Lifeboat Foundation

A movie montage for modern artificial intelligence might show a computer playing millions of games of chess or Go against itself to learn how to win. Now, researchers are exploring how the reinforcement learning technique that helped DeepMind’s AlphaZero conquer chess and Go could tackle an even more complex task—training a robotic knee to help amputees walk smoothly.

Computer algorithms help prosthetics wearers walk within minutes rather than requiring hours of training.

Law firms are under tremendous pressure to innovate to provide better value to their clients, who demand more value for their legal dollars. Providing higher-value services in turn boosts firms’ competitiveness.

However, much of the day-to-day work of any legal office – whether it’s in-house counsel, a boutique firm or one of the largest legal power houses – is the tedious, repetitive work of reading and preparing answers to complaints. Larger firms may have armies of junior associates do much of this necessary but mundane case-preparation work. At smaller firms, partners and senior associates are often involved in all stages of litigation. Preparing responses is time-consuming. It can take several hours to a full day to complete. Those are hours that both attorneys and firms would prefer to use tackling more strategic legal work.

We asked ourselves, what if, instead of taking hours, those high-volume, repetitive tasks could take a couple of minutes?

Continue reading “AI technology accelerates and augments legal work” »

A collaboration of researchers from MIT and Microsoft have developed a system that helps identify lapses in artificial intelligence knowledge in autonomous cars and robots. These lapses, referred to as “blind spots,” occur when there are significant differences between training examples and what a human would do in a certain situation — such as a driverless car not detecting the difference between a large white car and an ambulance with its sirens on, and thus not behaving appropriately.

An artificial intelligence solution (AI) can accurately identify precancerous changes that could require medical attention in images from a woman’s cervix. Researchers from the National Institutes of Health and Global Good developed the computer algorithm, which is called automated visual evaluation.

Researchers created the algorithm by using more than 60,000 cervical images from a National Cancer Institute (NCI) archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s.

More than 9,400 women participated in that population study, with follow up that lasted up to 18 years. Because of the prospective nature of the study, the researchers said that they gained nearly complete information on which cervical changes became pre-cancers and which did not.

Continue reading “Can AI Really Be a Game Changer in Cervical Cancer Screenings?” »

Using historical data to train risk assessment tools could mean that machines are copying the mistakes of the past.

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” »