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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.
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When? This is probably the question that futurists, AI experts, and even people with a keen interest in technology dread the most. It has proved famously difficult to predict when new developments in AI will take place. The scientists at the Dartmouth Summer Research Project on Artificial Intelligence in 1956 thought that perhaps two months would be enough to make “significant advances” in a whole range of complex problems, including computers that can understand language, improve themselves, and even understand abstract concepts.
Sixty years later, and these problems are not yet solved. The AI Index, from Stanford, is an attempt to measure how much progress has been made in artificial intelligence.
The index adopts a unique approach, and tries to aggregate data across many regimes. It contains Volume of Activity metrics, which measure things like venture capital investment, attendance at academic conferences, published papers, and so on. The results are what you might expect: tenfold increases in academic activity since 1996, an explosive growth in startups focused around AI, and corresponding venture capital investment. The issue with this metric is that it measures AI hype as much as AI progress. The two might be correlated, but then again, they may not.
The index also scrapes data from the popular coding website Github, which hosts more source code than anyone in the world. They can track the amount of AI-related software people are creating, as well as the interest levels in popular machine learning packages like Tensorflow and Keras. The index also keeps track of the sentiment of news articles that mention AI: surprisingly, given concerns about the apocalypse and an employment crisis, those considered “positive” outweigh the “negative” by three to one.
But again, this could all just be a measure of AI enthusiasm in general.
No one would dispute the fact that we’re in an age of considerable AI hype, but the progress of AI is littered by booms and busts in hype, growth spurts that alternate with AI winters. So the AI Index attempts to track the progress of algorithms against a series of tasks. How well does computer vision perform at the Large Scale Visual Recognition challenge? (Superhuman at annotating images since 2015, but they still can’t answer questions about images very well, combining natural language processing and image recognition). Speech recognition on phone calls is almost at parity.
In other narrow fields, AIs are still catching up to humans. Translation might be good enough that you can usually get the gist of what’s being said, but still scores poorly on the BLEU metric for translation accuracy. The AI index even keeps track of how well the programs can do on the SAT test, so if you took it, you can compare your score to an AI’s.
Measuring the performance of state-of-the-art AI systems on narrow tasks is useful and fairly easy to do. You can define a metric that’s simple to calculate, or devise a competition with a scoring system, and compare new software with old in a standardized way. Academics can always debate about the best method of assessing translation or natural language understanding. The Loebner prize, a simplified question-and-answer Turing Test, recently adopted Winograd Schema type questions, which rely on contextual understanding. AI has more difficulty with these.
Where the assessment really becomes difficult, though, is in trying to map these narrow-task performances onto general intelligence. This is hard because of a lack of understanding of our own intelligence. Computers are superhuman at chess, and now even a more complex game like Go. The braver predictors who came up with timelines thought AlphaGo’s success was faster than expected, but does this necessarily mean we’re closer to general intelligence than they thought?
Here is where it’s harder to track progress.
We can note the specialized performance of algorithms on tasks previously reserved for humans—for example, the index cites a Nature paper that shows AI can now predict skin cancer with more accuracy than dermatologists. We could even try to track one specific approach to general AI; for example, how many regions of the brain have been successfully simulated by a computer? Alternatively, we could simply keep track of the number of professions and professional tasks that can now be performed to an acceptable standard by AI.
“We are running a race, but we don’t know how to get to the endpoint, or how far we have to go.”
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.
The AI index doesn’t attempt to offer a timeline for general intelligence, as this is still too nebulous and confused a concept.
Michael Woodridge, head of Computer Science at the University of Oxford, notes, “The main reason general AI is not captured in the report is that neither I nor anyone else would know how to measure progress.” He is concerned about another AI winter, and overhyped “charlatans and snake-oil salesmen” exaggerating the progress that has been made.
A key concern that all the experts bring up is the ethics of artificial intelligence.
Of course, you don’t need general intelligence to have an impact on society; algorithms are already transforming our lives and the world around us. After all, why are Amazon, Google, and Facebook worth any money? The experts agree on the need for an index to measure the benefits of AI, the interactions between humans and AIs, and our ability to program values, ethics, and oversight into these systems.
Barbra Grosz of Harvard champions this view, saying, “It is important to take on the challenge of identifying success measures for AI systems by their impact on people’s lives.”
For those concerned about the AI employment apocalypse, tracking the use of AI in the fields considered most vulnerable (say, self-driving cars replacing taxi drivers) would be a good idea. Society’s flexibility for adapting to AI trends should be measured, too; are we providing people with enough educational opportunities to retrain? How about teaching them to work alongside the algorithms, treating them as tools rather than replacements? The experts also note that the data suffers from being US-centric.
We are running a race, but we don’t know how to get to the endpoint, or how far we have to go. We are judging by the scenery, and how far we’ve run already. For this reason, measuring progress is a daunting task that starts with defining progress. But the AI index, as an annual collection of relevant information, is a good start.
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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.
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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.
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In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.
When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
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