Tag Archives: build

#432009 How Swarm Intelligence Is Making Simple ...

Posted on February 9, 2018 by Android

As a group, simple creatures following simple rules can display a surprising amount of complexity, efficiency, and even creativity. Known as swarm intelligence, this trait is found throughout nature, but researchers have recently begun using it to transform various fields such as robotics, data mining, medicine, and blockchains.

Ants, for example, can only perform a limited range of functions, but an ant colony can build bridges, create superhighways of food and information, wage war, and enslave other ant species—all of which are beyond the comprehension of any single ant. Likewise, schools of fish, flocks of birds, beehives, and other species exhibit behavior indicative of planning by a higher intelligence that doesn’t actually exist.

It happens by a process called stigmergy. Simply put, a small change by a group member causes other members to behave differently, leading to a new pattern of behavior.

When an ant finds a food source, it marks the path with pheromones. This attracts other ants to that path, leads them to the food source, and prompts them to mark the same path with more pheromones. Over time, the most efficient route will become the superhighway, as the faster and easier a path is, the more ants will reach the food and the more pheromones will be on the path. Thus, it looks as if a more intelligent being chose the best path, but it emerged from the tiny, simple changes made by individuals.

So what does this mean for humans? Well, a lot. In the past few decades, researchers have developed numerous algorithms and metaheuristics, such as ant colony optimization and particle swarm optimization, and they are rapidly being adopted.

Swarm Robotics
A swarm of robots would work on the same principles as an ant colony: each member has a simple set of rules to follow, leading to self-organization and self-sufficiency.

For example, researchers at Georgia Robotics and InTelligent Systems (GRITS) created a small swarm of simple robots that can spell and play piano. The robots cannot communicate, but based solely on the position of surrounding robots, they are able to use their specially-created algorithm to determine the optimal path to complete their task.

This is also immensely useful for drone swarms.

Last February, Ehang, an aviation company out of China, created a swarm of a thousand drones that not only lit the sky with colorful, intricate displays, but demonstrated the ability to improvise and troubleshoot errors entirely autonomously.

Further, just recently, the University of Cambridge and Koc University unveiled their idea for what they call the Energy Neutral Internet of Drones. Amazingly, this drone swarm would take initiative to share information or energy with other drones that did not receive a communication or are running low on energy.

Militaries all of the world are utilizing this as well.

Last year, the US Department of Defense announced it had successfully tested a swarm of miniature drones that could carry out complex missions cheaper and more efficiently. They claimed, “The micro-drones demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying, and self-healing.”

Some experts estimate at least 30 nations are actively developing drone swarms—and even submersible drones—for military missions, including intelligence gathering, missile defense, precision missile strikes, and enhanced communication.

NASA also plans on deploying swarms of tiny spacecraft for space exploration, and the medical community is looking into using swarms of nanobots for precision delivery of drugs, microsurgery, targeting toxins, and biological sensors.

What If Humans Are the Ants?
The strength of any blockchain comes from the size and diversity of the community supporting it. Cryptocurrencies like Bitcoin, Ethereum, and Litecoin are driven by the people using, investing in, and, most importantly, mining them so their blockchains can function. Without an active community, or swarm, their blockchains wither away.

When viewed from a great height, a blockchain performs eerily like an ant colony in that it will naturally find the most efficient way to move vast amounts of information.

Miners compete with each other to perform the complex calculations necessary to add another block, for which the winner is rewarded with the blockchain’s native currency and agreed-upon fees. Of course, the miner with the more powerful computers is more likely to win the reward, thereby empowering the winner’s ability to mine and receive even more rewards. Over time, fewer and fewer miners are going to exist, as the winners are able to more efficiently shoulder more of the workload, in much the same way that ants build superhighways.

Further, a company called Unanimous AI has developed algorithms that allow humans to collectively make predictions. So far, the AI algorithms and their human participants have made some astoundingly accurate predictions, such as the first four winning horses of the Kentucky Derby, the Oscar winners, the Stanley Cup winners, and others. The more people involved in the swarm, the greater their predictive power will be.

To be clear, this is not a prediction based on group consensus. Rather, the swarm of humans uses software to input their opinions in real time, thus making micro-changes to the rest of the swarm and the inputs of other members.

Studies show that swarm intelligence consistently outperforms individuals and crowds working without the algorithms. While this is only the tip of the iceberg, some have suggested swarm intelligence can revolutionize how doctors diagnose a patient or how products are marketed to consumers. It might even be an essential step in truly creating AI.

While swarm intelligence is an essential part of many species’ success, it’s only a matter of time before humans harness its effectiveness as well.

