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#437957 Meet Assembloids, Mini Human Brains With ...

It’s not often that a twitching, snowman-shaped blob of 3D human tissue makes someone’s day.

But when Dr. Sergiu Pasca at Stanford University witnessed the tiny movement, he knew his lab had achieved something special. You see, the blob was evolved from three lab-grown chunks of human tissue: a mini-brain, mini-spinal cord, and mini-muscle. Each individual component, churned to eerie humanoid perfection inside bubbling incubators, is already a work of scientific genius. But Pasca took the extra step, marinating the three components together inside a soup of nutrients.

The result was a bizarre, Lego-like human tissue that replicates the basic circuits behind how we decide to move. Without external prompting, when churned together like ice cream, the three ingredients physically linked up into a fully functional circuit. The 3D mini-brain, through the information highway formed by the artificial spinal cord, was able to make the lab-grown muscle twitch on demand.

In other words, if you think isolated mini-brains—known formally as brain organoids—floating in a jar is creepy, upgrade your nightmares. The next big thing in probing the brain is assembloids—free-floating brain circuits—that now combine brain tissue with an external output.

The end goal isn’t to freak people out. Rather, it’s to recapitulate our nervous system, from input to output, inside the controlled environment of a Petri dish. An autonomous, living brain-spinal cord-muscle entity is an invaluable model for figuring out how our own brains direct the intricate muscle movements that allow us stay upright, walk, or type on a keyboard.

It’s the nexus toward more dexterous brain-machine interfaces, and a model to understand when brain-muscle connections fail—as in devastating conditions like Lou Gehrig’s disease or Parkinson’s, where people slowly lose muscle control due to the gradual death of neurons that control muscle function. Assembloids are a sort of “mini-me,” a workaround for testing potential treatments on a simple “replica” of a person rather than directly on a human.

From Organoids to Assembloids
The miniature snippet of the human nervous system has been a long time in the making.

It all started in 2014, when Dr. Madeleine Lancaster, then a post-doc at Stanford, grew a shockingly intricate 3D replica of human brain tissue inside a whirling incubator. Revolutionarily different than standard cell cultures, which grind up brain tissue to reconstruct as a flat network of cells, Lancaster’s 3D brain organoids were incredibly sophisticated in their recapitulation of the human brain during development. Subsequent studies further solidified their similarity to the developing brain of a fetus—not just in terms of neuron types, but also their connections and structure.

With the finding that these mini-brains sparked with electrical activity, bioethicists increasingly raised red flags that the blobs of human brain tissue—no larger than the size of a pea at most—could harbor the potential to develop a sense of awareness if further matured and with external input and output.

Despite these concerns, brain organoids became an instant hit. Because they’re made of human tissue—often taken from actual human patients and converted into stem-cell-like states—organoids harbor the same genetic makeup as their donors. This makes it possible to study perplexing conditions such as autism, schizophrenia, or other brain disorders in a dish. What’s more, because they’re grown in the lab, it’s possible to genetically edit the mini-brains to test potential genetic culprits in the search for a cure.

Yet mini-brains had an Achilles’ heel: not all were made the same. Rather, depending on the region of the brain that was reverse engineered, the cells had to be persuaded by different cocktails of chemical soups and maintained in isolation. It was a stark contrast to our own developing brains, where regions are connected through highways of neural networks and work in tandem.

Pasca faced the problem head-on. Betting on the brain’s self-assembling capacity, his team hypothesized that it might be possible to grow different mini-brains, each reflecting a different brain region, and have them fuse together into a synchronized band of neuron circuits to process information. Last year, his idea paid off.

In one mind-blowing study, his team grew two separate portions of the brain into blobs, one representing the cortex, the other a deeper part of the brain known to control reward and movement, called the striatum. Shockingly, when put together, the two blobs of human brain tissue fused into a functional couple, automatically establishing neural highways that resulted in one of the most sophisticated recapitulations of a human brain. Pasca crowned this tissue engineering crème-de-la-crème “assembloids,” a portmanteau between “assemble” and “organoids.”

“We have demonstrated that regionalized brain spheroids can be put together to form fused structures called brain assembloids,” said Pasca at the time.” [They] can then be used to investigate developmental processes that were previously inaccessible.”

