Tag Archives: humans

#434256 Singularity Hub’s Top Articles of the ...

2018 was a big year for science and technology. The first gene-edited babies were born, as were the first cloned monkeys. SpaceX successfully launched the Falcon Heavy, and NASA’s InSight lander placed a seismometer on Mars. Bitcoin’s value plummeted, as did the cost of renewable energy. The world’s biggest neuromorphic supercomputer was switched on, and quantum communication made significant progress.

As 2018 draws to a close and we start anticipating the developments that will happen in 2019, here’s a look back at our ten most-read articles of the year.

This 3D Printed House Goes Up in a Day for Under $10,000
Vanessa Bates Ramirez | 3/18/18
“ICON and New Story’s vision is one of 3D printed houses acting as a safe, affordable housing alternative for people in need. New Story has already built over 800 homes in Haiti, El Salvador, Bolivia, and Mexico, partnering with the communities they serve to hire local labor and purchase local materials rather than shipping everything in from abroad.”

Machines Teaching Each Other Could Be the Biggest Exponential Trend in AI
Aaron Frank | 1/21/18
“Data is the fuel of machine learning, but even for machines, some data is hard to get—it may be risky, slow, rare, or expensive. In those cases, machines can share experiences or create synthetic experiences for each other to augment or replace data. It turns out that this is not a minor effect, it actually is self-amplifying, and therefore exponential.”

Low-Cost Soft Robot Muscles Can Lift 200 Times Their Weight and Self-Heal
Edd Gent | 1/11/18
“Now researchers at the University of Colorado Boulder have built a series of low-cost artificial muscles—as little as 10 cents per device—using soft plastic pouches filled with electrically insulating liquids that contract with the force and speed of mammalian skeletal muscles when a voltage is applied to them.”

These Are the Most Exciting Industries and Jobs of the Future
Raya Bidshahri | 1/29/18
“Technological trends are giving rise to what many thought leaders refer to as the “imagination economy.” This is defined as “an economy where intuitive and creative thinking create economic value, after logical and rational thinking have been outsourced to other economies.” Unsurprisingly, humans continue to outdo machines when it comes to innovating and pushing intellectual, imaginative, and creative boundaries, making jobs involving these skills the hardest to automate.”

Inside a $1 Billion Real Estate Company Operating Entirely in VR
Aaron Frank | 4/8/18
“Incredibly, this growth is largely the result of eXp Realty’s use of an online virtual world similar to Second Life. That means every employee, contractor, and the thousands of agents who work at the company show up to work—team meetings, training seminars, onboarding sessions—all inside a virtual reality campus.To be clear, this is a traditional real estate brokerage helping people buy and sell physical homes—but they use a virtual world as their corporate offices.”

How Fast Is AI Progressing? Stanford’s New Report Card for Artificial Intelligence
Thomas Hornigold | 1/18/18
“Progress in AI over the next few years is far more likely to resemble a gradual rising tide—as more and more tasks can be turned into algorithms and accomplished by software—rather than the tsunami of a sudden intelligence explosion or general intelligence breakthrough. Perhaps measuring the ability of an AI system to learn and adapt to the work routines of humans in office-based tasks could be possible.”

When Will We Finally Achieve True Artificial Intelligence?
Thomas Hornigold | 1/1/18
“The issue with trying to predict the exact date of human-level AI is that we don’t know how far is left to go. This is unlike Moore’s Law. Moore’s Law, the doubling of processing power roughly every couple of years, makes a very concrete prediction about a very specific phenomenon. We understand roughly how to get there—improved engineering of silicon wafers—and we know we’re not at the fundamental limits of our current approach. You cannot say the same about artificial intelligence.”

IBM’s New Computer Is the Size of a Grain of Salt and Costs Less Than 10 Cents
Edd Gent | 3/26/18
“Costing less than 10 cents to manufacture, the company envisions the device being embedded into products as they move around the supply chain. The computer’s sensing, processing, and communicating capabilities mean it could effectively turn every item in the supply chain into an Internet of Things device, producing highly granular supply chain data that could streamline business operations.”

Why the Rise of Self-Driving Vehicles Will Actually Increase Car Ownership
Melba Kurman and Hod Lipson / 2/14/18
“When people predict the demise of car ownership, they are overlooking the reality that the new autonomous automotive industry is not going to be just a re-hash of today’s car industry with driverless vehicles. Instead, the automotive industry of the future will be selling what could be considered an entirely new product: a wide variety of intelligent, self-guiding transportation robots. When cars become a widely used type of transportation robot, they will be cheap, ubiquitous, and versatile.”

