In this week’s Sunday NYT Magazine, a discussion was recorded about the future of technology. One of my favorite writers, Sid Mukerjee, discussed chronic disease. In that discussion, he touched on a notion of immortality that I have been pondering for some time.
Here is what he said, and after is what I say in response.
MUKHERJEE: “In terms of longevity, the diseases that are most likely to kill us are neurological diseases and heart disease and cancer. In some other countries, there is tuberculosis and malaria and other infectious diseases, but here it’s the chronic diseases that dominate. There are three ways to think about these chronic diseases. One is the disease-specific way. So, you attack Alzheimer’s as Alzheimer’s; you attack cancer as cancer. The second one is that you forget about the disease-specific manners of attacking diseases and you attack longevity or aging reversal in general. You change diet, change genes, change whatever else — we might call them “trans factors,” which would simply override the “cis factors” that existed for individual diseases. And the third option is some combination of that and some digital form of immortality, which is that you record yourself forever, that you clone yourself and somehow pass along that recording. Which is to say that the body is just a repository of memories, images, times. And as a repository, there’s nothing special about it. The body per se, the mortal coil, is just a coil.”
This is the first time I have heard a major thinker put immortality into this context. And yet – its so obvious to do so!
– wouldn’t it be fair to say that every autobiography ever written would be a sincere attempt by the writer to achieve some form of immortality?
– in like manner, isn’t the task of the biographer, in part, to immortalize their subject?
– more broadly, how do societies around the world remember their ancestors? Their memories are their attempts to allow ancestors to live forever!
This point is nicely illustrated by the Irish culture. In my work on the History of Ireland, the centrality of “oral tradition” was crystal clear. I came continually across how the Irish told stories to revere their ancestors. The Irish would distill their ancestors into a wide variety of stories that helped the present generation understand the past.
So, by extrapolation from this point (which is obvious), can this be asked: “Can I be immortalized digitally?
Digital storage costs have plummeted. Methods of organizing and tagging video and audio recordings are now commonplace. Search engines are commonplace. Pattern recognition combined with search is exploding.
So what will prevent me in the future from immortalizing myself digitally? What prevents me from storing who I am, what I did, what I learned, where I have been, what I have experienced, who I knew, who my ancestors were, who my children and grandchildren were, etc etc?
Perhaps the answer is: nothing. Nothing prevents me from being digitally immortal.
We Need A Better Language for Climate Change – that Acts as a Call to Action
Below is as essay that makes the case for a new six-box classification system for global climate change – two columns and three rows. The core idea here is to move climate change out of a subject for the editorial page and into a subject for daily new – much like how storms, earthquakes and epidemics are covered. We want a language that serves as a “call-to-action”.
The news would inform the world about climate-change related occurrences that have impacts that are “major”, “disaster”, or “global disaster”, and that are either “incidents” (one-time) or “recurring”.
I worked this out with Karen . I am the scribe. Obviously, this is DRAFT 1.
Climate Change Language
CREDIT: Karen Flanders-Reid
Karen and I read today’s NYT article about California wildfires, and found ourselves musing – is the language of climate change right? Why is a “wildfire” just an isolated incident? Why isn’t it part of a larger wildfire classification system (“BREAKING NEWS: THE CALIFORNIA WILDFIRE HAS JUST BEEN RECLASSIFIED AS CATEGORY V.”?
We went on to ask: if climate change is the critical issue of our day, why Why isn’t the wildfire in California an climate change incident – part of a larger climate change classification system?
Why do the NYT editorial writers have to scream – everything is related to climate change!!!! After all, news breaks when a Hurricane is re-classified: “BREAKING NEWS: THE TROPICAL STORM OVER CUBA HAS JUST BEEN RE-CLASSIFIED BY THE WEATHER SERVICE AS A HURRICANE.”
Why doesn’t climate change have its own global classification system? How do we move from the editorial opinion desk to the news desk? How do we move from “The science is being ignored.” To “BREAKING NEWS: THE WILDFIRES IN CALIFORNIA HAVE JUST BEEN RECLASSIFIED BY THE WEATHER SERVICE FROM A CLIMATE-RELATED INCIDENT (CRI) TO A CLIMATE-RELATED DISASTER (CRD).”
EXAMPLES OF POWERFUL GLOBAL CLASSIFICATION SYSTEMS
To identify a powerful classification system, and the new language it implies, it first would be useful to identify the other global classification systems that exist – especially those with imply a call to action.
There are at least four:
Storms; Classified by the World Meteorological Organization (WMO), using the Saffir–Simpson scale:
Hurricane/Cyclone Categories 1-5
Earthquakes: Classified by the US Geological Service, using the Richter Scale:
Moderate (above 8)
Infectious Disease; Classified by the global centers for disease control, the classes are:
Outbreak (more incident than expected)
Epidemic (spreads rapidly to many people)
Pandemic (spreads rapidly to many people globally)
A NEW GLOBAL CLASSIFICATION SYSTEM FOR CLIMATE CHANGE
We recommend s simple structure, with easily understood terms, that evolves over time:
Starts with a few terms, and adds terms over time.
Begins classifying major occurrences only, and evolves to classify most occurrences.
Begins classifying evidence-based occurrences only (where science is conclusive that the occurrence is climate-change-related) and evolves as science becomes increasingly conclusive.
“Occurrence” – a natural phenomena that occurs somewhere
“Climate-Change-Related” (CR) – a shorthand for saying that the preponderance of science indicates that a given occurrence is a contributor to or the result of climate change.
“Incident” (I) – an episodic occurrence (with a beginning, middle, and end)
“Recurring” (R) – an on-going occurrence (no end in sight)
“Major” (M) – an occurrence with sufficient size to merit being classified.
“Disaster” (D) – an occurrence, with major impacts
“Global Disaster” (G) – an occurrence with major global impacts
Initial Classification System:
Climate-related Occurrences shall be identified.
Once identified, they shall be classified in one of six classes:
Either “incidents” or “recurring”.
Either “major”, “disaster”, or “global disaster”
“Climate-Change-Related Event” (CRE) – any occurrence that is deemed to be a contributor to climate-change.
“Climate-Change-Related Outcome” (CRO) – any occurrence that is deemed to be the result of to climate-change.
All major climate-change-related occurrences would be classified as follows:
CR Incident (CRE-I): An episodic event, with a beginning, a middle, and an end.
CR Disaster (CRE-D): An episodic event, with global impacts
The Weather Service would be tasked with implementation, and aligning with the World Meteorological Organization (WMO) and other agencies around the world.
The quantified self movement strikes again!
Smartwatch Technology Reliably Detects Afib Prior to Cardioversion
Study suggests a role for KardiaBand when paired with physician review
A newly FDA-approved smartwatch accessory can record heart rhythm and successfully differentiate atrial fibrillation (AF) from normal sinus rhythm (SR) through an automated algorithm, according to a Cleveland Clinic investigation. The study, which will be presented March 11 at the American College of Cardiology’s 67th Scientific Session, also showed that the accuracy of interpretation gets even better when the accessory is supported by physician review.
The findings suggest that the wearable technology, known as KardiaBand™, can help screen patients before presentation for elective cardioversion to avoid unnecessary procedures, among other potential uses.
KardiaBand, which consists of a software app for an Apple Watch® and a sensor band that replaces one of the watch’s straps, provides a 30-second recording of an ECG rhythm strip when the wearer places a thumb on the sensor band. The app contains an algorithm for automated detection of AF.
“Our objective was to determine how accurately KardiaBand and its algorithm can differentiate AF from sinus rhythm compared with physician-interpreted 12-lead ECGs,” says senior author Khaldoun Tarakji, MD, MPH, a Cleveland Clinic electrophysiologist. In November 2017, the device became the first smartwatch healthcare accessory to be approved by the FDA, “but we wanted to test it ourselves to determine how well it would perform in clinical practice,” Dr. Tarakji explains.
To that end, he and Cleveland Clinic colleagues prospectively enrolled 100 consecutive patients (mean age, 68 ± 11 years) with chronic AF who were scheduled to undergo cardioversion. Upon presenting for the cardioversion procedure, all patients were given a KardiaBand-equipped smartwatch and trained in its use, after which they underwent traditional ECG assessment and a 30-second KardiaBand recording. If cardioversion was still indicated, they underwent ECG and KardiaBand testing after the procedure. KardiaBand recordings were then compared with the physician-reviewed ECGs and also reviewed by two blinded electrophysiologists, with these readings compared to ECG interpretations.
Eight patients did not undergo cardioversion because they presented in SR; these patients were excluded. Among the remaining patients, a total of 169 pairs of ECG and KardiaBand recordings were available for comparison (each patient had two before and two after cardioversion).
• Of the 169 pre-cardioversion KardiaBand recordings, 57 fell out as “unclassified,” meaning that the KardiaBand algorithm did not draw a conclusion of either AF or SR.
• Among the remaining 112 pairs of recordings, the reviewing electrophysiologists determined that the KardiaBand algorithm correctly detected AF with 93 percent sensitivity and 84 percent specificity compared with ECG.
• When the blinded reviewers bypassed the automated algorithm and interpreted each patient’s KardiaBand strips against his or her ECG, sensitivity rose to 99 percent and specificity was 83 percent. Further, in the 57 unclassified cases, the reviewers were able to use the strips to correctly diagnose AF versus SR with 100 percent sensitivity and 80 percent specificity.