And if that’s possible for wiring up a lab-grown brain, why wouldn’t it work for larger neural circuits?

Assembloids, Assemble
The new study is the fruition of that idea.

The team started with human skin cells, scraped off of eight healthy people, and transformed them into a stem-cell-like state, called iPSCs. These cells have long been touted as the breakthrough for personalized medical treatment, before each reflects the genetic makeup of its original host.

Using two separate cocktails, the team then generated mini-brains and mini-spinal cords using these iPSCs. The two components were placed together “in close proximity” for three days inside a lab incubator, gently floating around each other in an intricate dance. To the team’s surprise, under the microscope using tracers that glow in the dark, they saw highways of branches extending from one organoid to the other like arms in a tight embrace. When stimulated with electricity, the links fired up, suggesting that the connections weren’t just for show—they’re capable of transmitting information.

“We made the parts,” said Pasca, “but they knew how to put themselves together.”

Then came the ménage à trois. Once the mini-brain and spinal cord formed their double-decker ice cream scoop, the team overlaid them onto a layer of muscle cells—cultured separately into a human-like muscular structure. The end result was a somewhat bizarre and silly-looking snowman, made of three oddly-shaped spherical balls.

Yet against all odds, the brain-spinal cord assembly reached out to the lab-grown muscle. Using a variety of tools, including measuring muscle contraction, the team found that this utterly Frankenstein-like snowman was able to make the muscle component contract—in a way similar to how our muscles twitch when needed.

“Skeletal muscle doesn’t usually contract on its own,” said Pasca. “Seeing that first twitch in a lab dish immediately after cortical stimulation is something that’s not soon forgotten.”

When tested for longevity, the contraption lasted for up to 10 weeks without any sort of breakdown. Far from a one-shot wonder, the isolated circuit worked even better the longer each component was connected.

Pasca isn’t the first to give mini-brains an output channel. Last year, the queen of brain organoids, Lancaster, chopped up mature mini-brains into slices, which were then linked to muscle tissue through a cultured spinal cord. Assembloids are a step up, showing that it’s possible to automatically sew multiple nerve-linked structures together, such as brain and muscle, sans slicing.

The question is what happens when these assembloids become more sophisticated, edging ever closer to the inherent wiring that powers our movements. Pasca’s study targets outputs, but what about inputs? Can we wire input channels, such as retinal cells, to mini-brains that have a rudimentary visual cortex to process those examples? Learning, after all, depends on examples of our world, which are processed inside computational circuits and delivered as outputs—potentially, muscle contractions.

To be clear, few would argue that today’s mini-brains are capable of any sort of consciousness or awareness. But as mini-brains get increasingly more sophisticated, at what point can we consider them a sort of AI, capable of computation or even something that mimics thought? We don’t yet have an answer—but the debates are on.

Image Credit: christitzeimaging.com / Shutterstock.com Continue reading

Posted in Human Robots

#437769 Q&A: Facebook’s CTO Is at War With ...

Photo: Patricia de Melo Moreira/AFP/Getty Images

Facebook chief technology officer Mike Schroepfer leads the company’s AI and integrity efforts.

Facebook’s challenge is huge. Billions of pieces of content—short and long posts, images, and combinations of the two—are uploaded to the site daily from around the world. And any tiny piece of that—any phrase, image, or video—could contain so-called bad content.

In its early days, Facebook relied on simple computer filters to identify potentially problematic posts by their words, such as those containing profanity. These automatically filtered posts, as well as posts flagged by users as offensive, went to humans for adjudication.

In 2015, Facebook started using artificial intelligence to cull images that contained nudity, illegal goods, and other prohibited content; those images identified as possibly problematic were sent to humans for further review.

By 2016, more offensive photos were reported by Facebook’s AI systems than by Facebook users (and that is still the case).

In 2018, Facebook CEO Mark Zuckerberg made a bold proclamation: He predicted that within five or ten years, Facebook’s AI would not only look for profanity, nudity, and other obvious violations of Facebook’s policies. The tools would also be able to spot bullying, hate speech, and other misuse of the platform, and put an immediate end to them.

