Tag Archives: cyborgs

#432193 Are ‘You’ Just Inside Your Skin or ...

In November 2017, a gunman entered a church in Sutherland Springs in Texas, where he killed 26 people and wounded 20 others. He escaped in his car, with police and residents in hot pursuit, before losing control of the vehicle and flipping it into a ditch. When the police got to the car, he was dead. The episode is horrifying enough without its unsettling epilogue. In the course of their investigations, the FBI reportedly pressed the gunman’s finger to the fingerprint-recognition feature on his iPhone in an attempt to unlock it. Regardless of who’s affected, it’s disquieting to think of the police using a corpse to break into someone’s digital afterlife.

Most democratic constitutions shield us from unwanted intrusions into our brains and bodies. They also enshrine our entitlement to freedom of thought and mental privacy. That’s why neurochemical drugs that interfere with cognitive functioning can’t be administered against a person’s will unless there’s a clear medical justification. Similarly, according to scholarly opinion, law-enforcement officials can’t compel someone to take a lie-detector test, because that would be an invasion of privacy and a violation of the right to remain silent.

But in the present era of ubiquitous technology, philosophers are beginning to ask whether biological anatomy really captures the entirety of who we are. Given the role they play in our lives, do our devices deserve the same protections as our brains and bodies?

After all, your smartphone is much more than just a phone. It can tell a more intimate story about you than your best friend. No other piece of hardware in history, not even your brain, contains the quality or quantity of information held on your phone: it ‘knows’ whom you speak to, when you speak to them, what you said, where you have been, your purchases, photos, biometric data, even your notes to yourself—and all this dating back years.

In 2014, the United States Supreme Court used this observation to justify the decision that police must obtain a warrant before rummaging through our smartphones. These devices “are now such a pervasive and insistent part of daily life that the proverbial visitor from Mars might conclude they were an important feature of human anatomy,” as Chief Justice John Roberts observed in his written opinion.

The Chief Justice probably wasn’t making a metaphysical point—but the philosophers Andy Clark and David Chalmers were when they argued in “The Extended Mind” (1998) that technology is actually part of us. According to traditional cognitive science, “thinking” is a process of symbol manipulation or neural computation, which gets carried out by the brain. Clark and Chalmers broadly accept this computational theory of mind, but claim that tools can become seamlessly integrated into how we think. Objects such as smartphones or notepads are often just as functionally essential to our cognition as the synapses firing in our heads. They augment and extend our minds by increasing our cognitive power and freeing up internal resources.

If accepted, the extended mind thesis threatens widespread cultural assumptions about the inviolate nature of thought, which sits at the heart of most legal and social norms. As the US Supreme Court declared in 1942: “freedom to think is absolute of its own nature; the most tyrannical government is powerless to control the inward workings of the mind.” This view has its origins in thinkers such as John Locke and René Descartes, who argued that the human soul is locked in a physical body, but that our thoughts exist in an immaterial world, inaccessible to other people. One’s inner life thus needs protecting only when it is externalized, such as through speech. Many researchers in cognitive science still cling to this Cartesian conception—only, now, the private realm of thought coincides with activity in the brain.

But today’s legal institutions are straining against this narrow concept of the mind. They are trying to come to grips with how technology is changing what it means to be human, and to devise new normative boundaries to cope with this reality. Justice Roberts might not have known about the idea of the extended mind, but it supports his wry observation that smartphones have become part of our body. If our minds now encompass our phones, we are essentially cyborgs: part-biology, part-technology. Given how our smartphones have taken over what were once functions of our brains—remembering dates, phone numbers, addresses—perhaps the data they contain should be treated on a par with the information we hold in our heads. So if the law aims to protect mental privacy, its boundaries would need to be pushed outwards to give our cyborg anatomy the same protections as our brains.

This line of reasoning leads to some potentially radical conclusions. Some philosophers have argued that when we die, our digital devices should be handled as remains: if your smartphone is a part of who you are, then perhaps it should be treated more like your corpse than your couch. Similarly, one might argue that trashing someone’s smartphone should be seen as a form of “extended” assault, equivalent to a blow to the head, rather than just destruction of property. If your memories are erased because someone attacks you with a club, a court would have no trouble characterizing the episode as a violent incident. So if someone breaks your smartphone and wipes its contents, perhaps the perpetrator should be punished as they would be if they had caused a head trauma.

