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#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.
Image Credit: Virgin Galactic / Virgin Galactic 2018. Continue reading
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#433298 Why the Pursuit of a “Killer App” ...
Tim Enwall, head of Misty Robotics, discusses the challenges of developing a personal robot for the mass market Continue reading
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#432469 ‘Killer Robot’ Lab Faces ...
The artificial intelligence (AI) community has a clear message for researchers in South Korea: Don't make killer robots. Continue reading
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#432249 New Malicious AI Report Outlines Biggest ...
Everyone’s talking about deep fakes: audio-visual imitations of people, generated by increasingly powerful neural networks, that will soon be indistinguishable from the real thing. Politicians are regularly laid low by scandals that arise from audio-visual recordings. Try watching the footage that could be created of Barack Obama from his speeches, and the Lyrebird impersonations. You could easily, today or in the very near future, create a forgery that might be indistinguishable from the real thing. What would that do to politics?
Once the internet is flooded with plausible-seeming tapes and recordings of this sort, how are we going to decide what’s real and what isn’t? Democracy, and our ability to counteract threats, is already threatened by a lack of agreement on the facts. Once you can’t believe the evidence of your senses anymore, we’re in serious trouble. Ultimately, you can dream up all kinds of utterly terrifying possibilities for these deep fakes, from fake news to blackmail.
How to solve the problem? Some have suggested that media websites like Facebook or Twitter should carry software that probes every video to see if it’s a deep fake or not and labels the fakes. But this will prove computationally intensive. Plus, imagine a case where we have such a system, and a fake is “verified as real” by news media algorithms that have been fooled by clever hackers.
The other alternative is even more dystopian: you can prove something isn’t true simply by always having an alibi. Lawfare describes a “solution” where those concerned about deep fakes have all of their movements and interactions recorded. So to avoid being blackmailed or having your reputation ruined, you just consent to some company engaging in 24/7 surveillance of everything you say or do and having total power over that information. What could possibly go wrong?
The point is, in the same way that you don’t need human-level, general AI or humanoid robotics to create systems that can cause disruption in the world of work, you also don’t need a general intelligence to threaten security and wreak havoc on society. Andrew Ng, AI researcher, says that worrying about the risks from superintelligent AI is like “worrying about overpopulation on Mars.” There are clearly risks that arise even from the simple algorithms we have today.
The looming issue of deep fakes is just one of the threats considered by the new malicious AI report, which has co-authors from the Future of Humanity Institute and the Centre for the Study of Existential Risk (among other organizations.) They limit their focus to the technologies of the next five years.
Some of the concerns the report explores are enhancements to familiar threats.
Automated hacking can get better, smarter, and algorithms can adapt to changing security protocols. “Phishing emails,” where people are scammed by impersonating someone they trust or an official organization, could be generated en masse and made more realistic by scraping data from social media. Standard phishing works by sending such a great volume of emails that even a very low success rate can be profitable. Spear phishing aims at specific targets by impersonating family members, but can be labor intensive. If AI algorithms enable every phishing scam to become sharper in this way, more people are going to get gouged.
Then there are novel threats that come from our own increasing use of and dependence on artificial intelligence to make decisions.
These algorithms may be smart in some ways, but as any human knows, computers are utterly lacking in common sense; they can be fooled. A rather scary application is adversarial examples. Machine learning algorithms are often used for image recognition. But it’s possible, if you know a little about how the algorithm is structured, to construct the perfect level of noise to add to an image, and fool the machine. Two images can be almost completely indistinguishable to the human eye. But by adding some cleverly-calculated noise, the hackers can fool the algorithm into thinking an image of a panda is really an image of a gibbon (in the OpenAI example). Research conducted by OpenAI demonstrates that you can fool algorithms even by printing out examples on stickers.
Now imagine that instead of tricking a computer into thinking that a panda is actually a gibbon, you fool it into thinking that a stop sign isn’t there, or that the back of someone’s car is really a nice open stretch of road. In the adversarial example case, the images are almost indistinguishable to humans. By the time anyone notices the road sign has been “hacked,” it could already be too late.
