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#435167 A Closer Look at the Robots Helping Us ...

Posted on June 5, 2019 by Android

Buck Rogers had Twiki. Luke Skywalker palled around with C-3PO and R2-D2. And astronauts aboard the International Space Station (ISS) now have their own robotic companions in space—Astrobee.

A pair of the cube-shaped robots were launched to the ISS during an April re-supply mission and are currently being commissioned for use on the space station. The free-flying space robots, dubbed Bumble and Honey, are the latest generation of robotic machines to join the human crew on the ISS.

Exploration of the solar system and beyond will require autonomous machines that can assist humans with numerous tasks—or go where we cannot. NASA has said repeatedly that robots will be instrumental in future space missions to the moon, Mars, and even to the icy moon Europa.

The Astrobee robots will specifically test robotic capabilities in zero gravity, replacing the SPHERES (Synchronized Position Hold, Engage, Reorient, Experimental Satellite) robots that have been on the ISS for more than a decade to test various technologies ranging from communications to navigation.

The 18-sided robots, each about the size of a volleyball or an oversized Dungeons and Dragons die, use CO2-based cold-gas thrusters for movement and a series of ultrasonic beacons for orientation. The Astrobee robots, on the other hand, can propel themselves autonomously around the interior of the ISS using electric fans and six cameras.

The modular design of the Astrobee robots means they are highly plug-and-play, capable of being reconfigured with different hardware modules. The robots’ software is also open-source, encouraging scientists and programmers to develop and test new algorithms and features.

And, yes, the Astrobee robots will be busy as bees once they are fully commissioned this fall, with experiments planned to begin next year. Scientists hope to learn more about how robots can assist space crews and perform caretaking duties on spacecraft.

Robots Working Together
The Astrobee robots are expected to be joined by a familiar “face” on the ISS later this year—the humanoid robot Robonaut.

Robonaut, also known as R2, was the first US-built robot on the ISS. It joined the crew back in 2011 without legs, which were added in 2014. However, the installation never entirely worked, as R2 experienced power failures that eventually led to its return to Earth last year to fix the problem. If all goes as planned, the space station’s first humanoid robot will return to the ISS to lend a hand to the astronauts and the new robotic arrivals.

In particular, NASA is interested in how the two different robotic platforms can complement each other, with an eye toward outfitting the agency’s proposed lunar orbital space station with various robots that can supplement a human crew.

“We don’t have definite plans for what would happen on the Gateway yet, but there’s a general recognition that intra-vehicular robots are important for space stations,” Astrobee technical lead Trey Smith in the NASA Intelligent Robotics Group told IEEE Spectrum. “And so, it would not be surprising to see a mobile manipulator like Robonaut, and a free flyer like Astrobee, on the Gateway.”

While the focus on R2 has been to test its capabilities in zero gravity and to use it for mundane or dangerous tasks in space, the technology enabling the humanoid robot has proven to be equally useful on Earth.

For example, R2 has amazing dexterity for a robot, with sensors, actuators, and tendons comparable to the nerves, muscles, and tendons in a human hand. Based on that design, engineers are working on a robotic glove that can help factory workers, for instance, do their jobs better while reducing the risk of repetitive injuries. R2 has also inspired development of a robotic exoskeleton for both astronauts in space and paraplegics on Earth.

Working Hard on Soft Robotics
While innovative and technologically sophisticated, Astrobee and Robonaut are typical robots in that neither one would do well in a limbo contest. In other words, most robots are limited in their flexibility and agility based on current hardware and materials.

A subfield of robotics known as soft robotics involves developing robots with highly pliant materials that mimic biological organisms in how they move. Scientists at NASA’s Langley Research Center are investigating how soft robots could help with future space exploration.

Specifically, the researchers are looking at a series of properties to understand how actuators—components responsible for moving a robotic part, such as Robonaut’s hand—can be built and used in space.

The team first 3D prints a mold and then pours a flexible material like silicone into the mold. Air bladders or chambers in the actuator expand and compress using just air.

Some of the first applications of soft robotics sound more tool-like than R2-D2-like. For example, two soft robots could connect to produce a temporary shelter for astronauts on the moon or serve as an impromptu wind shield during one of Mars’ infamous dust storms.

The idea is to use soft robots in situations that are “dangerous, dirty, or dull,” according to Jack Fitzpatrick, a NASA intern working on the soft robotics project at Langley.

Working on Mars
Of course, space robots aren’t only designed to assist humans. In many instances, they are the only option to explore even relatively close celestial bodies like Mars. Four American-made robotic rovers have been used to investigate the fourth planet from the sun since 1997.