A Model for the Future of Education
Peter Diamandis | 9/12/18
“I imagine a relatively near-term future in which robotics and artificial intelligence will allow any of us, from ages 8 to 108, to easily and quickly find answers, create products, or accomplish tasks, all simply by expressing our desires. From ‘mind to manufactured in moments.’ In short, we’ll be able to do and create almost whatever we want. In this future, what attributes will be most critical for our children to learn to become successful in their adult lives? What’s most important for educating our children today?”

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Posted in Human Robots

#434235 The Milestones of Human Progress We ...

When you look back at 2018, do you see a good or a bad year? Chances are, your perception of the year involves fixating on all the global and personal challenges it brought. In fact, every year, we tend to look back at the previous year as “one of the most difficult” and hope that the following year is more exciting and fruitful.

But in the grander context of human history, 2018 was an extraordinarily positive year. In fact, every year has been getting progressively better.

Before we dive into some of the highlights of human progress from 2018, let’s make one thing clear. There is no doubt that there are many overwhelming global challenges facing our species. From climate change to growing wealth inequality, we are far from living in a utopia.

Yet it’s important to recognize that both our news outlets and audiences have been disproportionately fixated on negative news. This emphasis on bad news is detrimental to our sense of empowerment as a species.

So let’s take a break from all the disproportionate negativity and have a look back on how humanity pushed boundaries in 2018.

On Track to Becoming an Interplanetary Species
We often forget how far we’ve come since the very first humans left the African savanna, populated the entire planet, and developed powerful technological capabilities. Our desire to explore the unknown has shaped the course of human evolution and will continue to do so.

This year, we continued to push the boundaries of space exploration. As depicted in the enchanting short film Wanderers, humanity’s destiny is the stars. We are born to be wanderers of the cosmos and the everlasting unknown.

SpaceX had 21 successful launches in 2018 and closed the year with a successful GPS launch. The latest test flight by Virgin Galactic was also an incredible milestone, as SpaceShipTwo was welcomed into space. Richard Branson and his team expect that space tourism will be a reality within the next 18 months.

Our understanding of the cosmos is also moving forward with continuous breakthroughs in astrophysics and astronomy. One notable example is the MARS InSight Mission, which uses cutting-edge instruments to study Mars’ interior structure and has even given us the first recordings of sound on Mars.

Understanding and Tackling Disease
Thanks to advancements in science and medicine, we are currently living longer, healthier, and wealthier lives than at any other point in human history. In fact, for most of human history, life expectancy at birth was around 30. Today it is more than 70 worldwide, and in the developed parts of the world, more than 80.

Brilliant researchers around the world are pushing for even better health outcomes. This year, we saw promising treatments emerge against Alzheimers disease, rheumatoid arthritis, multiple scleroris, and even the flu.

The deadliest disease of them all, cancer, is also being tackled. According to the American Association of Cancer Research, 22 revolutionary treatments for cancer were approved in the last year, and the death rate in adults is also in decline. Advancements in immunotherapy, genetic engineering, stem cells, and nanotechnology are all powerful resources to tackle killer diseases.

Breakthrough Mental Health Therapy
While cleaner energy, access to education, and higher employment rates can improve quality of life, they do not guarantee happiness and inner peace. According to the World Economic Forum, mental health disorders affect one in four people globally, and in many places they are significantly under-reported. More people are beginning to realize that our mental health is just as important as our physical health, and that we ought to take care of our minds just as much as our bodies.

We are seeing the rise of applications that put mental well-being at their center. Breakthrough advancements in genetics are allowing us to better understand the genetic makeup of disorders like clinical depression or Schizophrenia, and paving the way for personalized medical treatment. We are also seeing the rise of increasingly effective therapeutic treatments for anxiety.

This year saw many milestones for a whole new revolutionary area in mental health: psychedelic therapy. Earlier this summer, the FDA granted breakthrough therapy designation to MDMA for the treatment of PTSD, after several phases of successful trails. Similar research has discovered that Psilocybin (also known as magic mushrooms) combined with therapy is far more effective than traditional forms of treatment for depression and anxiety.

