Tag Archives: Market

#434637 AI Is Rapidly Augmenting Healthcare and ...

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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#434585 This Week’s Awesome Stories From ...

ARTIFICIAL INTELLIGENCE
The World’s Fastest Supercomputer Breaks an AI Record
Tom Simonite | Wired
“Summit, which occupies an area equivalent to two tennis courts, used more than 27,000 powerful graphics processors in the project. It tapped their power to train deep-learning algorithms, the technology driving AI’s frontier, chewing through the exercise at a rate of a billion billion operations per second, a pace known in supercomputing circles as an exaflop.”

ROBOTICS
iRobot Finally Announces Awesome New Terra Robotic Lawnmower
Evan Ackerman | IEEE Spectrum
“Since the first Roomba came out in 2002, it has seemed inevitable that one day iRobot would develop a robotic lawn mower. After all, a robot mower is basically just a Roomba that works outside, right? Of course, it’s not nearly that simple, as iRobot has spent the last decade or so discovering, but they’ve finally managed to pull it off.”

3D Printing
Watch This Super Speedy 3D Printer Make Objects Suddenly Appear
Erin Winick | MIT Technology Review
“The new machine—which the team nicknamed the ‘replicator’ after the machine from Star Trek—instead forms the entire item all in one go. It does this by shining light onto specific spots in a rotating resin that solidifies when exposed to a certain light level.”

GENETICS
The DIY Designer Baby Project Funded With Bitcoin
Antonio Regalado | MIT Technology Review
“i‘Is DIY bio anywhere close to making a CRISPR baby? No, not remotely,’ David Ishee says. ‘But if some rich guy pays a scientist to do the work, it’s going to happen.’ He adds: ‘What you are reporting on isn’t Bryan—it’s the unseen middle space, a layer of gray-market biotech and freelance science where people with resources can get things done.’i”

SCIENCE
The Complete Cancer Cure Story Is Both Bogus and Tragic
Megan Molteni | Wired
“You’d think creators and consumers of news would have learned their lesson by now. But the latest version of the fake cancer cure story is even more flagrantly flawed than usual. The public’s cancer cure–shaped amnesia, and media outlets’ willingness to exploit it for clicks, are as bottomless as ever. Hope, it would seem, trumps history.”

BOOKS
An AI Reading List—From Practical Primers to Sci-Fi Short Stories
James Vincent | The Verge
“The Verge has assembled a reading list: a brief but diverse compendium of books, short stories, and blogs, all chosen by leading figures in the AI world to help you better understand artificial intelligence.”

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Posted in Human Robots

#434569 From Parkour to Surgery, Here Are the ...

The robot revolution may not be here quite yet, but our mechanical cousins have made some serious strides. And now some of the leading experts in the field have provided a rundown of what they see as the 10 most exciting recent developments.

Compiled by the editors of the journal Science Robotics, the list includes some of the most impressive original research and innovative commercial products to make a splash in 2018, as well as a couple from 2017 that really changed the game.

1. Boston Dynamics’ Atlas doing parkour

It seems like barely a few months go by without Boston Dynamics rewriting the book on what a robot can and can’t do. Last year they really outdid themselves when they got their Atlas humanoid robot to do parkour, leaping over logs and jumping between wooden crates.

Atlas’s creators have admitted that the videos we see are cherry-picked from multiple attempts, many of which don’t go so well. But they say they’re meant to be inspirational and aspirational rather than an accurate picture of where robotics is today. And combined with the company’s dog-like Spot robot, they are certainly pushing boundaries.

2. Intuitive Surgical’s da Vinci SP platform
Robotic surgery isn’t new, but the technology is improving rapidly. Market leader Intuitive’s da Vinci surgical robot was first cleared by the FDA in 2000, but since then it’s come a long way, with the company now producing three separate systems.

The latest addition is the da Vinci SP (single port) system, which is able to insert three instruments into the body through a single 2.5cm cannula (tube) bringing a whole new meaning to minimally invasive surgery. The system was granted FDA clearance for urological procedures last year, and the company has now started shipping the new system to customers.

3. Soft robot that navigates through growth

Roboticists have long borrowed principles from the animal kingdom, but a new robot design that mimics the way plant tendrils and fungi mycelium move by growing at the tip has really broken the mold on robot navigation.

