Tag Archives: memory

#435172 DARPA’s New Project Is Investing ...

When Elon Musk and DARPA both hop aboard the cyborg hypetrain, you know brain-machine interfaces (BMIs) are about to achieve the impossible.

BMIs, already the stuff of science fiction, facilitate crosstalk between biological wetware with external computers, turning human users into literal cyborgs. Yet mind-controlled robotic arms, microelectrode “nerve patches”, or “memory Band-Aids” are still purely experimental medical treatments for those with nervous system impairments.

With the Next-Generation Nonsurgical Neurotechnology (N3) program, DARPA is looking to expand BMIs to the military. This month, the project tapped six academic teams to engineer radically different BMIs to hook up machines to the brains of able-bodied soldiers. The goal is to ditch surgery altogether—while minimizing any biological interventions—to link up brain and machine.

Rather than microelectrodes, which are currently surgically inserted into the brain to hijack neural communication, the project is looking to acoustic signals, electromagnetic waves, nanotechnology, genetically-enhanced neurons, and infrared beams for their next-gen BMIs.

It’s a radical departure from current protocol, with potentially thrilling—or devastating—impact. Wireless BMIs could dramatically boost bodily functions of veterans with neural damage or post-traumatic stress disorder (PTSD), or allow a single soldier to control swarms of AI-enabled drones with his or her mind. Or, similar to the Black Mirror episode Men Against Fire, it could cloud the perception of soldiers, distancing them from the emotional guilt of warfare.

When trickled down to civilian use, these new technologies are poised to revolutionize medical treatment. Or they could galvanize the transhumanist movement with an inconceivably powerful tool that fundamentally alters society—for better or worse.

Here’s what you need to know.

Radical Upgrades
The four-year N3 program focuses on two main aspects: noninvasive and “minutely” invasive neural interfaces to both read and write into the brain.

Because noninvasive technologies sit on the scalp, their sensors and stimulators will likely measure entire networks of neurons, such as those controlling movement. These systems could then allow soldiers to remotely pilot robots in the field—drones, rescue bots, or carriers like Boston Dynamics’ BigDog. The system could even boost multitasking prowess—mind-controlling multiple weapons at once—similar to how able-bodied humans can operate a third robotic arm in addition to their own two.

In contrast, minutely invasive technologies allow scientists to deliver nanotransducers without surgery: for example, an injection of a virus carrying light-sensitive sensors, or other chemical, biotech, or self-assembled nanobots that can reach individual neurons and control their activity independently without damaging sensitive tissue. The proposed use for these technologies isn’t yet well-specified, but as animal experiments have shown, controlling the activity of single neurons at multiple points is sufficient to program artificial memories of fear, desire, and experiences directly into the brain.

“A neural interface that enables fast, effective, and intuitive hands-free interaction with military systems by able-bodied warfighters is the ultimate program goal,” DARPA wrote in its funding brief, released early last year.

The only technologies that will be considered must have a viable path toward eventual use in healthy human subjects.

“Final N3 deliverables will include a complete integrated bidirectional brain-machine interface system,” the project description states. This doesn’t just include hardware, but also new algorithms tailored to these system, demonstrated in a “Department of Defense-relevant application.”

The Tools
Right off the bat, the usual tools of the BMI trade, including microelectrodes, MRI, or transcranial magnetic stimulation (TMS) are off the table. These popular technologies rely on surgery, heavy machinery, or personnel to sit very still—conditions unlikely in the real world.

The six teams will tap into three different kinds of natural phenomena for communication: magnetism, light beams, and acoustic waves.

Dr. Jacob Robinson at Rice University, for example, is combining genetic engineering, infrared laser beams, and nanomagnets for a bidirectional system. The $18 million project, MOANA (Magnetic, Optical and Acoustic Neural Access device) uses viruses to deliver two extra genes into the brain. One encodes a protein that sits on top of neurons and emits infrared light when the cell activates. Red and infrared light can penetrate through the skull. This lets a skull cap, embedded with light emitters and detectors, pick up these signals for subsequent decoding. Ultra-fast and utra-sensitvie photodetectors will further allow the cap to ignore scattered light and tease out relevant signals emanating from targeted portions of the brain, the team explained.

The other new gene helps write commands into the brain. This protein tethers iron nanoparticles to the neurons’ activation mechanism. Using magnetic coils on the headset, the team can then remotely stimulate magnetic super-neurons to fire while leaving others alone. Although the team plans to start in cell cultures and animals, their goal is to eventually transmit a visual image from one person to another. “In four years we hope to demonstrate direct, brain-to-brain communication at the speed of thought and without brain surgery,” said Robinson.

