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#436944 Is Digital Learning Still Second Best?
As Covid-19 continues to spread, the world has gone digital on an unprecedented scale. Tens of thousands of employees are working from home, and huge conferences, like the Google I/O and Apple WWDC software extravaganzas, plan to experiment with digital events.
Universities too are sending students home. This might have meant an extended break from school not too long ago. But no more. As lecture halls go empty, an experiment into digital learning at scale is ramping up. In the US alone, over 100 universities, from Harvard to Duke, are offering online classes to students to keep the semester going.
While digital learning has been improving for some time, Covid-19 may not only tip us further into a more digitally connected reality, but also help us better appreciate its benefits. This is important because historically, digital learning has been viewed as inferior to traditional learning. But that may be changing.
The Inversion
We often think about digital technologies as ways to reach people without access to traditional services—online learning for children who don’t have schools nearby or telemedicine for patients with no access to doctors. And while these solutions have helped millions of people, they’re often viewed as “second best” and “better than nothing.” Even in more resource-rich environments, there’s an assumption one should pay more to attend an event in person—a concert, a football game, an exercise class—while digital equivalents are extremely cheap or free. Why is this? And is the situation about to change?
Take the case of Dr. Sanjeev Arora, a professor of medicine at the University of New Mexico. Arora started Project Echo because he was frustrated by how many late-stage cases of hepatitis C he encountered in rural New Mexico. He realized that if he had reached patients sooner, he could have prevented needless deaths. The solution? Digital learning for local health workers.
Project Echo connects rural healthcare practitioners to specialists at top health centers by video. The approach is collaborative: Specialists share best practices and work through cases with participants to apply them in the real world and learn from edge cases. Added to expert presentations, there are lots of opportunities to ask questions and interact with specialists.
The method forms a digital loop of learning, practice, assessment, and adjustment.
Since 2003, Project Echo has scaled to 800 locations in 39 countries and trained over 90,000 healthcare providers. Most notably, a study in The New England Journal of Medicine found that the outcomes of hepatitis C treatment given by Project Echo trained healthcare workers in rural and underserved areas were similar to outcomes at university medical centers. That is, digital learning in this context was equivalent to high quality in-person learning.
If that is possible today, with simple tools, will they surpass traditional medical centers and schools in the future? Can digital learning more generally follow suit and have the same success? Perhaps. Going digital brings its own special toolset to the table too.
The Benefits of Digital
If you’re training people online, you can record the session to better understand their engagement levels—or even add artificial intelligence to analyze it in real time. Ahura AI, for example, founded by Bryan Talebi, aims to upskill workers through online training. Early study of their method suggests they can significantly speed up learning by analyzing users’ real-time emotions—like frustration or distraction—and adjusting the lesson plan or difficulty on the fly.
Other benefits of digital learning include the near-instantaneous download of course materials—rather than printing and shipping books—and being able to more easily report grades and other results, a requirement for many schools and social services organizations. And of course, as other digitized industries show, digital learning can grow and scale further at much lower costs.
To that last point, 360ed, a digital learning startup founded in 2016 by Hla Hla Win, now serves millions of children in Myanmar with augmented reality lesson plans. And Global Startup Ecosystem, founded by Christine Souffrant Ntim and Einstein Kofi Ntim in 2015, is the world’s first and largest digital accelerator program. Their entirely online programs support over 1,000 companies in 90 countries. It’s astonishing how fast both of these organizations have grown.
Notably, both examples include offline experiences too. Many of the 360ed lesson plans come with paper flashcards children use with their smartphones because the online-offline interaction improves learning. The Global Startup Ecosystem also hosts about 10 additional in-person tech summits around the world on various topics through a related initiative.
Looking further ahead, probably the most important benefit of online learning will be its potential to integrate with other digital systems in the workplace.