“This study shows that KardiaBand provides excellent sensitivity and good specificity in identifying AF,” says Dr. Tarakji. “The numbers improve further with physician overview of these recordings, indicating that even unclassified KardiaBand strip recordings could be of value to reading physicians.”
Smart devices demand smart use
KardiaBand carries the benefit of enabling patients to record their rhythm at any time, as opposed to only when they are wearing a Holter monitor or at a physician’s office. “We can catch intermittent episodes when they happen, and we’re not limited to a specific duration of monitoring time,” Dr. Tarakji says. He adds that wearable devices like this can also reduce time spent responding to false alarms if a recording taken at the same time shows normal rhythm.
Yet many questions remain about how KardiaBand and similar products may ultimately be used in practice. Dr. Tarakji cites a few examples:
• Which patients are best suited to this technology? For many patients dealing with AF, KardiaBand can provide reassurance when they need it. But for others, having constant access to their ECG data may lead them to check their rhythm obsessively, raising anxiety. “In general, however, patients value the instant feedback they get,” Dr. Tarakji observes.
• Do physicians have the IT infrastructure in place to make these devices part of their practice? Wearable devices can mean a flood of event reports to clinicians’ email boxes. At Cleveland Clinic, information from patients’ KardiaBands bypasses the email system and feeds into a cloud-computing platform that physicians can access anytime.
• How should clinicians respond to short episodes, particularly in asymptomatic patients? “We currently have a gap in our clinical knowledge about whether brief, random episodes that are asymptomatic warrant anticoagulation or not,” Dr. Tarakji explains, adding that ongoing studies are trying to address this important question.
“Future studies will focus on how we can use these smart devices intelligently to make sure they’re improving quality of care rather than just producing noise for physicians,” he observes.
A parallel goal, he says, is to ensure that the devices provide value by making care delivery more efficient. Noting that patients currently need to pay for KardiaBand out of pocket, Dr. Tarakji says that “developing a richer body of research evidence is the best way we can demonstrate cost-effectiveness to healthcare payers.”
Tech like this can’t be ignored
Indeed, KardiaBand could prove cost-effective by allowing patients who are in SR to avoid needless trips for elective procedures, such as in the case of the eight patients in the study who were found to be in SR when they presented for cardioversion and did not require the procedure. Other potential uses of KardiaBand for the longitudinal management of AF patients could well prove cost-effective too.
Regardless of how quickly such cost-effectiveness evidence may come, Dr. Tarakji says clinicians cannot be passive in the face of technologies like KardiaBand. “Patients will come to us with new products, and we can’t turn away,” he observes. “We need to test these products and find ways of responding to the information they deliver in a way that improves patient outcomes, all while remaining mindful of both patient and physician satisfaction.”
The researchers report that KardiaBand’s manufacturer, AliveCor, provided smartwatches for the study but was not involved in the study’s design, implementation, data analysis or interpretation.
With 1.2 million employees, Amazon, Berkshire Hathaway, and JP Morgan have decided to venture together into health care for their employees.
Following in the grand tradition of Henry Ford, who set up Henry Ford Hospital in Detroit, these three giants are stepping in too.
They have no illusions about how difficult it will be. But with premiums rising 19% per year, its clear that Congress is doing nothing, and someone has to do something.
“Planning for the new company is being led by Marvelle Sullivan Berchtold, a JPMorgan managing director who was previously head of the Swiss drugmaker Novartis’s mergers and acquisitions strategy; Mr. Combs; and Beth Galetti, a senior vice president at Amazon.”
The article points out that there are others working on this.
“Robert Andrews, chief executive of the Healthcare Transformation Alliance, a group of 46 companies, including Coca-Cola and American Express, that have banded together to lower health care costs.”
“Walmart contracted with groups like the Cleveland Clinic, Mayo and Geisinger, among others, to take care of employees who need organ transplants and heart and spine care.”
“Caterpillar, the construction equipment manufacturer, sets its own rules for drug coverage, which it has said saves it millions of dollars per year, even though it still uses a pharmacy benefit manager to process its claims.”
Suzanne Delbanco, the executive director for the Catalyst for Payment Reform, a nonprofit group that mainly represents employers”
Amazon, Berkshire Hathaway and JPMorgan Team Up to Try to Disrupt Health Care
By NICK WINGFIELD, KATIE THOMAS and REED ABELSON
JAN. 30, 2018
SEATTLE — Three corporate behemoths — Amazon, Berkshire Hathaway and JPMorgan Chase — announced on Tuesday that they would form an independent health care company for their employees in the United States.
The alliance was a sign of just how frustrated American businesses are with the state of the nation’s health care system and the rapidly spiraling cost of medical treatment. It also caused further turmoil in an industry reeling from attempts by new players to attack a notoriously inefficient, intractable web of doctors, hospitals, insurers and pharmaceutical companies.
It was unclear how extensively the three partners would overhaul their employees’ existing health coverage — whether they would simply help workers find a local doctor, steer employees to online medical advice or use their muscle to negotiate lower prices for drugs and procedures. While the alliance will apply only to their employees, these corporations are so closely watched that whatever successes they have could become models for other businesses.
Major employers, from Walmart to Caterpillar, have tried for years to tackle the high costs and complexity of health care, and have grown increasingly frustrated as Congress has deadlocked over the issue, leaving many of the thorniest issues to private industry. About 151 million Americans get their health insurance from an employer.
(Why will health care be so difficult for these companies to untangle? Analysis from The Upshot.)
But Tuesday’s announcement landed like a thunderclap — sending stocks for insurers and other major health companies tumbling. Shares of health care companies like UnitedHealth Group and Anthem plunged on Tuesday, dragging down the broader stock market.
That weakness reflects the strength of the new entrants. The partnership brings together Amazon, the online retail giant known for disrupting major industries; Berkshire Hathaway, the holding company led by the billionaire investor Warren E. Buffett; and JPMorgan Chase, the largest bank in the United States by assets.
They are moving into an industry where the lines between traditionally distinct areas, such as pharmacies, insurers and providers, are increasingly blurry. CVS Health’s deal last month to buy the health insurer Aetna for about $69 billion is just one example of the changes underway. Separately, Amazon’s potential entry into the pharmacy business continues to rattle major drug companies and distributors.
(Here’s a look at how the even the threat of Amazon’s entry into an industry can rattle stocks.)
The companies said the initiative, which is in its early stages, would be “free from profit-making incentives and constraints,” but did not specify whether that meant they would create a nonprofit organization. The tax implications were also unclear because so few details were released.
Jamie Dimon, the chief executive of JPMorgan Chase, said in a statement that the effort could eventually be expanded to benefit all Americans.
“The health care system is complex, and we enter into this challenge open-eyed about the degree of difficulty,” Jeff Bezos, Amazon’s founder and chief executive, said in a statement. “Hard as it might be, reducing health care’s burden on the economy while improving outcomes for employees and their families would be worth the effort.”
The announcement touched off a wave of speculation about what the new company might do, especially given Amazon’s extensive reach into the daily lives of Americans — from where they buy their paper towels to what they watch on television. It follows speculation that the company, which recently purchased the grocery chain Whole Foods, might use its stores as locations for pharmacies or clinics.
(We asked health care experts to imagine what the three corporations might do.)
“It could be big,” Ed Kaplan, who negotiates health coverage on behalf of large employers as the national health practice leader for the Segal Group, said of the announcement. “Those are three big players, and I think if they get into health care insurance or the health care coverage space, they are going to make a big impact.”
TAKING ON ‘THE HUNGRY TAPEWORM’
A look at the three companies that announced a joint health care initiative on Tuesday.
Total employees: 1.2 million
Berkshire Hathaway: 367,000
JPMorgan Chase: 252,000.
Individual strengths Amazon: logistics and technology Berkshire Hathaway: insurance JPMorgan Chase: finance.
Jeff Bezos of Amazon:
“The healthcare system is complex, and we enter into this challenge open-eyed about the degree of difficulty.”
Warren E. Buffett of Berkshire Hathaway: “The ballooning costs of healthcare act as a hungry tapeworm on the American economy. Our group does not come to this problem with answers. But we also do not accept it as inevitable.”
Jamie Dimon of JPMorgan Chase: “The three of our companies have extraordinary resources, and our goal is to create solutions that benefit our U.S. employees, their families and, potentially, all Americans.”
But others were less sure, noting that the three companies — which, combined, employ more than one million people — might still hold little sway over the largest insurers and pharmacy benefit managers, who oversee the benefits of tens of millions of Americans.
“This is not news in terms of jumbo employers being frustrated with what they can get through the traditional system,” said Sam Glick of the management consulting firm Oliver Wyman in San Francisco. He played down the notion that the three partners would have more success getting lower prices from hospitals and doctors. “The idea that they could have any sort of negotiation leverage with unit cost is a pretty far stretch.”
Even the three companies don’t seem to be sure of how to shake up health care. People briefed on the plan, who asked for anonymity because the discussions were private, said the executives decided to announce the initiative while still a concept in part so they can begin hiring staff for the new company.
Three people familiar with the partnership said it took shape as Mr. Bezos, Mr. Buffett, and Mr. Dimon, who are friends, discussed the challenges of providing insurance to their employees. They decided their combined access to data about how consumers make choices, along with an understanding of the intricacies of health insurance, would inevitably lead to some kind of new efficiency — whatever it might turn out to be.
