Рубрика: Robotics

On a normal day, humans encounter Artificial Intelligence (AI) numerous times. Artificial intelligence has become a part of human’s routine in many ways. They enter our lives in the form of smartphones, appliances in our homes and technology in our cars. They aid humans in different aspects starting from making appointments to diagnose illness.

Since humans are at the verge of accepting robotics into society, the question that lingers in everyone’s mind is ‘Can robots be trusted?’ Human has a mythical illustration of robots turning aggressive once they are provided with all the features of humans. We are pushed to such conclusions through movies, soap operas and dramas. So keeping all these confusing thoughts away, we should focus on the ways human-robot relationship could reach the milestone of trust.

Explainable artificial intelligence

Explainable artificial intelligence is a branch of artificial intelligence research that examines the ways an artificial agent could be presented in a more transparent and trustworthy form. Human-robot relationship building and leading it to a stage of trust is really important in the technology world. It is a mandatory move when humans start working with robots. Robots seek to develop systems that attract human attention and at the same time, it should perform well to fulfil the designated task.

A team at Center for Vision, Cognition, Learning, and Autonomy in the University of California, Los Angeles is researching on the factors that make machines more trustworthy. It is also looking into different algorithms that provoke building trust in humans. The lab is using a model knowledge representation that could interpret the surroundings and make decisions which are easily understandable by humans. This naturally improves trust in humans citing the clear explanation and transparency.

In recent research, the team experimented different ways a robot could explain its action to a human observer and which among them made humans feel more trustworthy about robots. Remarkably, the form of explanation that improved trust did not correspond to the learning algorithms that produced the best performance. The robot’s performance and explanation are not proportionally dependent on each other. But optimising for one of them is not a good idea. So the team is focusing on building robots that take into account both good task performance and trustworthy explanation.

Unravelling the performance and explanation skills of robots

Educating a robot to accomplish a task: The team undertook a study to know learn how robots perform particular tasks and how people respond to the robot’s explanation of the action.

The team taught a robot to learn from human demonstrations on how to open a medicine bottle with a safety lock. To make the robot scan and understand the moves, a person wore a tactile glove that recorded the poses, moves and forces involved in opening a bottle. The information was conveyed to the robot. The robot understood the moves in two ways: symbolic and haptic. Symbolic understanding in robot means the grasp of its representation in human action. Haptic is connected to body posture and motions during the moment.

As a primary step, the robot was made to learn a symbolic model that encodes the string of steps needed to complete the task of opening the bottle. Then, the robot learned a hectic model that features the robot to ‘imagine’ itself in the role of a human demonstrator. This move encourages the robot to predict the action a person would take while encountering a situation of opening a bottle. The robot also analyses the poses and force it needs to put in to finish the task.

Remarkably, the robot was able to achieve its best performance with the combination of symbolic and haptic process. With the help of symbolic representation, the robot was able to use its recorded knowledge for performing the task and real-time sensing and brought it to action through haptic by predicting the outcome.

Earning human trust through explanation: The robot is now aware of the actions it should make to accomplish a task. The next step would be explaining what it did to the humans to bring clarification and earn trust.

The robot has the ability to draw on its internal decisions and behaviour. The step-by-step description of robotic actions through symbolic models and the feeling and predictions through haptic model are unleashed. To make the experiment more lively and clear, the team has enabled the provision to make the robot do a write-up text of its actions. They wanted to know if the text summary is as effective as the rest.

The program involves 150 human participants, divided into four groups who were asked to observe the robot opening the medicine bottle. The types of explanations were divided as Symbolic, step-by-step, haptic expression of arm position and motion, text summary or symbolic and haptic together were given to the groups of people. One of the groups observed the robot opening the medicine bottle without getting any explanation.

The conclusion turned out to be that people who get to experience the robot explaining both symbolic and haptic process got to grow more trust in them. The other group that got explained by a text summary felt the same as the group that plainly watched the robot do its job. Both the process didn’t seem to foster much of human trust.

Take away for the future: Through the research, it was unfolded that people are not putting their trust in robots that only do actions without explaining. So the robots need both symbolic and haptic components to soothe humans.

The research gave an outlook for the future of artificial intelligence and promotes the researches to focus equally on the robots establishments and its ability to explain its actions. Both performance and explanation are a must while building an artificial intelligence system. The human-machine relationship should be further shaped in order to move it further.