Opportunity is perhaps the most famous, covering about 25 miles of terrain across Mars over 15 years. A dust storm knocked it out of commission last year, with NASA officially ending the mission in February.

However, the biggest and baddest of the Mars rovers, Curiosity, is still crawling across the Martian surface, sending back valuable data since 2012. The car-size robot carries 17 cameras, a laser to vaporize rocks for study, and a drill to collect samples. It is on the hunt for signs of biological life.

The next year or two could see a virtual traffic jam of robots to Mars. NASA’s Mars 2020 Rover is next in line to visit the Red Planet, sporting scientific gadgets like an X-ray fluorescence spectrometer for chemical analyses and ground-penetrating radar to see below the Martian surface.

This diagram shows the instrument payload for the Mars 2020 mission. Image Credit: NASA.
Meanwhile, the Europeans have teamed with the Russians on a rover called Rosalind Franklin, named after a famed British chemist, that will drill down into the Martian ground for evidence of past or present life as soon as 2021.

The Chinese are also preparing to begin searching for life on Mars using robots as soon as next year, as part of the country’s Mars Global Remote Sensing Orbiter and Small Rover program. The mission is scheduled to be the first in a series of launches that would culminate with bringing samples back from Mars to Earth.

Perhaps there is no more famous utterance in the universe of science fiction as “to boldly go where no one has gone before.” However, the fact is that human exploration of the solar system and beyond will only be possible with robots of different sizes, shapes, and sophistication.

Image Credit: NASA. Continue reading →

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#435106 Could Artificial Photosynthesis Help ...

Posted on May 17, 2019 by Android

Plants are the planet’s lungs, but they’re struggling to keep up due to rising CO2 emissions and deforestation. Engineers are giving them a helping hand, though, by augmenting their capacity with new technology and creating artificial substitutes to help them clean up our atmosphere.

Imperial College London, one of the UK’s top engineering schools, recently announced that it was teaming up with startup Arborea to build the company’s first outdoor pilot of its BioSolar Leaf cultivation system at the university’s White City campus in West London.

Arborea is developing large solar panel-like structures that house microscopic plants and can be installed on buildings or open land. The plants absorb light and carbon dioxide as they photosynthesize, removing greenhouse gases from the air and producing organic material, which can be processed to extract valuable food additives like omega-3 fatty acids.

The idea of growing algae to produce useful materials isn’t new, but Arborea’s pitch seems to be flexibility and affordability. The more conventional approach is to grow algae in open ponds, which are less efficient and open to contamination, or in photo-bioreactors, which typically require CO2 to be piped in rather than getting it from the air and can be expensive to run.

There’s little detail on how the technology deals with issues like nutrient supply and harvesting or how efficient it is. The company claims it can remove carbon dioxide as fast as 100 trees using the surface area of just a single tree, but there’s no published research to back that up, and it’s hard to compare the surface area of flat panels to that of a complex object like a tree. If you flattened out every inch of a tree’s surface it would cover a surprisingly large area.

Nonetheless, the ability to install these panels directly on buildings could present a promising way to soak up the huge amount of CO2 produced in our cities by transport and industry. And Arborea isn’t the only one trying to give plants a helping hand.

For decades researchers have been working on ways to use light-activated catalysts to split water into oxygen and hydrogen fuel, and more recently there have been efforts to fuse this with additional processes to combine the hydrogen with carbon from CO2 to produce all kinds of useful products.

Most notably, in 2016 Harvard researchers showed that water-splitting catalysts could be augmented with bacteria that combines the resulting hydrogen with CO2 to create oxygen and biomass, fuel, or other useful products. The approach was more efficient than plants at turning CO2 to fuel and was built using cheap materials, but turning it into a commercially viable technology will take time.

Not everyone is looking to mimic or borrow from biology in their efforts to suck CO2 out of the atmosphere. There’s been a recent glut of investment in startups working on direct-air capture (DAC) technology, which had previously been written off for using too much power and space to be practical. The looming climate change crisis appears to be rewriting some of those assumptions, though.

Most approaches aim to use the concentrated CO2 to produce synthetic fuels or other useful products, creating a revenue stream that could help improve their commercial viability. But we look increasingly likely to surpass the safe greenhouse gas limits, so attention is instead turning to carbon-negative technologies.