Image Credit: Nature Bird Photography / Shutterstock.com Continue reading →

Posted in Human Robots

#431995 The 10 Grand Challenges Facing Robotics ...

Posted on February 7, 2018 by Android

Robotics research has been making great strides in recent years, but there are still many hurdles to the machines becoming a ubiquitous presence in our lives. The journal Science Robotics has now identified 10 grand challenges the field will have to grapple with to make that a reality.

Editors conducted an online survey on unsolved challenges in robotics and assembled an expert panel of roboticists to shortlist the 30 most important topics, which were then grouped into 10 grand challenges that could have major impact in the next 5 to 10 years. Here’s what they came up with.

1. New Materials and Fabrication Schemes
Roboticists are beginning to move beyond motors, gears, and sensors by experimenting with things like artificial muscles, soft robotics, and new fabrication methods that combine multiple functions in one material. But most of these advances have been “one-off” demonstrations, which are not easy to combine.

Multi-functional materials merging things like sensing, movement, energy harvesting, or energy storage could allow more efficient robot designs. But combining these various properties in a single machine will require new approaches that blend micro-scale and large-scale fabrication techniques. Another promising direction is materials that can change over time to adapt or heal, but this requires much more research.

2. Bioinspired and Bio-Hybrid Robots
Nature has already solved many of the problems roboticists are trying to tackle, so many are turning to biology for inspiration or even incorporating living systems into their robots. But there are still major bottlenecks in reproducing the mechanical performance of muscle and the ability of biological systems to power themselves.

There has been great progress in artificial muscles, but their robustness, efficiency, and energy and power density need to be improved. Embedding living cells into robots can overcome challenges of powering small robots, as well as exploit biological features like self-healing and embedded sensing, though how to integrate these components is still a major challenge. And while a growing “robo-zoo” is helping tease out nature’s secrets, more work needs to be done on how animals transition between capabilities like flying and swimming to build multimodal platforms.

3. Power and Energy
Energy storage is a major bottleneck for mobile robotics. Rising demand from drones, electric vehicles, and renewable energy is driving progress in battery technology, but the fundamental challenges have remained largely unchanged for years.

That means that in parallel to battery development, there need to be efforts to minimize robots’ power utilization and give them access to new sources of energy. Enabling them to harvest energy from their environment and transmitting power to them wirelessly are two promising approaches worthy of investigation.

4. Robot Swarms
Swarms of simple robots that assemble into different configurations to tackle various tasks can be a cheaper, more flexible alternative to large, task-specific robots. Smaller, cheaper, more powerful hardware that lets simple robots sense their environment and communicate is combining with AI that can model the kind of behavior seen in nature’s flocks.

But there needs to be more work on the most efficient forms of control at different scales—small swarms can be controlled centrally, but larger ones need to be more decentralized. They also need to be made robust and adaptable to the changing conditions of the real world and resilient to deliberate or accidental damage. There also needs to be more work on swarms of non-homogeneous robots with complementary capabilities.

5. Navigation and Exploration
A key use case for robots is exploring places where humans cannot go, such as the deep sea, space, or disaster zones. That means they need to become adept at exploring and navigating unmapped, often highly disordered and hostile environments.

The major challenges include creating systems that can adapt, learn, and recover from navigation failures and are able to make and recognize new discoveries. This will require high levels of autonomy that allow the robots to monitor and reconfigure themselves while being able to build a picture of the world from multiple data sources of varying reliability and accuracy.

6. AI for Robotics
Deep learning has revolutionized machines’ ability to recognize patterns, but that needs to be combined with model-based reasoning to create adaptable robots that can learn on the fly.

Key to this will be creating AI that’s aware of its own limitations and can learn how to learn new things. It will also be important to create systems that are able to learn quickly from limited data rather than the millions of examples used in deep learning. Further advances in our understanding of human intelligence will be essential to solving these problems.

7. Brain-Computer Interfaces
BCIs will enable seamless control of advanced robotic prosthetics but could also prove a faster, more natural way to communicate instructions to robots or simply help them understand human mental states.

Most current approaches to measuring brain activity are expensive and cumbersome, though, so work on compact, low-power, and wireless devices will be important. They also tend to involve extended training, calibration, and adaptation due to the imprecise nature of reading brain activity. And it remains to be seen if they will outperform simpler techniques like eye tracking or reading muscle signals.

8. Social Interaction
If robots are to enter human environments, they will need to learn to deal with humans. But this will be difficult, as we have very few concrete models of human behavior and we are prone to underestimate the complexity of what comes naturally to us.