Other projects in N3 are just are ambitious.

The Carnegie Mellon team, for example, plans to use ultrasound waves to pinpoint light interaction in targeted brain regions, which can then be measured through a wearable “hat.” To write into the brain, they propose a flexible, wearable electrical mini-generator that counterbalances the noisy effect of the skull and scalp to target specific neural groups.

Similarly, a group at Johns Hopkins is also measuring light path changes in the brain to correlate them with regional brain activity to “read” wetware commands.

The Teledyne Scientific & Imaging group, in contrast, is turning to tiny light-powered “magnetometers” to detect small, localized magnetic fields that neurons generate when they fire, and match these signals to brain output.

The nonprofit Battelle team gets even fancier with their ”BrainSTORMS” nanotransducers: magnetic nanoparticles wrapped in a piezoelectric shell. The shell can convert electrical signals from neurons into magnetic ones and vice-versa. This allows external transceivers to wirelessly pick up the transformed signals and stimulate the brain through a bidirectional highway.

The magnetometers can be delivered into the brain through a nasal spray or other non-invasive methods, and magnetically guided towards targeted brain regions. When no longer needed, they can once again be steered out of the brain and into the bloodstream, where the body can excrete them without harm.

Four-Year Miracle
Mind-blown? Yeah, same. However, the challenges facing the teams are enormous.

DARPA’s stated goal is to hook up at least 16 sites in the brain with the BMI, with a lag of less than 50 milliseconds—on the scale of average human visual perception. That’s crazy high resolution for devices sitting outside the brain, both in space and time. Brain tissue, blood vessels, and the scalp and skull are all barriers that scatter and dissipate neural signals. All six teams will need to figure out the least computationally-intensive ways to fish out relevant brain signals from background noise, and triangulate them to the appropriate brain region to decipher intent.

In the long run, four years and an average $20 million per project isn’t much to potentially transform our relationship with machines—for better or worse. DARPA, to its credit, is keenly aware of potential misuse of remote brain control. The program is under the guidance of a panel of external advisors with expertise in bioethical issues. And although DARPA’s focus is on enabling able-bodied soldiers to better tackle combat challenges, it’s hard to argue that wireless, non-invasive BMIs will also benefit those most in need: veterans and other people with debilitating nerve damage. To this end, the program is heavily engaging the FDA to ensure it meets safety and efficacy regulations for human use.

Will we be there in just four years? I’m skeptical. But these electrical, optical, acoustic, magnetic, and genetic BMIs, as crazy as they sound, seem inevitable.

“DARPA is preparing for a future in which a combination of unmanned systems, AI, and cyber operations may cause conflicts to play out on timelines that are too short for humans to effectively manage with current technology alone,” said Al Emondi, the N3 program manager.

The question is, now that we know what’s in store, how should the rest of us prepare?

Image Credit: With permission from DARPA N3 project. Continue reading

Posted in Human Robots

#435056 How Researchers Used AI to Better ...

A few years back, DeepMind’s Demis Hassabis famously prophesized that AI and neuroscience will positively feed into each other in a “virtuous circle.” If realized, this would fundamentally expand our insight into intelligence, both machine and human.

We’ve already seen some proofs of concept, at least in the brain-to-AI direction. For example, memory replay, a biological mechanism that fortifies our memories during sleep, also boosted AI learning when abstractly appropriated into deep learning models. Reinforcement learning, loosely based on our motivation circuits, is now behind some of AI’s most powerful tools.

Hassabis is about to be proven right again.

Last week, two studies independently tapped into the power of ANNs to solve a 70-year-old neuroscience mystery: how does our visual system perceive reality?

The first, published in Cell, used generative networks to evolve DeepDream-like images that hyper-activate complex visual neurons in monkeys. These machine artworks are pure nightmare fuel to the human eye; but together, they revealed a fundamental “visual hieroglyph” that may form a basic rule for how we piece together visual stimuli to process sight into perception.

In the second study, a team used a deep ANN model—one thought to mimic biological vision—to synthesize new patterns tailored to control certain networks of visual neurons in the monkey brain. When directly shown to monkeys, the team found that the machine-generated artworks could reliably activate predicted populations of neurons. Future improved ANN models could allow even better control, giving neuroscientists a powerful noninvasive tool to study the brain. The work was published in Science.