Moral and Social Progress
Innovation is often associated with economic and technological progress. However, we also need leaps of progress in our morality, values, and policies. Throughout the 21st century, we’ve made massive strides in rights for women and children, civil rights, LGBT rights, animal rights, and beyond. However, with rising nationalism and xenophobia in many parts of the developed world, there is significant work to be done on this front.

All hope is not lost, as we saw many noteworthy milestones this year. In January 2018, Iceland introduced the equal wage law, bringing an end to the gender wage gap. On September 6th, the Indian Supreme Court decriminalized homosexuality, marking a historical moment. Earlier in December, the European Commission released a draft of ethics guidelines for trustworthy artificial intelligence. Such are just a few examples of positive progress in social justice, ethics, and policy.

We are also seeing a global rise in social impact entrepreneurship. Emerging startups are no longer valued simply based on their profits and revenue, but also on the level of positive impact they are having on the world at large. The world’s leading innovators are not asking themselves “How can I become rich?” but rather “How can I solve this global challenge?”

Intelligently Optimistic for 2019
It’s becoming more and more clear that we are living in the most exciting time in human history. Even more, we mustn’t be afraid to be optimistic about 2019.

An optimistic mindset can be grounded in rationality and evidence. Intelligent optimism is all about being excited about the future in an informed and rational way. The mindset is critical if we are to get everyone excited about the future by highlighting the rapid progress we have made and recognizing the tremendous potential humans have to find solutions to our problems.

In his latest TED talk, Steven Pinker points out, “Progress does not mean that everything becomes better for everyone everywhere all the time. That would be a miracle, and progress is not a miracle but problem-solving. Problems are inevitable and solutions create new problems which have to be solved in their turn.”

Let us not forget that in cosmic time scales, our entire species’ lifetime, including all of human history, is the equivalent of the blink of an eye. The probability of us existing both as an intelligent species and as individuals is so astoundingly low that it’s practically non-existent. We are the products of 14 billion years of cosmic evolution and extraordinarily good fortune. Let’s recognize and leverage this wondrous opportunity, and pave an exciting way forward.

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#434194 Educating the Wise Cyborgs of the Future

When we think of wisdom, we often think of ancient philosophers, mystics, or spiritual leaders. Wisdom is associated with the past. Yet some intellectual leaders are challenging us to reconsider wisdom in the context of the technological evolution of the future.

With the rise of exponential technologies like virtual reality, big data, artificial intelligence, and robotics, people are gaining access to increasingly powerful tools. These tools are neither malevolent nor benevolent on their own; human values and decision-making influence how they are used.

In future-themed discussions we often focus on technological progress far more than on intellectual and moral advancements. In reality, the virtuous insights that future humans possess will be even more powerful than their technological tools.

Tom Lombardo and Ray Todd Blackwood are advocating for exactly this. In their interdisciplinary paper “Educating the Wise Cyborg of the Future,” they propose a new definition of wisdom—one that is relevant in the context of the future of humanity.

We Are Already Cyborgs
The core purpose of Lombardo and Blackwood’s paper is to explore revolutionary educational models that will prepare humans, soon-to-be-cyborgs, for the future. The idea of educating such “cyborgs” may sound like science fiction, but if you pay attention to yourself and the world around you, cyborgs came into being a long time ago.

Techno-philosophers like Jason Silva point out that our tech devices are an abstract form of brain-machine interfaces. We use smartphones to store and retrieve information, perform calculations, and communicate with each other. Our devices are an extension of our minds.

According to philosophers Andy Clark and David Chalmers’ theory of the extended mind, we use this technology to expand the boundaries of our minds. We use tools like machine learning to enhance our cognitive skills or powerful telescopes to enhance our visual reach. Such is how technology has become a part of our exoskeletons, allowing us to push beyond our biological limitations.

In other words, you are already a cyborg. You have been all along.

Such an abstract definition of cyborgs is both relevant and thought-provoking. But it won’t stay abstract for much longer. The past few years have seen remarkable developments in both the hardware and software of brain-machine interfaces. Experts are designing more intricate electrodes while programming better algorithms to interpret the neural signals. Scientists have already succeeded in enabling paralyzed patients to type with their minds, and are even allowing people to communicate purely through brainwaves. Technologists like Ray Kurzweil believe that by 2030 we will connect the neocortex of our brains to the cloud via nanobots.

Given these trends, humans will continue to be increasingly cyborg-like. Our future schools may not necessarily educate people as we are today, but rather will be educating a new species of human-machine hybrid.