Making robots a part of everyday life is not a new idea. But to accomplish the move, humans need to put their belief on robots. Trusting robots through the explanation they provide for their actions is an important step towards enabling human and robots to a further level.

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Optimizing Workflows, Robots are the New Face of Laboratory Automation.

Tasks in the pharmaceutical, life sciences and biomedical industries have always been time-consuming and complex. With the advent of the Covid-19 pandemic, these undertakings will only grow in complexity. To ensure speed, accuracy and mitigate the infectivity stress among the humans, robots are called upon to meet the ever-increasing range of workflows in today’s research and development laboratories.

Laboratory automation, drug discovery and pharmaceutical manufacturing are emerging fields where the services of robots are leveraged for research and development. Robotic lab assistants help researchers and scientists focus on high-level tasks like the analysis of potential therapeutic compounds rather than mundanely mixing compounds to determine their curative characteristics.

Mobile Robot Scientists in the Labs

Automation has found its way into life sciences in applications that range from cell culture to new drug discovery. Robots perform a wide range of chores in life sciences, pharmaceutical and biomedical applications. They are used by clinical laboratories to assist in a wide range of tasks such as handling test tubes and flasks, sorting test tubes for the drug discovery processes. Mobile robotics are finding their way into intra-departmental and inter-departmental use, providing an on-demand and scheduled transport of laboratory specimens and pharmaceuticals.

Robotic Lab Assistant

Researchers from the UK’s University of Liverpool have developed a breakthrough robotic lab assistant, which is able to move around a laboratory and helps to conduct scientific experiments just like a human.

The machine, though far from being fully autonomous needs to be programmed with the location of the lab equipment. The robotic lab assistant works seven days a week, 22 hours a day (with two hours to recharge every night), and allows scientists to automate time-consuming and tedious research work which they wouldn’t otherwise tackle.

The robot’s creators, led by PhD student Benjamin Burger, say that the robotic lab assistant was able to perform experiments 1,000 times faster than a human lab assistant, in its initial test run. The speed was credited mostly due to the robot’s ability to work around the clock without breaks. Over an eight-day period, the machine carried out 688 experiments, mixing samples in glass vials, exposed them to light, and analysed the results using gas chromatography.

Robots for Medical Sample Collection

Robots are used in liquid sample collection like body fluids and blood samples for lab testing. In many labs, previously, the lab staff opened the transport boxes on arrival, removed the blood samples and sorted them for further clinical analysis. This led to tendon and muscle injuries as a result of repetitive work.

The process became more streamlined after the robots took over the job. The robotic lab assistants open the box, take the blood samples and set them for sorting. On average, the robots need 1.5 minutes per box, which is equivalent to a capacity of forty boxes per hour.

Liquid Handling Robots

LEGO-based liquid handling robots are an advanced 2-D robot who could slide both horizontally and vertically over sample-holders. Their liquid-dispensing mechanism efficiently handles sub-microliter liquid volumes stably over 1000 pipetting cycles. The pre-programmable robots are commonly used in biotechnology, currently deployed to increase the precision of handling small liquid volumes and discard contaminated tips.

Summing up, robot makers, integrators and end-users have several opportunities to learn more about the potential of flexible automation in biomedical, life sciences and pharmaceutical applications. Though we should not expect to see robots fully supplanting human researchers in the near future, but a giant step has been taken to automate some of the lab researches and drug discovery process for the benefit of the mankind.

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With new technologies advancing everyday in human life, Humanoid Robots are creating buzz in Social Media.

he series Black Mirror is all about using Artificial Intelligence to create new avatars that can carry out operations like humans. It presents an alternative universe where humans are replaced by Robots.

Humanoid Robots are not new to human civilization. Its history dates back to 1495 when Leonardo Da Vinci designed a ‘humanoid Robot” known as Leonard’s Robot, which operated using several pulleys and cables that could stand, sit, raise its visors and move its arms.

However, the first real Robot known as “Elektro Robot” was created by Joseph Barnett in 1937, who was capable of performing 26 different routines including walking, talking, counting, and smoking.

Humans encounter Artificial Intelligence on a day-to-day basis. From the manufacturing of AI-based products to everyday usable appliances, Artificial Intelligence is directing almost every aspect of human life.