The individual results, though fascinating, aren’t necessarily the point. Rather, they illustrate how scientists are now striving to complete the virtuous circle: tapping AI to probe natural intelligence. Vision is only the beginning—the tools can potentially be expanded into other sensory domains. And the more we understand about natural brains, the better we can engineer artificial ones.

It’s a “great example of leveraging artificial intelligence to study organic intelligence,” commented Dr. Roman Sandler at Kernel.co on Twitter.

Why Vision?
ANNs and biological vision have quite the history.

In the late 1950s, the legendary neuroscientist duo David Hubel and Torsten Wiesel became some of the first to use mathematical equations to understand how neurons in the brain work together.

In a series of experiments—many using cats—the team carefully dissected the structure and function of the visual cortex. Using myriads of images, they revealed that vision is processed in a hierarchy: neurons in “earlier” brain regions, those closer to the eyes, tend to activate when they “see” simple patterns such as lines. As we move deeper into the brain, from the early V1 to a nub located slightly behind our ears, the IT cortex, neurons increasingly respond to more complex or abstract patterns, including faces, animals, and objects. The discovery led some scientists to call certain IT neurons “Jennifer Aniston cells,” which fire in response to pictures of the actress regardless of lighting, angle, or haircut. That is, IT neurons somehow extract visual information into the “gist” of things.

That’s not trivial. The complex neural connections that lead to increasing abstraction of what we see into what we think we see—what we perceive—is a central question in machine vision: how can we teach machines to transform numbers encoding stimuli into dots, lines, and angles that eventually form “perceptions” and “gists”? The answer could transform self-driving cars, facial recognition, and other computer vision applications as they learn to better generalize.

Hubel and Wiesel’s Nobel-prize-winning studies heavily influenced the birth of ANNs and deep learning. Much of earlier ANN “feed-forward” model structures are based on our visual system; even today, the idea of increasing layers of abstraction—for perception or reasoning—guide computer scientists to build AI that can better generalize. The early romance between vision and deep learning is perhaps the bond that kicked off our current AI revolution.

It only seems fair that AI would feed back into vision neuroscience.

Hieroglyphs and Controllers
In the Cell study, a team led by Dr. Margaret Livingstone at Harvard Medical School tapped into generative networks to unravel IT neurons’ complex visual alphabet.

Scientists have long known that neurons in earlier visual regions (V1) tend to fire in response to “grating patches” oriented in certain ways. Using a limited set of these patches like letters, V1 neurons can “express a visual sentence” and represent any image, said Dr. Arash Afraz at the National Institute of Health, who was not involved in the study.

But how IT neurons operate remained a mystery. Here, the team used a combination of genetic algorithms and deep generative networks to “evolve” computer art for every studied neuron. In seven monkeys, the team implanted electrodes into various parts of the visual IT region so that they could monitor the activity of a single neuron.

The team showed each monkey an initial set of 40 images. They then picked the top 10 images that stimulated the highest neural activity, and married them to 30 new images to “evolve” the next generation of images. After 250 generations, the technique, XDREAM, generated a slew of images that mashed up contorted face-like shapes with lines, gratings, and abstract shapes.

This image shows the evolution of an optimum image for stimulating a visual neuron in a monkey. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“The evolved images look quite counter-intuitive,” explained Afraz. Some clearly show detailed structures that resemble natural images, while others show complex structures that can’t be characterized by our puny human brains.

This figure shows natural images (right) and images evolved by neurons in the inferotemporal cortex of a monkey (left). Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“What started to emerge during each experiment were pictures that were reminiscent of shapes in the world but were not actual objects in the world,” said study author Carlos Ponce. “We were seeing something that was more like the language cells use with each other.”

This image was evolved by a neuron in the inferotemporal cortex of a monkey using AI. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
Although IT neurons don’t seem to use a simple letter alphabet, it does rely on a vast array of characters like hieroglyphs or Chinese characters, “each loaded with more information,” said Afraz.

The adaptive nature of XDREAM turns it into a powerful tool to probe the inner workings of our brains—particularly for revealing discrepancies between biology and models.

The Science study, led by Dr. James DiCarlo at MIT, takes a similar approach. Using ANNs to generate new patterns and images, the team was able to selectively predict and independently control neuron populations in a high-level visual region called V4.

“So far, what has been done with these models is predicting what the neural responses would be to other stimuli that they have not seen before,” said study author Dr. Pouya Bashivan. “The main difference here is that we are going one step further and using the models to drive the neurons into desired states.”