“The ballooning costs of health care act as a hungry tapeworm on the American economy,” Mr. Buffett said in the statement. “Our group does not come to this problem with answers. But we also do not accept it as inevitable.”
Over the past several months, the three had met formally — along with Todd Combs, an investment officer at Berkshire Hathaway who is also on JPMorgan’s board — to discuss the idea, according to a person familiar with Mr. Buffett’s thinking.
The three chief executives saw one another at the Alfalfa Club dinner in Washington on Saturday, but by then each had already had dozens of conversations with the small in-house teams they had assembled. The plan was set.
Mr. Buffett’s motivation stems in part from conversations he has had with two people close to him who have been diagnosed with multiple sclerosis, according to the person. Mr. Buffett, the person said, believes the condition of the country’s health care system is a root cause of economic inequality, with wealthier people enjoying better, longer lives because they can afford good coverage As Mr. Buffett himself has aged — he is 87 — the contrast between his moneyed friends and others has grown starker, the person said.
The companies said they would initially focus on using technology to simplify care, but did not elaborate on how they intended to do that or bring down costs. One of the people briefed on the alliance said the new company wouldn’t replace existing health insurers or hospitals.
Planning for the new company is being led by Marvelle Sullivan Berchtold, a JPMorgan managing director who was previously head of the Swiss drugmaker Novartis’s mergers and acquisitions strategy; Mr. Combs; and Beth Galetti, a senior vice president at Amazon.
One potential avenue for the partnership might be an online health care dashboard that connects employees with the closest and best doctor specializing in whatever ailment they select from a drop-down menu. Perhaps the companies would strike deals to offer employee discounts with service providers like medical testing facilities.
“Each of those companies has extensive experience using transformative technology in their own businesses,” said John Sculley, the former chief executive of Apple who is now chairman of a health care start-up, RxAdvance. “I think it’s a great counterweight to what government leadership hasn’t done, which is to focus on how do we make this health care system sustainable.”
How Amazon Rattles Other Companies
The e-commerce giant’s actions – some big, like buying Whole Foods Markets; some smaller, like Amazon meal kits – have led to stock sell-offs for a wide range of businesses.
Erik Gordon, a professor at the University of Michigan’s Ross School of Business, predicted that the companies would attempt to modernize the cumbersome process of doctor appointments by making it more like booking a restaurant reservation on OpenTable, while eliminating the need to regularly fill out paper forms on clipboards.
“I think they will bring the customer-facing, patient-facing thing into your smartphone,” he said.
Amazon has long been mentioned by health care analysts and industry executives as a potential new player in the sector. While the company has remained quiet about its plans, some analysts noted that companies often use their own employees as a testing ground for future initiatives.
The entry of Amazon and its partners adds to the upheaval in an industry where much is changing, from government programs after the overhaul of the tax law to the uncertain future of the Affordable Care Act. All the while, medical costs have persistently been on the rise.
Nationwide, average premiums for family coverage for employees rose to $18,764 last year, an increase of 19 percent since 2012, according to the Kaiser Family Foundation. Workers are increasingly paying a greater share of those costs — they now pay 30 percent of the premium, in addition to high deductibles and growing co-payments.
“Our members’ balance sheets speak for themselves — health care is a growing cost at a time when other costs are either not rising or falling,” said Robert Andrews, chief executive of the Healthcare Transformation Alliance, a group of 46 companies, including Coca-Cola and American Express, that have banded together to lower health care costs.
Other major employers have also sought more direct control over their employees’ health care. Walmart contracted with groups like the Cleveland Clinic, Mayo and Geisinger, among others, to take care of employees who need organ transplants and heart and spine care. Caterpillar, the construction equipment manufacturer, sets its own rules for drug coverage, which it has said saves it millions of dollars per year, even though it still uses a pharmacy benefit manager to process its claims.
Suzanne Delbanco, the executive director for the Catalyst for Payment Reform, a nonprofit group that mainly represents employers, said controlling rising prices is especially hard in markets where a local hospital or medical group dominates. While some have tried to tackle the issue in different ways, like sending employees with heart conditions to a specific group, “it’s piecemeal,” she said.
She added, “There are so many opportunities to do this better.”
The issue is not solely a 21st-century concern: In 1915, Henry Ford became increasingly worried about the quality of health care available to his growing work force in Detroit, so he opened the Henry Ford Hospital. It is still in existence today.
Nick Wingfield reported from Seattle, Katie Thomas from Chicago and Reed Abelson from San Francisco. Michael J. de la Merced contributed reporting from London, and Emily Flitter from New York.
A version of this article appears in print on January 31, 2018, on Page A1 of the New York edition with the headline: 3 Giants Form Health Alliance, Rocking Insurers. Order Reprints| Today’s Paper|Subscribe
Note: This post is a continuation of prior posts on complex, adaptive systems. This post focusses on the virtual workplace, the virtual retailer, the virtual employer, and their myriad manifestations in today’s world. These particular complex, adaptive systems will have the ability to rapidly expand or contract based on demand. And this is the point of this post: to explore the notion of “on demand”.
Its so obvious …. but, then again, its not so obvious: “on demand” is the drumbeat of daily life. But the 21st century is putting the notion of “on demand” on steroids!
What is “on demand”?
I want a glass of wine, right now. I either pour myself one, buy one, or ask someone else to pour me one. “On demand”.
I need a hotel room, right now. Hotels inventory rooms. I rent one. “On demand”.
I need to haircut, right now. Barbers are open for business. I visit one. They are not busy so they take me. I buy the haircut. “On demand”.
Note that “on demand” wine needs an open bottle of wine to be available; the hotel room requires a hotel; the haircut requires a barber open for business;
In the 21st century, it seems clear to me that “on demand” will morph into smaller, more flexible slices. Consumers and companies will be able to purchase these slices when they want them, for as long as they want them.
It’s happening at lightning speed! There are so many examples. You can find them everywhere, in:
On Demand Transportation
The point: in the 20th century, you had to rent a car or bike or ride for a day from a business location, and now you can rent it for exactly as long as you want it from a street location.
Uber revolutionized the taxi business when they broke the paradigm and said “Effectively immediately, and car with driver can pick up a passenger and get paid to take them somewhere.” From a passenger’s POV, the result is revolutionary: I can get anywhere I want, anytime I want, by simply alerting a central intelligence on-line that I need ride from x to y at z time.
Every major city now rents bikes. Grab a bike at one stand and leave it at another stand. Take the bike from x to y at z time.
ZipCars are on-demand cars.
On Demand Work
The point: in the 20th century, you either had a job or you didn’t. Now you can have a job for a half hour of your choosing. “Temporary Help” agencies filled any gaps – when the job-holder was unable to work.
LiveOps revolutionized call center management by organizing workers to be available when the client wants the worker, for as long as the client wants the worker. They keep workers trained and on–the-ready, so they can deploy them virtually as needed.
On Demand Work Space
The point: In the 20th century, you worked someplace and employers employed workers in workplaces. Today workplaces are built for flexibility, so many employers can do their work with employees when they need the workplace and how they need the workplace for as long as they need the workplace.
Metro Studios and others are replacing the Hollywood “studio” with a flexible studio. Studios in the past built spaces for their filming needs. Metro Studios works with any client that will rent their massive spaces – for as long as the client needs the space, and not longer. Note that the “Studio” is a big box, easily repurposed to a warehouse or distribution center or big box store if demand shifts.
“Co-working” is exploding, and has revolutionized office work. A co-working space can be sized up or down as demand requires, for as long as demand requires. Co-working can suit the individual virtual worker, who can come in as they wish and stay as long as they wish. But, importantly, more and more companies are using co-working facilities in order to have flex space that suits them.
Self-storage is exploding, giving companies and consumers the ability to get storage space when they want it, for as long as they want it.
On Demand Housing
The point: Hotels, long term rentals and short term rentals will have their place in tomorrow’s economy but ordinary people with extra space in their houses will make places available when, where, and however long they are needed.
AirB&B and VRBO have revolutionized the way we access temporary housing. Go on line, check out who’s offering what, and when, and then make your selection.
On Demand Entertainment
The point: entertainment was made available at a certain time, at a certain place (a concert venue, a movie theater, a movie channel, or a TV channel). No longer. Increasingly, consumers will get what they want, when they want it, for as long as they want it.
Netflix revolutionized on demand movies by letting consumer get what they want when they want it. They started with on-line movie rentals that requires physical shipping of CD’s, but rapidly moved to on-line downloads and streaming. Amazon is chasing them, but with amazing speed.
Cable companies are perfecting “on demand” movies. Select the movie you want and when you want to view it (including immediately), and press “play”.
Amazon is perfecting “on demand” books. Select the book you want, and how you want to read it, and press “buy”. Download and start reading right now. No shipping. No library schlepping.
On Demand Tools
The point: In the 20th century, the norm was “if you want a tool, buy it and put it in a safe place until you need it. The norm is changing to “when you need a tool, order it up for as when you need it, for as long as you need it.”
Home Depot and Loews both have lucrative side businesses that allow businesses and consumers to rent the exact tool they need for as long as they need it.
On Demand Medicine
The point: in the 20th century, when you needed a doctor, you would call the office and make an appointment. If it was urgent, you would beg for the appointment to be sooner rather than later. We are not yet at an inflection point, but the trends seem clear enough: if you need a doctor, you can get a doctor – when you need it, and through the medium that makes the most sense to you.