That means capturing CO2 from the air and then putting it into long-term storage. One way could be to grow lots of biomass and then bury it, mimicking the process that created fossil fuels in the first place. Or DAC plants could pump the CO2 they produce into deep underground wells.

But the former would take up unreasonably large amounts of land to make a significant dent in emissions, while the latter would require huge amounts of already scant and expensive renewable power. According to a recent analysis, artificial photosynthesis could sidestep these issues because it’s up to five times more efficient than its natural counterpart and could be cheaper than DAC.

Whether the technology will develop quickly enough for it to be deployed at scale and in time to mitigate the worst effects of climate change remains to be seen. Emissions reductions certainly present a more sure-fire way to deal with the problem, but nonetheless, cyborg plants could soon be a common sight in our cities.

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#435046 The Challenge of Abundance: Boredom, ...

Posted on May 6, 2019 by Android

As technology continues to progress, the possibility of an abundant future seems more likely. Artificial intelligence is expected to drive down the cost of labor, infrastructure, and transport. Alternative energy systems are reducing the cost of a wide variety of goods. Poverty rates are falling around the world as more people are able to make a living, and resources that were once inaccessible to millions are becoming widely available.

But such a life presents fuel for the most common complaint against abundance: if robots take all the jobs, basic income provides us livable welfare for doing nothing, and healthcare is a guarantee free of charge, then what is the point of our lives? What would motivate us to work and excel if there are no real risks or rewards? If everything is simply given to us, how would we feel like we’ve ever earned anything?

Time has proven that humans inherently yearn to overcome challenges—in fact, this very desire likely exists as the root of most technological innovation. And the idea that struggling makes us stronger isn’t just anecdotal, it’s scientifically validated.

For instance, kids who use anti-bacterial soaps and sanitizers too often tend to develop weak immune systems, causing them to get sick more frequently and more severely. People who work out purposely suffer through torn muscles so that after a few days of healing their muscles are stronger. And when patients visit a psychologist to handle a fear that is derailing their lives, one of the most common treatments is exposure therapy: a slow increase of exposure to the suffering so that the patient gets stronger and braver each time, able to take on an incrementally more potent manifestation of their fears.

Different Kinds of Struggle
It’s not hard to understand why people might fear an abundant future as a terribly mundane one. But there is one crucial mistake made in this assumption, and it was well summarized by Indian mystic and author Sadhguru, who said during a recent talk at Google:

Stomach empty, only one problem. Stomach full—one hundred problems; because what we refer to as human really begins only after survival is taken care of.

This idea is backed up by Maslow’s hierarchy of needs, which was first presented in his 1943 paper “A Theory of Human Motivation.” Maslow shows the steps required to build to higher and higher levels of the human experience. Not surprisingly, the first two levels deal with physiological needs and the need for safety—in other words, with the body. You need to have food, water, and sleep, or you die. After that, you need to be protected from threats, from the elements, from dangerous people, and from disease and pain.

Maslow’s Hierarchy of Needs. Photo by Wikimedia User:Factoryjoe / CC BY-SA 3.0
The beauty of these first two levels is that they’re clear-cut problems with clear-cut solutions: if you’re hungry, then you eat; if you’re thirsty, then you drink; if you’re tired, then you sleep.

But what about the next tiers of the hierarchy? What of love and belonging, of self-esteem and self-actualization? If we’re lonely, can we just summon up an authentic friend or lover? If we feel neglected by society, can we demand it validate us? If we feel discouraged and disappointed in ourselves, can we simply dial up some confidence and self-esteem?

Of course not, and that’s because these psychological needs are nebulous; they don’t contain clear problems with clear solutions. They involve the external world and other people, and are complicated by the infinite flavors of nuance and compromise that are required to navigate human relationships and personal meaning.

These psychological difficulties are where we grow our personalities, outlooks, and beliefs. The truly defining characteristics of a person are dictated not by the physical situations they were forced into—like birth, socioeconomic class, or physical ailment—but instead by the things they choose. So a future of abundance helps to free us from the physical limitations so that we can truly commit to a life of purpose and meaning, rather than just feel like survival is our purpose.

The Greatest Challenge
And that’s the plot twist. This challenge to come to grips with our own individuality and freedom could actually be the greatest challenge our species has ever faced. Can you imagine waking up every day with infinite possibility? Every choice you make says no to the rest of reality, and so every decision carries with it truly life-defining purpose and meaning. That sounds overwhelming. And that’s probably because in our current socio-economic systems, it is.

