNA | KurzweilAI: Using a simple DNA gate architecture that allows experimental scale-up of multilayer digital circuits, the researchers systematically transformed arbitrary linear threshold circuits (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks.

That approach enabled them to implement a Hopfield associative memory with four fully connected artificial neurons made from 112 distinct DNA strands. After training in silico, it remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern.

DVD alloys help make computers that think like us - tech - 12 July 2011 - New Scientist

DVD alloys help make computers that think like us: GST is known as a "phase-change" alloy, because of its ability to change its molecular structure from a crystalline to a disordered amorphous "phase" when heated...
Different areas within a tiny spot of GST can be crystalline or amorphous to differing degrees, which means it can store information across a much wider range of values than simply 0 or 1...
Wright's neuron is able to mimic this threshold firing because GST's electrical resistance drops suddenly when it moves from its amorphous phase to the crystalline. So incoming signals in the form of pulses of current are applied to the artificial neuron - and it is deemed to have fired when its resistance plummets...
GST's ability to change its resistance has allowed them to program it to dynamically modify the strength of the nanoscale artificial synapses they have built...
a system with 10^10 synapses would consume just 10 watts...