CVS and Walgreen’s both are perfecting the mini-clinic. Modeled after the convenience store revolution of the 1960’s, mini-clinics are inside the store, and require a sign-up sheet, and that’s all. If the doctor is available, they will see you.
Telemedicine is taking full advantage of Skype and other two-way video conference platforms. In the best case, a patient’s blood work, vital signs, and medical history can be on-line while the patient is online, so the doctor can have as much context as possible. And when the doctor also has a genetic history, in the future a myriad of risks that cannot be currently understood will be known.
Other examples of “On Demand”
On Demand Meals Fast food showed the way to drive-throughs; Then Domino’s showed the way to pizza “on demand” – when you want it, how you want it. Yesterday, this was delivered to my house: a spaghetti made out of squash, in a coconut curry, with a fresh salad and lasagna for the kids. Costs a bit more, but so worth it!
On Demand Internet There are no good examples at the present time, but isn’t it plausible that the average consumer could summon ultra-high-speed internet “on-demand”? The consumer is just fine most of the day with low-speed internet, for emails and searches, etc. But if they want to watch a movie, an want to avoid slow downloads, or breaks in streaming quality, then they are happy to pay for “express lane service”.
On Demand Inventory This is old news, but further illustrates the mega trend: procurement can now demand that materials and components contracted and scheduled arrive when and as needed, minimizing inventory carrying costs.
On-demand Event Space
There has always been a demand for highly flexible event space. This is the world of clubs, hotels, etc, where it is usual to build a big box in your space that can be outfitted to a client’s needs. Today, though, that has been professionalized through companies like Convene, who specializes in this business.
========================== APPENDIX ==========================
On-Demand Work Articles and Commentary
The New York Times article below refers to a mega-trend: on-demand work. The author refers to it as a “tectonic shift in how jobs are structured“.
The focus of the article is Liveops, but this is only illustrative of this larger trend. https://www.liveops.com. Their competitor is https://workingsolutions.com .
On their front page, LiveOps says: “It’s a highly skilled workforce of virtual agents who flex to meet customer needs.”
On-demand work is exploding in customer service call centers and sales.
Some points I found interesting:
Roughly 3,000,000 Americans find work this way – as independent contractors working on a virtual basis.
Since 2001, apparently the move to outsource call centers to India has reversed. Today, the focus is on quality, and so the new trend is toward employing American workers, on a contract basis, and on a virtual basis.
They are only paid while on the phone. This is roughly 75% of the time they “commit” (“commits” are made in half hour blocks)
Top performers get the first call. “Performance-Based Routing, so the top-performing agents on your programs get more calls. By aligning our agents’ incentives with your goals, each agent who answers the call will be invested in your business objectives. What’s more, you won’t be paying call centers for idle time.”
Clients hire LiveOp. In this article, TruStage Insurance is the client.
Liveops CEO is Greg Hanover. Their competitor, Working Solutions, was founded in 1996. LiveOps says they have 20,000 agents, which they refer to as “Liveops Nation”.
“We hand-pick our agents for their great phone voices and warm and friendly personalities.”
“Scalable and flexible contract center outsourcing – leveraging an on-demand distributed network.”
Plugging Into the Gig Economy, From Home With a Headset
A company called Liveops has become the
Uber of call centers by doting on its agents.
But is the work liberating, or dehumanizing?
By NOAM SCHEIBERNOV. 11, 2017
DURHAM, N.C. — The gathering in a private dining room at a Mexican restaurant had the fervent energy of a megachurch service, or maybe an above-average “Oprah” episode — a mix of revival-style confession and extravagant empathy. There were souls to be won.
“By the end of the day, Kelly’s going to be an agent,” the group’s square-jawed leader said. “Kelly went through the process a while ago, then life happens, now she’s back. Her commitment to me that she made earlier, she looked me right in the eyes and told me she’s going to be an agent.”
Paradise, for these pilgrims, lies at one end of a phone line.
The company behind this spectacle, Liveops, had invited several dozen freelance call-center agents to a so-called road show. Some of them may have answered your customer-service calls to Home Depot or AAA. All were among the more than 100,000 agents who work as independent contractors through on-demand platforms like the one Liveops operates, which uses big data, algorithms and gamelike techniques to match its agents to clients. What Uber is to cars, Liveops is to call centers.
The agents are part of a tectonic shift in how jobs are structured. More companies are pushing work onto freelancers, temps, contractors and franchisees in the quest for an ever more nimble profit-making machine. It is one reason a job category seemingly headed offshore forever — customer service representatives — has been thriving in call centers and home offices across the United States, supporting roughly three million workers.
While critics of the arrangement cite rising insecurity, some of Liveops’ star agents — like Emmett Jones in Chicago, who knows of his rivals primarily as numbers on a leader board — say the opportunity has been transformative.
The earnest gratitude of the agents assembled here, not far from the Raleigh-Durham Airport, affirmed that. To them, Liveops is a sustaining force, a way to earn a living while being present at home. A few had driven hours to attend. Many brought friends and family members who were considering joining “Liveops Nation,” too.
There were icebreakers (“Liveops Nation Bingo”). Gift-card raffles (“$150?” the chief executive quipped. “Who approved these things?”). Free enchiladas. Everyone was invited to schmooze.
“John, I heard your story about how you got to us is pretty great,” said the master of ceremonies, an impossibly sunny woman named Tara. “Would you mind telling all these people?”
When the mic came to John, a former insurance claims adjuster with a gray beard and several earrings, there was a sense of imminent revelation.
“I was working in another glass box over near here for six years,” he began. “I reached the point where it was either jump off the roof or walk out the front door.” The other agents laughed knowingly.
He continued: “My commute now is I walk down the hall, close the bedroom door behind me.” More laughter.
Then John’s voice softened: “This is good, this is good. I get paid for when I’m working, instead of souring when you get paid for 40 hours and work some more. So, I’m here.”
“Awesome,” Tara said, applause drowning her out. “I feel like John’s story mimics a lot of what we hear from people.”
According to Greg Hanover, a longtime Liveops official who became chief executive this summer, the company’s goal is to make agents feel as if they’re part of a movement, not just earning a wage.
“Where we want to be with this is what Mary Kay has done, multilevel marketing companies,” Mr. Hanover said, referring to the cosmetics distributor and its independent sales force. “The direction we need to head in for the community within Liveops Nation is that the agents are so happy, so satisfied with the purpose and meaning there, that they’re telling their story.”
It’s an ambition that feels almost radical compared with Uber, whose best-known exercise in worker outreach is a video of its former chief executive berating a driver. It was heartening to discover that on-demand work could be both financially viable and emotionally fulfilling.
That is, until I began to speak with Mr. Jones and some of his Liveops competitors. The more you talk with them, the more you detect a kind of Darwinian struggle behind the facade of community and self-actualization. You start to wonder: Is there really such a thing as a righteous gig-economy job, even if the company is as apparently well intentioned as Liveops? Or is there something about the nature of gig work that’s inescapably dehumanizing?
Just the Right Tone
Mr. Jones, who lives in Chicago, was the top rated Liveops agent for an insurer called TruStage for much of this year.
An AT&T technician for decades, he decided that he needed to be at home not long after his wife was diagnosed with vertigo in 2008. “I can’t work and be worried about how she’s doing,” he said.
A few years later, when his daughter told him of a friend who worked with Liveops, he was eager to sign up — but refused to send in his required voice test until it was close to perfect. “I must have did the voice test four or five times,” he said. “I wanted to make sure I gave the right tone that they were looking for.”
As a Liveops agent, Mr. Jones sells life policies to callers, often those who have just seen a television commercial for TruStage insurance. He estimates that he works roughly 40 hours each week, beginning around 8 most mornings, and that he makes about $20 an hour. He is such a valued worker that TruStage invited him to its headquarters earlier this year for a two-day visit by an elite group of agents, in which executives pumped them for insights about how to increase sales.
Roughly two decades ago, Liveops and its competitors typically connected callers to psychic hotlines, and in some cases less reputable services. Such businesses had frequent spikes in call volume, making it helpful to have an on-demand work force that could be abruptly ramped up.
“The only thing people were interested in was the abandonment rate” — that is, the number of people who would hang up in frustration from being kept on hold — said Kim Houlne, the chief executive of a Liveops rival called Working Solutions, which she founded in 1996.
The call center industry took a hit during the 2001 recession, when cost consciousness unleashed a wave of outsourcing to India. But within 10 years, many companies decided that the practice, known as offshoring, had been oversold. The savings on wages were often wiped out by lost business from enraged customers, who preferred to communicate with native English speakers.
“People don’t feel comfortable,” Ms. Houlne said, alluding to the overseas agents.
By the early part of this decade, quality was in fashion. The enormous amounts of data that companies like Liveops and Working Solutions collect allowed them to connect callers to the best possible agent with remarkable precision, while allowing big clients to avoid the overhead of a physical call center and full-time workers.
Today, in addition to sales calls, Liveops agents handle calls from people trying to file insurance claims, those in need of roadside assistance, even those with medical or financial issues relating to prescription drugs. The agents must obtain a certification before they can handle such calls, which sometimes takes weeks of online coursework.
Liveops goes to great lengths to attend to their needs, addressing technical-support issues, even answering agents’ emails to the chief executive within 24 hours.