A report released by Mckinsey Insight states that by the year 2030, Robots will replace 800 million. So, it is no surprise when the AI is applied to Social Media, to introduce Robot Influencers, who display Human-Like Behaviours. But what are Robot influencers and how are they affecting human influencers on social media?

Android and Humanoid Robots

Humanoid Robots display Human-like behavior. Categorized as a male Humanoid called Android and female Humanoid called Gynoid, it is administered by Humanoid Science.

A report by MIT, authored by Hiroshi Ishiguro states that humanoid Science forms the basis of synergistic intelligence. With the expected effects over the brain, neuroscience, cognitive science, and developmental psychology, synergistic intelligence emerges through the interaction between the environment.

Synergistic intelligence paves the path of a new design that amalgamates human-like robots and human-related science. It utilizes Cognitive Developmental Robotics, which aligns self-developing structures inside the brain of robots and environmental designs, and aims into actual programming languages and implementation for high-level control programs for autonomous systems.

On the other hand, Androids display human-like appearance and are supervised by Android science, which displays an interdisciplinary framework of robotics engineering and cognitive science. Android science enables the sharing of knowledge about the development of androids and understanding of humans in cognitive science.

Android robots use actuators, governed by 3D-motion-capture-system, represents, conscious and unconscious movements such as chest movements and movements of arms and legs respectively, and generates facial expression.

Hiroshi Ishiguro, concludes that appearance and behavior are imperative for a cognitive science tool to be successful. The Android robots thus display both human-like behavior and human-like appearance to be used as Robot Influencers in social media, apart from its many other applications in day to day life.

Improving Creativity through GAN

Another technology that has been successful in the popularity of Bot Influencers is the use of Generative Adversarial Networks.

A report states that GAN allows the AI to generate different versions of the database by learning from its existing ones. It pairs two AI systems together, where one system creates whereas others provide outcomes, learn from the experience, and improves the quality of results.

Successful Robot Influencers

Shudu, with 2.6 million followers is the world’s first Digital Model. Developed in 2016, by a London based Cameron- James Wilson, Shudu is created using 3-D digital animation. Having collaborated with brands like Bahmain, Calvin Klein, and Dior, Shudu has also graced the magazine covers with celebrities like Kim Kardashian and Bella Hadid. It generates an income of $US10,000 to $US15,000 which is four times more than that generated by many human influencers.

Lil Miquela is another successful bot influencer with a 2.6million follower database. Developed by Trevor McFredies and Sara DeCou, it received $US125 from investors. The Bot identifies itself as a change-seeking Robot and has also landed a job at Dazed Magazine as a contributing arts Editor.

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Robots have disrupted every industry. They are everywhere. But can they help fight loneliness?

The current pandemic has made us befriend one technology which is often considered as our intellectual rival: robots. We have read several accounts of how robots have been resourceful in helping us fight the harrowing effects of COVID, like assisting us in our mission to find a cure drug and even sanitize public spaces. However, the crisis has also shown that robots can be our emotional support too. The scientists from Heriot-Watt University in Edinburgh, have programmed robots to address the instances of loneliness caused due to social distancing and isolation that have become new normal and mandatory due to COVID. This is not the first time that researchers around the world have been experimenting with emotional AI for the greater good of humans. Mauro Dragone, who is the project’s lead scientist, believes that this study can help understand the needs of the most vulnerable at this time and what technology could be used to make their lives better.

Her team at Heriot-Watt University, are working to incorporate robots in social care as a potential solution to reach out to vulnerable groups affected by the social distancing measures that have resulted in decreased visits and restricted activities. In this study named, Ambient Assisted Living, the scientists observed robots like Pepper perform everyday household tasks. The objective was to see if robots can assist healthcare workers with stretched hours of work and responsibilities by taking over simple household chores.

The work of these robots was similar to a nurse bot that not only reminds older patients on long-term medical programs to take their medication but also converses with them every day to monitor their overall wellbeing.

Around the same time, another group of scientists from Ohio State University’s College of Nursing and Vanderbilt University received a grant of US$3.13 million to develop socially-assistive robots that can promote social interaction among older adults. The humanoid and animal-like robots are scheduled to be trialed next summer. According to one of the faculties, Dr. Lorraine Mion, robots can be a great assistive technology to the nursing homes and the assisted living areas that can then be used to facilitate older adults to engage with one another.