The extended mind thesis also challenges the law’s role in protecting both the content and the means of thought—that is, shielding what and how we think from undue influence. Regulation bars non-consensual interference in our neurochemistry (for example, through drugs), because that meddles with the contents of our mind. But if cognition encompasses devices, then arguably they should be subject to the same prohibitions. Perhaps some of the techniques that advertisers use to hijack our attention online, to nudge our decision-making or manipulate search results, should count as intrusions on our cognitive process. Similarly, in areas where the law protects the means of thought, it might need to guarantee access to tools such as smartphones—in the same way that freedom of expression protects people’s right not only to write or speak, but also to use computers and disseminate speech over the internet.

The courts are still some way from arriving at such decisions. Besides the headline-making cases of mass shooters, there are thousands of instances each year in which police authorities try to get access to encrypted devices. Although the Fifth Amendment to the US Constitution protects individuals’ right to remain silent (and therefore not give up a passcode), judges in several states have ruled that police can forcibly use fingerprints to unlock a user’s phone. (With the new facial-recognition feature on the iPhone X, police might only need to get an unwitting user to look at her phone.) These decisions reflect the traditional concept that the rights and freedoms of an individual end at the skin.

But the concept of personal rights and freedoms that guides our legal institutions is outdated. It is built on a model of a free individual who enjoys an untouchable inner life. Now, though, our thoughts can be invaded before they have even been developed—and in a way, perhaps this is nothing new. The Nobel Prize-winning physicist Richard Feynman used to say that he thought with his notebook. Without a pen and pencil, a great deal of complex reflection and analysis would never have been possible. If the extended mind view is right, then even simple technologies such as these would merit recognition and protection as a part of the essential toolkit of the mind.This article was originally published at Aeon and has been republished under Creative Commons.

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#431872 AI Uses Titan Supercomputer to Create ...