Mr. Jones, like many of his fellow agents, thinks of himself as helping others in need. He said that many families will gather around a table after a loved one has died to discuss the burial. If the deceased relative had no insurance, he said, “A lot of times that table is going to clear.” If, on the other hand, he had even $2,000 in life insurance — the minimum that TruStage sells — “the family members are more inclined to say, ‘He did what he could, let me see what I could do to help out.’ You end up with $5,000 to $6,000. You can do a decent burial rather than none at all.”
Still, there is undeniably a brass-tacks quality to the work. Shortly after we hung up, I turned my attention to an assignment due that afternoon, only to receive more calls from Mr. Jones’s number. When I finally answered, he apologized for interrupting me, then came to the point. “I have a question for you,” he said. “Do you have life insurance?”
‘Where the Price Point Is’
Like Uber, Liveops expends considerable effort calculating demand for its agents. For example, if an auto insurance company is running a commercial on ESPN, Liveops will ask the company’s media buyer — that is, the intermediary that placed the ad — to predict how many calls such an ad is likely to generate. Liveops will adjust that prediction, using its own data showing how many calls similar ads have produced from similar audiences during a comparable time of year.
And like Uber, the Liveops focuses on “utilization” — in the Liveops case, the percentage of working agents actually on a call. Depending on the client, Liveops strives for rates of 65 percent to 75 percent. Lower than that and the agents, who make money only when they’re on a call, will complain that they’re not busy enough. Significantly higher and the system is vulnerable to a sudden increase in demand that could tie up the phone lines and keep callers waiting.
Liveops asks agents to schedule themselves in half-hour blocks, known as “commits,” for the upcoming week. If the company expects demand to be higher than the number of commits, it sends agents a message urging them to sign up. (Uber does something similar, except without formal scheduling.) Sometimes it will even offer financial incentives, like a bump in the rate earned for each minute they’re on a call, or a raffle-type scheme in which people accumulate tickets for the giveaway of an iPad or a cruise.
Again like Uber, Liveops relentlessly tests the effectiveness of these tools. Referring to financial incentives, Jon Brown, the Liveops senior director of client services, said, “We’ve zeroed in on exactly what we need for an agent to go from 10 to 15 commits, from 15 commits to 20 commits. We know where the price point is, what drives behavior.”
And then there are the performance metrics. Liveops agents are rated according to what are called key performance indicators, which, depending on the customer, can include the number of sales they make, their success at upselling customers, and whether a caller would recommend the service based on their interaction.
Liveops makes clear that its agents’ ability to earn more money is closely tied to performance. “You’ve heard the term meritocracy?” said a Liveops official named Aimee Matolka at the North Carolina event. “When a call comes in, it routes in to that best agent. Yes, our router is that smart. You guys want to be that agent, I know you do. Otherwise you wouldn’t be here.”
It allows the agents to track their rankings obsessively through internal leader boards. (Liveops officials say that while the pressures of the job can preoccupy agents, it is up to them how much time to invest.)
“I lost the No. 1 spot, now I’m No. 2,” Mr. Jones said in early August, acknowledging that he checks his ranking frequently. “I thought about researching to find out who it is — you always want to know who’s the competition — but I said leave it.”
He added: “I’m a competitive person. We just toggle back and forth. If they see me jump back in, they work harder. They want that spot back.”
‘This Is My Phone Call’
My flight to Bangor, Me., was due after 9 p.m., and apparently sensing my unease with the North Country, the firefighter seated next to me asked if I had to far to drive when we landed. “About three hours north,” I confessed. “Watch out for moose,” he said. I assured him I’d driven around deer before. He stopped me short: If you hit a deer, you’ll kill them, he said. If you hit a moose, they’ll kill you.
I found Troy Carter, the agent who had recently surpassed Emmett Jones, at his home in Fort Fairfield the next day, wearing jeans, a button-down short-sleeve shirt, and a New England Patriots hat. There were no shoes on his feet, only white socks.
Like Mr. Jones, Mr. Carter said Liveops had been a blessing, allowing him to earn a living in a part of Maine so remote that my cellphone carrier welcomed me to Canada shortly after I pulled into his driveway.
When I told Mr. Carter that I had been in touch with his top competitor, he quickly pulled up the latest monthly rankings of Liveops agents selling TruStage insurance. He pointed out that while Mr. Jones, whom he recognized only by his identification number, 141806, had more sales — 87 to his 82 — he had far fewer paid sales, charged at the time of purchase rather than by invoice.
“The real thing is the paid application rate — they want it around 95 percent,” Mr. Carter said. “He has 87 sales, but only 65 percent paid, compared to my 94 percent.” This, he explained, was why he enjoyed the right to call himself the top agent for the month.
Mr. Carter is what you might call a serial entrepreneur. He once started an art supply website that folded within a few months, and a penny auction site called Bid Tree that foundered for lack of a marketing budget.
He sees Liveops, on which he spends 40 to 50 hours per week, as of a piece with these entrepreneurial efforts. In fact, it is something of a family business. His wife, Lori, handles incoming calls while he’s busy with customers. “I’m a housewife/secretary/receptionist,” she said. Even Mr. Carter’s 9-year-old son, Logan, plays a role. “At nighttime, he says the last part of his prayer based on how many sales I did today,” Mr. Carter said. “If it was a lot of sales, he’ll pray, ‘Dear Lord, help my dad get the same amount of sales tomorrow.’”
Though Liveops agents work from a script, Mr. Carter, like Mr. Jones, adds his own flourishes. Before asking a caller’s gender, as he is required to do, he will say, “Now I already know the answer to this question, but please confirm if you’re male or female.” Upon receiving the answer, he will pause momentarily before saying, “I told you I already knew the answer,” and break into a laugh.
He might make this identical joke, with identical timing, dozens of times in a workday. “It’s like a comedian has a little pause before a joke,” he told me. “It relaxes them right off.”
Even with these touches, results can vary widely. Two days earlier, Mr. Carter had made seven sales, only a few shy of his record. The day I turned up, he managed only one. He said some callers had the impression they could receive $25,000 of insurance for $9.95 per month — the commercial mentions both figures — and begged off when Mr. Carter told them that
Mr. Carter has done research on how to comport himself, including watching an instructional YouTube video by the former stockbroker who was the subject of the movie “The Wolf of Wall Street.” He believes the key is to come off as the alpha presence. “The one that asks the most questions is the one in control,” he said. “If they ask me questions — ‘How are you doing?’ — I’ll come back, ‘The question is how are you doing?’ This is my phone call, as much as I can make it.”
But on this day he repeatedly ran up against the limits of his powers. Even those who remained interested after 10 or 15 minutes of painstaking back-and-forth often demurred when Mr. Carter asked them for payment information. “This one guy was outside in a wheelchair,” Mr. Carter said of a caller who couldn’t produce his credit card. “He didn’t want to go in and get it. I said, ‘I’m fine waiting,’ but I can’t push him.”
These setbacks only seem to make Mr. Carter focus more. At one point, he made a swiping motion between his face and his headset with his index finger and middle finger. “They recommend that you keep the microphone two fingers away,” he said. “I’m always doing that — checking that it’s two fingers. I’ll do that for the rest of my life.”
It seemed, all in all, like a grueling way to make the slightly more than $30,000 that Mr. Carter estimates he takes in before taxes. “The good thing is he can take hours off,” Lori told me. “But then he can lose his spot. It’s always a fight for the top.”
I was reminded of the Alec Baldwin monologue from the movie “Glengarry Glen Ross,” except that the prize for having the most sales wouldn’t be a Cadillac, it would be a set of steak knives, because the Liveops analytics team had calculated that agents would give nearly as much effort for a prize worth a small fraction of the cost.
Of course, unlike the salesmen in that movie, the Liveops agents can’t really be fired — the third prize — because they weren’t employees to begin with.
A while later, Mr. Carter described a recent initiative in which agents were promised a bonus if 95 percent of their collective sales were paid up front. “I knew it wasn’t going to work as soon as they said it,” he told me, because a handful of agents with low paid rates could ruin everyone else’s chances.
“They did do a pullover sweatshirt for the top two,” he added, brightening. “I was second, so that’s coming.”
A version of this article appears in print on November 12, 2017, on Page BU1 of the New York edition with the headline: Paradise at the End of a Phone Line. Order Reprints| Today’s Paper|Subscribe
The Economist kicked off their 2018 year with a bold prediction: “Brain-computer interfaces may change what it means to be human.”
In their lead article, they suggest that BCIS (Brain Computer Interfaces) like the BrainGate System are leading the way into a new world: where mind control works.
I feel like I did in 1979 when I first heard about the Apple II. The whole world was mainframe computing and time-sharing of those monsters, and yet two guys in a garage blow a massive hole through this paradigm, turn it on its head, and invent personal computing.
Think about it: personal computing had been evolving and constantly improving now for almost forty year!
Back then, I could see the future vaguely, in very partial outlines, without much practical effect, but with intense curiosity.
Another example is voice recognition. I still remember being introduced to the subject, way back in …. 1970? I got all excited about it, until I realized …. it sucked! And it wasn’t going to get much better anytime soon. But I remember saying to myself: I can’t be fooled by the first versions of voice recognition. I can’t lull myself to sleep. I need to watch this space because it will evolve and improve over time.