The SoftBank Robotics, who holds credit for developing Pepper, emotional robots would not replace humans but complement them. By coexisting with us, these robots hold promise for leading all people to a smarter, safer, healthier, and happier existence.

Some months ago, Japanese robotic startup Groove X designed Lovot, a tiny, plush robot designed to spread love. Lovot, which looks like an adorable mix between a penguin and a teddy bear, is supposed to help lonely people cope with their emotional needs. Speaking to a Forbes interview, Lovot creator and Groove X CEO Kaname Hayashi said, “Our robot doesn’t do any work for humans, and it doesn’t have any contents for entertainment purposes. But neither do dogs or cats. What it does is recognize you and bother you. That’s the aim of our robot.”

Meanwhile, we have, Phobot, an interactive robot developed by student researchers at the University of Amsterdam, serves as a strong visual and learning aid to help children who suffer from anxiety and phobias. Another robot studio called BeatBots, based in San Francisco and Sendai, Japan—created Keepon Pro in 2003 specifically for children with developmental disorders like autism. People with autism often have trouble keeping eye contact with other people, so a therapist can use Keepon to interact with a child in a social setting without the child shutting down.

All these notable examples cite that robots can ready to become our emotional companion. This is quite encouraging both in robotics and Emotional AI too. All the COVID pandemic worsen, the demand for care workers rise, these ‘social robots’ will prove helpful in an often ignored aspect of healthcare. The noble work at Heriot-Watt University and SoftBank’s Pepper are just a start!

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In the mid-twentieth century when the computer and its applications were starting to bring changes to the world, sociologist David Reisman had something stuck in his mind. He wondered what people would do once machine automation comes to effect and humans have no compulsion to do daily physical chores and strain their brain to come up with solutions. He was excited to see what people would do with all the free time.

More than half a century later when the world has exactly what Reisman wondered, humans are still working on a full-time scale. Work alleviated by industrious machines such as robotics systems has only freed humans to create more elaborate new tasks to be laboured over. To counter attack all the predictions of the previous century, machines gave humans more time to work and not to relax.

Like how we currently imagine robots taking over the human society and doing all the work by themselves including physical and intellectual labour without human assistance as they are well programmed and set to adapt to any environment and take the accurate decision without human help, the previous century people too dreamed that robots will take over all the physical work during the era of the space race. But today, robots are used for their intelligence more vigorously than their physical assistance. Humans can only teach robots and make them follow instructions up to an extent. So when humans lack, machine learning makes its way to discipline robotics.

What is machine learning?

Machine learning is one of the advanced and innovative technological fields today in which robotics is being influenced. Machine learning aids robots to function with their developed applications and a deep vision.

According to a recent survey published by the Evans Data Corporation Global Development, machine learning and robotics is at the top developers’ priority for 2016. It is calculated that 56.4% of participants build robotic apps and 24.7% of them indicate the use of machine learning in their project.

Machine learning involves enormous caches of data to be taught to the robot for its perfect learning. The procedure contains algorithms and physical machines to aid the robots in the learning process.

Different disciplines of teaching a robot through machine learning

Deep Learning educates the purpose of the robot

Deep Learning has been in the machine learning field for more than 30 years. But it was recognised and brought into continuous use recently when Deep Neutral Network algorithms and hardware advancements started having high potential. Deep learning can be accomplished through computational capacity and the required datasets that are ultimately the powerful assets of machine learning.

The process of teaching robots machine learning necessitates engineers and scientists to decide how AI learns. Domain experts take the next role of advising on how robots need to function and operate within the scope of the task. They also specify the features of robots being of assistance at logistics experts and security consultants. Deep learning focuses on the sector that a robot needs to be specialised from its root.

Feeding robots with planning and learning

AI robots through machine learning acquire two important processes namely planning and learning. Planning is like a physical way of teaching robot that presumes the robots to work on what pace it has to move every joint to complete a task. For example, grabbing an object by a robot is a planning input.

Meanwhile, learning involves different inputs and reacts according to the data added to it on a dynamic environment. Learning process takes place through physical demonstrations in which movements are trained, stimulation of 3D artificial environments and feeding video and data of a person or another robot performing the task it is hoping to master for itself. The stimulation is a training data where a set of labelled or annotated datasets that an AI algorithm can use to recognize and learn from it.