Wisdom-Based Education
Whether you take an abstract or literal definition of a cyborg, we need to completely revamp our educational models. Even if you don’t buy into the scenario where humans integrate powerful brain-machine interfaces into our minds, there is still a desperate need for wisdom-based education to equip current generations to tackle 21st-century issues.

With an emphasis on isolated subjects, standardized assessments, and content knowledge, our current educational models were designed for the industrial era, with the intended goal of creating masses of efficient factory workers—not to empower critical thinkers, innovators, or wise cyborgs.

Currently, the goal of higher education is to provide students with the degree that society tells them they need, and ostensibly to prepare them for the workforce. In contrast, Lombardo and Blackwood argue that wisdom should be the central goal of higher education, and they elaborate on how we can practically make this happen. Lombardo has developed a comprehensive two-year foundational education program for incoming university students aimed at the development of wisdom.

What does such an educational model look like? Lombardo and Blackwood break wisdom down into individual traits and capacities, each of which can be developed and measured independently or in combination with others. The authors lay out an expansive list of traits that can influence our decision-making as we strive to tackle global challenges and pave a more exciting future. These include big-picture thinking, curiosity, wonder, compassion, self-transcendence, love of learning, optimism, and courage.

As the authors point out, “given the complex and transforming nature of the world we live in, the development of wisdom provides a holistic, perspicacious, and ethically informed foundation for understanding the world, identifying its critical problems and positive opportunities, and constructively addressing its challenges.”

After all, many of the challenges we see in our world today boil down to out-dated ways of thinking, be they regressive mindsets, superficial value systems, or egocentric mindsets. The development of wisdom would immunize future societies against such debilitating values; imagine what our world would be like if wisdom was ingrained in all leaders and participating members of society.

The Wise Cyborg
Lombardo and Blackwood invite us to imagine how the wise cyborgs of the future would live their lives. What would happen if the powerful human-machine hybrids of tomorrow were also purpose-driven, compassionate, and ethical?

They would perceive the evolving digital world through a lens of wonder, awe, and curiosity. They would use digital information as a tool for problem-solving and a source of infinite knowledge. They would leverage immersive mediums like virtual reality to enhance creative expression and experimentation. They would continue to adapt and thrive in an unpredictable world of accelerating change.

Our media often depict a dystopian future for our species. It is worth considering a radically positive yet plausible scenario where instead of the machines taking over, we converge with them into wise cyborgs. This is just a glimpse of what is possible if we combine transcendent wisdom with powerful exponential technologies.

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#434151 Life-or-Death Algorithms: The Black Box ...

When it comes to applications for machine learning, few can be more widely hyped than medicine. This is hardly surprising: it’s a huge industry that generates a phenomenal amount of data and revenue, where technological advances can improve or save the lives of millions of people. Hardly a week passes without a study that suggests algorithms will soon be better than experts at detecting pneumonia, or Alzheimer’s—diseases in complex organs ranging from the eye to the heart.

The problems of overcrowded hospitals and overworked medical staff plague public healthcare systems like Britain’s NHS and lead to rising costs for private healthcare systems. Here, again, algorithms offer a tantalizing solution. How many of those doctor’s visits really need to happen? How many could be replaced by an interaction with an intelligent chatbot—especially if it can be combined with portable diagnostic tests, utilizing the latest in biotechnology? That way, unnecessary visits could be reduced, and patients could be diagnosed and referred to specialists more quickly without waiting for an initial consultation.

As ever with artificial intelligence algorithms, the aim is not to replace doctors, but to give them tools to reduce the mundane or repetitive parts of the job. With an AI that can examine thousands of scans in a minute, the “dull drudgery” is left to machines, and the doctors are freed to concentrate on the parts of the job that require more complex, subtle, experience-based judgement of the best treatments and the needs of the patient.

High Stakes
But, as ever with AI algorithms, there are risks involved with relying on them—even for tasks that are considered mundane. The problems of black-box algorithms that make inexplicable decisions are bad enough when you’re trying to understand why that automated hiring chatbot was unimpressed by your job interview performance. In a healthcare context, where the decisions made could mean life or death, the consequences of algorithmic failure could be grave.

A new paper in Science Translational Medicine, by Nicholson Price, explores some of the promises and pitfalls of using these algorithms in the data-rich medical environment.