You don’t have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world.
The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we’re far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins.
The first big boost this year came from Google. The tech giant announced it was developing automated machine learning (AutoML), writing algorithms that can do some of the heavy lifting by identifying the right neural networks for a specific job. Now researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL), using the most powerful supercomputer in the US, have developed an AI system that can generate neural networks as good if not better than any developed by a human in less than a day.
It can take months for the brainiest, best-paid data scientists to develop deep learning software, which sends data through a complex web of mathematical algorithms. The system is modeled after the human brain and known as an artificial neural network. Even Google’s AutoML took weeks to design a superior image recognition system, one of the more standard operations for AI systems today.
Computing Power
Of course, Google Brain project engineers only had access to 800 graphic processing units (GPUs), a type of computer hardware that works especially well for deep learning. Nvidia, which pioneered the development of GPUs, is considered the gold standard in today’s AI hardware architecture. Titan, the supercomputer at ORNL, boasts more than 18,000 GPUs.
The ORNL research team’s algorithm, called MENNDL for Multinode Evolutionary Neural Networks for Deep Learning, isn’t designed to create AI systems that cull cute cat photos from the internet. Instead, MENNDL is a tool for testing and training thousands of potential neural networks to work on unique science problems.
That requires a different approach from the Google and Facebook AI platforms of the world, notes Steven Young, a postdoctoral research associate at ORNL who is on the team that designed MENNDL.
“We’ve discovered that those [neural networks] are very often not the optimal network for a lot of our problems, because our data, while it can be thought of as images, is different,” he explains to Singularity Hub. “These images, and the problems, have very different characteristics from object detection.”
AI for Science
One application of the technology involved a particle physics experiment at the Fermi National Accelerator Laboratory. Fermilab researchers are interested in understanding neutrinos, high-energy subatomic particles that rarely interact with normal matter but could be a key to understanding the early formation of the universe. One Fermilab experiment involves taking a sort of “snapshot” of neutrino interactions.
The team wanted the help of an AI system that could analyze and classify Fermilab’s detector data. MENNDL evaluated 500,000 neural networks in 24 hours. Its final solution proved superior to custom models developed by human scientists.
In another case involving a collaboration with St. Jude Children’s Research Hospital in Memphis, MENNDL improved the error rate of a human-designed algorithm for identifying mitochondria inside 3D electron microscopy images of brain tissue by 30 percent.
“We are able to do better than humans in a fraction of the time at designing networks for these sort of very different datasets that we’re interested in,” Young says.
What makes MENNDL particularly adept is its ability to define the best or most optimal hyperparameters—the key variables—to tackle a particular dataset.
“You don’t always need a big, huge deep network. Sometimes you just need a small network with the right hyperparameters,” Young says.
A Virtual Data Scientist
That’s not dissimilar to the approach of a company called H20.ai, a startup out of Silicon Valley that uses open source machine learning platforms to “democratize” AI. It applies machine learning to create business solutions for Fortune 500 companies, including some of the world’s biggest banks and healthcare companies.
“Our software is more [about] pattern detection, let’s say anti-money laundering or fraud detection or which customer is most likely to churn,” Dr. Arno Candel, chief technology officer at H2O.ai, tells Singularity Hub. “And that kind of insight-generating software is what we call AI here.”
The company’s latest product, Driverless AI, promises to deliver the data scientist equivalent of a chessmaster to its customers (the company claims several such grandmasters in its employ and advisory board). In other words, the system can analyze a raw dataset and, like MENNDL, automatically identify what features should be included in the computer model to make the most of the data based on the best “chess moves” of its grandmasters.
“So we’re using those algorithms, but we’re giving them the human insights from those data scientists, and we automate their thinking,” he explains. “So we created a virtual data scientist that is relentless at trying these ideas.”
Inside the Black Box
Not unlike how the human brain reaches a conclusion, it’s not always possible to understand how a machine, despite being designed by humans, reaches its own solutions. The lack of transparency is often referred to as the AI “black box.” Experts like Young say we can learn something about the evolutionary process of machine learning by generating millions of neural networks and seeing what works well and what doesn’t.
“You’re never going to be able to completely explain what happened, but maybe we can better explain it than we currently can today,” Young says.
Transparency is built into the “thought process” of each particular model generated by Driverless AI, according to Candel.
The computer even explains itself to the user in plain English at each decision point. There is also real-time feedback that allows users to prioritize features, or parameters, to see how the changes improve the accuracy of the model. For example, the system may include data from people in the same zip code as it creates a model to describe customer turnover.
“That’s one of the advantages of our automatic feature engineering: it’s basically mimicking human thinking,” Candel says. “It’s not just neural nets that magically come up with some kind of number, but we’re trying to make it statistically significant.”
Moving Forward
Much digital ink has been spilled over the dearth of skilled data scientists, so automating certain design aspects for developing artificial neural networks makes sense. Experts agree that automation alone won’t solve that particular problem. However, it will free computer scientists to tackle more difficult issues, such as parsing the inherent biases that exist within the data used by machine learning today.
“I think the world has an opportunity to focus more on the meaning of things and not on the laborious tasks of just fitting a model and finding the best features to make that model,” Candel notes. “By automating, we are pushing the burden back for the data scientists to actually do something more meaningful, which is think about the problem and see how you can address it differently to make an even bigger impact.”
The team at ORNL expects it can also make bigger impacts beginning next year when the lab’s next supercomputer, Summit, comes online. While Summit will boast only 4,600 nodes, it will sport the latest and greatest GPU technology from Nvidia and CPUs from IBM. That means it will deliver more than five times the computational performance of Titan, the world’s fifth-most powerful supercomputer today.
“We’ll be able to look at much larger problems on Summit than we were able to with Titan and hopefully get to a solution much faster,” Young says.
It’s all in a day’s work.
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#431664 Forget Cyborgs — Biohybrid Robots Are ...

There may come a day when humans take on the form of cyborgs with integrated, robotic parts to enhance our abilities. But long before that, look for "biohybrid" robots… Continue reading

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#431142 Will Privacy Survive the Future?