Educating and training with accurate data

The process of educating a robot needs accuracy and abundance. Inaccurate or corrupt data is going to bring nothing except for chaos. Inaccurate data will lead to a robot drawing to the wrong conclusion. For example, if the database is focused on green apples, and you input a picture of a blueberry muffin, they would still get a green apple. This kind of data disruption is a major threat. Insufficient training data will bar the robot from acquiring the full potential it is designed to perform.

Reaping the maximum of physical help

Machine learning will push robots to do physical work at its best. Recently, these kinds of robots are used in industries for various purposes. For example, unmanned vehicles are stealing the spotlight at construction sites.

It is not just the construction sector that is reaping a handful of help from machine learning. Medical industry makes use of it by involving computer vision models to recognise tumours within MRIs and CT scans. Through further training, an AI robot will be capable of doing life-saving surgeries and other medical procedures through its machine learning input.

With the emergence of robots in the society, the opportunity of training data, machine learning and Artificial Intelligence (AI) are playing a critical role in bringing it to enforcement. Tech companies involved in the robot creating and training process should spend some time to sensitize people on the robots help to humanity. If things work well and the AI department comes up with advanced robots that are well-trained, built and purposed AI, Reisman’s dream of humans having leisure time could come true.

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Researchers say that this new innovation will prove useful by equipping the robots with instrumental canes so that the robots can identify the objects by tapping on them.

When robots were first discovered in the year 1495 by Leonardo Da Vinci, the world would not have that they would become an integral part of human life. However, with the advancement in technology, robotics has seen innovation and intervention that was earlier not possible.

At present, Humanoid robots present a vast range of scope to the daily mundane task. Alexa, Chatbots in Financial spaces, and Siri are some of the examples where robots exhibited assistance to humans in a mundane task. Be it playing a song or assuring emotional support, especially during COVID 19, the humanoid robots halved the task conducted by humans.

In most cases, these robots relied on touch, vision, and ability to move around. Nonetheless, with the power to hear these robots can be a game-changer in the world of AI and Robotics.

The researchers at Carnegie Mellon University’s (CMU) Robotics Institute states that Robot perception can be improved by adding another element to its reliability: Hearing.

The addition of this new element in its functioning would enable Robots to differentiate between different kinds of objects, determine the action behind the sound, and would help them to predict the physical properties of different objects by utilizing the sound.

In an interview, Lerrel Pinto, the researcher who was part of the CMU’s Robotics team stated, “A lot of preliminary work in other fields indicated that sound could be useful, but it wasn’t clear how useful it would be in Robotics”.

The Methodology used By Hearing Robots

The researchers used a square tray, which was attached to the arms of the Swayer Robot to create an apparatus known as Tilt bot, that captured interactions. An object was placed at the tray, letting Swayer spend some hours with it, by moving the tray with the object in different directions.

The large dataset of 60 different objects such as balls and toys, was then recorded by using videos and cameras. The dataset cataloged 15000 interactions to be used by other researchers.

Some other datasets were also collected by enabling the robot to push objects on a surface. A detailed report of this finding was presented last month, at the Virtual Robotics Science and Systems Conference. The researchers pointed out that the performance rate of Robot was considerably high and in 76% of the cases, the robot was able to differentiate between objects.

Other Type of Robots

Apart from this newly discovered ability of robots, other innovations in robotics have also assisted human beings.

• Collaborative Robots- Collaborative Robots also known as Cobots, is governed by augmented intelligence that enables them to enhance human capabilities like data capabilities, precision, and strength, to operate near its human counterparts for performing the tasks assigned to them.
• Robots with Learning-Based Particle Stimulator- Last year, the researchers at MIT developed a new learning system for improving the abilities of the robots to mold materials into shapes and identify whether the material is solid or liquid. This new system is governed by using physical stimulators that captures the response of different material to force. This new system will act as a guide for the robots in determining the reaction of the force when it touches a particular object and would also enhance their robotic controls.
• Humanoid Robots- Humanoid Robot is governed by Humanoid science and replicates human behavior, emotions, expression, and intelligence.
• Android Robots- Android Robots are governed by android science and thus display human-like behavior by displaying conscious and unconscious movements like that in a human body. It is an interdisciplinary framework of robotic engineering and cognitive sciences.

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Did you know that today educational institutes like schools, training centers can design their robotics lab?