It suggests that our current ANN models for visual computation “implicitly capture a great deal of visual knowledge” which we can’t really describe, but which the brain uses to turn vision information into perception, the authors said. By testing AI-generated images on biological vision, however, the team concluded that today’s ANNs have a degree of understanding and generalization. The results could potentially help engineer even more accurate ANN models of biological vision, which in turn could feed back into machine vision.

“One thing is clear already: Improved ANN models … have led to control of a high-level neural population that was previously out of reach,” the authors said. “The results presented here have likely only scratched the surface of what is possible with such implemented characterizations of the brain’s neural networks.”

To Afraz, the power of AI here is to find cracks in human perception—both our computational models of sensory processes, as well as our evolved biological software itself. AI can be used “as a perfect adversarial tool to discover design cracks” of IT, said Afraz, such as finding computer art that “fools” a neuron into thinking the object is something else.

“As artificial intelligence researchers develop models that work as well as the brain does—or even better—we will still need to understand which networks are more likely to behave safely and further human goals,” said Ponce. “More efficient AI can be grounded by knowledge of how the brain works.”

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Posted in Human Robots

#434658 The Next Data-Driven Healthtech ...

Increasing your healthspan (i.e. making 100 years old the new 60) will depend to a large degree on artificial intelligence. And, as we saw in last week’s blog, healthcare AI systems are extremely data-hungry.

Fortunately, a slew of new sensors and data acquisition methods—including over 122 million wearables shipped in 2018—are bursting onto the scene to meet the massive demand for medical data.

From ubiquitous biosensors, to the mobile healthcare revolution, to the transformative power of the Health Nucleus, converging exponential technologies are fundamentally transforming our approach to healthcare.

In Part 4 of this blog series on Longevity & Vitality, I expand on how we’re acquiring the data to fuel today’s AI healthcare revolution.

In this blog, I’ll explore:

How the Health Nucleus is transforming “sick care” to healthcare
Sensors, wearables, and nanobots
The advent of mobile health

Let’s dive in.

Health Nucleus: Transforming ‘Sick Care’ to Healthcare
Much of today’s healthcare system is actually sick care. Most of us assume that we’re perfectly healthy, with nothing going on inside our bodies, until the day we travel to the hospital writhing in pain only to discover a serious or life-threatening condition.

Chances are that your ailment didn’t materialize that morning; rather, it’s been growing or developing for some time. You simply weren’t aware of it. At that point, once you’re diagnosed as “sick,” our medical system engages to take care of you.

What if, instead of this retrospective and reactive approach, you were constantly monitored, so that you could know the moment anything was out of whack?

Better yet, what if you more closely monitored those aspects of your body that your gene sequence predicted might cause you difficulty? Think: your heart, your kidneys, your breasts. Such a system becomes personalized, predictive, and possibly preventative.

This is the mission of the Health Nucleus platform built by Human Longevity, Inc. (HLI). While not continuous—that will come later, with the next generation of wearable and implantable sensors—the Health Nucleus was designed to ‘digitize’ you once per year to help you determine whether anything is going on inside your body that requires immediate attention.

The Health Nucleus visit provides you with the following tests during a half-day visit:

Whole genome sequencing (30x coverage)
Whole body (non-contrast) MRI
Brain magnetic resonance imaging/angiography (MRI/MRA)
CT (computed tomography) of the heart and lungs
Coronary artery calcium scoring
Electrocardiogram
Echocardiogram
Continuous cardiac monitoring
Clinical laboratory tests and metabolomics

In late 2018, HLI published the results of the first 1,190 clients through the Health Nucleus. The results were eye-opening—especially since these patients were all financially well-off, and already had access to the best doctors.

Following are the physiological and genomic findings in these clients who self-selected to undergo evaluation at HLI’s Health Nucleus.

Physiological Findings [TG]

Two percent had previously unknown tumors detected by MRI
2.5 percent had previously undetected aneurysms detected by MRI
Eight percent had cardiac arrhythmia found on cardiac rhythm monitoring, not previously known
Nine percent had moderate-severe coronary artery disease risk, not previously known
16 percent discovered previously unknown cardiac structure/function abnormalities
30 percent had elevated liver fat, not previously known

Genomic Findings [TG]

24 percent of clients uncovered a rare (unknown) genetic mutation found on WGS
63 percent of clients had a rare genetic mutation with a corresponding phenotypic finding

In summary, HLI’s published results found that 14.4 percent of clients had significant findings that are actionable, requiring immediate or near-term follow-up and intervention.

