Tag Archives: the mind

#439042 How Scientists Used Ultrasound to Read ...

Thanks to neural implants, mind reading is no longer science fiction.

As I’m writing this sentence, a tiny chip with arrays of electrodes could sit on my brain, listening in on the crackling of my neurons firing as my hands dance across the keyboard. Sophisticated algorithms could then decode these electrical signals in real time. My brain’s inner language to plan and move my fingers could then be used to guide a robotic hand to do the same. Mind-to-machine control, voilà!

Yet as the name implies, even the most advanced neural implant has a problem: it’s an implant. For electrodes to reliably read the brain’s electrical chatter, they need to pierce through the its protective membrane and into brain tissue. Danger of infection aside, over time, damage accumulates around the electrodes, distorting their signals or even rendering them unusable.

Now, researchers from Caltech have paved a way to read the brain without any physical contact. Key to their device is a relatively new superstar in neuroscience: functional ultrasound, which uses sound waves to capture activity in the brain.

In monkeys, the technology could reliably predict their eye movement and hand gestures after just a single trial—without the usual lengthy training process needed to decode a movement. If adopted by humans, the new mind-reading tech represents a triple triumph: it requires minimal surgery and minimal learning, but yields maximal resolution for brain decoding. For people who are paralyzed, it could be a paradigm shift in how they control their prosthetics.

“We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement,” said study author Dr. Sumner Norman.

To Dr. Krishna Shenoy at Stanford, who was not involved, the study will finally put ultrasound “on the map as a brain-machine interface technique. Adding to this toolkit is spectacular,” he said.

Breaking the Sound Barrier
Using sound to decode brain activity might seem preposterous, but ultrasound has had quite the run in medicine. You’ve probably heard of its most common use: taking photos of a fetus in pregnancy. The technique uses a transducer, which emits ultrasound pulses into the body and finds boundaries in tissue structure by analyzing the sound waves that bounce back.

Roughly a decade ago, neuroscientists realized they could adapt the tech for brain scanning. Rather than directly measuring the brain’s electrical chatter, it looks at a proxy—blood flow. When certain brain regions or circuits are active, the brain requires much more energy, which is provided by increased blood flow. In this way, functional ultrasound works similarly to functional MRI, but at a far higher resolution—roughly ten times, the authors said. Plus, people don’t have to lie very still in an expensive, claustrophobic magnet.

“A key question in this work was: If we have a technique like functional ultrasound that gives us high-resolution images of the brain’s blood flow dynamics in space and over time, is there enough information from that imaging to decode something useful about behavior?” said study author Dr. Mikhail Shapiro.

There’s plenty of reasons for doubt. As the new kid on the block, functional ultrasound has some known drawbacks. A major one: it gives a far less direct signal than electrodes. Previous studies show that, with multiple measurements, it can provide a rough picture of brain activity. But is that enough detail to guide a robotic prosthesis?

One-Trial Wonder
The new study put functional ultrasound to the ultimate test: could it reliably detect movement intention in monkeys? Because their brains are the most similar to ours, rhesus macaque monkeys are often the critical step before a brain-machine interface technology is adapted for humans.

The team first inserted small ultrasound transducers into the skulls of two rhesus monkeys. While it sounds intense, the surgery doesn’t penetrate the brain or its protective membrane; it’s only on the skull. Compared to electrodes, this means the brain itself isn’t physically harmed.

The device is linked to a computer, which controls the direction of sound waves and captures signals from the brain. For this study, the team aimed the pulses at the posterior parietal cortex, a part of the “motor” aspect of the brain, which plans movement. If right now you’re thinking about scrolling down this page, that’s the brain region already activated, before your fingers actually perform the movement.

Then came the tests. The first looked at eye movements—something pretty necessary before planning actual body movements without tripping all over the place. Here, the monkeys learned to focus on a central dot on a computer screen. A second dot, either left or right, then flashed. The monkeys’ task was to flicker their eyes to the most recent dot. It’s something that seems easy for us, but requires sophisticated brain computation.

The second task was more straightforward. Rather than just moving their eyes to the second target dot, the monkeys learned to grab and manipulate a joystick to move a cursor to that target.

Using brain imaging to decode the mind and control movement. Image Credit: S. Norman, Caltech
As the monkeys learned, so did the device. Ultrasound data capturing brain activity was fed into a sophisticated machine learning algorithm to guess the monkeys’ intentions. Here’s the kicker: once trained, using data from just a single trial, the algorithm was able to correctly predict the monkeys’ actual eye movement—whether left or right—with roughly 78 percent accuracy. The accuracy for correctly maneuvering the joystick was even higher, at nearly 90 percent.

That’s crazy accurate, and very much needed for a mind-controlled prosthetic. If you’re using a mind-controlled cursor or limb, the last thing you’d want is to have to imagine the movement multiple times before you actually click the web button, grab the door handle, or move your robotic leg.

Even more impressive is the resolution. Sound waves seem omnipresent, but with focused ultrasound, it’s possible to measure brain activity at a resolution of 100 microns—roughly 10 neurons in the brain.