Studies have shown that people in wealthier nations tend to experience more anxiety and depression. Ron Kessler, professor of health care policy at Harvard and World Health Organization (WHO) researcher, summarized his findings of global mental health by saying, “When you’re literally trying to survive, who has time for depression? Americans, on the other hand, many of whom lead relatively comfortable lives, blow other nations away in the depression factor, leading some to suggest that depression is a ‘luxury disorder.’”

This might explain why America scores in the top rankings for the most depressed and anxious country on the planet. We surpassed our survival needs, and instead became depressed because our jobs and relationships don’t fulfill our expectations for the next three levels of Maslow’s hierarchy (belonging, esteem, and self-actualization).

But a future of abundance would mean we’d have to deal with these levels. This is the challenge for the future; this is what keeps things from being mundane.

As a society, we would be forced to come to grips with our emotional intelligence, to reckon with philosophy rather than simply contemplate it. Nearly every person you meet will be passionately on their own customized life journey, not following a routine simply because of financial limitations. Such a world seems far more vibrant and interesting than one where most wander sleep-deprived and numb while attempting to survive the rat race.

We can already see the forceful hand of this paradigm shift as self-driving cars become ubiquitous. For example, consider the famous psychological and philosophical “trolley problem.” In this thought experiment, a person sees a trolley car heading towards five people on the train tracks; they see a lever that will allow them to switch the trolley car to a track that instead only has one person on it. Do you switch the lever and have a hand in killing one person, or do you let fate continue and kill five people instead?

For the longest time, this was just an interesting quandary to consider. But now, massive corporations have to have an answer, so they can program their self-driving cars with the ability to choose between hitting a kid who runs into the road or swerving into an oncoming car carrying a family of five. When companies need philosophers to make business decisions, it’s a good sign of what’s to come.

Luckily, it’s possible this forceful reckoning with philosophy and our own consciousness may be exactly what humanity needs. Perhaps our great failure as a species has been a result of advanced cognition still trapped in the first two levels of Maslow’s hierarchy due to a long history of scarcity.

As suggested in the opening scenes in 2001: A Space Odyssey, our ape-like proclivity for violence has long stayed the same while the technology we fight with and live amongst has progressed. So while well-off Americans may have comfortable lives, they still know they live in a system where there is no safety net, where a single tragic failure could still mean hunger and homelessness. And because of this, that evolutionarily hard-wired neurotic part of our brain that fears for our survival has never been able to fully relax, and so that anxiety and depression that come with too much freedom but not enough security stays ever present.

Not only might this shift in consciousness help liberate humanity, but it may be vital if we’re to survive our future creations as well. Whatever values we hold dear as a species are the ones we will imbue into the sentient robots we create. If machine learning is going to take its guidance from humanity, we need to level up humanity’s emotional maturity.

While the physical struggles of the future may indeed fall to the wayside amongst abundance, it’s unlikely to become a mundane world; instead, it will become a vibrant culture where each individual is striving against the most important struggle that affects all of us: the challenge to find inner peace, to find fulfillment, to build meaningful relationships, and ultimately, the challenge to find ourselves.

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#435023 Inflatable Robot Astronauts and How to ...

Posted on May 1, 2019 by Android

The typical cultural image of a robot—as a steel, chrome, humanoid bucket of bolts—is often far from the reality of cutting-edge robotics research. There are difficulties, both social and technological, in realizing the image of a robot from science fiction—let alone one that can actually help around the house. Often, it’s simply the case that great expense in producing a humanoid robot that can perform dozens of tasks quite badly is less appropriate than producing some other design that’s optimized to a specific situation.

A team of scientists from Brigham Young University has received funding from NASA to investigate an inflatable robot called, improbably, King Louie. The robot was developed by Pneubotics, who have a long track record in the world of soft robotics.

In space, weight is at a premium. The world watched in awe and amusement when Commander Chris Hadfield sang “Space Oddity” from the International Space Station—but launching that guitar into space likely cost around $100,000. A good price for launching payload into outer space is on the order of $10,000 per pound ($22,000/kg).

For that price, it would cost a cool $1.7 million to launch Boston Dynamics’ famous ATLAS robot to the International Space Station, and its bulk would be inconvenient in the cramped living quarters available. By contrast, an inflatable robot like King Louie is substantially lighter and can simply be deflated and folded away when not in use. The robot can be manufactured from cheap, lightweight, and flexible materials, and minor damage is easy to repair.