The editors point out that this is the perfect example of bio-inspired design; the researchers didn’t simply copy nature, they took a general principle and expanded on it. The tube-like robot unfolds from the front as pneumatic pressure is applied, but unlike a plant, it can grow at the speed of an animal walking and can navigate using visual feedback from a camera.

4. 3D printed liquid crystal elastomers for soft robotics
Soft robotics is one of the fastest-growing sub-disciplines in the field, but powering these devices without rigid motors or pumps is an ongoing challenge. A variety of shape-shifting materials have been proposed as potential artificial muscles, including liquid crystal elastomeric actuators.

Harvard engineers have now demonstrated that these materials can be 3D printed using a special ink that allows the designer to easily program in all kinds of unusual shape-shifting abilities. What’s more, their technique produces actuators capable of lifting significantly more weight than previous approaches.

5. Muscle-mimetic, self-healing, and hydraulically amplified actuators
In another effort to find a way to power soft robots, last year researchers at the University of Colorado Boulder designed a series of super low-cost artificial muscles that can lift 200 times their own weight and even heal themselves.

The devices rely on pouches filled with a liquid that makes them contract with the force and speed of mammalian skeletal muscles when a voltage is applied. The most promising for robotics applications is the so-called Peano-HASEL, which features multiple rectangular pouches connected in series that contract linearly, just like real muscle.

6. Self-assembled nanoscale robot from DNA

While you may think of robots as hulking metallic machines, a substantial number of scientists are working on making nanoscale robots out of DNA. And last year German researchers built the first remote-controlled DNA robotic arm.

They created a length of tightly-bound DNA molecules to act as the arm and attached it to a DNA base plate via a flexible joint. Because DNA carries a charge, they were able to get the arm to swivel around like the hand of a clock by applying a voltage and switch direction by reversing that voltage. The hope is that this arm could eventually be used to build materials piece by piece at the nanoscale.

7. DelFly nimble bioinspired robotic flapper

Robotics doesn’t only borrow from biology—sometimes it gives back to it, too. And a new flapping-winged robot designed by Dutch engineers that mimics the humble fruit fly has done just that, by revealing how the animals that inspired it carry out predator-dodging maneuvers.

The lab has been building flapping robots for years, but this time they ditched the airplane-like tail used to control previous incarnations. Instead, they used insect-inspired adjustments to the motions of its twin pairs of flapping wings to hover, pitch, and roll with the agility of a fruit fly. That has provided a useful platform for investigating insect flight dynamics, as well as more practical applications.

8. Soft exosuit wearable robot

Exoskeletons could prevent workplace injuries, help people walk again, and even boost soldiers’ endurance. Strapping on bulky equipment isn’t ideal, though, so researchers at Harvard are working on a soft exoskeleton that combines specially-designed textiles, sensors, and lightweight actuators.

And last year the team made an important breakthrough by combining their novel exoskeleton with a machine-learning algorithm that automatically tunes the device to the user’s particular walking style. Using physiological data, it is able to adjust when and where the device needs to deliver a boost to the user’s natural movements to improve walking efficiency.

9. Universal Robots (UR) e-Series Cobots
Robots in factories are nothing new. The enormous mechanical arms you see in car factories normally have to be kept in cages to prevent them from accidentally crushing people. In recent years there’s been growing interest in “co-bots,” collaborative robots designed to work side-by-side with their human colleagues and even learn from them.

Earlier this year saw the demise of ReThink robotics, the pioneer of the approach. But the simple single arm devices made by Danish firm Universal Robotics are becoming ubiquitous in workshops and warehouses around the world, accounting for about half of global co-bot sales. Last year they released their latest e-Series, with enhanced safety features and force/torque sensing.

10. Sony’s aibo
After a nearly 20-year hiatus, Sony’s robotic dog aibo is back, and it’s had some serious upgrades. As well as a revamp to its appearance, the new robotic pet takes advantage of advances in AI, with improved environmental and command awareness and the ability to develop a unique character based on interactions with its owner.

The editors note that this new context awareness mark the device out as a significant evolution in social robots, which many hope could aid in childhood learning or provide companionship for the elderly.

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#434336 These Smart Seafaring Robots Have a ...

Drones. Self-driving cars. Flying robo taxis. If the headlines of the last few years are to be believed, terrestrial transportation in the future will someday be filled with robotic conveyances and contraptions that will require little input from a human other than to download an app.