Imagine a medical center that has perfect information about every patient and treatment in real time and that this information is (anonymously and privately) centralized, analyzed, and shared with medical centers, research labs, pharmaceutical companies, clinical trials, policy makers, and medical students around the world. Just as self-driving cars can learn to drive better by having access to the experiences of other self-driving cars, so too can any group working to solve complex, time-sensitive challenges learn from and build on each other’s experiences.
Why This Matters
While in the long term the world will likely end up combining the best aspects of traditional and digital learning, it’s important in the near term to be more aware of the assumptions we make about digital technologies. Some of the most pioneering work in education, healthcare, and other industries may not be highly visible right now because it is in a virtual setting. Most people are unaware, for example, that the busiest emergency room in rural America is already virtual.
Once they start converging with other digital technologies, these innovations will likely become the mainstream system for all of us. Which raises more questions: What is the best business model for these virtual services? If they start delivering better healthcare and educational outcomes than traditional institutions, should they charge more? Hopefully, we will see an even bigger shift occurring, in which technology allows us to provide high quality education, healthcare, and other services to everyone at more affordable prices than today.
These are some of the topics we can consider as Covid-19 forces us into uncharted territory.
Image Credit: Andras Vas / Unsplash Continue reading
#436578 AI Just Discovered a New Antibiotic to ...
Penicillin, one of the greatest discoveries in the history of medicine, was a product of chance.
After returning from summer vacation in September 1928, bacteriologist Alexander Fleming found a colony of bacteria he’d left in his London lab had sprouted a fungus. Curiously, wherever the bacteria contacted the fungus, their cell walls broke down and they died. Fleming guessed the fungus was secreting something lethal to the bacteria—and the rest is history.
Fleming’s discovery of penicillin and its later isolation, synthesis, and scaling in the 1940s released a flood of antibiotic discoveries in the next few decades. Bacteria and fungi had been waging an ancient war against each other, and the weapons they’d evolved over eons turned out to be humanity’s best defense against bacterial infection and disease.
In recent decades, however, the flood of new antibiotics has slowed to a trickle.
Their development is uneconomical for drug companies, and the low-hanging fruit has long been picked. We’re now facing the emergence of strains of super bacteria resistant to one or more antibiotics and an aging arsenal to fight them with. Gone unchallenged, an estimated 700,000 deaths worldwide due to drug resistance could rise to as many as 10 million in 2050.
Increasingly, scientists warn the tide is turning, and we need a new strategy to keep pace with the remarkably quick and boundlessly creative tactics of bacterial evolution.
But where the golden age of antibiotics was sparked by serendipity, human intelligence, and natural molecular weapons, its sequel may lean on the uncanny eye of artificial intelligence to screen millions of compounds—and even design new ones—in search of the next penicillin.
Hal Discovers a Powerful Antibiotic
In a paper published this week in the journal, Cell, MIT researchers took a step in this direction. The team says their machine learning algorithm discovered a powerful new antibiotic.
Named for the AI in 2001: A Space Odyssey, the antibiotic, halicin, successfully wiped out dozens of bacterial strains, including some of the most dangerous drug-resistant bacteria on the World Health Organization’s most wanted list. The bacteria also failed to develop resistance to E. coli during a month of observation, in stark contrast to existing antibiotic ciprofloxacin.
“In terms of antibiotic discovery, this is absolutely a first,” Regina Barzilay, a senior author on the study and computer science professor at MIT, told The Guardian.
The algorithm that discovered halicin was trained on the molecular features of 2,500 compounds. Nearly half were FDA-approved drugs, and another 800 naturally occurring. The researchers specifically tuned the algorithm to look for molecules with antibiotic properties but whose structures would differ from existing antibiotics (as halicin’s does). Using another machine learning program, they screened the results for those likely to be safe for humans.
Early study suggests halicin attacks the bacteria’s cell membranes, disrupting their ability to produce energy. Protecting the cell membrane from halicin might take more than one or two genetic mutations, which could account for its impressive ability to prevent resistance.