Social robots will need to be able to perceive minute social cues like facial expression or intonation, understand the cultural and social context they are operating in, and model the mental states of people they interact with to tailor their dealings with them, both in the short term and as they develop long-standing relationships with them.

9. Medical Robotics
Medicine is one of the areas where robots could have significant impact in the near future. Devices that augment a surgeon’s capabilities are already in regular use, but the challenge will be to increase the autonomy of these systems in such a high-stakes environment.

Autonomous robot assistants will need to be able to recognize human anatomy in a variety of contexts and be able to use situational awareness and spoken commands to understand what’s required of them. In surgery, autonomous robots could perform the routine steps of a procedure, giving way to the surgeon for more complicated patient-specific bits.

Micro-robots that operate inside the human body also hold promise, but there are still many roadblocks to their adoption, including effective delivery systems, tracking and control methods, and crucially, finding therapies where they improve on current approaches.

10. Robot Ethics and Security
As the preceding challenges are overcome and robots are increasingly integrated into our lives, this progress will create new ethical conundrums. Most importantly, we may become over-reliant on robots.

That could lead to humans losing certain skills and capabilities, making us unable to take the reins in the case of failures. We may end up delegating tasks that should, for ethical reasons, have some human supervision, and allow people to pass the buck to autonomous systems in the case of failure. It could also reduce self-determination, as human behaviors change to accommodate the routines and restrictions required for robots and AI to work effectively.

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Posted in Human Robots

#431939 This Awesome Robot Is the Size of a ...

Posted on January 22, 2018 by Android

They say size isn’t everything, but when it comes to delta robots it seems like it’s pretty important.

The speed and precision of these machines sees them employed in delicate pick-and-place tasks in all kinds of factories, as well as to control 3D printer heads. But Harvard researchers have found that scaling them down to millimeter scale makes them even faster and more precise, opening up applications in everything from microsurgery to manipulating tiny objects like circuit board components or even living cells.

Unlike the industrial robots you’re probably more familiar with, delta robots consist of three individually controlled arms supporting a platform. Different combinations of movements can move the platform in three directions, and a variety of tools can be attached to this platform.



The benefit of this design is that unlike a typical robotic arm, all the motors are housed at the base rather than at the joints, which reduces their mechanical complexity, but also—importantly—the weight of the arms. That means they can move and accelerate faster and with greater precision.

It’s been known for a while that the physics of these robots means the smaller you can make them, the more pronounced these advantages become, but scientists had struggled to build them at scales below tens of centimeters.

In a recent paper in the journal Science Robotics, the researchers describe how they used an origami-inspired micro-fabrication approach that relies on folding flat sheets of composite materials to create a robot measuring just 15 millimeters by 15 millimeters by 20 millimeters.

The robot dubbed “milliDelta” features joints that rely on a flexible polymer core to bend—a simplified version of the more complicated joints found in large-scale delta robots. The machine was powered by three piezoelectric actuators, which flex when a voltage is applied, and could perform movements at frequencies 15 to 20 times higher than current delta robots, with precisions down to roughly 5 micrometers.

One potential use for the device is to cancel out surgeons’ hand tremors as they carry out delicate microsurgery procedures, such as operations on the eye’s retina. The researchers actually investigated this application in their paper. They got volunteers to hold a toothpick and measured the movement of the tip to map natural hand tremors. They fed this data to the milliDelta, which was able to match the movements and therefore cancel them out.

In an email to Singularity Hub, the researchers said that adding the robot to the end of a surgical tool could make it possible to stabilize needles or scalpels, though this would require some design optimization. For a start, the base would have to be redesigned to fit on a surgical tool, and sensors would have to be added to the robot to allow it to measure tremors in real time.

Another promising application for the device would be placing components on circuit boards at very high speeds, which could prove valuable in electronics manufacturing. The researchers even think the device’s precision means it could be used for manipulating living cells in research and clinical laboratories.

The researchers even said it would be feasible to integrate the devices onto microrobots to give them similarly impressive manipulation capabilities, though that would require considerable work to overcome control and sensing challenges.

Image Credit: Wyss institute / Harvard Continue reading →

Posted in Human Robots

#431925 How the Science of Decision-Making Will ...

Posted on January 18, 2018 by Android

Neuroscientist Brie Linkenhoker believes that leaders must be better prepared for future strategic challenges by continually broadening their worldviews.
As the director of Worldview Stanford, Brie and her team produce multimedia content and immersive learning experiences to make academic research and insights accessible and useable by curious leaders. These future-focused topics are designed to help curious leaders understand the forces shaping the future.
Worldview Stanford has tackled such interdisciplinary topics as the power of minds, the science of decision-making, environmental risk and resilience, and trust and power in the age of big data.
We spoke with Brie about why understanding our biases is critical to making better decisions, particularly in a time of increasing change and complexity.