Long-term value findings were found in 40 percent of the clients we screened. Long-term clinical findings include discoveries that require medical attention or monitoring but are not immediately life-threatening.

The bottom line: most people truly don’t know their actual state of health. The ability to take a fully digital deep dive into your health status at least once per year will enable you to detect disease at stage zero or stage one, when it is most curable.

Sensors, Wearables, and Nanobots
Wearables, connected devices, and quantified self apps will allow us to continuously collect enormous amounts of useful health information.

Wearables like the Quanttus wristband and Vital Connect can transmit your electrocardiogram data, vital signs, posture, and stress levels anywhere on the planet.

In April 2017, we were proud to grant $2.5 million in prize money to the winning team in the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices.

Using a group of noninvasive sensors that collect data on vital signs, body chemistry, and biological functions, Final Frontier integrates this data in their powerful, AI-based DxtER diagnostic engine for rapid, high-precision assessments.

Their engine combines learnings from clinical emergency medicine and data analysis from actual patients.

Google is developing a full range of internal and external sensors (e.g. smart contact lenses) that can monitor the wearer’s vitals, ranging from blood sugar levels to blood chemistry.

In September 2018, Apple announced its Series 4 Apple Watch, including an FDA-approved mobile, on-the-fly ECG. Granted its first FDA approval, Apple appears to be moving deeper into the sensing healthcare market.

Further, Apple is reportedly now developing sensors that can non-invasively monitor blood sugar levels in real time for diabetic treatment. IoT-connected sensors are also entering the world of prescription drugs.

Last year, the FDA approved the first sensor-embedded pill, Abilify MyCite. This new class of digital pills can now communicate medication data to a user-controlled app, to which doctors may be granted access for remote monitoring.

Perhaps what is most impressive about the next generation of wearables and implantables is the density of sensors, processing, networking, and battery capability that we can now cheaply and compactly integrate.

Take the second-generation OURA ring, for example, which focuses on sleep measurement and management.

The OURA ring looks like a slightly thick wedding band, yet contains an impressive array of sensors and capabilities, including:

Two infrared LED
One infrared sensor
Three temperature sensors
One accelerometer
A six-axis gyro
A curved battery with a seven-day life
The memory, processing, and transmission capability required to connect with your smartphone

Disrupting Medical Imaging Hardware
In 2018, we saw lab breakthroughs that will drive the cost of an ultrasound sensor to below $100, in a packaging smaller than most bandages, powered by a smartphone. Dramatically disrupting ultrasound is just the beginning.

Nanobots and Nanonetworks
While wearables have long been able to track and transmit our steps, heart rate, and other health data, smart nanobots and ingestible sensors will soon be able to monitor countless new parameters and even help diagnose disease.

Some of the most exciting breakthroughs in smart nanotechnology from the past year include:

Researchers from the École Polytechnique Fédérale de Lausanne (EPFL) and the Swiss Federal Institute of Technology in Zurich (ETH Zurich) demonstrated artificial microrobots that can swim and navigate through different fluids, independent of additional sensors, electronics, or power transmission.

Researchers at the University of Chicago proposed specific arrangements of DNA-based molecular logic gates to capture the information contained in the temporal portion of our cells’ communication mechanisms. Accessing the otherwise-lost time-dependent information of these cellular signals is akin to knowing the tune of a song, rather than solely the lyrics.

MIT researchers built micron-scale robots able to sense, record, and store information about their environment. These tiny robots, about 100 micrometers in diameter (approximately the size of a human egg cell), can also carry out pre-programmed computational tasks.

Engineers at University of California, San Diego developed ultrasound-powered nanorobots that swim efficiently through your blood, removing harmful bacteria and the toxins they produce.

But it doesn’t stop there.

As nanosensor and nanonetworking capabilities develop, these tiny bots may soon communicate with each other, enabling the targeted delivery of drugs and autonomous corrective action.

Mobile Health
The OURA ring and the Series 4 Apple Watch are just the tip of the spear when it comes to our future of mobile health. This field, predicted to become a $102 billion market by 2022, puts an on-demand virtual doctor in your back pocket.

Step aside, WebMD.

In true exponential technology fashion, mobile device penetration has increased dramatically, while image recognition error rates and sensor costs have sharply declined.

As a result, AI-powered medical chatbots are flooding the market; diagnostic apps can identify anything from a rash to diabetic retinopathy; and with the advent of global connectivity, mHealth platforms enable real-time health data collection, transmission, and remote diagnosis by medical professionals.