A robotics lab is a hub of various technologies working together under the expertise of the human mind under a single roof. While anyone can make their own robotics lab, housing numerous robots in a room does not qualify as a robotics lab. In layman’s terms to constitute an ideal robotics lab, one needs a perfect balance of hardware, software, design tools, and human resource. However, many people fail to understand this simple idea and, as a result, are often stressed when planning to build one. In short, the problem areas are lack of information resources and time to understand them. Since the future of robotics is dependent on the vast exposure and experiences gained in the robotics field, it is important to set up a lab, at least in educational institutions and spaces. Hence we present the interested parties, a guide on pointers to consider when building a robotics lab of their own.

1. Determine the space pre-requisites: The first and foremost thing is to find out how much space the set of equipment will occupy. For instance, a school will need a few rooms to house robots to teach students the basics of robotics and some programming, whereas a research lab would not share similar space requirements like a school. The latter would need a minimum of dozen robot stations, few demonstration stations, workstation, and equipment storage. Further, some labs need security fencing, especially to ensure the safety of students and researchers from accidental contact with robots of industrial configuration and to prevent theft of robotics equipment too. Also, if an educational body has more space and resources, it can invest in having full-size robotics equipment for higher education.

2. Flexible Design Strategy: Though most of the robots are not mobile, yet one cannot ignore having a flexible design strategy. For instance, at Tri Star Career Compact, Celina, Ohio, the robotics lab also focused on flexibility in design. After paying a visit to the Honda North American Training Center in Marysville, Ohio, the school authorities notice that the lab has ceiling-mounted bus duct with movable electrical disconnects for power drops, and all utilities are located in the ceiling, including power and compressed air. This allowed the lab to reconfigure robots anywhere along the bus duct line. This inspired Tri Star’s robotics instructor to reconfigure the room layout during construction and implement a different configuration on the first day of school. These flexible strategies apply to both demonstration and full-size robotics equipment.

3. Medium of Instruction: While the lab can be made in a state of the art asset but it is equally important to work on how the learners are going to be taught about robotics, through experimentation and illustration. Hence the classrooms or workstation will require proper LED lighting, coding stations, soldering equipment, and overhead tool storage. And programming can be taught using digital tools, such as Scratch, Tynker, Alice, or Code.org. The larger classroom area can accommodate desks with computer monitors that raise and lower via remote control. Such configuration gives the teacher the ability to move from lecture to project-based learning with the click of a button. Further, if there are budget constraints, institutes can opt for free robotic sets like Scratch VPL for programming LEGO WeDo robots.

It is imperative to note that whatever be the scale of the robotics labs being built, concerned authorities should not compromise of quality of the resources used and deployed. They can also ensure that the robotics pieces of equipment are well maintained, and the lab and classroom both provide a welcoming and comforting atmosphere. Moreover, if they want to encourage more participation and enrollment, the robotics lab can be constructed in an area that is easily accessible by road and possesses a characteristic architecture.

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Artificial brains could mimic biological neural networks and make robots smarter

The recent advances in robotics have made it possible to perform complex tasks that were previously risky for human workers. There is no wonder that robots are gaining unprecedented interest as companies are seeking to deliver seamless customer experience. Robots today are also capable of picking up a can of soft drink or cooking foods in the same way humans do. Yet, they have much to learn as they require to locate an object, infer its shape, determine the right amount of strength, and hold the object without letting it slip. In order to perform such tasks with precision, robots need an extraordinary sense of touch equivalent to the human skin.

In this way, a team of computer scientists and materials engineers from the National University of Singapore has recently developed a sensory integrated artificial brain system. The system mimics biological neural networks, and can run on a power-efficient neuromorphic processor, such as Loihi chip from Intel. The system also assimilates artificial skin and vision sensors that can equip a robot with the ability to draw accurate conclusions about an object it grasps based on the data captured by the vision and touch sensors in real-time.

As the field of robotic manipulation has made great progress in recent years, fusing both vision and tactile information to provide a highly precise response in milliseconds remains a technology challenge, according to assistant professor Benjamin Tee at the NUS Department of Materials Science and Engineering. He said, “Our recent work combines our ultra-fast electronic skins and nervous systems with the latest innovations in vision sensing and AI for robots so that they can become smarter and more intuitive in physical interactions.”