But what about the other 70 percent of the planet’s surface—the part that’s made up of water?

Sure, there are underwater drones that can capture 4K video for the next BBC documentary. Remotely operated vehicles (ROVs) are capable of diving down thousands of meters to investigate ocean vents or repair industrial infrastructure.

Yet most of the robots on or below the water today still lean heavily on the human element to operate. That’s not surprising given the unstructured environment of the seas and the poor communication capabilities for anything moving below the waves. Autonomous underwater vehicles (AUVs) are probably the closest thing today to smart cars in the ocean, but they generally follow pre-programmed instructions.

A new generation of seafaring robots—leveraging artificial intelligence, machine vision, and advanced sensors, among other technologies—are beginning to plunge into the ocean depths. Here are some of the latest and most exciting ones.

The Transformer of the Sea
Nic Radford, chief technology officer of Houston Mechatronics Inc. (HMI), is hesitant about throwing around the word “autonomy” when talking about his startup’s star creation, Aquanaut. He prefers the term “shared control.”

Whatever you want to call it, Aquanaut seems like something out of the script of a Transformers movie. The underwater robot begins each mission in a submarine-like shape, capable of autonomously traveling up to 200 kilometers on battery power, depending on the assignment.

When Aquanaut reaches its destination—oil and gas is the primary industry HMI hopes to disrupt to start—its four specially-designed and built linear actuators go to work. Aquanaut then unfolds into a robot with a head, upper torso, and two manipulator arms, all while maintaining proper buoyancy to get its job done.

The lightbulb moment of how to engineer this transformation from submarine to robot came one day while Aquanaut’s engineers were watching the office’s stand-up desks bob up and down. The answer to the engineering challenge of the hull suddenly seemed obvious.

“We’re just gonna build a big, gigantic, underwater stand-up desk,” Radford told Singularity Hub.

Hardware wasn’t the only problem the team, comprised of veteran NASA roboticists like Radford, had to solve. In order to ditch the expensive support vessels and large teams of humans required to operate traditional ROVs, Aquanaut would have to be able to sense its environment in great detail and relay that information back to headquarters using an underwater acoustics communications system that harkens back to the days of dial-up internet connections.

To tackle that problem of low bandwidth, HMI equipped Aquanaut with a machine vision system comprised of acoustic, optical, and laser-based sensors. All of that dense data is compressed using in-house designed technology and transmitted to a single human operator who controls Aquanaut with a few clicks of a mouse. In other words, no joystick required.

“I don’t know of anyone trying to do this level of autonomy as it relates to interacting with the environment,” Radford said.

HMI got $20 million earlier this year in Series B funding co-led by Transocean, one of the world’s largest offshore drilling contractors. That should be enough money to finish the Aquanaut prototype, which Radford said is about 99.8 percent complete. Some “high-profile” demonstrations are planned for early next year, with commercial deployments as early as 2020.

“What just gives us an incredible advantage here is that we have been born and bred on doing robotic systems for remote locations,” Radford noted. “This is my life, and I’ve bet the farm on it, and it takes this kind of fortitude and passion to see these things through, because these are not easy problems to solve.”

On Cruise Control
Meanwhile, a Boston-based startup is trying to solve the problem of making ships at sea autonomous. Sea Machines is backed by about $12.5 million in capital venture funding, with Toyota AI joining the list of investors in a $10 million Series A earlier this month.

Sea Machines is looking to the self-driving industry for inspiration, developing what it calls “vessel intelligence” systems that can be retrofitted on existing commercial vessels or installed on newly-built working ships.

For instance, the startup announced a deal earlier this year with Maersk, the world’s largest container shipping company, to deploy a system of artificial intelligence, computer vision, and LiDAR on the Danish company’s new ice-class container ship. The technology works similar to advanced driver-assistance systems found in automobiles to avoid hazards. The proof of concept will lay the foundation for a future autonomous collision avoidance system.

It’s not just startups making a splash in autonomous shipping. Radford noted that Rolls Royce—yes, that Rolls Royce—is leading the way in the development of autonomous ships. Its Intelligence Awareness system pulls in nearly every type of hyped technology on the market today: neural networks, augmented reality, virtual reality, and LiDAR.