If you think about it, technology version 1-10 always sucks. The history of speech recognition in the 1950’s and 1960’s is, well, pathetic.
IBM’s SHOEBOX was introduced at the 1962 World’s Fair.
DARPA got involved in the late 1970’s, and then partnered with Carnegie Mellon on HARPY – a major advance.
Threshold Technology was formed then, in order to advance commercialization of primitive speech recognition.
And now we have SIRI.
And sure, enough, after almost 40 years of trying, voice recognition is getting really, really good. Can we see a time within the next 10 years when voice recognition replaces most keyboard applications?
I think so.
And so it is with this subject. We are at the very, very beginning, when it all sounds vague, with partial outlines, without much practical effect, and yet ….. it fills me with intense curiosity.
What could the next fifty years bring?
Is it possible that we will be able to think something, and have that something (a thought? a prescribed action? an essay?) become physical?
TECHNOLOGIES are often billed as transformative. For William Kochevar, the term is justified. Mr Kochevar is paralysed below the shoulders after a cycling accident, yet has managed to feed himself by his own hand. This remarkable feat is partly thanks to electrodes, implanted in his right arm, which stimulate muscles. But the real magic lies higher up. Mr Kochevar can control his arm using the power of thought. His intention to move is reflected in neural activity in his motor cortex; these signals are detected by implants in his brain and processed into commands to activate the electrodes in his arms.
An ability to decode thought in this way may sound like science fiction. But brain-computer interfaces (BCIs) like the BrainGate system used by Mr Kochevar provide evidence that mind-control can work. Researchers are able to tell what words and images people have heard and seen from neural activity alone. Information can also be encoded and used to stimulate the brain. Over 300,000 people have cochlear implants, which help them to hear by converting sound into electrical signals and sending them into the brain. Scientists have “injected” data into monkeys’ heads, instructing them to perform actions via electrical pulses.
As our Technology Quarterly in this issue explains, the pace of research into BCIs and the scale of its ambition are increasing. Both America’s armed forces and Silicon Valley are starting to focus on the brain. Facebook dreams of thought-to-text typing. Kernel, a startup, has $100m to spend on neurotechnology. Elon Musk has formed a firm called Neuralink; he thinks that, if humanity is to survive the advent of artificial intelligence, it needs an upgrade. Entrepreneurs envisage a world in which people can communicate telepathically, with each other and with machines, or acquire superhuman abilities, such as hearing at very high frequencies.
These powers, if they ever materialise, are decades away. But well before then, BCIs could open the door to remarkable new applications. Imagine stimulating the visual cortex to help the blind, forging new neural connections in stroke victims or monitoring the brain for signs of depression. By turning the firing of neurons into a resource to be harnessed, BCIs may change the idea of what it means to be human.
That thinking feeling
Sceptics scoff. Taking medical BCIs out of the lab into clinical practice has proved very difficult. The BrainGate system used by Mr Kochevar was developed more than ten years ago, but only a handful of people have tried it out. Turning implants into consumer products is even harder to imagine. The path to the mainstream is blocked by three formidable barriers—technological, scientific and commercial.
Start with technology. Non-invasive techniques like an electroencephalogram (EEG) struggle to pick up high-resolution brain signals through intervening layers of skin, bone and membrane. Some advances are being made—on EEG caps that can be used to play virtual-reality games or control industrial robots using thought alone. But for the time being at least, the most ambitious applications require implants that can interact directly with neurons. And existing devices have lots of drawbacks. They involve wires that pass through the skull; they provoke immune responses; they communicate with only a few hundred of the 85bn neurons in the human brain. But that could soon change. Helped by advances in miniaturisation and increased computing power, efforts are under way to make safe, wireless implants that can communicate with hundreds of thousands of neurons. Some of these interpret the brain’s electrical signals; others experiment with light, magnetism and ultrasound.
Clear the technological barrier, and another one looms. The brain is still a foreign country. Scientists know little about how exactly it works, especially when it comes to complex functions like memory formation. Research is more advanced in animals, but experiments on humans are hard. Yet, even today, some parts of the brain, like the motor cortex, are better understood. Nor is complete knowledge always needed. Machine learning can recognise patterns of neural activity; the brain itself gets the hang of controlling BCIS with extraordinary ease. And neurotechnology will reveal more of the brain’s secrets.
Like a hole in the head
The third obstacle comprises the practical barriers to commercialisation. It takes time, money and expertise to get medical devices approved. And consumer applications will take off only if they perform a function people find useful. Some of the applications for brain-computer interfaces are unnecessary—a good voice-assistant is a simpler way to type without fingers than a brain implant, for example. The idea of consumers clamouring for craniotomies also seems far-fetched. Yet brain implants are already an established treatment for some conditions. Around 150,000 people receive deep-brain stimulation via electrodes to help them control Parkinson’s disease. Elective surgery can become routine, as laser-eye procedures show.
All of which suggests that a route to the future imagined by the neurotech pioneers is arduous but achievable. When human ingenuity is applied to a problem, however hard, it is unwise to bet against it. Within a few years, improved technologies may be opening up new channels of communications with the brain. Many of the first applications hold out unambiguous promise—of movement and senses restored. But as uses move to the augmentation of abilities, whether for military purposes or among consumers, a host of concerns will arise. Privacy is an obvious one: the refuge of an inner voice may disappear. Security is another: if a brain can be reached on the internet, it can also be hacked. Inequality is a third: access to superhuman cognitive abilities could be beyond all except a self-perpetuating elite. Ethicists are already starting to grapple with questions of identity and agency that arise when a machine is in the neural loop.
These questions are not urgent. But the bigger story is that neither are they the realm of pure fantasy. Technology changes the way people live. Beneath the skull lies the next frontier.
This article appeared in the Leaders section of the print edition under the headline “The next frontier”
================== REFERENCE: History of Speech Recognition =====
Speech Recognition Through the Decades: How We Ended Up With Siri
By Melanie Pinola
PCWorld | NOV 2, 2011 6:00 PM PT
Looking back on the development of speech recognition technology is like watching a child grow up, progressing from the baby-talk level of recognizing single syllables, to building a vocabulary of thousands of words, to answering questions with quick, witty replies, as Apple’s supersmart virtual assistant Siri does.
Listening to Siri, with its slightly snarky sense of humor, made us wonder how far speech recognition has come over the years. Here’s a look at the developments in past decades that have made it possible for people to control devices using only their voice.
1950s and 1960s: Baby Talk
The first speech recognition systems could understand only digits. (Given the complexity of human language, it makes sense that inventors and engineers first focused on numbers.) Bell Laboratories designed in 1952 the “Audrey” system, which recognized digits spoken by a single voice. Ten years later, IBM demonstrated at the 1962 World’s Fair its “Shoebox” machine, which could understand 16 words spoken in English.
Labs in the United States, Japan, England, and the Soviet Union developed other hardware dedicated to recognizing spoken sounds, expanding speech recognition technology to support four vowels and nine consonants.
They may not sound like much, but these first efforts were an impressive start, especially when you consider how primitive computers themselves were at the time.
1970s: Speech Recognition Takes Off
Speech recognition technology made major strides in the 1970s, thanks to interest and funding from the U.S. Department of Defense. The DoD’s DARPA Speech Understanding Research (SUR) program, from 1971 to 1976, was one of the largest of its kind in the history of speech recognition, and among other things it was responsible for Carnegie Mellon’s “Harpy” speech-understanding system. Harpy could understand 1011 words, approximately the vocabulary of an average three-year-old.
Harpy was significant because it introduced a more efficient search approach, called beam search, to “prove the finite-state network of possible sentences,” according to Readings in Speech Recognition by Alex Waibel and Kai-Fu Lee. (The story of speech recognition is very much tied to advances in search methodology and technology, as Google’s entrance into speech recognition on mobile devices proved just a few years ago.)
The ’70s also marked a few other important milestones in speech recognition technology, including the founding of the first commercial speech recognition company, Threshold Technology, as well as Bell Laboratories’ introduction of a system that could interpret multiple people’s voices.
1980s: Speech Recognition Turns Toward Prediction
Over the next decade, thanks to new approaches to understanding what people say, speech recognition vocabulary jumped from about a few hundred words to several thousand words, and had the potential to recognize an unlimited number of words. One major reason was a new statistical method known as the hidden Markov model.
Rather than simply using templates for words and looking for sound patterns, HMM considered the probability of unknown sounds’ being words. This foundation would be in place for the next two decades (see Automatic Speech Recognition—A Brief History of the Technology Development by B.H. Juang and Lawrence R. Rabiner).
Equipped with this expanded vocabulary, speech recognition started to work its way into commercial applications for business and specialized industry (for instance, medical use). It even entered the home, in the form of Worlds of Wonder’s Julie doll (1987), which children could train to respond to their voice. (“Finally, the doll that understands you.”)
See how well Julie could speak:
However, whether speech recognition software at the time could recognize 1000 words, as the 1985 Kurzweil text-to-speech program did, or whether it could support a 5000-word vocabulary, as IBM’s system did, a significant hurdle remained: These programs took discrete dictation, so you had … to … pause … after … each … and … every … word.