Today, automated systems using algorithms developed with AI scan every piece of content between the time when a user completes a post and when it is visible to others on the site—just fractions of a second. In most cases, a violation of Facebook’s standards is clear, and the AI system automatically blocks the post. In other cases, the post goes to human reviewers for a final decision, a workforce that includes 15,000 content reviewers and another 20,000 employees focused on safety and security, operating out of more than 20 facilities around the world.

In the first quarter of this year, Facebook removed or took other action (like appending a warning label) on more than 9.6 million posts involving hate speech, 8.6 million involving child nudity or exploitation, almost 8 million posts involving the sale of drugs, 2.3 million posts involving bullying and harassment, and tens of millions of posts violating other Facebook rules.

Right now, Facebook has more than 1,000 engineers working on further developing and implementing what the company calls “integrity” tools. Using these systems to screen every post that goes up on Facebook, and doing so in milliseconds, is sucking up computing resources. Facebook chief technology officer Mike Schroepfer, who is heading up Facebook’s AI and integrity efforts, spoke with IEEE Spectrum about the team’s progress on building an AI system that detects bad content.

Since that discussion, Facebook’s policies around hate speech have come under increasing scrutiny, with particular attention on divisive posts by political figures. A group of major advertisers in June announced that they would stop advertising on the platform while reviewing the situation, and civil rights groups are putting pressure on others to follow suit until Facebook makes policy changes related to hate speech and groups that promote hate, misinformation, and conspiracies.

Facebook CEO Mark Zuckerberg responded with news that Facebook will widen the category of what it considers hateful content in ads. Now the company prohibits claims that people from a specific race, ethnicity, national origin, religious affiliation, caste, sexual orientation, gender identity, or immigration status are a threat to the physical safety, health, or survival of others. The policy change also aims to better protect immigrants, migrants, refugees, and asylum seekers from ads suggesting these groups are inferior or expressing contempt. Finally, Zuckerberg announced that the company will label some problematic posts by politicians and government officials as content that violates Facebook’s policies.

However, civil rights groups say that’s not enough. And an independent audit released in July also said that Facebook needs to go much further in addressing civil rights concerns and disinformation.

Schroepfer indicated that Facebook’s AI systems are designed to quickly adapt to changes in policy. “I don’t expect considerable technical changes are needed to adjust,” he told Spectrum.

This interview has been edited and condensed for clarity.

IEEE Spectrum: What are the stakes of content moderation? Is this an existential threat to Facebook? And is it critical that you deal well with the issue of election interference this year?

Schroepfer: It’s probably existential; it’s certainly massive. We are devoting a tremendous amount of our attention to it.

The idea that anyone could meddle in an election is deeply disturbing and offensive to all of us here, just as people and citizens of democracies. We don’t want to see that happen anywhere, and certainly not on our watch. So whether it’s important to the company or not, it’s important to us as people. And I feel a similar way on the content-moderation side.

There are not a lot of easy choices here. The only way to prevent people, with certainty, from posting bad things is to not let them post anything. We can take away all voice and just say, “Sorry, the Internet’s too dangerous. No one can use it.” That will certainly get rid of all hate speech online. But I don’t want to end up in that world. And there are variants of that world that various governments are trying to implement, where they get to decide what’s true or not, and you as a person don’t. I don’t want to get there either.

My hope is that we can build a set of tools that make it practical for us to do a good enough job, so that everyone is still excited about the idea that anyone can share what they want, and so that Facebook is a safe and reasonable place for people to operate in.

Spectrum: You joined Facebook in 2008, before AI was part of the company’s toolbox. When did that change? When did you begin to think that AI tools would be useful to Facebook?

Schroepfer: Ten years ago, AI wasn’t commercially practical; the technology just didn’t work very well. In 2012, there was one of those moments that a lot of people point to as the beginning of the current revolution in deep learning and AI. A computer-vision model—a neural network—was trained using what we call supervised training, and it turned out to be better than all the existing models.

Spectrum: How is that training done, and how did computer-vision models come to Facebook?

Image: Facebook

Just Broccoli? Facebook’s image analysis algorithms can tell the difference between marijuana [left] and tempura broccoli [right] better than some humans.

