"Many computer games are expensive and require a lot of data and power. We need games that require little computing power to train algorithms in industrial environments," says Per-Arne Andersen, assistant professor at the University of Agder's (UiA's) Department of ICT. He recently earned his PhD with a thesis on how artificial intelligence in computer games can function well even if there is not much computing power. Andersen has developed artificial intelligence algorithms that can be used in systems where frequent decisions have to be made. Here, computer games are widely used to train artificial intelligence in game environments that are modelled on complicated industrial environments.

Andersen's area of research is called deep reinforcement learning, a combination of deep learning and reinforcement learning.

Businesses in all industries are developing innovative artificial intelligence (AI) applications, tapping into the technology for diverse use cases ranging from virtual assistants to medical-grade diagnostic algorithms. In fact, research firm Omdia has predicted that the AI-based software market will be a $99 billion industry by the middle of the decade. But with any AI and machine learning project, there needs to be an emphasis on'learning' in order for it to be successful. Here's a look at what AI training is, how it works and what is required to do it well. When you train AI, you're teaching it to properly interpret data and learn from it in order to perform a task with accuracy.

A reliable early warning system to detect tsunamis could be a step closer thanks to research from Cardiff University. Researchers say their analysis of ocean soundwaves triggered by underwater earthquakes has enabled them to develop artificial intelligence (AI) that allow prediction of when a tsunami might occur. The results are published today in the journal Scientific Reports. It is hoped this technology could assist experts in gaining accurate real-time assessments of these geological events. Dr. Usama Kadri, from Cardiff University's School of Mathematics, said: "Tsunamis have a devastating impact on communities. Developing accurate methods to detect them quickly is key to saving lives. "Our findings show we are able to classify the type of earthquake and retrieve its main properties from acoustic signals, in near real time.

When using the system, the student must achieve the target level, i.e. master all skills with the confidence not lower than a certain threshold value, e.g. This means that the student will have this skill at the required level with 90% probability. The system must provide the amount of various level learning materials that should be sufficient to master each skill. To exclude the chance of the student memorizing the right answer, the content must be redundant. The system must provide the student with an individual sequence of learning materials corresponding with his/her current state.

Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons. In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. "If the robot goes right, it does not receive a reward, but if it goes left it receives a reward. That's the classical version of the experiment, but the quantum version would allow it to go left and right simultaneously at each guess, requiring far fewer guesses before it learns to always go left. Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons. In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. "If the robot goes right, it does not receive a reward, but if it goes left it receives a reward.

Click to learn more about author Irfan Ak. Artificial intelligence and big data are two of the hottest and most discussed topics in the tech circle. Despite this, there are many misconceptions surrounding both big data and artificial intelligence. There is a lot of hype around both these topics as well, which can sometimes lead to even more misconceptions and myths. That is exactly what has happened in the case of big data and artificial intelligence.

There's been a reckoning in recent years when it comes to measuring bias in machine learning. We now know that these "unbiased" automated tools are actually far from unprejudiced, and there's a growing demand that researchers think about how their products might screw over or endanger the lives of others before they unleash them on society. It's not just the final products we should be worried about, however, but also the consequences of building them. As the world burns in Facebook feeds and in backyards, the carbon footprints of even the most innocuous things are coming under scrutiny. It's sparked debates around AC units, straws, face scrubs, plastic bags, air travel.

Over the last few days the #faceappchallenge has taken over social media. This "challenge" involves downloading a selfie-editing tool called FaceApp and using one of its filters to digitally age your face. You then post the photo of your wizened old self on the internet and everyone laughs uproariously. You get a small surge of dopamine from gathering a few online likes before existential ennui sets in once again. On Monday, as the #faceappchallenge went viral, Joshua Nozzi, a software developer, warned people to "BE CAREFUL WITH FACEAPP….it Some media outlets picked this claim up and privacy concerns about the app began to mount. Concern escalated further when people started to point out that FaceApp is Russian. "The app that you're willingly giving all your facial data to says the company's location is in Saint-Petersburg, Russia," tweeted the New York Times's Charlie Warzel. And we all know what those Russians are like, don't we? They want to harvest your data for nefarious ...

REFUNITE, based in California, is trialing the app in Uganda where it has launched a pilot project involving 5,000 refugees – mainly form South Sudan and Democratic Republic of Congo. It hopes to scale up to 25,000 refugees within two years.