Neural networks excel at churning through vast quantities of training data and making connections, absorbing the underlying patterns or logic for the system in hidden layers of linear algebra; whether it’s detecting skin cancer from photographs or learning to write in pseudo-Shakespearean script. They are terrible, however, at explaining the underlying logic behind the relationships that they’ve found: there is often little more than a string of numbers, the statistical “weights” between the layers. They struggle to distinguish between correlation and causation.

This raises interesting dilemmas for healthcare providers. The dream of big data in medicine is to feed a neural network on “huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients.” What if, inevitably, such an algorithm proves to be unreasonably effective at diagnosing a medical condition or prescribing a treatment, but you have no scientific understanding of how this link actually works?

Too Many Threads to Unravel?
The statistical models that underlie such neural networks often assume that variables are independent of each other, but in a complex, interacting system like the human body, this is not always the case.

In some ways, this is a familiar concept in medical science—there are many phenomena and links which have been observed for decades but are still poorly understood on a biological level. Paracetamol is one of the most commonly-prescribed painkillers, but there’s still robust debate about how it actually works. Medical practitioners may be keen to deploy whatever tool is most effective, regardless of whether it’s based on a deeper scientific understanding. Fans of the Copenhagen interpretation of quantum mechanics might spin this as “Shut up and medicate!”

But as in that field, there’s a debate to be had about whether this approach risks losing sight of a deeper understanding that will ultimately prove more fruitful—for example, for drug discovery.

Away from the philosophical weeds, there are more practical problems: if you don’t understand how a black-box medical algorithm is operating, how should you approach the issues of clinical trials and regulation?

Price points out that, in the US, the “21st-Century Cures Act” allows the FDA to regulate any algorithm that analyzes images, or doesn’t allow a provider to review the basis for its conclusions: this could completely exclude “black-box” algorithms of the kind described above from use.

Transparency about how the algorithm functions—the data it looks at, and the thresholds for drawing conclusions or providing medical advice—may be required, but could also conflict with the profit motive and the desire for secrecy in healthcare startups.

One solution might be to screen algorithms that can’t explain themselves, or don’t rely on well-understood medical science, from use before they enter the healthcare market. But this could prevent people from reaping the benefits that they can provide.

Evaluating Algorithms
New healthcare algorithms will be unable to do what physicists did with quantum mechanics, and point to a track record of success, because they will not have been deployed in the field. And, as Price notes, many algorithms will improve as they’re deployed in the field for a greater amount of time, and can harvest and learn from the performance data that’s actually used. So how can we choose between the most promising approaches?

Creating a standardized clinical trial and validation system that’s equally valid across algorithms that function in different ways, or use different input or training data, will be a difficult task. Clinical trials that rely on small sample sizes, such as for algorithms that attempt to personalize treatment to individuals, will also prove difficult. With a small sample size and little scientific understanding, it’s hard to tell whether the algorithm succeeded or failed because it’s bad at its job or by chance.

Add learning into the mix and the picture gets more complex. “Perhaps more importantly, to the extent that an ideal black-box algorithm is plastic and frequently updated, the clinical trial validation model breaks down further, because the model depends on a static product subject to stable validation.” As Price describes, the current system for testing and validation of medical products needs some adaptation to deal with this new software before it can successfully test and validate the new algorithms.

Striking a Balance
The story in healthcare reflects the AI story in so many other fields, and the complexities involved perhaps illustrate why even an illustrious company like IBM appears to be struggling to turn its famed Watson AI into a viable product in the healthcare space.

A balance must be struck, both in our rush to exploit big data and the eerie power of neural networks, and to automate thinking. We must be aware of the biases and flaws of this approach to problem-solving: to realize that it is not a foolproof panacea.

But we also need to embrace these technologies where they can be a useful complement to the skills, insights, and deeper understanding that humans can provide. Much like a neural network, our industries need to train themselves to enhance this cooperation in the future.

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#433954 The Next Great Leap Forward? Combining ...

The Internet of Things is a popular vision of objects with internet connections sending information back and forth to make our lives easier and more comfortable. It’s emerging in our homes, through everything from voice-controlled speakers to smart temperature sensors. To improve our fitness, smart watches and Fitbits are telling online apps how much we’re moving around. And across entire cities, interconnected devices are doing everything from increasing the efficiency of transport to flood detection.

In parallel, robots are steadily moving outside the confines of factory lines. They’re starting to appear as guides in shopping malls and cruise ships, for instance. As prices fall and the artificial intelligence (AI) and mechanical technology continues to improve, we will get more and more used to them making independent decisions in our homes, streets and workplaces.

