Tag Archives: hand

#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.

Image Credit: angellodeco / Shutterstock.com Continue reading

Posted in Human Robots

#434324 Big Brother Nation: The Case for ...

Powerful surveillance cameras have crept into public spaces. We are filmed and photographed hundreds of times a day. To further raise the stakes, the resulting video footage is fed to new forms of artificial intelligence software that can recognize faces in real time, read license plates, even instantly detect when a particular pre-defined action or activity takes place in front of a camera.

As most modern cities have quietly become surveillance cities, the law has been slow to catch up. While we wait for robust legal frameworks to emerge, the best way to protect our civil liberties right now is to fight technology with technology. All cities should place local surveillance video into a public cloud-based data trust. Here’s how it would work.

In Public Data We Trust
To democratize surveillance, every city should implement three simple rules. First, anyone who aims a camera at public space must upload that day’s haul of raw video file (and associated camera meta-data) into a cloud-based repository. Second, this cloud-based repository must have open APIs and a publicly-accessible log file that records search histories and tracks who has accessed which video files. And third, everyone in the city should be given the same level of access rights to the stored video data—no exceptions.

This kind of public data repository is called a “data trust.” Public data trusts are not just wishful thinking. Different types of trusts are already in successful use in Estonia and Barcelona, and have been proposed as the best way to store and manage the urban data that will be generated by Alphabet’s planned Sidewalk Labs project in Toronto.

It’s true that few people relish the thought of public video footage of themselves being looked at by strangers and friends, by ex-spouses, potential employers, divorce attorneys, and future romantic prospects. In fact, when I propose this notion when I give talks about smart cities, most people recoil in horror. Some turn red in the face and jeer at my naiveté. Others merely blink quietly in consternation.

The reason we should take this giant step towards extreme transparency is to combat the secrecy that surrounds surveillance. Openness is a powerful antidote to oppression. Edward Snowden summed it up well when he said, “Surveillance is not about public safety, it’s about power. It’s about control.”

Let Us Watch Those Watching Us
If public surveillance video were put back into the hands of the people, citizens could watch their government as it watches them. Right now, government cameras are controlled by the state. Camera locations are kept secret, and only the agencies that control the cameras get to see the footage they generate.

Because of these information asymmetries, civilians have no insight into the size and shape of the modern urban surveillance infrastructure that surrounds us, nor the uses (or abuses) of the video footage it spawns. For example, there is no swift and efficient mechanism to request a copy of video footage from the cameras that dot our downtown. Nor can we ask our city’s police force to show us a map that documents local traffic camera locations.

By exposing all public surveillance videos to the public gaze, cities could give regular people tools to assess the size, shape, and density of their local surveillance infrastructure and neighborhood “digital dragnet.” Using the meta-data that’s wrapped around video footage, citizens could geo-locate individual cameras onto a digital map to generate surveillance “heat maps.” This way people could assess whether their city’s camera density was higher in certain zip codes, or in neighborhoods populated by a dominant ethnic group.

Surveillance heat maps could be used to document which government agencies were refusing to upload their video files, or which neighborhoods were not under surveillance. Given what we already know today about the correlation between camera density, income, and social status, these “dark” camera-free regions would likely be those located near government agencies and in more affluent parts of a city.

Extreme transparency would democratize surveillance. Every city’s data trust would keep a publicly-accessible log of who’s searching for what, and whom. People could use their local data trust’s search history to check whether anyone was searching for their name, face, or license plate. As a result, clandestine spying on—and stalking of—particular individuals would become difficult to hide and simpler to prove.

Protect the Vulnerable and Exonerate the Falsely Accused
Not all surveillance video automatically works against the underdog. As the bungled (and consequently no longer secret) assassination of journalist Jamal Khashoggi demonstrated, one of the unexpected upsides of surveillance cameras has been the fact that even kings become accountable for their crimes. If opened up to the public, surveillance cameras could serve as witnesses to justice.

Video evidence has the power to protect vulnerable individuals and social groups by shedding light onto messy, unreliable (and frequently conflicting) human narratives of who did what to whom, and why. With access to a data trust, a person falsely accused of a crime could prove their innocence. By searching for their own face in video footage or downloading time/date stamped footage from a particular camera, a potential suspect could document their physical absence from the scene of a crime—no lengthy police investigation or high-priced attorney needed.

Given Enough Eyeballs, All Crimes Are Shallow
Placing public surveillance video into a public trust could make cities safer and would streamline routine police work. Linus Torvalds, the developer of open-source operating system Linux, famously observed that “given enough eyeballs, all bugs are shallow.” In the case of public cameras and a common data repository, Torvald’s Law could be restated as “given enough eyeballs, all crimes are shallow.”