Enabling a Sense of Touch in Robotics

Just like humans, researchers nowadays are exploring to enable a sense of touch for robots. Meanwhile, recent years have seen a great stride towards it. For the new robotic system, the NUS researchers built Asynchronous Coded Electronic Skin (ACES), an advanced artificial skin, in 2019. The skin has ultra-high responsiveness and robustness to damage and can be paired with any kind of sensor skin layers to function effectively as an electronic skin.

Led by Asst Prof Tee, ACES is made up of a network of sensors connected through a single electrical conductor and varies from current electronic skins that have interlinked wiring systems that can make them sensitive to damage and intricate to scale up. This novel sensor senses touch over 1,000 times faster than the human sensory nervous system. Besides, it can detect the shape, texture and hardness of an object 10 times faster than the blink of an eye. ACES is able to adapt and can remain functional following physical damage largely thanks to its all the sensors that can be connected to a common electrical conductor with each sensor operating independently.

Moreover, scientists across the world are also exploring ways to give robots a human-like sense of touch. In a study published in the journal Science Robotics, Stanford scientists developed an electronic glove comprising sensors that could give robotic hands the human-like sense of touch and dexterity. According to researchers, the sensors in the glove’s fingertips at the same time measure the intensity and direction of pressure, two qualities essential to achieving manual dexterity.

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The Prosocial Behaviour strengthens the Human-Robot Interaction by prompting human participants to intervene when the robot shows a sad response.

Just like Human abuse, robot abuse is very normal. Ever since its innovation, robots have been subjected to abuse. Several posts and videos, floating around social media can testify the abuse of robots by children and adults. Infact, one such distinctive video by Boston Dynamics, a Softbank Robotic firm, demonstrated a parody video of robot abuse. Followed by which, the social media was flooded with Robot Lovers criticizing the abuse.

The Human-Robot Interaction forms the basis of strong robotics. Robots have been designed to hear, touch, and provide emotional support to humans, especially during a crisis. But can the same be said to humans, especially when it comes to Robot abuse? Or Can Robots prevent abuse from happening? These are some of the questions which define the complexity of Human-Robot Interaction. That’s why it becomes imperative to understand the prosocial behavior of robots towards each other during abuse and the reaction prompted by robots for human beings to act through this interaction.

Prosocial Behaviour during Robot Abuse

An experimental study named “Prompting Prosocial Human Intervention in Response to Robot Mistreatment” by Interactive Machine Groups at Yale University, investigated the emotional reactions of bystander robots for motivating human participants in response to human abuse.

Prosocial Behaviour is the actions taken at the personal cost, thus fostering unselfishness and collaboration among the people involved.

The study involved a confederate, a human participant, and three cozmo robots which were painted- yellow, green, and blue in 15 groups. Cozmo robots is a programmable toy robot, which can express emotions, utter non-linguistic phases, move through confined spaces, and sense changes in pose. The group was assigned a collaborative block building task.

In each group one robot made mistakes repeatedly thus, being subjected to physical and verbal abuse by the confederate. It was also imperative in understanding the dynamics of human bullying with group compositions and the role of bystanders.

The conditions of the experiments were No response i.e. the bystander robots did not react to the abuse endured by the other robot, and Sad response, where the bystander robots turned towards the abused robot, expressing sadness over the abuse with the help of preset animation, which was highly anthropomorphic and displayed audio and facial reactions.

It was hypothesized that the sad response would increase the perception of robot mistreatment, induce more empathy for the abused robot, and would lead to more prosocial intervention from participants.

The abuse was detected automatically with the help of the installed camera. A significant change in the head position of the robot was noticed, and the yellow robot reacted by displaying a sad face and shutting down for 10 seconds.

The result of the experiment had strong interventions. It included:

• Interruptions to the Abuse Script: The reactions from the bystander robots prompted the participant human to interrupt the abuse by preventing the yellow robot from making the same mistakes again, thus decreasing the chances of confederate abusing the yellow robot.
• Direct Stop: The sad reaction of the bystander robots, let the human participants stop the abuse by saying “You should Stop”,” Don’t do that”.
• Social Pressure: In other instances, the comments used by the participants put the confederate about continuing the abuse. Example: “You hurt its feelings”.

The study concludes that the effect of the experiment is possible. Researchers conclude that more often the participants in the study intervened when robots in their group expressed sadness due to abuse. If such an experiment comes into effect, the chances of robot breaking after abuse would be reduced thus ensuring the safety of robots due to robot malfunctioning.