Lisa Kay Solomon: What is Worldview Stanford?
Brie Linkenhoker: Leaders and decision makers are trying to navigate this complex hairball of a planet that we live on and that requires keeping up on a lot of diverse topics across multiple fields of study and research. Universities like Stanford are where that new knowledge is being created, but it’s not getting out and used as readily as we would like, so that’s what we’re working on.
Worldview is designed to expand our individual and collective worldviews about important topics impacting our future. Your worldview is not a static thing, it’s constantly changing. We believe it should be informed by lots of different perspectives, different cultures, by knowledge from different domains and disciplines. This is more important now than ever.
At Worldview, we create learning experiences that are an amalgamation of all of those things.
LKS: One of your marquee programs is the Science of Decision Making. Can you tell us about that course and why it’s important?
BL: We tend to think about decision makers as being people in leadership positions, but every person who works in your organization, every member of your family, every member of the community is a decision maker. You have to decide what to buy, who to partner with, what government regulations to anticipate.
You have to think not just about your own decisions, but you have to anticipate how other people make decisions too. So, when we set out to create the Science of Decision Making, we wanted to help people improve their own decisions and be better able to predict, understand, anticipate the decisions of others.

“I think in another 10 or 15 years, we’re probably going to have really rich models of how we actually make decisions and what’s going on in the brain to support them.”

We realized that the only way to do that was to combine a lot of different perspectives, so we recruited experts from economics, psychology, neuroscience, philosophy, biology, and religion. We also brought in cutting-edge research on artificial intelligence and virtual reality and explored conversations about how technology is changing how we make decisions today and how it might support our decision-making in the future.
There’s no single set of answers. There are as many unanswered questions as there are answered questions.
LKS: One of the other things you explore in this course is the role of biases and heuristics. Can you explain the importance of both in decision-making?
BL: When I was a strategy consultant, executives would ask me, “How do I get rid of the biases in my decision-making or my organization’s decision-making?” And my response would be, “Good luck with that. It isn’t going to happen.”
As human beings we make, probably, thousands of decisions every single day. If we had to be actively thinking about each one of those decisions, we wouldn’t get out of our house in the morning, right?
We have to be able to do a lot of our decision-making essentially on autopilot to free up cognitive resources for more difficult decisions. So, we’ve evolved in the human brain a set of what we understand to be heuristics or rules of thumb.
And heuristics are great in, say, 95 percent of situations. It’s that five percent, or maybe even one percent, that they’re really not so great. That’s when we have to become aware of them because in some situations they can become biases.
For example, it doesn’t matter so much that we’re not aware of our rules of thumb when we’re driving to work or deciding what to make for dinner. But they can become absolutely critical in situations where a member of law enforcement is making an arrest or where you’re making a decision about a strategic investment or even when you’re deciding who to hire.
Let’s take hiring for a moment.
How many years is a hire going to impact your organization? You’re potentially looking at 5, 10, 15, 20 years. Having the right person in a role could change the future of your business entirely. That’s one of those areas where you really need to be aware of your own heuristics and biases—and we all have them. There’s no getting rid of them.
LKS: We seem to be at a time when the boundaries between different disciplines are starting to blend together. How has the advancement of neuroscience help us become better leaders? What do you see happening next?
BL: Heuristics and biases are very topical these days, thanks in part to Michael Lewis’s fantastic book, The Undoing Project, which is the story of the groundbreaking work that Nobel Prize winner Danny Kahneman and Amos Tversky did in the psychology and biases of human decision-making. Their work gave rise to the whole new field of behavioral economics.
In the last 10 to 15 years, neuroeconomics has really taken off. Neuroeconomics is the combination of behavioral economics with neuroscience. In behavioral economics, they use economic games and economic choices that have numbers associated with them and have real-world application.
For example, they ask, “How much would you spend to buy A versus B?” Or, “If I offered you X dollars for this thing that you have, would you take it or would you say no?” So, it’s trying to look at human decision-making in a format that’s easy to understand and quantify within a laboratory setting.
Now you bring neuroscience into that. You can have people doing those same kinds of tasks—making those kinds of semi-real-world decisions—in a brain scanner, and we can now start to understand what’s going on in the brain while people are making decisions. You can ask questions like, “Can I look at the signals in someone’s brain and predict what decision they’re going to make?” That can help us build a model of decision-making.
I think in another 10 or 15 years, we’re probably going to have really rich models of how we actually make decisions and what’s going on in the brain to support them. That’s very exciting for a neuroscientist.
Image Credit: Black Salmon / Shutterstock.com Continue reading →

Posted in Human Robots

#431920 If We Could Engineer Animals to Be as ...