If thousands of citizen eyeballs were given access to a city’s public surveillance videos, local police forces could crowdsource the work of solving crimes and searching for missing persons. Unfortunately, at the present time, cities are unable to wring any social benefit from video footage of public spaces. The most formidable barrier is not government-imposed secrecy, but the fact that as cameras and computers have grown cheaper, a large and fast-growing “mom and pop” surveillance state has taken over most of the filming of public spaces.

While we fear spooky government surveillance, the reality is that we’re much more likely to be filmed by security cameras owned by shopkeepers, landlords, medical offices, hotels, homeowners, and schools. These businesses, organizations, and individuals install cameras in public areas for practical reasons—to reduce their insurance costs, to prevent lawsuits, or to combat shoplifting. In the absence of regulations governing their use, private camera owners store video footage in a wide variety of locations, for varying retention periods.

The unfortunate (and unintended) result of this informal and decentralized network of public surveillance is that video files are not easy to access, even for police officers on official business. After a crime or terrorist attack occurs, local police (or attorneys armed with a subpoena) go from door to door to manually collect video evidence. Once they have the videos in hand, their next challenge is searching for the right “codex” to crack the dozens of different file formats they encounter so they can watch and analyze the footage.

The result of these practical barriers is that as it stands today, only people with considerable legal or political clout are able to successfully gain access into a city’s privately-owned, ad-hoc collections of public surveillance videos. Not only are cities missing the opportunity to streamline routine evidence-gathering police work, they’re missing a radically transformative benefit that would become possible once video footage from thousands of different security cameras were pooled into a single repository: the ability to apply the power of citizen eyeballs to the work of improving public safety.

Why We Need Extreme Transparency
When regular people can’t access their own surveillance videos, there can be no data justice. While we wait for the law to catch up with the reality of modern urban life, citizens and city governments should use technology to address the problem that lies at the heart of surveillance: a power imbalance between those who control the cameras and those who don’t.

Cities should permit individuals and organizations to install and deploy as many public-facing cameras as they wish, but with the mandate that camera owners must place all resulting video footage into the mercilessly bright sunshine of an open data trust. This way, cloud computing, open APIs, and artificial intelligence software can help combat abuses of surveillance and give citizens insight into who’s filming us, where, and why.

Image Credit: VladFotoMag / Shutterstock.com 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.

Image Credit: jamesteohart / Shutterstock.com Continue reading

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.

Image Credit: enzozo / Shutterstock.com Continue reading

Posted in Human Robots

#434260 The Most Surprising Tech Breakthroughs ...

Development across the entire information technology landscape certainly didn’t slow down this year. From CRISPR babies, to the rapid decline of the crypto markets, to a new robot on Mars, and discovery of subatomic particles that could change modern physics as we know it, there was no shortage of headline-grabbing breakthroughs and discoveries.

As 2018 comes to a close, we can pause and reflect on some of the biggest technology breakthroughs and scientific discoveries that occurred this year.

I reached out to a few Singularity University speakers and faculty across the various technology domains we cover asking what they thought the biggest breakthrough was in their area of expertise. The question posed was:

“What, in your opinion, was the biggest development in your area of focus this year? Or, what was the breakthrough you were most surprised by in 2018?”

I can share that for me, hands down, the most surprising development I came across in 2018 was learning that a publicly-traded company that was briefly valued at over $1 billion, and has over 12,000 employees and contractors spread around the world, has no physical office space and the entire business is run and operated from inside an online virtual world. This is Ready Player One stuff happening now.

For the rest, here’s what our experts had to say.

DIGITAL BIOLOGY
Dr. Tiffany Vora | Faculty Director and Vice Chair, Digital Biology and Medicine, Singularity University

“That’s easy: CRISPR babies. I knew it was technically possible, and I’ve spent two years predicting it would happen first in China. I knew it was just a matter of time but I failed to predict the lack of oversight, the dubious consent process, the paucity of publicly-available data, and the targeting of a disease that we already know how to prevent and treat and that the children were at low risk of anyway.

I’m not convinced that this counts as a technical breakthrough, since one of the girls probably isn’t immune to HIV, but it sure was a surprise.”

For more, read Dr. Vora’s summary of this recent stunning news from China regarding CRISPR-editing human embryos.

