A five-year European Union (EU) project to develop a collaborative humanoid that helps workers in industrial environments has concluded, with the participants touting breakthroughs in “AI learning, natural language processing, and robotic manipulation.”

The SecondHands project, born from the EU’s €80 billion ($87 billion) Horizon 2020 research program, set out back in 2015 to develop what it called a “second pair of hands” for workers in factories, warehouses, and other industrial locations. The aim was to create a robotic assistant that is “proactive” in helping technicians lift or carry objects, acting as an apprentice-like helper that carries out the less-skilled facets of a job. The project was developed under the auspices of a consortium of researchers and computer scientists from a number of organizations, including Ecole Polytechnique Fédérale de Lausanne (EPFL); Karlsruhe Institute of Technology (KIT); Sapienza, University of Rome; University College London (UCL); and Ocado Technology, the technology division of the U.K.’s online-only grocery giant Ocado.

The culmination of the program was a robot called ARMAR-6, which was developed at KIT in Germany to advance research into human-robot interaction in a structured, supervised environment.

Collaboration

Over the past couple of years, ARMAR-6 has been tested at Ocado’s automated customer fulfillment centers in the U.K. to perform maintenance on its conveyor belt system — essentially trialing the use of robots to help fix other robots. The idea is that a maintenance technician could be at the top of a ladder with a tool in their hand, and as they stretch their arm out to place the tool down, the robot would observe their action and take it from them.

Moreover, ARMAR-6 was designed to learn and adapt to real-world situations, such as grasping an object when someone wants to move it from one location to another.

ARMAR-6 sports a range of sensors and cameras, along with a telescopic torso and rotatable arms, hands, and fingers that can grasp. Crucially, it can interact with its environment using just visual data, detecting where humans are and estimating their posture purely from real-time images.

A major part of the project was also showcasing how ARMAR-6 could avoid collisions in a fast-moving industrial environment, where static and dynamic obstacles — including humans — are common.

Among other notable developments from the project is the creation of a speech interface based entirely on neural models, including all-neural speech synthesis and all-neural speech recognition. Such breakthroughs, according to KIT’s Dr. Sebastian Stüker, will lead to “better acceptance of ‘cobots’ by humans” and facilitate a more natural interaction between humans and robots.

Transition

SecondHands’ broader goal was to help transition humanoid assistants from research labs to industry settings, with Ocado the first stepping stone on that journey. With the project now officially concluded, plans are in place to apply findings to other sectors and use cases, including autonomous vehicles and the oil and gas industry.

Ocado won’t be deploying ARMAR-6 at is fulfillment centers, which suggests it’s not quite ready for a commercial environment. But various undisclosed ARMAR-6 projects are underway in other industries, so it could help accelerate the use of humanoid assistants in the real world.

“The results of this project have shown categorically how robots can amplify the benefits of human expertise,” said Graham Deacon, robotics research fellow at Ocado Technology. “We’ll continue to build on these learnings, looking forward to a future when we can use these breakthroughs to apply them in a real-world setting.”

This project also highlights the need for academia and industry to work in tandem. There’s no point developing AI-infused robots in a laboratory setting if they fail at the first hurdle in the real world.

The timing of SecondHands’ conclusion is also notable, as countries are embracing more automation due to social-distancing measures enforced by the COVID-19 crisis. In the future, the technologies that underpin ARMAR-6 could be redeployed in other environments, such as “helping to reduce contamination, or in assisted living,” according to Deacon.

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A team of researchers from DeepMind, University College and Harvard University has found that lessons learned in applying learning techniques to AI systems may help explain how reward pathways work in the brain. In their paper published in the journal Nature, the group describes comparing distributional reinforcement learning in a computer with dopamine processing in the mouse brain, and what they learned from it.

Prior research has shown that dopamine produced in the brain is involved in reward processing—it is produced when something good happens, and its expression results in feelings of pleasure. Some studies have also suggested that the neurons in the brain that respond to the presence of dopamine all respond in the same ways—an event causes a person or a mouse to feel either good or bad. Other studies have suggested that neuronal response is more of a gradient. In this new effort, the researchers have found evidence supporting the latter theory.

Distributional reinforcement learning is a type of machine learning based on reinforcement. It is often used when designing games such as Starcraft II or Go. It keeps track of good moves versus bad moves and learns to reduce the number of bad moves, improving its performance the more it plays. But such systems do not treat all good and bad moves the same—each move is weighted as it is recorded and the weights are part of the calculations used when making future move choices.

Researchers have noted that humans appear to use a similar strategy to improve their level of play, as well. The researchers in London suspected that the similarities between the AI systems and the way the brain carries out reward processing were likely similar, as well. To find out if they were correct, they carried out experiments with mice. They inserted devices into their brains that were capable of recording responses from individual dopamine neurons. The mice were then trained to carry out a task in which they received rewards for responding in a desired way.

The mouse neuron responses revealed that they did not all respond the same way, as prior theory had predicted. Instead, they responded in reliably different ways—an indication that the levels of pleasure the mice were experiencing were more of a gradient, as the team had predicted.

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The following editorial appeared in Sunday’s Japan News-Yomiuri:

Fake videos misusing artificial intelligence are spreading on the internet. Is the information coming from a credible source? Is the content reliable? It is important to be aware of them on a daily basis.

A video of U.S. House of Representatives Speaker Nancy Pelosi looking drunk while giving a lecture was disseminated online in May. The video was deliberately slowed down, and it is not clear who posted it online. Seemingly aimed at smearing her, it was viewed more than 3 million times.