In augmented reality mode, for example, a live feed video from the ship’s sensors can detect both static and moving objects, overlaying the scene with details about the types of vessels in the area, as well as their distance, heading, and other pertinent data.

While safety is a primary motivation for vessel automation—more than 1,100 ships have been lost over the past decade—these new technologies could make ships more efficient and less expensive to operate, according to a story in Wired about the Rolls Royce Intelligence Awareness system.

Sea Hunt Meets Science
As Singularity Hub noted in a previous article, ocean robots can also play a critical role in saving the seas from environmental threats. One poster child that has emerged—or, invaded—is the spindly lionfish.

A venomous critter endemic to the Indo-Pacific region, the lionfish is now found up and down the east coast of North America and beyond. And it is voracious, eating up to 30 times its own stomach volume and reducing juvenile reef fish populations by nearly 90 percent in as little as five weeks, according to the Ocean Support Foundation.

That has made the colorful but deadly fish Public Enemy No. 1 for many marine conservationists. Both researchers and startups are developing autonomous robots to hunt down the invasive predator.

At the Worcester Polytechnic Institute, for example, students are building a spear-carrying robot that uses machine learning and computer vision to distinguish lionfish from other aquatic species. The students trained the algorithms on thousands of different images of lionfish. The result: a lionfish-killing machine that boasts an accuracy of greater than 95 percent.

Meanwhile, a small startup called the American Marine Research Corporation out of Pensacola, Florida is applying similar technology to seek and destroy lionfish. Rather than spearfishing, the AMRC drone would stun and capture the lionfish, turning a profit by selling the creatures to local seafood restaurants.

Lionfish: It’s what’s for dinner.

Water Bots
A new wave of smart, independent robots are diving, swimming, and cruising across the ocean and its deepest depths. These autonomous systems aren’t necessarily designed to replace humans, but to venture where we can’t go or to improve safety at sea. And, perhaps, these latest innovations may inspire the robots that will someday plumb the depths of watery planets far from Earth.

Image Credit: Houston Mechatronics, Inc. Continue reading

Posted in Human Robots

#434303 Making Superhumans Through Radical ...

Imagine trying to read War and Peace one letter at a time. The thought alone feels excruciating. But in many ways, this painful idea holds parallels to how human-machine interfaces (HMI) force us to interact with and process data today.

Designed back in the 1970s at Xerox PARC and later refined during the 1980s by Apple, today’s HMI was originally conceived during fundamentally different times, and specifically, before people and machines were generating so much data. Fast forward to 2019, when humans are estimated to produce 44 zettabytes of data—equal to two stacks of books from here to Pluto—and we are still using the same HMI from the 1970s.

These dated interfaces are not equipped to handle today’s exponential rise in data, which has been ushered in by the rapid dematerialization of many physical products into computers and software.

Breakthroughs in perceptual and cognitive computing, especially machine learning algorithms, are enabling technology to process vast volumes of data, and in doing so, they are dramatically amplifying our brain’s abilities. Yet even with these powerful technologies that at times make us feel superhuman, the interfaces are still crippled with poor ergonomics.

Many interfaces are still designed around the concept that human interaction with technology is secondary, not instantaneous. This means that any time someone uses technology, they are inevitably multitasking, because they must simultaneously perform a task and operate the technology.

If our aim, however, is to create technology that truly extends and amplifies our mental abilities so that we can offload important tasks, the technology that helps us must not also overwhelm us in the process. We must reimagine interfaces to work in coherence with how our minds function in the world so that our brains and these tools can work together seamlessly.

Embodied Cognition
Most technology is designed to serve either the mind or the body. It is a problematic divide, because our brains use our entire body to process the world around us. Said differently, our minds and bodies do not operate distinctly. Our minds are embodied.

Studies using MRI scans have shown that when a person feels an emotion in their gut, blood actually moves to that area of the body. The body and the mind are linked in this way, sharing information back and forth continuously.

Current technology presents data to the brain differently from how the brain processes data. Our brains, for example, use sensory data to continually encode and decipher patterns within the neocortex. Our brains do not create a linguistic label for each item, which is how the majority of machine learning systems operate, nor do our brains have an image associated with each of these labels.

Our bodies move information through us instantaneously, in a sense “computing” at the speed of thought. What if our technology could do the same?