Inflatable Robots Under Pressure
The concept of inflatable robots is not new: indeed, earlier prototypes of King Louie were exhibited back in 2013 at Google I/O’s After Hours, flailing away at each other in a boxing ring. Sparks might fly in fights between traditional robots, but the aim here was to demonstrate that the robots are passively safe: the soft, inflatable figures won’t accidentally smash delicate items when moving around.

Health and safety regulations form part of the reason why robots don’t work alongside humans more often, but soft robots would be far safer to use in healthcare or around children (whose first instinct, according to BYU’s promotional video, is either to hug or punch King Louie.) It’s also much harder to have nightmarish fantasies about robotic domination with these friendlier softbots: Terminator would’ve been a much shorter franchise if Skynet’s droids were inflatable.

Robotic exoskeletons are increasingly used for physical rehabilitation therapies, as well as for industrial purposes. As countries like Japan seek to care for their aging populations with robots and alleviate the burden on nurses, who suffer from some of the highest rates of back injuries of any profession, soft robots will become increasingly attractive for use in healthcare.

Precision and Proprioception
The main issue is one of control. Rigid, metallic robots may be more expensive and more dangerous, but the simple fact of their rigidity makes it easier to map out and control the precise motions of each of the robot’s limbs, digits, and actuators. Individual motors attached to these rigid robots can allow for a great many degrees of freedom—individual directions in which parts of the robot can move—and precision control.

For example, ATLAS has 28 degrees of freedom, while Shadow’s dexterous robot hand alone has 20. This is much harder to do with an inflatable robot, for precisely the same reasons that make it safer. Without hard and rigid bones, other methods of control must be used.

In the case of King Louie, the robot is made up of many expandable air chambers. An air-compressor changes the pressure levels in these air chambers, allowing them to expand and contract. This harks back to some of the earliest pneumatic automata. Pairs of chambers act antagonistically, like muscles, such that when one chamber “tenses,” another relaxes—allowing King Louie to have, for example, four degrees of freedom in each of its arms.

The robot is also surprisingly strong. Professor Killpack, who works at BYU on the project, estimates that its payload is comparable to other humanoid robots on the market, like Rethink Robotics’ Baxter (RIP).

Proprioception, that sixth sense that allows us to map out and control our own bodies and muscles in fine detail, is being enhanced for a wider range of soft, flexible robots with the use of machine learning algorithms connected to input from a whole host of sensors on the robot’s body.

Part of the reason this is so complicated with soft, flexible robots is that the shape and “map” of the robot’s body can change; that’s the whole point. But this means that every time King Louie is inflated, its body is a slightly different shape; when it becomes deformed, for example due to picking up objects, the shape changes again, and the complex ways in which the fabric can twist and bend are far more difficult to model and sense than the behavior of the rigid metal of King Louie’s hard counterparts. When you’re looking for precision, seemingly-small changes can be the difference between successfully holding an object or dropping it.

Learning to Move
Researchers at BYU are therefore spending a great deal of time on how to control the soft-bot enough to make it comparably useful. One method involves the commercial tracking technology used in the Vive VR system: by moving the game controller, which provides a constant feedback to the robot’s arm, you can control its position. Since the tracking software provides an estimate of the robot’s joint angles and continues to provide feedback until the arm is correctly aligned, this type of feedback method is likely to work regardless of small changes to the robot’s shape.

The other technologies the researchers are looking into for their softbot include arrays of flexible, tactile sensors to place on the softbot’s skin, and minimizing the complex cross-talk between these arrays to get coherent information about the robot’s environment. As with some of the new proprioception research, the project is looking into neural networks as a means of modeling the complicated dynamics—the motion and response to forces—of the softbot. This method relies on large amounts of observational data, mapping how the robot is inflated and how it moves, rather than explicitly understanding and solving the equations that govern its motion—which hopefully means the methods can work even as the robot changes.

There’s still a long way to go before soft and inflatable robots can be controlled sufficiently well to perform all the tasks they might be used for. Ultimately, no one robotic design is likely to be perfect for any situation.

Nevertheless, research like this gives us hope that one day, inflatable robots could be useful tools, or even companions, at which point the advertising slogans write themselves: Don’t let them down, and they won’t let you down!

Image Credit: Brigham Young University. Continue reading →

Posted in Human Robots

#434837 In Defense of Black Box AI

Posted on April 17, 2019 by Android

Deep learning is powering some amazing new capabilities, but we find it hard to scrutinize the workings of these algorithms. Lack of interpretability in AI is a common concern and many are trying to fix it, but is it really always necessary to know what’s going on inside these “black boxes”?