QUANTUM COMPUTING
Andrew Fursman | Co-Founder/CEO 1Qbit, Faculty, Quantum Computing, Singularity University

“There were two last-minute holiday season surprise quantum computing funding and technology breakthroughs:

First, right before the government shutdown, one priority legislative accomplishment will provide $1.2 billion in quantum computing research over the next five years. Second, there’s the rise of ions as a truly viable, scalable quantum computing architecture.”

*Read this Gizmodo profile on an exciting startup in the space to learn more about this type of quantum computing

ENERGY
Ramez Naam | Chair, Energy and Environmental Systems, Singularity University

“2018 had plenty of energy surprises. In solar, we saw unsubsidized prices in the sunny parts of the world at just over two cents per kwh, or less than half the price of new coal or gas electricity. In the US southwest and Texas, new solar is also now cheaper than new coal or gas. But even more shockingly, in Germany, which is one of the least sunny countries on earth (it gets less sunlight than Canada) the average bid for new solar in a 2018 auction was less than 5 US cents per kwh. That’s as cheap as new natural gas in the US, and far cheaper than coal, gas, or any other new electricity source in most of Europe.

In fact, it’s now cheaper in some parts of the world to build new solar or wind than to run existing coal plants. Think tank Carbon Tracker calculates that, over the next 10 years, it will become cheaper to build new wind or solar than to operate coal power in most of the world, including specifically the US, most of Europe, and—most importantly—India and the world’s dominant burner of coal, China.

Here comes the sun.”

GLOBAL GRAND CHALLENGES
Darlene Damm | Vice Chair, Faculty, Global Grand Challenges, Singularity University

“In 2018 we saw a lot of areas in the Global Grand Challenges move forward—advancements in robotic farming technology and cultured meat, low-cost 3D printed housing, more sophisticated types of online education expanding to every corner of the world, and governments creating new policies to deal with the ethics of the digital world. These were the areas we were watching and had predicted there would be change.

What most surprised me was to see young people, especially teenagers, start to harness technology in powerful ways and use it as a platform to make their voices heard and drive meaningful change in the world. In 2018 we saw teenagers speak out on a number of issues related to their well-being and launch digital movements around issues such as gun and school safety, global warming and environmental issues. We often talk about the harm technology can cause to young people, but on the flip side, it can be a very powerful tool for youth to start changing the world today and something I hope we see more of in the future.”

BUSINESS STRATEGY
Pascal Finette | Chair, Entrepreneurship and Open Innovation, Singularity University

“Without a doubt the rapid and massive adoption of AI, specifically deep learning, across industries, sectors, and organizations. What was a curiosity for most companies at the beginning of the year has quickly made its way into the boardroom and leadership meetings, and all the way down into the innovation and IT department’s agenda. You are hard-pressed to find a mid- to large-sized company today that is not experimenting or implementing AI in various aspects of its business.

On the slightly snarkier side of answering this question: The very rapid decline in interest in blockchain (and cryptocurrencies). The blockchain party was short, ferocious, and ended earlier than most would have anticipated, with a huge hangover for some. The good news—with the hot air dissipated, we can now focus on exploring the unique use cases where blockchain does indeed offer real advantages over centralized approaches.”

*Author note: snark is welcome and appreciated

ROBOTICS
Hod Lipson | Director, Creative Machines Lab, Columbia University

“The biggest surprise for me this year in robotics was learning dexterity. For decades, roboticists have been trying to understand and imitate dexterous manipulation. We humans seem to be able to manipulate objects with our fingers with incredible ease—imagine sifting through a bunch of keys in the dark, or tossing and catching a cube. And while there has been much progress in machine perception, dexterous manipulation remained elusive.

There seemed to be something almost magical in how we humans can physically manipulate the physical world around us. Decades of research in grasping and manipulation, and millions of dollars spent on robot-hand hardware development, has brought us little progress. But in late 2018, the Berkley OpenAI group demonstrated that this hurdle may finally succumb to machine learning as well. Given 200 years worth of practice, machines learned to manipulate a physical object with amazing fluidity. This might be the beginning of a new age for dexterous robotics.”

MACHINE LEARNING
Jeremy Howard | Founding Researcher, fast.ai, Founder/CEO, Enlitic, Faculty Data Science, Singularity University

“The biggest development in machine learning this year has been the development of effective natural language processing (NLP).

The New York Times published an article last month titled “Finally, a Machine That Can Finish Your Sentence,” which argued that NLP neural networks have reached a significant milestone in capability and speed of development. The “finishing your sentence” capability mentioned in the title refers to a type of neural network called a “language model,” which is literally a model that learns how to finish your sentences.