Technological progress has radically transformed our concept of privacy. How we share information and display our identities has changed as we’ve migrated to the digital world.
As the Guardian states, “We now carry with us everywhere devices that give us access to all the world’s information, but they can also offer almost all the world vast quantities of information about us.” We are all leaving digital footprints as we navigate through the internet. While sometimes this information can be harmless, it’s often valuable to various stakeholders, including governments, corporations, marketers, and criminals.
The ethical debate around privacy is complex. The reality is that our definition and standards for privacy have evolved over time, and will continue to do so in the next few decades.
Implications of Emerging Technologies
Protecting privacy will only become more challenging as we experience the emergence of technologies such as virtual reality, the Internet of Things, brain-machine interfaces, and much more.
Virtual reality headsets are already gathering information about users’ locations and physical movements. In the future all of our emotional experiences, reactions, and interactions in the virtual world will be able to be accessed and analyzed. As virtual reality becomes more immersive and indistinguishable from physical reality, technology companies will be able to gather an unprecedented amount of data.
It doesn’t end there. The Internet of Things will be able to gather live data from our homes, cities and institutions. Drones may be able to spy on us as we live our everyday lives. As the amount of genetic data gathered increases, the privacy of our genes, too, may be compromised.
It gets even more concerning when we look farther into the future. As companies like Neuralink attempt to merge the human brain with machines, we are left with powerful implications for privacy. Brain-machine interfaces by nature operate by extracting information from the brain and manipulating it in order to accomplish goals. There are many parties that can benefit and take advantage of the information from the interface.
Marketing companies, for instance, would take an interest in better understanding how consumers think and consequently have their thoughts modified. Employers could use the information to find new ways to improve productivity or even monitor their employees. There will notably be risks of “brain hacking,” which we must take extreme precaution against. However, it is important to note that lesser versions of these risks currently exist, i.e., by phone hacking, identify fraud, and the like.
A New Much-Needed Definition of Privacy
In many ways we are already cyborgs interfacing with technology. According to theories like the extended mind hypothesis, our technological devices are an extension of our identities. We use our phones to store memories, retrieve information, and communicate. We use powerful tools like the Hubble Telescope to extend our sense of sight. In parallel, one can argue that the digital world has become an extension of the physical world.
These technological tools are a part of who we are. This has led to many ethical and societal implications. Our Facebook profiles can be processed to infer secondary information about us, such as sexual orientation, political and religious views, race, substance use, intelligence, and personality. Some argue that many of our devices may be mapping our every move. Your browsing history could be spied on and even sold in the open market.
While the argument to protect privacy and individuals’ information is valid to a certain extent, we may also have to accept the possibility that privacy will become obsolete in the future. We have inherently become more open as a society in the digital world, voluntarily sharing our identities, interests, views, and personalities.

“The question we are left with is, at what point does the tradeoff between transparency and privacy become detrimental?”

There also seems to be a contradiction with the positive trend towards mass transparency and the need to protect privacy. Many advocate for a massive decentralization and openness of information through mechanisms like blockchain.
The question we are left with is, at what point does the tradeoff between transparency and privacy become detrimental? We want to live in a world of fewer secrets, but also don’t want to live in a world where our every move is followed (not to mention our every feeling, thought and interaction). So, how do we find a balance?
Traditionally, privacy is used synonymously with secrecy. Many are led to believe that if you keep your personal information secret, then you’ve accomplished privacy. Danny Weitzner, director of the MIT Internet Policy Research Initiative, rejects this notion and argues that this old definition of privacy is dead.
From Witzner’s perspective, protecting privacy in the digital age means creating rules that require governments and businesses to be transparent about how they use our information. In other terms, we can’t bring the business of data to an end, but we can do a better job of controlling it. If these stakeholders spy on our personal information, then we should have the right to spy on how they spy on us.
The Role of Policy and Discourse
Almost always, policy has been too slow to adapt to the societal and ethical implications of technological progress. And sometimes the wrong laws can do more harm than good. For instance, in March, the US House of Representatives voted to allow internet service providers to sell your web browsing history on the open market.
More often than not, the bureaucratic nature of governance can’t keep up with exponential growth. New technologies are emerging every day and transforming society. Can we confidently claim that our world leaders, politicians, and local representatives are having these conversations and debates? Are they putting a focus on the ethical and societal implications of emerging technologies? Probably not.
We also can’t underestimate the role of public awareness and digital activism. There needs to be an emphasis on educating and engaging the general public about the complexities of these issues and the potential solutions available. The current solution may not be robust or clear, but having these discussions will get us there.
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#430854 Get a Live Look Inside Singularity ...