Already available to residents across North London, Babylon Health offers immediate medical advice through AI-powered chatbots and video consultations with doctors via its app.

Babylon now aims to build up its AI for advanced diagnostics and even prescription. Others, like Woebot, take on mental health, using cognitive behavioral therapy in communications over Facebook messenger with patients suffering from depression.

In addition to phone apps and add-ons that test for fertility or autism, the now-FDA-approved Clarius L7 Linear Array Ultrasound Scanner can connect directly to iOS and Android devices and perform wireless ultrasounds at a moment’s notice.

Next, Healthy.io, an Israeli startup, uses your smartphone and computer vision to analyze traditional urine test strips—all you need to do is take a few photos.

With mHealth platforms like ClickMedix, which connects remotely-located patients to medical providers through real-time health data collection and transmission, what’s to stop us from delivering needed treatments through drone delivery or robotic telesurgery?

Welcome to the age of smartphone-as-a-medical-device.

Conclusion
With these DIY data collection and diagnostic tools, we save on transportation costs (time and money), and time bottlenecks.

No longer will you need to wait for your urine or blood results to go through the current information chain: samples will be sent to the lab, analyzed by a technician, results interpreted by your doctor, and only then relayed to you.

Just like the “sage-on-the-stage” issue with today’s education system, healthcare has a “doctor-on-the-dais” problem. Current medical procedures are too complicated and expensive for a layperson to perform and analyze on their own.

The coming abundance of healthcare data promises to transform how we approach healthcare, putting the power of exponential technologies in the patient’s hands and revolutionizing how we live.

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Posted in Human Robots

#434508 The Top Biotech and Medicine Advances to ...

2018 was bonkers for science.

From a woman who gave birth using a transplanted uterus, to the infamous CRISPR baby scandal, to forensics adopting consumer-based genealogy test kits to track down criminals, last year was a factory churning out scientific “whoa” stories with consequences for years to come.

With CRISPR still in the headlines, Britain ready to bid Europe au revoir, and multiple scientific endeavors taking off, 2019 is shaping up to be just as tumultuous.

Here are the science and health stories that may blow up in the new year. But first, a note of caveat: predicting the future is tough. Forecasting is the lovechild between statistics and (a good deal of) intuition, and entire disciplines have been dedicated to the endeavor. But January is the perfect time to gaze into the crystal ball for wisps of insight into the year to come. Last year we predicted the widespread approval of gene therapy products—on the most part, we nailed it. This year we’re hedging our bets with multiple predictions.

Gene Drives Used in the Wild
The concept of gene drives scares many, for good reason. Gene drives are a step up in severity (and consequences) from CRISPR and other gene-editing tools. Even with germline editing, in which the sperm, egg, or embryos are altered, gene editing affects just one genetic line—one family—at least at the beginning, before they reproduce with the general population.

Gene drives, on the other hand, have the power to wipe out entire species.

In a nutshell, they’re little bits of DNA code that help a gene transfer from parent to child with almost 100 percent perfect probability. The “half of your DNA comes from dad, the other comes from mom” dogma? Gene drives smash that to bits.

In other words, the only time one would consider using a gene drive is to change the genetic makeup of an entire population. It sounds like the plot of a supervillain movie, but scientists have been toying around with the idea of deploying the technology—first in mosquitoes, then (potentially) in rodents.

By releasing just a handful of mutant mosquitoes that carry gene drives for infertility, for example, scientists could potentially wipe out entire populations that carry infectious scourges like malaria, dengue, or Zika. The technology is so potent—and dangerous—the US Defense Advances Research Projects Agency is shelling out $65 million to suss out how to deploy, control, counter, or even reverse the effects of tampering with ecology.

Last year, the U.N. gave a cautious go-ahead for the technology to be deployed in the wild in limited terms. Now, the first release of a genetically modified mosquito is set for testing in Burkina Faso in Africa—the first-ever field experiment involving gene drives.

The experiment will only release mosquitoes in the Anopheles genus, which are the main culprits transferring disease. As a first step, over 10,000 male mosquitoes are set for release into the wild. These dudes are genetically sterile but do not cause infertility, and will help scientists examine how they survive and disperse as a preparation for deploying gene-drive-carrying mosquitoes.

Hot on the project’s heels, the nonprofit consortium Target Malaria, backed by the Bill and Melinda Gates foundation, is engineering a gene drive called Mosq that will spread infertility across the population or kill out all female insects. Their attempt to hack the rules of inheritance—and save millions in the process—is slated for 2024.