As the OpenAI foundation freely admits, worrying about whether we’d be able to tame a superintelligent AI is a hard problem. It looks all the more difficult when you realize some of our best algorithms can be fooled by stickers; even “modern simple algorithms can behave in ways we do not intend.”
There are ways around this approach.
Adversarial training can generate lots of adversarial examples and explicitly train the algorithm not to be fooled by them—but it’s costly in terms of time and computation, and puts you in an arms race with hackers. Many strategies for defending against adversarial examples haven’t proved adaptive enough; correcting against vulnerabilities one at a time is too slow. Moreover, it demonstrates a point that can be lost in the AI hype: algorithms can be fooled in ways we didn’t anticipate. If we don’t learn about these vulnerabilities until the algorithms are everywhere, serious disruption can occur. And no matter how careful you are, some vulnerabilities are likely to remain to be exploited, even if it takes years to find them.
Just look at the Meltdown and Spectre vulnerabilities, which weren’t widely known about for more than 20 years but could enable hackers to steal personal information. Ultimately, the more blind faith we put into algorithms and computers—without understanding the opaque inner mechanics of how they work—the more vulnerable we will be to these forms of attack. And, as China dreams of using AI to predict crimes and enhance the police force, the potential for unjust arrests can only increase.
This is before you get into the truly nightmarish territory of “killer robots”—not the Terminator, but instead autonomous or consumer drones which could potentially be weaponized by bad actors and used to conduct attacks remotely. Some reports have indicated that terrorist organizations are already trying to do this.
As with any form of technology, new powers for humanity come with new risks. And, as with any form of technology, closing Pandora’s box will prove very difficult.
Somewhere between the excessively hyped prospects of AI that will do everything for us and AI that will destroy the world lies reality: a complex, ever-changing set of risks and rewards. The writers of the malicious AI report note that one of their key motivations is ensuring that the benefits of new technology can be delivered to people as quickly, but as safely, as possible. In the rush to exploit the potential for algorithms and create 21st-century infrastructure, we must ensure we’re not building in new dangers.
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#432051 What Roboticists Are Learning From Early ...
You might not have heard of Hanson Robotics, but if you’re reading this, you’ve probably seen their work. They were the company behind Sophia, the lifelike humanoid avatar that’s made dozens of high-profile media appearances. Before that, they were the company behind that strange-looking robot that seemed a bit like Asimo with Albert Einstein’s head—or maybe you saw BINA48, who was interviewed for the New York Times in 2010 and featured in Jon Ronson’s books. For the sci-fi aficionados amongst you, they even made a replica of legendary author Philip K. Dick, best remembered for having books with titles like Do Androids Dream of Electric Sheep? turned into films with titles like Blade Runner.
Hanson Robotics, in other words, with their proprietary brand of life-like humanoid robots, have been playing the same game for a while. Sometimes it can be a frustrating game to watch. Anyone who gives the robot the slightest bit of thought will realize that this is essentially a chat-bot, with all the limitations this implies. Indeed, even in that New York Times interview with BINA48, author Amy Harmon describes it as a frustrating experience—with “rare (but invariably thrilling) moments of coherence.” This sensation will be familiar to anyone who’s conversed with a chatbot that has a few clever responses.
The glossy surface belies the lack of real intelligence underneath; it seems, at first glance, like a much more advanced machine than it is. Peeling back that surface layer—at least for a Hanson robot—means you’re peeling back Frubber. This proprietary substance—short for “Flesh Rubber,” which is slightly nightmarish—is surprisingly complicated. Up to thirty motors are required just to control the face; they manipulate liquid cells in order to make the skin soft, malleable, and capable of a range of different emotional expressions.
A quick combinatorial glance at the 30+ motors suggests that there are millions of possible combinations; researchers identify 62 that they consider “human-like” in Sophia, although not everyone agrees with this assessment. Arguably, the technical expertise that went into reconstructing the range of human facial expressions far exceeds the more simplistic chat engine the robots use, although it’s the second one that allows it to inflate the punters’ expectations with a few pre-programmed questions in an interview.
