Some jobs are simply too dangerous for humans. Near the top of that list is nuclear cleanup -- so to keep us mere homo sapiens safe, we're sending in robots. To help improve the bots' decision making skills, scientists at the University of Lincoln have won a grant to develop artificial intelligence systems for self-learning robots deployed at nuclear sites. The team received £1.1 million ($1.5 million) from the UK Engineering and Physical Sciences Research Council to develop machine learning AI. The algorithms will help robots handle tasks like decommissioning, waste handling and site monitoring.
One of the longest-running challenges in the logistics industry is finding the shortest routes. First articulated in the 1930s, the "traveling salesman problem" seeks to deduce the shortest route connecting a group of cities to ensure optimal use of time and resources. Karim Beguir, co-founder and CEO of London-based AI startup InstaDeep, told GPU Technology Conference attendees this week that GPU-powered deep learning and reinforcement learning may have the answer. Previous efforts to address the traveling salesman problem include optimization solvers, heuristics and Monte Carlo Tree Search algorithms. But, according to Beguir, these approaches all suffer from the same shortcoming: They don't learn.
Q: Could you provide our readers with a brief introduction to Invacio? A: My name is William J.D. West, and I'm the Founder of Invacio. Invacio is a cutting-edge technology company that has created a level-3 (Self-learning) artificial intelligence and is ready to bring it to the global market. Invacio's "Jean" is a state-of-the-art, multi-faceted A.I. system that can work as a stand-alone "market specific" module, or as a bigger network of more than 30 individual modules, working together to create one of the most sophisticated Artificial Intelligence platforms on Earth. Invacio brings together the world's largest repository of organized data with the most advanced A.I. technology known to man… and is working to make it accessible to EVERYONE on the planet.
Many tasks in which humans excel are extremely difficult for robots and computers to perform. Especially challenging are decision-making tasks that are non-deterministic and, to use human terms, are based on experience and intuition rather than on predetermined algorithmic response. A good example of a task that is difficult to formalize and encode using procedural programming is image recognition and classification. For instance, teaching a computer to recognize that the animal in a picture is a cat is difficult to accomplish using traditional programming.
It has almost become regular news for this to occur, and these rapid pendulum swings are looking more and more normal for gold's trading pattern. Of course, as consistently reported, this trading pattern has been heavily dependent on geopolitical tensions. So much so, in fact, that analysts are calling this type of uncertainty the new normal. A recent report by Citi analysts stated, "Event-driven bids for gold seem to be occurring more frequently and may be the new normal […] In short, even as the rates and forex channel dominate the outlook for gold pricing, the yellow metal is increasingly being used by investors as a policy and tail risk hedge". This closely ties in to what was discussed in previous articles: gold has become something of a paper trading commodity, such that it it heavily reactive to events and bases itself in very reactive movement.
The research was led by Marco Zorzi at the University of Padova and funded with a starting grant from the European Research Centre (ERC). The project – GENMOD – demonstrated that it is possible to build an artificial neural network that observes the world and generates its own internal representation based on sensory data. For example, the network was able by itself to develop approximate number sense, the ability to determine basic numerical qualities, such as greater or lesser, without actually understanding the numbers themselves, just like human babies and some animals. "We have shown that generative learning in a probabilistic framework can be a crucial step forward for developing more plausible neural network models of human cognition," Zorzi says. Tests on visual numerosity show the network's capabilities, and offer insight into how the ability to judge the amount of objects in a set emerges in humans and animals without any pre-existing knowledge of numbers or arithmetic.
Imagine a future where complex decisions could be made faster and adapt over time. Where societal and industrial problems can be autonomously solved using learned experiences. It's a future where first responders using image-recognition applications can analyze streetlight camera images and quickly solve missing or abducted person reports. It's a future where stoplights automatically adjust their timing to sync with the flow of traffic, reducing gridlock and optimizing starts and stops. It's a future where robots are more autonomous and performance efficiency is dramatically increased.