The forgery of still images and falsification of printed materials have been around for a long time. In recent years, it has become possible to superimpose the movements of other people’s lips and facial expressions onto celebrities’ faces using AI learning functions. Artificial audio can be used to create videos of real-life figures saying falsified lines.

“Deep fake” is a coined word that combines AI deep learning and fake.

It is concerning that this technology could be abused to manipulate information in order to disrupt society.

In the 2016 U.S. presidential election, false information to attack candidates was circulated online, and Russia was accused of involvement. There are fears that more sophisticated intervention could take place using fake videos in the 2020 presidential election.

This situation threatens fair elections, which are the foundation of democracy. Thus measures to deal with this should be urgently devised.

Experts warn that if such false information spreads in the field of military information, national security could be threatened.

Short videos are posted on social media one after another. They are shared among like-minded people and spread instantly. The nature of the internet heightens the danger of fake videos.

Concerns over the manipulation of information are also growing in Japan.

During the House of Councillors election in July, a bogus post spread that claimed “the salaries of Diet members will be raised.”

In 2013, the ban on online campaigning for national and local elections was lifted. In the recent upper house election, an increasing number of people posted videos on social media. Caution cannot be neglected against politically motivated inducements.

Broadcasters receive images of accident sites, among other materials, from outside sources. There was a case in which a broadcaster was forced to apologize for airing video of a similar incident that had happened in the past.

In May, a panel of experts under the Internal Affairs and Communications Ministry began full-fledged discussions on measures to deal with false information. It must draw up effective measures by referring to each country.

The internet is an indispensable platform as a tool for obtaining information and connecting with other people. Information should be understood correctly while using the internet.

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Today, artificial intelligence is dominating most of the games — from board games to interactive fiction games. They are providing complex, decision-making environments for AI to experiment with. The ability of games to provide interesting and complex problems, offering creativity and expression, has made them one of the most popular and meaningful domain for AI researchers.

Games offer one of the most meaningful domains that can process, interpret and stimulate human behaviour. The current gaming industry is not only deploying better graphics but is also exploring the area of virtual gameplay. The two-way relationship of gaming and AI has begun to tread a new road and it can be said that the gaming industry is largely revolutionising the way AI works.

AI In Gaming Industry

Application of AI to the gaming industry can be dated back to 1956 by Arthur Samuel’s checkers program. Since its first application which could beat professional players to the present day’s AlphaGo, AI in gaming has come a long way.

Today we see an enormous upsurge of AI in game. First Encounter Assault Recon, popularly known as F.E.A.R. and The Last Of Us are some of the most popular games that give a very realistic experience with the use of AI.

How Does Gaming Aid AI?

Games are difficult because of the complexity and the skill that demands of them to play. This complexity of games makes it very desirable for AI to work on. A typical game has about 101685 possible states, whereas the number of protons in the observable universe are just of the order of 1080. This can tell about the degree to which the gaming industry is complicated and rich with data. And where there is plenty of data, AI is always a privilege. With larger sets of training data, AI would have the ability to be less predictable and more spontaneous, thereby making the gameinfinitely interesting and impulsive.

Interaction:

As every game involves players, the interaction of the player with the game is advantageous to AI, as it gives access to the algorithm to study the player experience an emotional behaviour. The study of this game and human interaction proves a key to not only study the human behaviour, but it also makes a way for AI to build a better human-computer interaction system. It further pushes the AI boundaries to study and understand the human-computer interaction systems and address the challenges faced by its applications in the real world.

Decision-Making:

This is the main crux of AI. AI must be able to make decisions by looking at the opponent’s action. There are various models used for decision-making in the game. Markov model is the most popular model. Fine State Machine (FSM) is one of the many AI methods used for decision-making.

Prediction Ability:

Prediction involves anticipating the next move of the player, so that decision-making can be done based on it. This is done using methods like past-pattern recognition and random guess. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Tree Learning are the algorithms used for prediction. Regression algorithms are used for predicting player behaviour. This process includes situations like predicting times when the player is expected to be in the particular level of the game, what item will the player pick next, when will he move to the other lane, are made. Experimenting with this is virtual games, we implement these algorithms and models in the real world as well.

Intelligence:

Social intelligence and human-computer interaction are the most supreme objectives of AI. These two things are taken into consideration by games and that way they help in AI development. Virtual characters exhibiting human behaviour as well as intelligence.

AI had learnt about the intelligence of computers the most from games, than from any other application, because they provide a virtual platform to test every kind of algorithm. Moreover, they also provide complicated mathematical problems to deal with, so the AI learning is not just restricted to the gaming world.

The success of deep Q-learning in learning to play arcade games with a human-level performance by just looking at and processing the pixels on the screen, is an example of intelligence. The study of intelligence within games not only lets us know more about human intelligence but also about AI intelligence.

The recent Dota2 tournament, ‘The International’, had bots competing with professional players. Although they couldn’t win the match, it must be noted that the ability that AI can be bestowed with, to play games as complicated as Dota2, is remarkable. Another example into the future of AI in games is at the Michigan State University, where a group of researchers have deployed AI to learn a game by learning from every player’s behaviour. It will adapt to individual player’s behaviour and play the next move.

Games offer both entertainment and interaction, in turn having a very high realisation of the affective loop which is very important in gaming. They provide a multitude of fancy features at once — visual art, sound design, graphic design, beautification, are narrative, virtual cinematography, all in one single software. Games are perfect testbeds for AI because they act as the best application of computer creativity. As a result, with the use of computational creativity in the gaming industry, provides a way to advance AI. It not only challenges computer creativity but also advances it.

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