Schroepfer: Say I take a bunch of photos and I have people look at them. If they see a photo of a cat, they put a text label that says cat; if it’s one of a dog, the text label says dog. If you build a big enough data set and feed that to the neural net, it learns how to tell the difference between cats and dogs.

Prior to 2012, it didn’t work very well. And then in 2012, there was this moment where it seemed like, “Oh wow, this technique might work.” And a few years later we were deploying that form of technology to help us detect problematic imagery.

Spectrum: Do your AI systems work equally well on all types of prohibited content?

Schroepfer: Nudity was technically easiest. I don’t need to understand language or culture to understand that this is either a naked human or not. Violence is a much more nuanced problem, so it was harder technically to get it right. And with hate speech, not only do you have to understand the language, it may be very contextual, even tied to recent events. A week before the Christchurch shooting [New Zealand, 2019], saying “I wish you were in the mosque” probably doesn’t mean anything. A week after, that might be a terrible thing to say.

Spectrum: How much progress have you made on hate speech?

Schroepfer: AI, in the first quarter of 2020, proactively detected 88.8 percent of the hate-speech content we removed, up from 80.2 percent in the previous quarter. In the first quarter of 2020, we took action on 9.6 million pieces of content for violating our hate-speech policies.

Image: Facebook

Off Label: Sometimes image analysis isn’t enough to determine whether a picture posted violates the company’s policies. In considering these candy-colored vials of marijuana, for example, the algorithms can look at any accompanying text and, if necessary, comments on the post.

Spectrum: It sounds like you’ve expanded beyond tools that analyze images and are also using AI tools that analyze text.

Schroepfer: AI started off as very siloed. People worked on language, people worked on computer vision, people worked on video. We’ve put these things together—in production, not just as research—into multimodal classifiers.

[Schroepfer shows a photo of a pan of Rice Krispies treats, with text referring to it as a “potent batch”] This is a case in which you have an image, and then you have the text on the post. This looks like Rice Krispies. On its own, this image is fine. You put the text together with it in a bigger model; that can then understand what’s going on. That didn’t work five years ago.

Spectrum: Today, every post that goes up on Facebook is immediately checked by automated systems. Can you explain that process?

Image: Facebook

Bigger Picture: Identifying hate speech is often a matter of context. Either the text or the photo in this post isn’t hateful standing alone, but putting them together tells a different story.

Schroepfer: You upload an image and you write some text underneath it, and the systems look at both the image and the text to try to see which, if any, policies it violates. Those decisions are based on our Community Standards. It will also look at other signals on the posts, like the comments people make.

It happens relatively instantly, though there may be times things happen after the fact. Maybe you uploaded a post that had misinformation in it, and at the time you uploaded it, we didn’t know it was misinformation. The next day we fact-check something and scan again; we may find your post and take it down. As we learn new things, we’re going to go back through and look for violations of what we now know to be a problem. Or, as people comment on your post, we might update our understanding of it. If people are saying, “That’s terrible,” or “That’s mean,” or “That looks fake,” those comments may be an interesting signal.

Spectrum: How is Facebook applying its AI tools to the problem of election interference?

Schroepfer: I would split election interference into two categories. There are times when you’re going after the content, and there are times you’re going after the behavior or the authenticity of the person.

On content, if you’re sharing misinformation, saying, “It’s super Wednesday, not super Tuesday, come vote on Wednesday,” that’s a problem whether you’re an American sitting in California or a foreign actor.

Other times, people create a series of Facebook pages pretending they’re Americans, but they’re really a foreign entity. That is a problem on its own, even if all the content they’re sharing completely meets our Community Standards. The problem there is that you have a foreign government running an information operation.

There, you need different tools. What you’re trying to do is put pieces together, to say, “Wait a second. All of these pages—Martians for Justice, Moonlings for Justice, and Venusians for Justice”—are all run by an administrator with an IP address that’s outside the United States. So they’re all connected, even though they’re pretending to not be connected. That’s a very different problem than me sitting in my office in Menlo Park [Calif.] sharing misinformation.