In a recent perspective piece for Science, Elizabeth Holm, a professor of materials science and engineering at Carnegie Mellon University, argued in defense of the black box algorithm. I caught up with her last week to find out more.

Edd Gent: What’s your experience with black box algorithms?

Elizabeth Holm: I got a dual PhD in materials science and engineering and scientific computing. I came to academia about six years ago and part of what I wanted to do in making this career change was to refresh and revitalize my computer science side.

I realized that computer science had changed completely. It used to be about algorithms and making codes run fast, but now it’s about data and artificial intelligence. There are the interpretable methods like random forest algorithms, where we can tell how the machine is making its decisions. And then there are the black box methods, like convolutional neural networks.

Once in a while we can find some information about their inner workings, but most of the time we have to accept their answers and kind of probe around the edges to figure out the space in which we can use them and how reliable and accurate they are.

EG: What made you feel like you had to mount a defense of these black box algorithms?

EH: When I started talking with my colleagues, I found that the black box nature of many of these algorithms was a real problem for them. I could understand that because we’re scientists, we always want to know why and how.

It got me thinking as a bit of a contrarian, “Are black boxes all bad? Must we reject them?” Surely not, because human thought processes are fairly black box. We often rely on human thought processes that the thinker can’t necessarily explain.

It’s looking like we’re going to be stuck with these methods for a while, because they’re really helpful. They do amazing things. And so there’s a very pragmatic realization that these are the best methods we’ve got to do some really important problems, and we’re not right now seeing alternatives that are interpretable. We’re going to have to use them, so we better figure out how.

EG: In what situations do you think we should be using black box algorithms?

EH: I came up with three rules. The simplest rule is: when the cost of a bad decision is small and the value of a good decision is high, it’s worth it. The example I gave in the paper is targeted advertising. If you send an ad no one wants it doesn’t cost a lot. If you’re the receiver it doesn’t cost a lot to get rid of it.

There are cases where the cost is high, and that’s then we choose the black box if it’s the best option to do the job. Things get a little trickier here because we have to ask “what are the costs of bad decisions, and do we really have them fully characterized?” We also have to be very careful knowing that our systems may have biases, they may have limitations in where you can apply them, they may be breakable.

But at the same time, there are certainly domains where we’re going to test these systems so extensively that we know their performance in virtually every situation. And if their performance is better than the other methods, we need to do it. Self driving vehicles are a significant example—it’s almost certain they’re going to have to use black box methods, and that they’re going to end up being better drivers than humans.

The third rule is the more fun one for me as a scientist, and that’s the case where the black box really enlightens us as to a new way to look at something. We have trained a black box to recognize the fracture energy of breaking a piece of metal from a picture of the broken surface. It did a really good job, and humans can’t do this and we don’t know why.

What the computer seems to be seeing is noise. There’s a signal in that noise, and finding it is very difficult, but if we do we may find something significant to the fracture process, and that would be an awesome scientific discovery.

EG: Do you think there’s been too much emphasis on interpretability?

EH: I think the interpretability problem is a fundamental, fascinating computer science grand challenge and there are significant issues where we need to have an interpretable model. But how I would frame it is not that there’s too much emphasis on interpretability, but rather that there’s too much dismissiveness of uninterpretable models.

I think that some of the current social and political issues surrounding some very bad black box outcomes have convinced people that all machine learning and AI should be interpretable because that will somehow solve those problems.

Asking humans to explain their rationale has not eliminated bias, or stereotyping, or bad decision-making in humans. Relying too much on interpreted ability perhaps puts the responsibility in the wrong place for getting better results. I can make a better black box without knowing exactly in what way the first one was bad.

EG: Looking further into the future, do you think there will be situations where humans will have to rely on black box algorithms to solve problems we can’t get our heads around?

EH: I do think so, and it’s not as much of a stretch as we think it is. For example, humans don’t design the circuit map of computer chips anymore. We haven’t for years. It’s not a black box algorithm that designs those circuit boards, but we’ve long since given up trying to understand a particular computer chip’s design.

With the billions of circuits in every computer chip, the human mind can’t encompass it, either in scope or just the pure time that it would take to trace every circuit. There are going to be cases where we want a system so complex that only the patience that computers have and their ability to work in very high-dimensional spaces is going to be able to do it.

So we can continue to argue about interpretability, but we need to acknowledge that we’re going to need to use black boxes. And this is our opportunity to do our due diligence to understand how to use them responsibly, ethically, and with benefits rather than harm. And that’s going to be a social conversation as well as as a scientific one.

*Responses have been edited for length and style

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