Next page: Speech recognition for the masses, and the future of speech recognition
This Cat Sensed Death. What if Computers Could, Too
By Siddhartha Mukherjee
Jan. 3, 2018
Of the many small humiliations heaped on a young oncologist in his final year of fellowship, perhaps this one carried the oddest bite: A 2-year-old black-and-white cat named Oscar was apparently better than most doctors at predicting when a terminally ill patient was about to die. The story appeared, astonishingly, in The New England Journal of Medicine in the summer of 2007. Adopted as a kitten by the medical staff, Oscar reigned over one floor of the Steere House nursing home in Rhode Island. When the cat would sniff the air, crane his neck and curl up next to a man or woman, it was a sure sign of impending demise. The doctors would call the families to come in for their last visit. Over the course of several years, the cat had curled up next to 50 patients. Every one of them died shortly thereafter.
No one knows how the cat acquired his formidable death-sniffing skills. Perhaps Oscar’s nose learned to detect some unique whiff of death — chemicals released by dying cells, say. Perhaps there were other inscrutable signs. I didn’t quite believe it at first, but Oscar’s acumen was corroborated by other physicians who witnessed the prophetic cat in action. As the author of the article wrote: “No one dies on the third floor unless Oscar pays a visit and stays awhile.”
The story carried a particular resonance for me that summer, for I had been treating S., a 32-year-old plumber with esophageal cancer. He had responded well to chemotherapy and radiation, and we had surgically resected his esophagus, leaving no detectable trace of malignancy in his body. One afternoon, a few weeks after his treatment had been completed, I cautiously broached the topic of end-of-life care. We were going for a cure, of course, I told S., but there was always the small possibility of a relapse. He had a young wife and two children, and a mother who had brought him weekly to the chemo suite. Perhaps, I suggested, he might have a frank conversation with his family about his goals?
But S. demurred. He was regaining strength week by week. The conversation was bound to be “a bummah,” as he put it in his distinct Boston accent. His spirits were up. The cancer was out. Why rain on his celebration? I agreed reluctantly; it was unlikely that the cancer would return.
When the relapse appeared, it was a full-on deluge. Two months after he left the hospital, S. returned to see me with sprays of metastasis in his liver, his lungs and, unusually, in his bones. The pain from these lesions was so terrifying that only the highest doses of painkilling drugs would treat it, and S. spent the last weeks of his life in a state bordering on coma, unable to register the presence of his family around his bed. His mother pleaded with me at first to give him more chemo, then accused me of misleading the family about S.’s prognosis. I held my tongue in shame: Doctors, I knew, have an abysmal track record of predicting which of our patients are going to die. Death is our ultimate black box.
In a survey led by researchers at University College London of over 12,000 prognoses of the life span of terminally ill patients, the hits and misses were wide-ranging. Some doctors predicted deaths accurately. Others underestimated death by nearly three months; yet others overestimated it by an equal magnitude. Even within oncology, there were subcultures of the worst offenders: In one story, likely apocryphal, a leukemia doctor was found instilling chemotherapy into the veins of a man whose I.C.U. monitor said that his heart had long since stopped.
But what if an algorithm could predict death? In late 2016 a graduate student named Anand Avati at Stanford’s computer-science department, along with a small team from the medical school, tried to “teach” an algorithm to identify patients who were very likely to die within a defined time window. “The palliative-care team at the hospital had a challenge,” Avati told me. “How could we find patients who are within three to 12 months of dying?” This window was “the sweet spot of palliative care.” A lead time longer than 12 months can strain limited resources unnecessarily, providing too much, too soon; in contrast, if death came less than three months after the prediction, there would be no real preparatory time for dying — too little, too late. Identifying patients in the narrow, optimal time period, Avati knew, would allow doctors to use medical interventions more appropriately and more humanely. And if the algorithm worked, palliative-care teams would be relieved from having to manually scour charts, hunting for those most likely to benefit.
Avati and his team identified about 200,000 patients who could be studied. The patients had all sorts of illnesses — cancer, neurological diseases, heart and kidney failure. The team’s key insight was to use the hospital’s medical records as a proxy time machine. Say a man died in January 2017. What if you scrolled time back to the “sweet spot of palliative care” — the window between January and October 2016 when care would have been most effective? But to find that spot for a given patient, Avati knew, you’d presumably need to collect and analyze medical information before that window. Could you gather information about this man during this prewindow period that would enable a doctor to predict a demise in that three-to-12-month section of time? And what kinds of inputs might teach such an algorithm to make predictions?
Avati drew on medical information that had already been coded by doctors in the hospital: a patient’s diagnosis, the number of scans ordered, the number of days spent in the hospital, the kinds of procedures done, the medical prescriptions written. The information was admittedly limited — no questionnaires, no conversations, no sniffing of chemicals — but it was objective, and standardized across patients.
These inputs were fed into a so-called deep neural network — a kind of software architecture thus named because it’s thought to loosely mimic the way the brain’s neurons are organized. The task of the algorithm was to adjust the weights and strengths of each piece of information in order to generate a probability score that a given patient would die within three to 12 months.
The “dying algorithm,” as we might call it, digested and absorbed information from nearly 160,000 patients to train itself. Once it had ingested all the data, Avati’s team tested it on the remaining 40,000 patients. The algorithm performed surprisingly well. The false-alarm rate was low: Nine out of 10 patients predicted to die within three to 12 months did die within that window. And 95 percent of patients assigned low probabilities by the program survived longer than 12 months. (The data used by this algorithm can be vastly refined in the future. Lab values, scan results, a doctor’s note or a patient’s own assessment can be added to the mix, enhancing the predictive power.)
So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?” It is, like death, another black box.
Still, when you pry the box open to look at individual cases, you see expected and unexpected patterns. One man assigned a score of 0.946 died within a few months, as predicted. He had had bladder and prostate cancer, had undergone 21 scans, had been hospitalized for 60 days — all of which had been picked up by the algorithm as signs of impending death. But a surprising amount of weight was seemingly put on the fact that scans were made of his spine and that a catheter had been used in his spinal cord — features that I and my colleagues might not have recognized as predictors of dying (an M.R.I. of the spinal cord, I later realized, was most likely signaling cancer in the nervous system — a deadly site for metastasis).
It’s hard for me to read about the “dying algorithm” without thinking about my patient S. If a more sophisticated version of such an algorithm had been available, would I have used it in his case? Absolutely. Might that have enabled the end-of-life conversation S. never had with his family? Yes. But I cannot shake some inherent discomfort with the thought that an algorithm might understand patterns of mortality better than most humans. And why, I kept asking myself, would such a program seem so much more acceptable if it had come wrapped in a black-and-white fur box that, rather than emitting probabilistic outputs, curled up next to us with retracted claws?
Siddhartha Mukherjee is the author of “The Emperor of All Maladies: A Biography of Cancer” and, more recently, “The Gene: An Intimate History.”
This beta site NeoLife link beyond the splash pagee is tracking the “neobiological revolution”. I wholeheartedly agree that some of our best and brightest are on the case. Here they are:
Making Sense of the Neobiological Revolution
NOTE FROM THE EDITOR
Mapping the brain, sequencing the genome, decoding the microbiome, extending life, curing diseases, editing mutations. We live in a time of awe and possibility — and also enormous responsibility. Are you prepared?
Founder of Neo.life. Entrepreneur in media (Wired) and food (TCHO). Lover of mountains, horses, roses, and kimchee, though not necessarily in that order.
Story seeker and story teller. Editor at NEO.LIFE. Former executive editor of MIT Technology Review; former technology & media editor at The Associated Press
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founder @subcasthq. used to work here.
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“To oppose something is to maintain it.” — Ursula K. Le Guin
writes for the New Yorker and Neo.life, and is a former medical columnist for Slate. @abschaffer
freelance journalist in Los Angeles
Health/Science journalist passionate about human health, cool researcher and telling stories.
Science and tech journalist. Writing in Nature, National Geographic, Smithsonian, mental_floss, & others.
Best-selling author, Managing Director of Excel Venture Management.
Tech and features writer. @Stanford grad.
Making sense of the Neobiological Revolution. Get the email at www.neo.life.
I’m an author and tell stories across multiple mediums including prose, food, gardens, technology & narrative mapping. www.mariafinn.com Instagram maria_finn1.
I write about science, technology and the things people do with them.
Neuroscientist at Stanford, internationally bestselling author of fiction and non-fiction, creator and presenter of PBS’ The Brain.
Kristen V. Brown
Reporter @Gizmodo covering biotech.
David Ewing Duncan
Life science journalist; bestselling author, 9 books; NY Times, Atlantic, Wired, Daily Beast, NPR, ABC News, more; Curator, Arc Fusion www.davidewingduncan.com
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Dr. Sophie Zaaijer
CEO of PlayDNA, Postdoctoral fellow at the New York Genome Center, Runway postdoc at Cornell Tech.
I’m a freelance tech writer based in Oakland, CA. You can find my work at Neo.Life, the MIT Technology Review, Popular Science, and many other places.
Fledgling science journalist here, hoping to foster discussion about the ways science acts as a catalyst for social change #biology
Calling for a radical aging movement. Anti-ageism blog+talk+book
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Science and other sundries.
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Freelance science and technology journalist and editor, formerly on staff at Fast Company, Vocativ, MIT Technology Review, and ClimateWire.
Jessica Carew Kraft
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Senior editor, The Atlantic, five-time James Beard Journalism Award winner, restaurant reviewer for New York, Boston, and Atlanta magazines
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I’m a journalist living in Berkeley. I write about health, science, social justice and policy. Father of 1. From Detroit.
writes for MIT Technology Review and Neo.life from Beijing, and was based in Accra, Ghana, in 2014 and 2015.