I’m not going to go into lots of technical detail, because this is an area of adversarial nature. The fundamental problem you’re trying to solve is that there’s one entity coordinating the activity of a bunch of things that look like they’re not all one thing. So this is a series of Instagram accounts, or a series of Facebook pages, or a series of WhatsApp accounts, and they’re pretending to be totally different things. We’re looking for signals that these things are related in some way. And we’re looking through the graph [what Facebook calls its map of relationships between users] to understand the properties of this network.

Spectrum: What cutting-edge AI tools and methods have you been working on lately?

Schroepfer: Supervised learning, with humans setting up the instruction process for the AI systems, is amazingly effective. But it has a very obvious flaw: the speed at which you can develop these things is limited by how fast you can curate the data sets. If you’re dealing in a problem domain where things change rapidly, you have to rebuild a new data set and retrain the whole thing.

Self-supervision is inspired by the way people learn, by the way kids explore the world around them. To get computers to do it themselves, we take a bunch of raw data and build a way for the computer to construct its own tests. For language, you scan a bunch of Web pages, and the computer builds a test where it takes a sentence, eliminates one of the words, and figures out how to predict what word belongs there. And because it created the test, it actually knows the answer. I can use as much raw text as I can find and store because it’s processing everything itself and doesn’t require us to sit down and build the information set. In the last two years there has been a revolution in language understanding as a result of AI self-supervised learning.

Spectrum: What else are you excited about?

Schroepfer: What we’ve been working on over the last few years is multilingual understanding. Usually, when I’m trying to figure out, say, whether something is hate speech or not I have to go through the whole process of training the model in every language. I have to do that one time for every language. When you make a post, the first thing we have to figure out is what language your post is in. “Ah, that’s Spanish. So send it to the Spanish hate-speech model.”

We’ve started to build a multilingual model—one box where you can feed in text in 40 different languages and it determines whether it’s hate speech or not. This is way more effective and easier to deploy.

To geek out for a second, just the idea that you can build a model that understands a concept in multiple languages at once is crazy cool. And it not only works for hate speech, it works for a variety of things.

When we started working on this multilingual model years ago, it performed worse than every single individual model. Now, it not only works as well as the English model, but when you get to the languages where you don’t have enough data, it’s so much better. This rapid progress is very exciting.

Spectrum: How do you move new AI tools from your research labs into operational use?

Schroepfer: Engineers trying to make the next breakthrough will often say, “Cool, I’ve got a new thing and it achieved state-of-the-art results on machine translation.” And we say, “Great. How long does it take to run in production?” They say, “Well, it takes 10 seconds for every sentence to run on a CPU.” And we say, “It’ll eat our whole data center if we deploy that.” So we take that state-of-the-art model and we make it 10 or a hundred or a thousand times more efficient, maybe at the cost of a little bit of accuracy. So it’s not as good as the state-of-the-art version, but it’s something we can actually put into our data centers and run in production.

Spectrum: What’s the role of the humans in the loop? Is it true that Facebook currently employs 35,000 moderators?

Schroepfer: Yes. Right now our goal is not to reduce that. Our goal is to do a better job catching bad content. People often think that the end state will be a fully automated system. I don’t see that world coming anytime soon.

As automated systems get more sophisticated, they take more and more of the grunt work away, freeing up the humans to work on the really gnarly stuff where you have to spend an hour researching.

We also use AI to give our human moderators power tools. Say I spot this new meme that is telling everyone to vote on Wednesday rather than Tuesday. I have a tool in front of me that says, “Find variants of that throughout the system. Find every photo with the same text, find every video that mentions this thing and kill it in one shot.” Rather than, I found this one picture, but then a bunch of other people upload that misinformation in different forms.

Another important aspect of AI is that anything I can do to prevent a person from having to look at terrible things is time well spent. Whether it’s a person employed by us as a moderator or a user of our services, looking at these things is a terrible experience. If I can build systems that take the worst of the worst, the really graphic violence, and deal with that in an automated fashion, that’s worth a lot to me. Continue reading

Posted in Human Robots

#437543 This Is How We’ll Engineer Artificial ...

Take a Jeopardy! guess: this body part was once referred to as the “consummation of all perfection as an instrument.”

Answer: “What is the human hand?”