Earlier this year, two systems (one, called ELMO, is from the Allen Institute for AI, and the other, called ULMFiT, was developed by me and Sebastian Ruder) showed that such a model could be fine-tuned to dramatically improve the state-of-the-art in nearly every NLP task that researchers study. This work was further developed by OpenAI, which in turn was greatly scaled up by Google Brain, who created a system called BERT which reached human-level performance on some of NLP’s toughest challenges.

Over the next year, expect to see fine-tuned language models used for everything from understanding medical texts to building disruptive social media troll armies.”

DIGITAL MANUFACTURING
Andre Wegner | Founder/CEO Authentise, Chair, Digital Manufacturing, Singularity University

“Most surprising to me was the extent and speed at which the industry finally opened up.

While previously, only few 3D printing suppliers had APIs and knew what to do with them, 2018 saw nearly every OEM (or original equipment manufacturer) enabling data access and, even more surprisingly, shying away from proprietary standards and adopting MTConnect, as stalwarts such as 3D Systems and Stratasys have been. This means that in two to three years, data access to machines will be easy, commonplace, and free. The value will be in what is being done with that data.

Another example of this openness are the seemingly endless announcements of integrated workflows: GE’s announcement with most major software players to enable integrated solutions, EOS’s announcement with Siemens, and many more. It’s clear that all actors in the additive ecosystem have taken a step forward in terms of openness. The result is a faster pace of innovation, particularly in the software and data domains that are crucial to enabling comprehensive digital workflow to drive agile and resilient manufacturing.

I’m more optimistic we’ll achieve that now than I was at the end of 2017.”

SCIENCE AND DISCOVERY
Paul Saffo | Chair, Future Studies, Singularity University, Distinguished Visiting Scholar, Stanford Media-X Research Network

“The most important development in technology this year isn’t a technology, but rather the astonishing science surprises made possible by recent technology innovations. My short list includes the discovery of the “neptmoon”, a Neptune-scale moon circling a Jupiter-scale planet 8,000 lightyears from us; the successful deployment of the Mars InSight Lander a month ago; and the tantalizing ANITA detection (what could be a new subatomic particle which would in turn blow the standard model wide open). The highest use of invention is to support science discovery, because those discoveries in turn lead us to the future innovations that will improve the state of the world—and fire up our imaginations.”

ROBOTICS
Pablos Holman | Inventor, Hacker, Faculty, Singularity University

“Just five or ten years ago, if you’d asked any of us technologists “What is harder for robots? Eyes, or fingers?” We’d have all said eyes. Robots have extraordinary eyes now, but even in a surgical robot, the fingers are numb and don’t feel anything. Stanford robotics researchers have invented fingertips that can feel, and this will be a kingpin that allows robots to go everywhere they haven’t been yet.”

BLOCKCHAIN
Nathana Sharma | Blockchain, Policy, Law, and Ethics, Faculty, Singularity University

“2017 was the year of peak blockchain hype. 2018 has been a year of resetting expectations and technological development, even as the broader cryptocurrency markets have faced a winter. It’s now about seeing adoption and applications that people want and need to use rise. An incredible piece of news from December 2018 is that Facebook is developing a cryptocurrency for users to make payments through Whatsapp. That’s surprisingly fast mainstream adoption of this new technology, and indicates how powerful it is.”

ARTIFICIAL INTELLIGENCE
Neil Jacobstein | Chair, Artificial Intelligence and Robotics, Singularity University

“I think one of the most visible improvements in AI was illustrated by the Boston Dynamics Parkour video. This was not due to an improvement in brushless motors, accelerometers, or gears. It was due to improvements in AI algorithms and training data. To be fair, the video released was cherry-picked from numerous attempts, many of which ended with a crash. However, the fact that it could be accomplished at all in 2018 was a real win for both AI and robotics.”

NEUROSCIENCE
Divya Chander | Chair, Neuroscience, Singularity University

“2018 ushered in a new era of exponential trends in non-invasive brain modulation. Changing behavior or restoring function takes on a new meaning when invasive interfaces are no longer needed to manipulate neural circuitry. The end of 2018 saw two amazing announcements: the ability to grow neural organoids (mini-brains) in a dish from neural stem cells that started expressing electrical activity, mimicking the brain function of premature babies, and the first (known) application of CRISPR to genetically alter two fetuses grown through IVF. Although this was ostensibly to provide genetic resilience against HIV infections, imagine what would happen if we started tinkering with neural circuitry and intelligence.”

Image Credit: Yurchanka Siarhei / Shutterstock.com Continue reading

Posted in Human Robots