Curious amateur. Years of near-daily microbiome experiments. US CEO of AI healthcare startup http://airdoc.com
Bob Parks ✂
Connoisseur of the slap dash . . . maker . . . runner . . . writer of Outside magazine’s Gear Guy blog . . . freelance writer and reporter.
Richard Sprague provides a useful update about the microbiome landscape below. Microbiome is exploding. Your gut can be measured, and your gut can influence your health and well-being. But now …. these gut measurements can offer people a first: personalized nutrition information.
Among the more relevant points:
– Israel’s Weitzman Institute is the global leader academically. Eran Elinav, a physician and immunologist at the Weizmann Institute and one of their lead investigators (see prior post).
– The older technology for measuring the gut is called “16S” sequencing. It tell you at a high level which kinds of microbes are present. It’s cheap and easy, but 16S can see only broad categories,
– The companies competing to measure your microbiome are uBiome, American Gut, Thryve, DayTwo and Viome. DayTwo and Viome offer more advanced technology (see below).
– The latest technology seems to be “metagenomic sequencing”. It is better because it is more specific and detailed.
– By combining “metagenomic sequencing” information with extensive research about how certain species interact with particular foods, machine-learning algorithms can recommend what you should eat.
– DayTwo offers a metagenomic sequencing for $299, and then combines that with all available research to offer personalized nutrition information.
– DayTwo recently completed a $12 million financing round from, among others, Mayo Clinic, which announced it would be validating the research in the U.S.
– DayTwo draws its academic understandings from Israel’s Weitzman Institute. The app is based on more than five years of highly cited research showing, for example, that while people on average respond similarly to white bread versus whole grain sourdough bread, the differences between individuals can be huge: what’s good for one specific person may be bad for another.
CREDIT: Article on Microbiome Advances
When a Double-Chocolate Brownie is Better for You Than Quinoa
A $299 microbiome test from DayTwo turns up some counterintuitive dietary advice.
Why do certain diets work well for some people but not others? Although several genetic tests try to answer that question and might help you craft ideal nutrition plans, your DNA reveals only part of the picture. A new generation of tests from DayTwo and Viome offer diet advice based on a more complete view: they look at your microbiome, the invisible world of bacteria that help you metabolize food, and, unlike your DNA, change constantly throughout your life.
These bugs are involved in the synthesis of vitamins and other compounds in food, and they even play a role in the digestion of gluten. Artificial sweeteners may not contain calories, but they do modify the bacteria in your gut, which may explain why some people continue to gain weight on diet soda. Everyone’s microbiome is different.
So how well do these new tests work?
Basic microbiome tests, long available from uBiome, American Gut, Thryve, and others, based on older “16S” sequencing, can tell you at a high level which kinds of microbes are present. It’s cheap and easy, but 16S can see only broad categories, the bacterial equivalent of, say, canines versus felines. But just as your life might depend on knowing the difference between a wolf and a Chihuahua, your body’s reaction to food often depends on distinctions that can be known only at the species level. The difference between a “good” microbe and a pathogen can be a single DNA base pair.
New tests use more precise “metagenomic” sequencing that can make those distinctions. And by combining that information with extensive research about how those species interact with particular foods, machine-learning algorithms can recommend what you should eat. (Disclosure: I am a former “citizen scientist in residence” at uBiome. But I have no current relationship with any of these companies; I’m just an enthusiast about the microbiome.)
I recently tested myself with DayTwo ($299) to see what it would recommend for me, and I was pleased that the advice was not always the standard “eat more vegetables” that you’ll get from other products claiming to help you eat healthily. DayTwo’s advice is much more specific and often refreshingly counterintuitive. It’s based on more than five years of highly cited research at Israel’s Weizmann Institute, showing, for example, that while people on average respond similarly to white bread versus whole grain sourdough bread, the differences between individuals can be huge: what’s good for one specific person may be bad for another.
In my case, whole grain breads all rate C-. French toast with challah bread: A.
The DayTwo test was pretty straightforward: you collect what comes out of your, ahem, gut, which involves mailing a sample from your time on the toilet. Unlike the other tests, which can analyze the DNA found in just a tiny swab from a stain on a piece of toilet paper, DayTwo requires more like a tablespoon. The extra amount is needed for DayTwo’s more comprehensive metagenomics sequencing.
Since you can get a microbiome test from other companies for under $100, does the additional metagenomic information from DayTwo justify its much higher price? Generally, I found the answer is yes.
About two months after I sent my sample, my iPhone lit up with my results in a handy app that gave me a personalized rating for most common foods, graded from A+ to C-. In my case, whole grain breads all rate C-. Slightly better are pasta and oatmeal, each ranked C+. Even “healthy” quinoa — a favorite of gluten-free diets — was a mere B-. Why? DayTwo’s algorithm can’t say precisely, but among the hundreds of thousands of gut microbe and meal combinations it was trained on, it finds that my microbiome doesn’t work well with these grains. They make my blood sugar rise too high.
So what kinds of bread are good for me? How about a butter croissant (B+) or cheese ravioli (A-)? The ultimate bread winner for me: French toast with challah bread (A). These recommendations are very different from the one-size-fits-all advice from the U.S. Department of Agriculture or the American Diabetes Association.
I was also pleased to learn that a Starbucks double chocolate brownie is an A- for me, while a 100-calorie pack of Snyder’s of Hanover pretzels gets a C-. That might go against general diet advice, but an algorithm determined that the thousands of bacterial species inside me tend to metabolize fatty foods in a way that results in healthier blood sugar levels than what I get from high-carb foods. Of course, that’s advice just for me; your mileage may vary.
Although the research behind DayTwo has been well-reviewed for more than five years, the app is new to the U.S., so the built-in food suggestions often seem skewed toward Middle Eastern eaters, perhaps the Israeli subjects who formed the original research cohort. That might explain why the app’s suggestions for me include lamb souvlaki with yogurt garlic dip for dinner (A+) and lamb kabob and a side of lentils (A) for lunch. They sound delicious, but to many American ears they might not have the ring of “pork ribs” or “ribeye steak,” which have the same A+ rating. Incidentally, DayTwo recently completed a $12 million financing round from, among others, Mayo Clinic, which announced it would be validating the research in the U.S., so I expect the menu to expand with more familiar fare.
Fortunately you’re not limited to the built-in menu choices. The app includes a “build a meal” function that lets you enter combinations of foods from a large database that includes packaged items from Trader Joe’s and Whole Foods.
There is much more to the product, such as a graphical rendering of where my microbiome fits on the spectrum of the rest of the population that eats a particular food. Since the microbiome changes constantly, this will help me see what is different when I do a retest and when I try Viome and other tests.
I’ve had my DayTwo results for only a few weeks, so it’s too soon to know what happens if I take the app’s advice over the long term. Thankfully I’m in good health and reasonably fit, but for now I’ll be eating more strawberries (A+) and blackberries (A-), and fewer apples (B-) and bananas (C+). And overall I’m looking forward to a future where each of us will insist on personalized nutritional information. We all have unique microbiomes, and an app like DayTwo lets us finally eat that way too.
Richard Sprague is a technology executive and quantified-self enthusiast who has worked at Apple, Microsoft, and other tech companies. He is now the U.S. CEO of an AI healthcare startup, Airdoc.
====================APPENDIX: Older Posts about the microbiome =========
CREDIT: https://www.wsj.com/articles/how-disrupting-your-guts-rhythm-affects-your-health-1488164400?mod=e2tw A healthy community of microbes in the gut maintains regular daily cycles of activities. A healthy community of microbes in the gut maintains regular daily cycles of activities.PHOTO: WEIZMANN INSTITUTE By LARRY M. GREENBERG Updated Feb. 27, 2017 3:33 p.m. ET 4 COMMENTS New research is helping to unravel the mystery of how […]
Vibrant Health measures microbiome
My last research on this subject was in August, 2014. I looked at both microbiomes and proteomics. Today, the New York Times published a very comprehensive update on microbiome research: Link to New York Time Microbiome Article Here is the article itself: = = = = = = = ARTICLE BEGINS HERE = = = […]
Science is advancing on microbiomes in the gut. The key to food is fiber, and the key to best fiber is long fibers, like cellulose, uncooked or slightly sauteed (cooking shortens fiber length). The best vegetable, in the view of Jeff Leach, is a leek. Eating Well Article on Microbiome = = = = = […]
Arivale Launches LABS company
“Arivale” Launched and Moving Fast. They launched last month. They have 19 people in the Company and a 107 person pilot – but their plans are way more ambitious than that. Moreover: “The founders said they couldn’t envision Arivale launching even two or three years ago.” Read on …. This is an important development: the […]
Precision Wellness at Mt Sinai
My Sinai announcement Mount Sinai to Establish Precision Wellness Center to Advance Personalized Healthcare Mount Sinai Health System Launches Telehealth Initiatives Joshua Harris, co-Founder of Apollo Global Management, and his wife, Marjorie has made a $5 million gift to the Icahn School of Medicine at Mount Sinai to establish the Harris Center for Precision Wellness. […]
“Systems biology…is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different….It means changing our philosophy, in the full sense of the term” (Denis Noble). Proteomics From Wikipedia, the free encyclopedia For the journal […]