Our hands are insanely complex feats of evolutionary engineering. Densely-packed sensors provide intricate and ultra-sensitive feelings of touch. Dozens of joints synergize to give us remarkable dexterity. A “sixth sense” awareness of where our hands are in space connects them to the mind, making it possible to open a door, pick up a mug, and pour coffee in total darkness based solely on what they feel.

So why can’t robots do the same?

In a new article in Science, Dr. Subramanian Sundaram at Boston and Harvard University argues that it’s high time to rethink robotic touch. Scientists have long dreamed of artificially engineering robotic hands with the same dexterity and feedback that we have. Now, after decades, we’re at the precipice of a breakthrough thanks to two major advances. One, we better understand how touch works in humans. Two, we have the mega computational powerhouse called machine learning to recapitulate biology in silicon.

Robotic hands with a sense of touch—and the AI brain to match it—could overhaul our idea of robots. Rather than charming, if somewhat clumsy, novelties, robots equipped with human-like hands are far more capable of routine tasks—making food, folding laundry—and specialized missions like surgery or rescue. But machines aren’t the only ones to gain. For humans, robotic prosthetic hands equipped with accurate, sensitive, and high-resolution artificial touch is the next giant breakthrough to seamlessly link a biological brain to a mechanical hand.

Here’s what Sundaram laid out to get us to that future.

How Does Touch Work, Anyway?
Let me start with some bad news: reverse engineering the human hand is really hard. It’s jam-packed with over 17,000 sensors tuned to mechanical forces alone, not to mention sensors for temperature and pain. These force “receptors” rely on physical distortions—bending, stretching, curling—to signal to the brain.

The good news? We now have a far clearer picture of how biological touch works. Imagine a coin pressed into your palm. The sensors embedded in the skin, called mechanoreceptors, capture that pressure, and “translate” it into electrical signals. These signals pulse through the nerves on your hand to the spine, and eventually make their way to the brain, where they gets interpreted as “touch.”

At least, that’s the simple version, but one too vague and not particularly useful for recapitulating touch. To get there, we need to zoom in.

The cells on your hand that collect touch signals, called tactile “first order” neurons (enter Star Wars joke) are like upside-down trees. Intricate branches extend from their bodies, buried deep in the skin, to a vast area of the hand. Each neuron has its own little domain called “receptor fields,” although some overlap. Like governors, these neurons manage a semi-dedicated region, so that any signal they transfer to the higher-ups—spinal cord and brain—is actually integrated from multiple sensors across a large distance.

It gets more intricate. The skin itself is a living entity that can regulate its own mechanical senses through hydration. Sweat, for example, softens the skin, which changes how it interacts with surrounding objects. Ever tried putting a glove onto a sweaty hand? It’s far more of a struggle than a dry one, and feels different.

In a way, the hand’s tactile neurons play a game of Morse Code. Through different frequencies of electrical beeps, they’re able to transfer information about an object’s size, texture, weight, and other properties, while also asking the brain for feedback to better control the object.

Biology to Machine
Reworking all of our hands’ greatest features into machines is absolutely daunting. But robots have a leg up—they’re not restricted to biological hardware. Earlier this year, for example, a team from Columbia engineered a “feeling” robotic finger using overlapping light emitters and sensors in a way loosely similar to receptor fields. Distortions in light were then analyzed with deep learning to translate into contact location and force.

Although a radical departure from our own electrical-based system, the Columbia team’s attempt was clearly based on human biology. They’re not alone. “Substantial progress is being made in the creation of soft, stretchable electronic skins,” said Sundaram, many of which can sense forces or pressure, although they’re currently still limited.

What’s promising, however, is the “exciting progress in using visual data,” said Sundaram. Computer vision has gained enormously from ubiquitous cameras and large datasets, making it possible to train powerful but data-hungry algorithms such as deep convolutional neural networks (CNNs).

By piggybacking on their success, we can essentially add “eyes” to robotic hands, a superpower us humans can’t imagine. Even better, CNNs and other classes of algorithms can be readily adopted for processing tactile data. Together, a robotic hand could use its eyes to scan an object, plan its movements for grasp, and use touch for feedback to adjust its grip. Maybe we’ll finally have a robot that easily rescues the phone sadly dropped into a composting toilet. Or something much grander to benefit humanity.