Here lies a major opportunity. Robots become considerably more capable with internet connections. There is a growing view that the next evolution of the Internet of Things will be to incorporate them into the network, opening up thrilling possibilities along the way.

Home Improvements
Even simple robots become useful when connected to the internet—getting updates about their environment from sensors, say, or learning about their users’ whereabouts and the status of appliances in the vicinity. This lets them lend their bodies, eyes, and ears to give an otherwise impersonal smart environment a user-friendly persona. This can be particularly helpful for people at home who are older or have disabilities.

We recently unveiled a futuristic apartment at Heriot-Watt University to work on such possibilities. One of a few such test sites around the EU, our whole focus is around people with special needs—and how robots can help them by interacting with connected devices in a smart home.

Suppose a doorbell rings that has smart video features. A robot could find the person in the home by accessing their location via sensors, then tell them who is at the door and why. Or it could help make video calls to family members or a professional carer—including allowing them to make virtual visits by acting as a telepresence platform.

Equally, it could offer protection. It could inform them the oven has been left on, for example—phones or tablets are less reliable for such tasks because they can be misplaced or not heard.

Similarly, the robot could raise the alarm if its user appears to be in difficulty.Of course, voice-assistant devices like Alexa or Google Home can offer some of the same services. But robots are far better at moving, sensing and interacting with their environment. They can also engage their users by pointing at objects or acting more naturally, using gestures or facial expressions. These “social abilities” create bonds which are crucially important for making users more accepting of the support and making it more effective.

To help incentivize the various EU test sites, our apartment also hosts the likes of the European Robotic League Service Robot Competition—a sort of Champions League for robots geared to special needs in the home. This brought academics from around Europe to our laboratory for the first time in January this year. Their robots were tested in tasks like welcoming visitors to the home, turning the oven off, and fetching objects for their users; and a German team from Koblenz University won with a robot called Lisa.

Robots Offshore
There are comparable opportunities in the business world. Oil and gas companies are looking at the Internet of Things, for example; experimenting with wireless sensors to collect information such as temperature, pressure, and corrosion levels to detect and possibly predict faults in their offshore equipment.

In the future, robots could be alerted to problem areas by sensors to go and check the integrity of pipes and wells, and to make sure they are operating as efficiently and safely as possible. Or they could place sensors in parts of offshore equipment that are hard to reach, or help to calibrate them or replace their batteries.

The likes of the ORCA Hub, a £36m project led by the Edinburgh Centre for Robotics, bringing together leading experts and over 30 industry partners, is developing such systems. The aim is to reduce the costs and the risks of humans working in remote hazardous locations.

ORCA tests a drone robot. ORCA
Working underwater is particularly challenging, since radio waves don’t move well under the sea. Underwater autonomous vehicles and sensors usually communicate using acoustic waves, which are many times slower (1,500 meters a second vs. 300m meters a second for radio waves). Acoustic communication devices are also much more expensive than those used above the water.

This academic project is developing a new generation of low-cost acoustic communication devices, and trying to make underwater sensor networks more efficient. It should help sensors and underwater autonomous vehicles to do more together in future—repair and maintenance work similar to what is already possible above the water, plus other benefits such as helping vehicles to communicate with one another over longer distances and tracking their location.

Beyond oil and gas, there is similar potential in sector after sector. There are equivalents in nuclear power, for instance, and in cleaning and maintaining the likes of bridges and buildings. My colleagues and I are also looking at possibilities in areas such as farming, manufacturing, logistics, and waste.

First, however, the research sectors around the Internet of Things and robotics need to properly share their knowledge and expertise. They are often isolated from one another in different academic fields. There needs to be more effort to create a joint community, such as the dedicated workshops for such collaboration that we organized at the European Robotics Forum and the IoT Week in 2017.

To the same end, industry and universities need to look at setting up joint research projects. It is particularly important to address safety and security issues—hackers taking control of a robot and using it to spy or cause damage, for example. Such issues could make customers wary and ruin a market opportunity.

We also need systems that can work together, rather than in isolated applications. That way, new and more useful services can be quickly and effectively introduced with no disruption to existing ones. If we can solve such problems and unite robotics and the Internet of Things, it genuinely has the potential to change the world.

Mauro Dragone, Assistant Professor, Cognitive Robotics, Multiagent systems, Internet of Things, Heriot-Watt University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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