“I think this is one of the more powerful antibiotics that has been discovered to date,” James Collins, an MIT professor of bioengineering and senior author told The Guardian. “It has remarkable activity against a broad range of antibiotic-resistant pathogens.”
Beyond tests in petri-dish bacterial colonies, the team also tested halicin in mice. The antibiotic cleared up infections of a strain of bacteria resistant to all known antibiotics in a day. The team plans further study in partnership with a pharmaceutical company or nonprofit, and they hope to eventually prove it safe and effective for use in humans.
This last bit remains the trickiest step, given the cost of getting a new drug approved. But Collins hopes algorithms like theirs will help. “We could dramatically reduce the cost required to get through clinical trials,” he told the Financial Times.
A Universe of Drugs Awaits
The bigger story may be what happens next.
How many novel antibiotics await discovery, and how far can AI screening take us? The initial 6,000 compounds scanned by Barzilay and Collins’s team is a drop in the bucket.
They’ve already begun digging deeper by setting the algorithm loose on 100 million molecules from an online library of 1.5 billion compounds called the ZINC15 database. This first search took three days and turned up 23 more candidates that, like halicin, differ structurally from existing antibiotics and may be safe for humans. Two of these—which the team will study further—appear to be especially powerful.
Even more ambitiously, Barzilay hopes the approach can find or even design novel antibiotics that kill bad bacteria with alacrity while sparing the good guys. In this way, a round of antibiotics would cure whatever ails you without taking out your whole gut microbiome in the process.
All this is part of a larger movement to use machine learning algorithms in the long, expensive process of drug discovery. Other players in the area are also training AI on the vast possibility space of drug-like compounds. Last fall, one of the leaders in the area, Insilico, was challenged by a partner to see just how fast their method could do the job. The company turned out a new a proof-of-concept drug candidate in only 46 days.
The field is still developing, however, and it has yet to be seen exactly how valuable these approaches will be in practice. Barzilay is optimistic though.
“There is still a question of whether machine-learning tools are really doing something intelligent in healthcare, and how we can develop them to be workhorses in the pharmaceuticals industry,” she said. “This shows how far you can adapt this tool.”
Image Credit: Halicin (top row) prevented the development of antibiotic resistance in E. coli, while ciprofloxacin (bottom row) did not. Collins Lab at MIT Continue reading
#436470 Retail Robots Are on the Rise—at Every ...
The robots are coming! The robots are coming! On our sidewalks, in our skies, in our every store… Over the next decade, robots will enter the mainstream of retail.
As countless robots work behind the scenes to stock shelves, serve customers, and deliver products to our doorstep, the speed of retail will accelerate.
These changes are already underway. In this blog, we’ll elaborate on how robots are entering the retail ecosystem.
Let’s dive in.
Robot Delivery
On August 3rd, 2016, Domino’s Pizza introduced the Domino’s Robotic Unit, or “DRU” for short. The first home delivery pizza robot, the DRU looks like a cross between R2-D2 and an oversized microwave.
LIDAR and GPS sensors help it navigate, while temperature sensors keep hot food hot and cold food cold. Already, it’s been rolled out in ten countries, including New Zealand, France, and Germany, but its August 2016 debut was critical—as it was the first time we’d seen robotic home delivery.
And it won’t be the last.
A dozen or so different delivery bots are fast entering the market. Starship Technologies, for instance, a startup created by Skype founders Janus Friis and Ahti Heinla, has a general-purpose home delivery robot. Right now, the system is an array of cameras and GPS sensors, but upcoming models will include microphones, speakers, and even the ability—via AI-driven natural language processing—to communicate with customers. Since 2016, Starship has already carried out 50,000 deliveries in over 100 cities across 20 countries.