Teaching Robots to Fight Back Abuse

Another way through which Human-Robot interaction can be demonstrated with Prosocial Behaviour is by teaching the robot to fight back abuse. A video released by Boston Dynamics in which an experiment was conducted by researchers over SpotMini, the firm headless Robotic Dog.

The experiment demonstrated Spotmini entering the door where a human is already standing with an ice hockey stick. Undisturbed by this the robot continues to grab the door when Spotmini lost its fifth arm on claw due to the attack by a human.

The assault continues with human closing the door on the robot, and grabbing its leash, and yanking so that Spotmini can’t cross the threshold. This led to the robot fighting back the human by looking like real dogfighting its owner. As the human gives in SpotMini trudges through the threshold and enters the door.

The firm describes this video as a test for Spotmini to adjust to disturbances. The Boston Dynamics also pointed out that this would not harm the robot, and would improve the operations of the robot.

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Did Robots shape the outcome of the 2016 US Presidential Election?

A debate that is still hot in the discussion as the United States Presidential election 2020 date draws near.

There are many reasons to deploy robots in an election, trade pundits believe that robots are imperative to control the narrative during political debates and skew its discourse. However, it is still not clear to what extent the automated AI-driven bots can manipulate social media activity and what kind of influence they have on public opinion and election outcomes. Bots can exist in all kinds of social media. They can, for example, be active in various forms of discussion forums or commentary fields, Twitter for instance.

What may be beneficial for politicians and their engineers, however, may not necessarily be good for a Democracy!

Many pieces of evidence point to the influx of artificial intelligence-powered technologies been systematically misused to manipulate citizens in recent elections.

Propagating Fake News

The regular use of political bots to spread right-wing propaganda and fake news on social media cannot be ruled out. These bots are autonomous accounts programmed to aggressively spread favour political messages to create a false notion of public support rallying around a candidate. This is an increasingly widespread tactic which attempts to shape the multifaced public discourse and distort political sentiment.

Typically disguised as ordinary human accounts, these malicious bots are responsible for spreading misinformation and contributing to an acrimonious political climate on social media sites like Twitter, Reddit and Facebook. These bots are very powerful at attacking loyalist voters from the opposing camp and even discouraging them from going to the voting booth. Pro-Trump bots regularly infiltrated the online spaces used by pro-Clinton campaigners to disseminate the highly automated content, generating one-quarter of Twitter traffic in the 2016 election.

Manipulating Voter Choices

In addition to shaping the online debate, bots can also be used to target and manipulate individual voters. Back in 2016, during the U.S. presidential election, Robert Mercer-backed data-analytics firm Cambridge Analytica rolled out an extensive advertising campaign that targeted persuadable voters based on their psychology. This highly sophisticated micro-targeting operation used big data and machine learning that influenced people’s emotions and fuelled the fire to the data privacy debate.

• According to Howard’s research, in the period leading up to the Brexit vote in the UK in June 2016, a third of the 1.5 million tweets with referendum-related hashtags were generated by only 1 per cent of accounts, pointing fingers at the malicious bots.

• Consider this, a set of 984,713 tweets related to the 2017 German federal election were tweeted by bot-driven automation. In total, around 7.4% of the total traffic around the German election was from bot-driven automation accounts!

• During the 2018 Swedish general election, the authorities analysed over 11500 URLs for fake news. The results assessed that 40% of the links were directed to news media and 28% links were pointed at other sources of political news and information, including fake news.

Promoting Free and Fair Elections

All is not lost. AI itself is not harmful.

The same AI algorithms and bots which are used to mislead, misinform and confuse voters can be re-purposed to support democracy and increase public participation. An ethical approach to AI is the need of the hour as the USA will select its new president in a few months from now.

AI start-ups like Factmata and Avantgarde Analytics are providing technological solutions programmed to spread information debunking known falsehoods, that surround the political campaigns and electoral candidates. Similarly, micro-targeting campaigns can educate voters on a variety of political issues to assist them with the free and fair voting choice.

The use of AI techniques like robotics, ml and deep learning to tide the electoral in politics is not going away anytime soon, for it is simply too valuable to politicians and their campaigns! However, to push the ethos of democracy they must commit to using AI as ethically and judiciously as possible to ensure that their attempts to sway voters do not undermine democracy as a whole.

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