A Universal Flu Vaccine
People often brush off flu as a mere annoyance, but the infection kills hundreds of thousands each year based on the CDC’s statistical estimates.

The flu virus is actually as difficult of a nemesis as HIV—it mutates at an extremely rapid rate, making effective vaccines almost impossible to engineer on time. Scientists currently use data to forecast the strains that will likely explode into an epidemic and urge the public to vaccinate against those predictions. That’s partly why, on average, flu vaccines only have a success rate of roughly 50 percent—not much better than a coin toss.

Tired of relying on educated guesses, scientists have been chipping away at a universal flu vaccine that targets all strains—perhaps even those we haven’t yet identified. Often referred to as the “holy grail” in epidemiology, these vaccines try to alert our immune systems to parts of a flu virus that are least variable from strain to strain.

Last November, a first universal flu vaccine developed by BiondVax entered Phase 3 clinical trials, which means it’s already been proven safe and effective in a small numbers and is now being tested in a broader population. The vaccine doesn’t rely on dead viruses, which is a common technique. Rather, it uses a small chain of amino acids—the chemical components that make up proteins—to stimulate the immune system into high alert.

With the government pouring $160 million into the research and several other universal candidates entering clinical trials, universal flu vaccines may finally experience a breakthrough this year.

In-Body Gene Editing Shows Further Promise
CRISPR and other gene editing tools headed the news last year, including both downers suggesting we already have immunity to the technology and hopeful news of it getting ready for treating inherited muscle-wasting diseases.

But what wasn’t widely broadcasted was the in-body gene editing experiments that have been rolling out with gusto. Last September, Sangamo Therapeutics in Richmond, California revealed that they had injected gene-editing enzymes into a patient in an effort to correct a genetic deficit that prevents him from breaking down complex sugars.

The effort is markedly different than the better-known CAR-T therapy, which extracts cells from the body for genetic engineering before returning them to the hosts. Rather, Sangamo’s treatment directly injects viruses carrying the edited genes into the body. So far, the procedure looks to be safe, though at the time of reporting it was too early to determine effectiveness.

This year the company hopes to finally answer whether it really worked.

If successful, it means that devastating genetic disorders could potentially be treated with just a few injections. With a gamut of new and more precise CRISPR and other gene-editing tools in the works, the list of treatable inherited diseases is likely to grow. And with the CRISPR baby scandal potentially dampening efforts at germline editing via regulations, in-body gene editing will likely receive more attention if Sangamo’s results return positive.

Neuralink and Other Brain-Machine Interfaces
Neuralink is the stuff of sci fi: tiny implanted particles into the brain could link up your biological wetware with silicon hardware and the internet.

But that’s exactly what Elon Musk’s company, founded in 2016, seeks to develop: brain-machine interfaces that could tinker with your neural circuits in an effort to treat diseases or even enhance your abilities.

Last November, Musk broke his silence on the secretive company, suggesting that he may announce something “interesting” in a few months, that’s “better than anyone thinks is possible.”

Musk’s aspiration for achieving symbiosis with artificial intelligence isn’t the driving force for all brain-machine interfaces (BMIs). In the clinics, the main push is to rehabilitate patients—those who suffer from paralysis, memory loss, or other nerve damage.

2019 may be the year that BMIs and neuromodulators cut the cord in the clinics. These devices may finally work autonomously within a malfunctioning brain, applying electrical stimulation only when necessary to reduce side effects without requiring external monitoring. Or they could allow scientists to control brains with light without needing bulky optical fibers.

Cutting the cord is just the first step to fine-tuning neurological treatments—or enhancements—to the tune of your own brain, and 2019 will keep on bringing the music.

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Posted in Human Robots

#434270 AI Will Create Millions More Jobs Than ...

In the past few years, artificial intelligence has advanced so quickly that it now seems hardly a month goes by without a newsworthy AI breakthrough. In areas as wide-ranging as speech translation, medical diagnosis, and gameplay, we have seen computers outperform humans in startling ways.

This has sparked a discussion about how AI will impact employment. Some fear that as AI improves, it will supplant workers, creating an ever-growing pool of unemployable humans who cannot compete economically with machines.

This concern, while understandable, is unfounded. In fact, AI will be the greatest job engine the world has ever seen.

New Technology Isn’t a New Phenomenon
On the one hand, those who predict massive job loss from AI can be excused. It is easier to see existing jobs disrupted by new technology than to envision what new jobs the technology will enable.