Along similar lines, Nuro—co-founded by Jiajun Zhu, one of the engineers who helped develop Google’s self-driving car—has a miniature self-driving car of its own. Half the size of a sedan, the Nuro looks like a toaster on wheels, except with a mission. This toaster has been designed to carry cargo—about 12 bags of groceries (version 2.0 will carry 20)—which it’s been doing for select Kroger stores since 2018. Domino’s also partnered with Nuro in 2019.
As these delivery bots take to our streets, others are streaking across the sky.
Back in 2016, Amazon came first, announcing Prime Air—the e-commerce giant’s promise of drone delivery in 30 minutes or less. Almost immediately, companies ranging from 7-Eleven and Walmart to Google and Alibaba jumped on the bandwagon.
While critics remain doubtful, the head of the FAA’s drone integration department recently said that drone deliveries may be “a lot closer than […] the skeptics think. [Companies are] getting ready for full-blown operations. We’re processing their applications. I would like to move as quickly as I can.”
In-Store Robots
While delivery bots start to spare us trips to the store, those who prefer shopping the old-fashioned way—i.e., in person—also have plenty of human-robot interaction in store. In fact, these robotics solutions have been around for a while.
In 2010, SoftBank introduced Pepper, a humanoid robot capable of understanding human emotion. Pepper is cute: 4 feet tall, with a white plastic body, two black eyes, a dark slash of a mouth, and a base shaped like a mermaid’s tail. Across her chest is a touch screen to aid in communication. And there’s been a lot of communication. Pepper’s cuteness is intentional, as it matches its mission: help humans enjoy life as much as possible.
Over 12,000 Peppers have been sold. She serves ice cream in Japan, greets diners at a Pizza Hut in Singapore, and dances with customers at a Palo Alto electronics store. More importantly, Pepper’s got company.
Walmart uses shelf-stocking robots for inventory control. Best Buy uses a robo-cashier, allowing select locations to operate 24-7. And Lowe’s Home Improvement employs the LoweBot—a giant iPad on wheels—to help customers find the items they need while tracking inventory along the way.
Warehouse Bots
Yet the biggest benefit robots provide might be in-warehouse logistics.
In 2012, when Amazon dished out $775 million for Kiva Systems, few could predict that just 6 years later, 45,000 Kiva robots would be deployed at all of their fulfillment centers, helping process a whopping 306 items per second during the Christmas season.
And many other retailers are following suit.
Order jeans from the Gap, and soon they’ll be sorted, packed, and shipped with the help of a Kindred robot. Remember the old arcade game where you picked up teddy bears with a giant claw? That’s Kindred, only her claw picks up T-shirts, pants, and the like, placing them in designated drop-off zones that resemble tiny mailboxes (for further sorting or shipping).
The big deal here is democratization. Kindred’s robot is cheap and easy to deploy, allowing smaller companies to compete with giants like Amazon.
Final Thoughts
For retailers interested in staying in business, there doesn’t appear to be much choice in the way of robotics.
By 2024, the US minimum wage is projected to be $15 an hour (the House of Representatives has already passed the bill, but the wage hike is meant to unfold gradually between now and 2025), and many consider that number far too low.
Yet, as human labor costs continue to climb, robots won’t just be coming, they’ll be here, there, and everywhere. It’s going to become increasingly difficult for store owners to justify human workers who call in sick, show up late, and can easily get injured. Robots work 24-7. They never take a day off, never need a bathroom break, health insurance, or parental leave.
Going forward, this spells a growing challenge of technological unemployment (a blog topic I will cover in the coming month). But in retail, robotics usher in tremendous benefits for companies and customers alike.
And while professional re-tooling initiatives and the transition of human capital from retail logistics to a booming experience economy take hold, robotic retail interaction and last-mile delivery will fundamentally transform our relationship with commerce.
This blog comes from The Future is Faster Than You Think—my upcoming book, to be released Jan 28th, 2020. To get an early copy and access up to $800 worth of pre-launch giveaways, sign up here!
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