Using Cognitive Ergonomics to Design Better Interfaces
Well-designed physical tools, as philosopher Martin Heidegger once meditated on while using the metaphor of a hammer, seem to disappear into the “hand.” They are designed to amplify a human ability and not get in the way during the process.

The aim of physical ergonomics is to understand the mechanical movement of the human body and then adapt a physical system to amplify the human output in accordance. By understanding the movement of the body, physical ergonomics enables ergonomically sound physical affordances—or conditions—so that the mechanical movement of the body and the mechanical movement of the machine can work together harmoniously.

Cognitive ergonomics applied to HMI design uses this same idea of amplifying output, but rather than focusing on physical output, the focus is on mental output. By understanding the raw materials the brain uses to comprehend information and form an output, cognitive ergonomics allows technologists and designers to create technological affordances so that the brain can work seamlessly with interfaces and remove the interruption costs of our current devices. In doing so, the technology itself “disappears,” and a person’s interaction with technology becomes fluid and primary.

By leveraging cognitive ergonomics in HMI design, we can create a generation of interfaces that can process and present data the same way humans process real-world information, meaning through fully-sensory interfaces.

Several brain-machine interfaces are already on the path to achieving this. AlterEgo, a wearable device developed by MIT researchers, uses electrodes to detect and understand nonverbal prompts, which enables the device to read the user’s mind and act as an extension of the user’s cognition.

Another notable example is the BrainGate neural device, created by researchers at Stanford University. Just two months ago, a study was released showing that this brain implant system allowed paralyzed patients to navigate an Android tablet with their thoughts alone.

These are two extraordinary examples of what is possible for the future of HMI, but there is still a long way to go to bring cognitive ergonomics front and center in interface design.

Disruptive Innovation Happens When You Step Outside Your Existing Users
Most of today’s interfaces are designed by a narrow population, made up predominantly of white, non-disabled men who are prolific in the use of technology (you may recall The New York Times viral article from 2016, Artificial Intelligence’s White Guy Problem). If you ask this population if there is a problem with today’s HMIs, most will say no, and this is because the technology has been designed to serve them.

This lack of diversity means a limited perspective is being brought to interface design, which is problematic if we want HMI to evolve and work seamlessly with the brain. To use cognitive ergonomics in interface design, we must first gain a more holistic understanding of how people with different abilities understand the world and how they interact with technology.

Underserved groups, such as people with physical disabilities, operate on what Clayton Christensen coined in The Innovator’s Dilemma as the fringe segment of a market. Developing solutions that cater to fringe groups can in fact disrupt the larger market by opening a downward, much larger market.

Learning From Underserved Populations
When technology fails to serve a group of people, that group must adapt the technology to meet their needs.

The workarounds created are often ingenious, specifically because they have not been arrived at by preferences, but out of necessity that has forced disadvantaged users to approach the technology from a very different vantage point.

When a designer or technologist begins learning from this new viewpoint and understanding challenges through a different lens, they can bring new perspectives to design—perspectives that otherwise can go unseen.

Designers and technologists can also learn from people with physical disabilities who interact with the world by leveraging other senses that help them compensate for one they may lack. For example, some blind people use echolocation to detect objects in their environments.

The BrainPort device developed by Wicab is an incredible example of technology leveraging one human sense to serve or compliment another. The BrainPort device captures environmental information with a wearable video camera and converts this data into soft electrical stimulation sequences that are sent to a device on the user’s tongue—the most sensitive touch receptor in the body. The user learns how to interpret the patterns felt on their tongue, and in doing so, become able to “see” with their tongue.

Key to the future of HMI design is learning how different user groups navigate the world through senses beyond sight. To make cognitive ergonomics work, we must understand how to leverage the senses so we’re not always solely relying on our visual or verbal interactions.

Radical Inclusion for the Future of HMI
Bringing radical inclusion into HMI design is about gaining a broader lens on technology design at large, so that technology can serve everyone better.

Interestingly, cognitive ergonomics and radical inclusion go hand in hand. We can’t design our interfaces with cognitive ergonomics without bringing radical inclusion into the picture, and we also will not arrive at radical inclusion in technology so long as cognitive ergonomics are not considered.

This new mindset is the only way to usher in an era of technology design that amplifies the collective human ability to create a more inclusive future for all.

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Posted in Human Robots