But on the other hand, radical technological advances aren’t a new phenomenon. Technology has progressed nonstop for 250 years, and in the US unemployment has stayed between 5 to 10 percent for almost all that time, even when radical new technologies like steam power and electricity came on the scene.

But you don’t have to look back to steam, or even electricity. Just look at the internet. Go back 25 years, well within the memory of today’s pessimistic prognosticators, to 1993. The web browser Mosaic had just been released, and the phrase “surfing the web,” that most mixed of metaphors, was just a few months old.

If someone had asked you what would be the result of connecting a couple billion computers into a giant network with common protocols, you might have predicted that email would cause us to mail fewer letters, and the web might cause us to read fewer newspapers and perhaps even do our shopping online. If you were particularly farsighted, you might have speculated that travel agents and stockbrokers would be adversely affected by this technology. And based on those surmises, you might have thought the internet would destroy jobs.

But now we know what really happened. The obvious changes did occur. But a slew of unexpected changes happened as well. We got thousands of new companies worth trillions of dollars. We bettered the lot of virtually everyone on the planet touched by the technology. Dozens of new careers emerged, from web designer to data scientist to online marketer. The cost of starting a business with worldwide reach plummeted, and the cost of communicating with customers and leads went to nearly zero. Vast storehouses of information were made freely available and used by entrepreneurs around the globe to build new kinds of businesses.

But yes, we mail fewer letters and buy fewer newspapers.

The Rise of Artificial Intelligence
Then along came a new, even bigger technology: artificial intelligence. You hear the same refrain: “It will destroy jobs.”

Consider the ATM. If you had to point to a technology that looked as though it would replace people, the ATM might look like a good bet; it is, after all, an automated teller machine. And yet, there are more tellers now than when ATMs were widely released. How can this be? Simple: ATMs lowered the cost of opening bank branches, and banks responded by opening more, which required hiring more tellers.

In this manner, AI will create millions of jobs that are far beyond our ability to imagine. For instance, AI is becoming adept at language translation—and according to the US Bureau of Labor Statistics, demand for human translators is skyrocketing. Why? If the cost of basic translation drops to nearly zero, the cost of doing business with those who speak other languages falls. Thus, it emboldens companies to do more business overseas, creating more work for human translators. AI may do the simple translations, but humans are needed for the nuanced kind.

In fact, the BLS forecasts faster-than-average job growth in many occupations that AI is expected to impact: accountants, forensic scientists, geological technicians, technical writers, MRI operators, dietitians, financial specialists, web developers, loan officers, medical secretaries, and customer service representatives, to name a very few. These fields will not experience job growth in spite of AI, but through it.

But just as with the internet, the real gains in jobs will come from places where our imaginations cannot yet take us.

Parsing Pessimism
You may recall waking up one morning to the news that “47 percent of jobs will be lost to technology.”

That report by Carl Frey and Michael Osborne is a fine piece of work, but readers and the media distorted their 47 percent number. What the authors actually said is that some functions within 47 percent of jobs will be automated, not that 47 percent of jobs will disappear.

Frey and Osborne go on to rank occupations by “probability of computerization” and give the following jobs a 65 percent or higher probability: social science research assistants, atmospheric and space scientists, and pharmacy aides. So what does this mean? Social science professors will no longer have research assistants? Of course they will. They will just do different things because much of what they do today will be automated.

The intergovernmental Organization for Economic Co-operation and Development released a report of their own in 2016. This report, titled “The Risk of Automation for Jobs in OECD Countries,” applies a different “whole occupations” methodology and puts the share of jobs potentially lost to computerization at nine percent. That is normal churn for the economy.

But what of the skills gap? Will AI eliminate low-skilled workers and create high-skilled job opportunities? The relevant question is whether most people can do a job that’s just a little more complicated than the one they currently have. This is exactly what happened with the industrial revolution; farmers became factory workers, factory workers became factory managers, and so on.

Embracing AI in the Workplace
A January 2018 Accenture report titled “Reworking the Revolution” estimates that new applications of AI combined with human collaboration could boost employment worldwide as much as 10 percent by 2020.

Electricity changed the world, as did mechanical power, as did the assembly line. No one can reasonably claim that we would be better off without those technologies. Each of them bettered our lives, created jobs, and raised wages. AI will be bigger than electricity, bigger than mechanization, bigger than anything that has come before it.

This is how free economies work, and why we have never run out of jobs due to automation. There are not a fixed number of jobs that automation steals one by one, resulting in progressively more unemployment. There are as many jobs in the world as there are buyers and sellers of labor.

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