Singularity University’s (SU) second annual Global Summit begins today in San Francisco, and the Singularity Hub team will be there to give you a live look inside the event, exclusive speaker interviews, and articles on great talks.
Whereas SU’s other summits each focus on a specific field or industry, Global Summit is a broad look at emerging technologies and how they can help solve the world’s biggest challenges.
Talks will cover the latest in artificial intelligence, the brain and technology, augmented and virtual reality, space exploration, the future of work, the future of learning, and more.
We’re bringing three full days of live Facebook programming, streaming on Singularity Hub’s Facebook page, complete with 30+ speaker interviews, tours of the EXPO innovation hall, and tech demos. You can also livestream main stage talks at Singularity University’s Facebook page.
Interviews include Peter Diamandis, cofounder and chairman of Singularity University; Sylvia Earle, National Geographic explorer-in-residence; Esther Wojcicki, founder of the Palo Alto High Media Arts Center; Bob Richards, founder and CEO of Moon Express; Matt Oehrlein, cofounder of MegaBots; and Craig Newmark, founder of Craigslist and the Craig Newmark Foundation.
Pascal Finette, SU vice president of startup solutions, and Alison Berman, SU staff writer and digital producer, will host the show, and Lisa Kay Solomon, SU chair of transformational practices, will put on a special daily segment on exponential leadership with thought leaders.
Make sure you don’t miss anything by ‘liking’ the Singularity Hub and Singularity University Facebook pages and turn on notifications from both pages so you know when we go live. And to get a taste of what’s in store, check out the below selection of stories from last year’s event.
Are We at the Edge of a Second Sexual Revolution?By Vanessa Bates Ramirez
“Brace yourself, because according to serial entrepreneur Martin Varsavsky, all our existing beliefs about procreation are about to be shattered again…According to Varsavsky, the second sexual revolution will decouple procreation from sex, because sex will no longer be the best way to make babies.”
VR Pioneer Chris Milk: Virtual Reality Will Mirror Life Like Nothing Else BeforeBy Jason Ganz
“Milk is already a legend in the VR community…But [he] is just getting started. His company Within has plans to help shape the language we use for virtual reality storytelling. Because let’s be clear, VR storytelling is still very much in its infancy. This fact makes it even crazier there are already VR films out there that can inspire and captivate on such a profound level. And we’re only going up from here.”
7 Key Factors Driving the Artificial Intelligence RevolutionBy David Hill
“Jacobstein calmly and optimistically assures that this revolution isn’t going to disrupt humans completely, but usher in a future in which there’s a symbiosis between human and machine intelligence. He highlighted 7 factors driving this revolution.”
Are There Other Intelligent Civilizations Out There? Two Views on the Fermi ParadoxBy Alison Berman
“Cliché or not, when I stare up at the sky, I still wonder if we’re alone in the galaxy. Could there be another technologically advanced civilization out there? During a panel discussion on space exploration at Singularity University’s Global Summit, Jill Tarter, the Bernard M. Oliver chair at the SETI Institute, was asked to explain the Fermi paradox and her position on it. Her answer was pretty brilliant.”
Engineering Will Soon Be ‘More Parenting Than Programming’By Sveta McShane
“In generative design, the user states desired goals and constraints and allows the computer to generate entire designs, iterations and solution sets based on those constraints. It is, in fact, a lot like parents setting boundaries for their children’s activities. The user basically says, ‘Yes, it’s ok to do this, but it’s not ok to do that.’ The resulting solutions are ones you might never have thought of on your own.”
Biohacking Will Let You Connect Your Body to Anything You WantBy Vanessa Bates Ramirez
“How many cyborgs did you see during your morning commute today? I would guess at least five. Did they make you nervous? Probably not; you likely didn’t even realize they were there…[Hannes] Sjoblad said that the cyborgs we see today don’t look like Hollywood prototypes; they’re regular people who have integrated technology into their bodies to improve or monitor some aspect of their health.”
Peter Diamandis: We’ll Radically Extend Our Lives With New TechnologiesBy Jason Dorrier
“[Diamandis] said humans aren’t the longest-lived animals. Other species have multi-hundred-year lifespans. Last year, a study “dating” Greenland sharks found they can live roughly 400 years. Though the technique isn’t perfectly precise, they estimated one shark to be about 392. Its approximate birthday was 1624…Diamandis said he asked himself: If these animals can live centuries—why can’t I?” Continue reading

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