That said, relying too heavily on vision could also be a downfall. Take a robot that scans a wide area of rubble for signs of life during a disaster response. If touch relies on sight, then it would have to keep a continuous line-of-sight in a complex and dynamic setting—something computer vision doesn’t do well in, at least for now.

A Neuromorphic Way Forward
Too Debbie Downer? I got your back! It’s hard to overstate the challenges, but what’s clear is that emerging machine learning tools can tackle data processing challenges. For vision, it’s distilling complex images into “actionable control policies,” said Sundaram. For touch, it’s easy to imagine the same. Couple the two together, and that’s a robotic super-hand in the making.

Going forward, argues Sundaram, we need to closely adhere to how the hand and brain process touch. Hijacking our biological “touch machinery” has already proved useful. In 2019, one team used a nerve-machine interface for amputees to control a robotic arm—the DEKA LUKE arm—and sense what the limb and attached hand were feeling. Pressure on the LUKE arm and hand activated an implanted neural interface, which zapped remaining nerves in a way that the brain processes as touch. When the AI analyzed pressure data similar to biological tactile neurons, the person was able to better identify different objects with their eyes closed.

“Neuromorphic tactile hardware (and software) advances will strongly influence the future of bionic prostheses—a compelling application of robotic hands,” said Sundaram, adding that the next step is to increase the density of sensors.

Two additional themes made the list of progressing towards a cyborg future. One is longevity, in that sensors on a robot need to be able to reliably produce large quantities of high-quality data—something that’s seemingly mundane, but is a practical limitation.

The other is going all-in-one. Rather than just a pressure sensor, we need something that captures the myriad of touch sensations. From feather-light to a heavy punch, from vibrations to temperatures, a tree-like architecture similar to our hands would help organize, integrate, and otherwise process data collected from those sensors.

Just a decade ago, mind-controlled robotics were considered a blue sky, stretch-goal neurotechnological fantasy. We now have a chance to “close the loop,” from thought to movement to touch and back to thought, and make some badass robots along the way.

Image Credit: PublicDomainPictures from Pixabay Continue reading

Posted in Human Robots

#437477 If a Robot Is Conscious, Is It OK to ...

In the Star Trek: The Next Generation episode “The Measure of a Man,” Data, an android crew member of the Enterprise, is to be dismantled for research purposes unless Captain Picard can argue that Data deserves the same rights as a human being. Naturally the question arises: What is the basis upon which something has rights? What gives an entity moral standing?

The philosopher Peter Singer argues that creatures that can feel pain or suffer have a claim to moral standing. He argues that nonhuman animals have moral standing, since they can feel pain and suffer. Limiting it to people would be a form of speciesism, something akin to racism and sexism.

Without endorsing Singer’s line of reasoning, we might wonder if it can be extended further to an android robot like Data. It would require that Data can either feel pain or suffer. And how you answer that depends on how you understand consciousness and intelligence.

As real artificial intelligence technology advances toward Hollywood’s imagined versions, the question of moral standing grows more important. If AIs have moral standing, philosophers like me reason, it could follow that they have a right to life. That means you cannot simply dismantle them, and might also mean that people shouldn’t interfere with their pursuing their goals.

Two Flavors of Intelligence and a Test
IBM’s Deep Blue chess machine was successfully trained to beat grandmaster Gary Kasparov. But it could not do anything else. This computer had what’s called domain-specific intelligence.

On the other hand, there’s the kind of intelligence that allows for the ability to do a variety of things well. It is called domain-general intelligence. It’s what lets people cook, ski, and raise children—tasks that are related, but also very different.

Artificial general intelligence, AGI, is the term for machines that have domain-general intelligence. Arguably no machine has yet demonstrated that kind of intelligence. This summer, a startup called OpenAI released a new version of its Generative Pre-Training language model. GPT-3 is a natural language processing system, trained to read and write so that it can be easily understood by people.

It drew immediate notice, not just because of its impressive ability to mimic stylistic flourishes and put together plausible content, but also because of how far it had come from a previous version. Despite this impressive performance, GPT-3 doesn’t actually know anything beyond how to string words together in various ways. AGI remains quite far off.