Posted on January 17, 2018 by Android

Advances in neural implants and genetic engineering suggest that in the not–too–distant future we may be able to boost human intelligence. If that’s true, could we—and should we—bring our animal cousins along for the ride?
Human brain augmentation made headlines last year after several tech firms announced ambitious efforts to build neural implant technology. Duke University neuroscientist Mikhail Lebedev told me in July it could be decades before these devices have applications beyond the strictly medical.
But he said the technology, as well as other pharmacological and genetic engineering approaches, will almost certainly allow us to boost our mental capacities at some point in the next few decades.
Whether this kind of cognitive enhancement is a good idea or not, and how we should regulate it, are matters of heated debate among philosophers, futurists, and bioethicists, but for some it has raised the question of whether we could do the same for animals.
There’s already tantalizing evidence of the idea’s feasibility. As detailed in BBC Future, a group from MIT found that mice that were genetically engineered to express the human FOXP2 gene linked to learning and speech processing picked up maze routes faster. Another group at Wake Forest University studying Alzheimer’s found that neural implants could boost rhesus monkeys’ scores on intelligence tests.
The concept of “animal uplift” is most famously depicted in the Planet of the Apes movie series, whose planet–conquering protagonists are likely to put most people off the idea. But proponents are less pessimistic about the outcomes.
Science fiction author David Brin popularized the concept in his “Uplift” series of novels, in which humans share the world with various other intelligent animals that all bring their own unique skills, perspectives, and innovations to the table. “The benefits, after a few hundred years, could be amazing,” he told Scientific American.
Others, like George Dvorsky, the director of the Rights of Non-Human Persons program at the Institute for Ethics and Emerging Technologies, go further and claim there is a moral imperative. He told the Boston Globe that denying augmentation technology to animals would be just as unethical as excluding certain groups of humans.
Others are less convinced. Forbes’ Alex Knapp points out that developing the technology to uplift animals will likely require lots of very invasive animal research that will cause huge suffering to the animals it purports to help. This is problematic enough with normal animals, but could be even more morally dubious when applied to ones whose cognitive capacities have been enhanced.
The whole concept could also be based on a fundamental misunderstanding of the nature of intelligence. Humans are prone to seeing intelligence as a single, self-contained metric that progresses in a linear way with humans at the pinnacle.
In an opinion piece in Wired arguing against the likelihood of superhuman artificial intelligence, Kevin Kelly points out that science has no such single dimension with which to rank the intelligence of different species. Each one combines a bundle of cognitive capabilities, some of which are well below our own capabilities and others which are superhuman. He uses the example of the squirrel, which can remember the precise location of thousands of acorns for years.
Uplift efforts may end up being less about boosting intelligence and more about making animals more human-like. That represents “a kind of benevolent colonialism” that assumes being more human-like is a good thing, Paul Graham Raven, a futures researcher at the University of Sheffield in the United Kingdom, told the Boston Globe. There’s scant evidence that’s the case, and it’s easy to see how a chimpanzee with the mind of a human might struggle to adjust.
There are also fundamental barriers that may make it difficult to achieve human-level cognitive capabilities in animals, no matter how advanced brain augmentation technology gets. In 2013 Swedish researchers selectively bred small fish called guppies for bigger brains. This made them smarter, but growing the energy-intensive organ meant the guppies developed smaller guts and produced fewer offspring to compensate.
This highlights the fact that uplifting animals may require more than just changes to their brains, possibly a complete rewiring of their physiology that could prove far more technically challenging than human brain augmentation.
Our intelligence is intimately tied to our evolutionary history—our brains are bigger than other animals’; opposable thumbs allow us to use tools; our vocal chords make complex communication possible. No matter how much you augment a cow’s brain, it still couldn’t use a screwdriver or talk to you in English because it simply doesn’t have the machinery.
Finally, from a purely selfish point of view, even if it does become possible to create a level playing field between us and other animals, it may not be a smart move for humanity. There’s no reason to assume animals would be any more benevolent than we are, having evolved in the same ‘survival of the fittest’ crucible that we have. And given our already endless capacity to divide ourselves along national, religious, or ethnic lines, conflict between species seems inevitable.
We’re already likely to face considerable competition from smart machines in the coming decades if you believe the hype around AI. So maybe adding a few more intelligent species to the mix isn’t the best idea.
Image Credit: Ron Meijer / Shutterstock.com Continue reading →

Posted in Human Robots