Hanson Robotics’ belief is that, ultimately, a lot of how humans will eventually relate to robots is going to depend on their faces and voices, as well as on what they’re saying. “The perception of identity is so intimately bound up with the perception of the human form,” says David Hanson, company founder.
Yet anyone attempting to design a robot that won’t terrify people has to contend with the uncanny valley—that strange blend of concern and revulsion people react with when things appear to be creepily human. Between cartoonish humanoids and genuine humans lies what has often been a no-go zone in robotic aesthetics.
The uncanny valley concept originated with roboticist Masahiro Mori, who argued that roboticists should avoid trying to replicate humans exactly. Since anything that wasn’t perfect, but merely very good, would elicit an eerie feeling in humans, shirking the challenge entirely was the only way to avoid the uncanny valley. It’s probably a task made more difficult by endless streams of articles about AI taking over the world that inexplicably conflate AI with killer humanoid Terminators—which aren’t particularly likely to exist (although maybe it’s best not to push robots around too much).
The idea behind this realm of psychological horror is fairly simple, cognitively speaking.
We know how to categorize things that are unambiguously human or non-human. This is true even if they’re designed to interact with us. Consider the popularity of Aibo, Jibo, or even some robots that don’t try to resemble humans. Something that resembles a human, but isn’t quite right, is bound to evoke a fear response in the same way slightly distorted music or slightly rearranged furniture in your home will. The creature simply doesn’t fit.
You may well reject the idea of the uncanny valley entirely. David Hanson, naturally, is not a fan. In the paper Upending the Uncanny Valley, he argues that great art forms have often resembled humans, but the ultimate goal for humanoid roboticists is probably to create robots we can relate to as something closer to humans than works of art.
Meanwhile, Hanson and other scientists produce competing experiments to either demonstrate that the uncanny valley is overhyped, or to confirm it exists and probe its edges.
The classic experiment involves gradually morphing a cartoon face into a human face, via some robotic-seeming intermediaries—yet it’s in movement that the real horror of the almost-human often lies. Hanson has argued that incorporating cartoonish features may help—and, sometimes, that the uncanny valley is a generational thing which will melt away when new generations grow used to the quirks of robots. Although Hanson might dispute the severity of this effect, it’s clearly what he’s trying to avoid with each new iteration.
Hiroshi Ishiguro is the latest of the roboticists to have dived headlong into the valley.
Building on the work of pioneers like Hanson, those who study human-robot interaction are pushing at the boundaries of robotics—but also of social science. It’s usually difficult to simulate what you don’t understand, and there’s still an awful lot we don’t understand about how we interpret the constant streams of non-verbal information that flow when you interact with people in the flesh.
Ishiguro took this imitation of human forms to extreme levels. Not only did he monitor and log the physical movements people made on videotapes, but some of his robots are based on replicas of people; the Repliee series began with a ‘replicant’ of his daughter. This involved making a rubber replica—a silicone cast—of her entire body. Future experiments were focused on creating Geminoid, a replica of Ishiguro himself.
As Ishiguro aged, he realized that it would be more effective to resemble his replica through cosmetic surgery rather than by continually creating new casts of his face, each with more lines than the last. “I decided not to get old anymore,” Ishiguro said.
We love to throw around abstract concepts and ideas: humans being replaced by machines, cared for by machines, getting intimate with machines, or even merging themselves with machines. You can take an idea like that, hold it in your hand, and examine it—dispassionately, if not without interest. But there’s a gulf between thinking about it and living in a world where human-robot interaction is not a field of academic research, but a day-to-day reality.
As the scientists studying human-robot interaction develop their robots, their replicas, and their experiments, they are making some of the first forays into that world. We might all be living there someday. Understanding ourselves—decrypting the origins of empathy and love—may be the greatest challenge to face. That is, if you want to avoid the valley.
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