Named after pioneering AI researcher Alan Turing, the Turing test helps determine when an AI is intelligent. Can a person conversing with a hidden AI tell whether it’s an AI or a human being? If he can’t, then for all practical purposes, the AI is intelligent. But this test says nothing about whether the AI might be conscious.

Two Kinds of Consciousness
There are two parts to consciousness. First, there’s the what-it’s-like-for-me aspect of an experience, the sensory part of consciousness. Philosophers call this phenomenal consciousness. It’s about how you experience a phenomenon, like smelling a rose or feeling pain.

In contrast, there’s also access consciousness. That’s the ability to report, reason, behave, and act in a coordinated and responsive manner to stimuli based on goals. For example, when I pass the soccer ball to my friend making a play on the goal, I am responding to visual stimuli, acting from prior training, and pursuing a goal determined by the rules of the game. I make the pass automatically, without conscious deliberation, in the flow of the game.

Blindsight nicely illustrates the difference between the two types of consciousness. Someone with this neurological condition might report, for example, that they cannot see anything in the left side of their visual field. But if asked to pick up a pen from an array of objects in the left side of their visual field, they can reliably do so. They cannot see the pen, yet they can pick it up when prompted—an example of access consciousness without phenomenal consciousness.

Data is an android. How do these distinctions play out with respect to him?

The Data Dilemma
The android Data demonstrates that he is self-aware in that he can monitor whether or not, for example, he is optimally charged or there is internal damage to his robotic arm.

Data is also intelligent in the general sense. He does a lot of distinct things at a high level of mastery. He can fly the Enterprise, take orders from Captain Picard and reason with him about the best path to take.

He can also play poker with his shipmates, cook, discuss topical issues with close friends, fight with enemies on alien planets, and engage in various forms of physical labor. Data has access consciousness. He would clearly pass the Turing test.

However, Data most likely lacks phenomenal consciousness—he does not, for example, delight in the scent of roses or experience pain. He embodies a supersized version of blindsight. He’s self-aware and has access consciousness—can grab the pen—but across all his senses he lacks phenomenal consciousness.

Now, if Data doesn’t feel pain, at least one of the reasons Singer offers for giving a creature moral standing is not fulfilled. But Data might fulfill the other condition of being able to suffer, even without feeling pain. Suffering might not require phenomenal consciousness the way pain essentially does.

For example, what if suffering were also defined as the idea of being thwarted from pursuing a just cause without causing harm to others? Suppose Data’s goal is to save his crewmate, but he can’t reach her because of damage to one of his limbs. Data’s reduction in functioning that keeps him from saving his crewmate is a kind of nonphenomenal suffering. He would have preferred to save the crewmate, and would be better off if he did.

In the episode, the question ends up resting not on whether Data is self-aware—that is not in doubt. Nor is it in question whether he is intelligent—he easily demonstrates that he is in the general sense. What is unclear is whether he is phenomenally conscious. Data is not dismantled because, in the end, his human judges cannot agree on the significance of consciousness for moral standing.

Should an AI Get Moral Standing?
Data is kind; he acts to support the well-being of his crewmates and those he encounters on alien planets. He obeys orders from people and appears unlikely to harm them, and he seems to protect his own existence. For these reasons he appears peaceful and easier to accept into the realm of things that have moral standing.

But what about Skynet in the Terminator movies? Or the worries recently expressed by Elon Musk about AI being more dangerous than nukes, and by Stephen Hawking on AI ending humankind?

Human beings don’t lose their claim to moral standing just because they act against the interests of another person. In the same way, you can’t automatically say that just because an AI acts against the interests of humanity or another AI it doesn’t have moral standing. You might be justified in fighting back against an AI like Skynet, but that does not take away its moral standing. If moral standing is given in virtue of the capacity to nonphenomenally suffer, then Skynet and Data both get it even if only Data wants to help human beings.

There are no artificial general intelligence machines yet. But now is the time to consider what it would take to grant them moral standing. How humanity chooses to answer the question of moral standing for nonbiological creatures will have big implications for how we deal with future AIs—whether kind and helpful like Data, or set on destruction, like Skynet.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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