Lifeboat Foundation

One of the most powerful techniques in machine learning, generative adversarial networks, has been tested on a quantum computer for the first time.

Prof Pugh is using motion-tracking sensors to test how trainee surgeons use the instruments, for example in a simulated hernia repair. Their performance is measured, videoed and compared with best practice at each stage, so they can understand where they need to improve.

“Like Olympic athletes, they can practise repeatedly until they understand the routine and where they need to improve. That is the goal in training surgeons.” The next step is to use sensors in real operations.

Being able to measure pressure will help create better surgical robots, says Richard Trimlett, a cardiothoracic surgeon and head of mechanical support at the Royal Brompton and Harefield Trust, London.

In May, 2016 I stumbled upon a highly controversial Aeon article titled “The Empty Brain: Your brain does not process information, retrieve knowledge or store memories. In short: your brain is not a computer” by psychologist Rob Epstein. This article attested to me once again just how wide the range of professional opinions may be when it comes to brain and mind in general. Unsurprisingly, the article drew an outrage from the reading audience. I myself disagree with the author on most fronts but one thing, I actually agree with him is that yes, our brains are not “digital computers.” They are, rather, neural networks where each neuron might function sort of like a quantum computer. The author has never offered his version of what human brains are like, but only criticized IT metaphors in his article. It’s my impression, that at the time of writing the psychologist hadn’t even come across such terms as neuromorphic computing, quantum computing, cognitive computing, deep learning, evolutionary computing, computational neuroscience, deep neural networks, and alike. All these IT concepts clearly indicate that today’s AI research and computer science derive their inspiration from human brain information processing — notably neuromorphic neural networks aspiring to incorporate quantum computing into AI cognitive architecture. Deep neural networks learn by doing just children.

By Alex Vikoulov.

Continue reading “Our Neural Code: A Pathway to AI Minds?” »

The human brain has amazing capabilities making it in many ways more powerful than the world’s most advanced computers. So it’s not surprising that engineers have long been trying to copy it. Today, artificial neural networks inspired by the structure of the brain are used to tackle some of the most difficult problems in artificial intelligence (AI). But this approach typically involves building software so information is processed in a similar way to the brain, rather than creating hardware that mimics neurons.

My colleagues and I instead hope to build the first dedicated neural network computer, using the latest “quantum” technology rather than AI software. By combining these two branches of computing, we hope to produce a breakthrough which leads to AI that operates at unprecedented speed, automatically making very complex decisions in a very short time.

We need much more advanced AI if we want it to help us create things like truly autonomous self-driving cars and systems for accurately managing the traffic flow of an entire city in real-time. Many attempts to build this kind of software involve writing code that mimics the way neurons in the human brain work and combining many of these artificial neurons into a network. Each neuron mimics a decision-making process by taking a number of input signals and processing them to give an output corresponding to either “yes” or “no”.

Continue reading “Quantum Computer: We’re Planning to Create One That Acts Like a Brain” »

Buried deep within the DNA of Asian individuals is a genetic clue pointing to the existence of an unknown human ancestor. Remarkably, it wasn’t a human who reached this startling conjecture, but rather an artificially intelligent algorithm. Welcome to archaeology in the 21st century.

New research published last week in Nature Communications suggests a yet-to-be discovered hominid interbred with modern humans tens of thousands of years ago. This mystery species eventually went extinct, but an AI developed by researchers from the Institute of Evolutionary Biology (IBE) and several other European institutions found traces of its existence in the DNA of present-day people with Asian ancestry. A press release issued by the Centre for Genomic Regulation said it’s the first time deep learning has been used to explain human history, “paving the way for this technology to be applied in other questions in biology, genomics and evolution.”

Many of the new developments in artificial intelligence that we hear about nowadays are actually just applications of machine learning techniques that have been hammered out for years.

And as the research community’s attention shifts from deep learning, it remains unclear what will take its place, according to MIT Tech. In the past, older types of artificial intelligence that didn’t really take off when they were first developed later resurfaced and taken off with new life. For instance, scientists first developed machine learning decades ago, but it only became commonplace about a decade ago.

MIT Tech didn’t predict what will come next. It may be that some form of existing technology will finally hits its stride, but it’s also possible that an AI engineer will develop some brand-new type of AI that’ll shape the future.

Here is another advanced prosthetic device that can make life easier for amputees. BrainRobotics’ EMG Prosthetic Hand features a modular mechanical design. It comes with 8 signal detection channels for precise EMG readings. An advanced machine learning algorithm is used to allow users to intuitively control this robotic prosthesis.

More like this ➡️ here.

Blue Danube Robotics focuses on safe human robot collaboration in mass production. The Austrian company produces state of the art safety solutions for robots, cobots and End-of-Arm-Tooling (EOAT). These safety solutions make robots safe for humans and thus efficient for enterprises.

“Human players must explicitly manage an ‘economy of attention’ to decide where to focus the camera,” said the DeepMind team. “However, analysis of AlphaStar’s games suggests that it manages an implicit focus of attention. On average, agents ‘switched context’ about 30 times per minute,” the team wrote, which was similar to Komincz’s and Wünsch’s behaviors.

Ultimately, the DeepMind team concluded that AlphaStar won against both pros because of “superior macro and micro-strategic decision-making, rather than superior click-rate, faster reaction times, or the raw interface.”