A Cyborg Future?
Before you start worrying about scientists blasting your brain with sound waves to hack your mind, don’t worry. The new tech still requires skull surgery, meaning that a small chunk of skull needs to be removed. However, the brain itself is spared. This means that compared to electrodes, ultrasound could offer less damage and potentially a far longer mind reading than anything currently possible.

There are downsides. Focused ultrasound is far younger than any electrode-based neural implants, and can’t yet reliably decode 360-degree movement or fine finger movements. For now, the tech requires a wire to link the device to a computer, which is off-putting to many people and will prevent widespread adoption. Add to that the inherent downside of focused ultrasound, which lags behind electrical recordings by roughly two seconds.

All that aside, however, the tech is just tiptoeing into a future where minds and machines seamlessly connect. Ultrasound can penetrate the skull, though not yet at the resolution needed for imaging and decoding brain activity. The team is already working with human volunteers with traumatic brain injuries, who had to have a piece of their skulls removed, to see how well ultrasound works for reading their minds.

“What’s most exciting is that functional ultrasound is a young technique with huge potential. This is just our first step in bringing high performance, less invasive brain-machine interface to more people,” said Norman.

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#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.

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#437150 AI Is Getting More Creative. But Who ...

Creativity is a trait that makes humans unique from other species. We alone have the ability to make music and art that speak to our experiences or illuminate truths about our world. But suddenly, humans’ artistic abilities have some competition—and from a decidedly non-human source.

Over the last couple years there have been some remarkable examples of art produced by deep learning algorithms. They have challenged the notion of an elusive definition of creativity and put into perspective how professionals can use artificial intelligence to enhance their abilities and produce beyond the known boundaries.

But when creativity is the result of code written by a programmer, using a format given by a software engineer, featuring private and public datasets, how do we assign ownership of AI-generated content, and particularly that of artwork? McKinsey estimates AI will annually generate value of $3.5 to $5.8 trillion across various sectors.

In 2018, a portrait that was christened Edmond de Belamy was made in a French art collective called Obvious. It used a database with 15,000 portraits from the 1300s to the 1900s to train a deep learning algorithm to produce a unique portrait. The painting sold for $432,500 in a New York auction. Similarly, a program called Aiva, trained on thousands of classical compositions, has released albums whose pieces are being used by ad agencies and movies.

The datasets used by these algorithms were different, but behind both there was a programmer who changed the brush strokes or musical notes into lines of code and a data scientist or engineer who fitted and “curated” the datasets to use for the model. There could also have been user-based input, and the output may be biased towards certain styles or unintentionally infringe on similar pieces of art. This shows that there are many collaborators with distinct roles in producing AI-generated content, and it’s important to discuss how they can protect their proprietary interests.

A perspective article published in Nature Machine Intelligence by Jason K. Eshraghian in March looks into how AI artists and the collaborators involved should assess their ownership, laying out some guiding principles that are “only applicable for as long as AI does not have legal parenthood, the way humans and corporations are accorded.”

Before looking at how collaborators can protect their interests, it’s useful to understand the basic requirements of copyright law. The artwork in question must be an “original work of authorship fixed in a tangible medium.” Given this principle, the author asked whether it’s possible for AI to exercise creativity, skill, or any other indicator of originality. The answer is still straightforward—no—or at least not yet. Currently, AI’s range of creativity doesn’t exceed the standard used by the US Copyright Office, which states that copyright law protects the “fruits of intellectual labor founded in the creative powers of the mind.”

Due to the current limitations of narrow AI, it must have some form of initial input that helps develop its ability to create. At the moment AI is a tool that can be used to produce creative work in the same way that a video camera is a tool used to film creative content. Video producers don’t need to comprehend the inner workings of their cameras; as long as their content shows creativity and originality, they have a proprietary claim over their creations.

The same concept applies to programmers developing a neural network. As long as the dataset they use as input yields an original and creative result, it will be protected by copyright law; they don’t need to understand the high-level mathematics, which in this case are often black box algorithms whose output it’s impossible to analyze.

Will robots and algorithms eventually be treated as creative sources able to own copyrights? The author pointed to the recent patent case of Warner-Lambert Co Ltd versus Generics where Lord Briggs, Justice of the Supreme Court of the UK, determined that “the court is well versed in identifying the governing mind of a corporation and, when the need arises, will no doubt be able to do the same for robots.”

In the meantime, Dr. Eshraghian suggests four guiding principles to allow artists who collaborate with AI to protect themselves.

First, programmers need to document their process through online code repositories like GitHub or BitBucket.

Second, data engineers should also document and catalog their datasets and the process they used to curate their models, indicating selectivity in their criteria as much as possible to demonstrate their involvement and creativity.

Third, in cases where user data is utilized, the engineer should “catalog all runs of the program” to distinguish the data selection process. This could be interpreted as a way of determining whether user-based input has a right to claim the copyright too.

Finally, the output should avoid infringing on others’ content through methods like reverse image searches and version control, as mentioned above.

AI-generated artwork is still a very new concept, and the ambiguous copyright laws around it give a lot of flexibility to AI artists and programmers worldwide. The guiding principles Eshraghian lays out will hopefully shed some light on the legislation we’ll eventually need for this kind of art, and start an important conversation between all the stakeholders involved.

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