from the world's big
Work Starts at 9AM? It’ll Cost You
Joseph Sussman: Behavioral changes are very difficult. On some dimensions I’m optimistic on others I am less optimistic. Certainly there are a lot of forces in place that makes change away from the automobile very difficult. The land use that has grown up around the cities, I mentioned sprawl, makes transportation by anything other than automobile very, very difficult. You can’t provide say heavy rail public transportation of the sort you have in New York City out somewhere in the suburbs. There just isn’t the volume to pay, if you will, for the extraordinary costs there are of building those kinds of systems.
But, yes, there are ways of improving the system and improving behavior. We have this idea of congestion pricing in the transportation field, and this is enabled by the Intelligent Transportation System ideas that we talked about earlier. So, the notion here would be to use pricing as a way of changing people’s behavior, to make particular kinds of trips more expensive, particular kinds of trips less expensive to try to entice them into behaviors that would make sense from a systemic point of view. Make sense not only for them, but make sense for the traveling public at large.
So, you know all about EZ Pass in New York City so one can electronically collect tolls as one zips by the tollbooth at speed without stopping. Well once we have that kind of electronic capability, one can change those tolls and one can do it not only at toll booths, but one can do it along the entire structured infrastructure and one can even start thinking about doing it with GPS so that one is monitoring the flows of vehicles and one is charging the drivers for using the roads as a function of the kind of car they are driving, for example, a less polluting car would be charged less. As a function of where they are driving. If they are driving in a congested area, we want to give them incentive to not drive during the congested period, we would charge them more. It depends on the time of day they are driving. If they are driving at the height of the rush hour, well that’s going to be more expensive then it will be to drive two hours before, or two hours after the peak hour.
So the notion here is that by changing these prices dynamically as a function of time of day, as a function of location, as a function of vehicle type, that one can give incentives to drivers to make different kinds of decisions. So the notion her is that one can perhaps have a lower need, a lesser need, I should say, of building more infrastructure that is built for peak hour capacity by enticing people to drive at some time outside the peak hour.
Now, that sounds well and good, politically it’s not quite so easy. One has questions, the third “E” in sustainability. The social equity questions. So, the well healed banker can maybe change his hours relatively easily and drive whenever he or she wants to get to work, where perhaps some blue collar worker says to his boss, “I’d rather get here at 11:00 rather than at 9:00 because it will cost me less to drive.” And his boss says, “We start at 9:00,” and that’s the end of that discussion. So, there are equity discussions that go along with it. But this idea of universal road pricing is in fact something that I believe will happen over an extended period, perhaps 20 or 30 years. But eventually, that’s going to be the way we pay for this infrastructure. Rather than paying it in indirect terms through things like the gas tax, we’ll pay for using the roads themselves.
Question: What technology will help deal with those issues?
Joseph Sussman: So, we have the question of different kinds of fuels that we might use that would be less polluting and fuels that would lessen our international energy dependency on unstable regions of the world, like the Arabian Gulf, for example, where our foreign policy is, to a certain extent, driven by our insatiable need for that fuel. One could see a change in the mix of fuels that we are using that would make us less energy dependent and perhaps create a less polluting system. But it’s going to be a long hard slog. Our land use patterns have been developed in a particular way, the idea of the American Dream of a house in the suburbs with some land and some privacy still exists in much of the country. Surely there’s been some modes flow of people back from the suburbs into the cities, which of course would be a good thing from a transportation perspective, but these land use changes are very long term and slow to happen. So, we have to work on some of these issues in the meantime, congestion pricing and technology enhancements and so on.
Dynamic road pricing could solve congestion problems, but is it socially equitable?
If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.
- Does AI—and, more specifically, conscious AI—deserve moral rights? In this thought exploration, evolutionary biologist Richard Dawkins, ethics and tech professor Joanna Bryson, philosopher and cognitive scientist Susan Schneider, physicist Max Tegmark, philosopher Peter Singer, and bioethicist Glenn Cohen all weigh in on the question of AI rights.
- Given the grave tragedy of slavery throughout human history, philosophers and technologists must answer this question ahead of technological development to avoid humanity creating a slave class of conscious beings.
- One potential safeguard against that? Regulation. Once we define the context in which AI requires rights, the simplest solution may be to not build that thing.
Duke University researchers might have solved a half-century old problem.
- Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
- The blend of three polymers provides enough flexibility and durability to mimic the knee.
- The next step is to test this hydrogel in sheep; human use can take at least three years.
Duke researchers have developed the first gel-based synthetic cartilage with the strength of the real thing. A quarter-sized disc of the material can withstand the weight of a 100-pound kettlebell without tearing or losing its shape.
Photo: Feichen Yang.<p>That's the word from a team in the Department of Chemistry and Department of Mechanical Engineering and Materials Science at Duke University. Their <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202003451" target="_blank">new paper</a>, published in the journal,<em> Advanced Functional Materials</em>, details this exciting evolution of this frustrating joint.<br></p><p>Researchers have sought materials strong and versatile enough to repair a knee since at least the seventies. This new hydrogel, comprised of three polymers, might be it. When two of the polymers are stretched, a third keeps the entire structure intact. When pulled 100,000 times, the cartilage held up as well as materials used in bone implants. The team also rubbed the hydrogel against natural cartilage a million times and found it to be as wear-resistant as the real thing. </p><p>The hydrogel has the appearance of Jell-O and is comprised of 60 percent water. Co-author, Feichen Yang, <a href="https://today.duke.edu/2020/06/lab-first-cartilage-mimicking-gel-strong-enough-knees" target="_blank">says</a> this network of polymers is particularly durable: "Only this combination of all three components is both flexible and stiff and therefore strong." </p><p> As with any new material, a lot of testing must be conducted. They don't foresee this hydrogel being implanted into human bodies for at least three years. The next step is to test it out in sheep. </p><p>Still, this is an exciting step forward in the rehabilitation of one of our trickiest joints. Given the potential reward, the wait is worth it. </p><p><span></span>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>, <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a> and <a href="https://derekberes.substack.com/" target="_blank">Substack</a>. His next book is</em> "<em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy."</em></p>
What would it be like to experience the 4th dimension?
Physicists have understood at least theoretically, that there may be higher dimensions, besides our normal three. The first clue came in 1905 when Einstein developed his theory of special relativity. Of course, by dimensions we’re talking about length, width, and height. Generally speaking, when we talk about a fourth dimension, it’s considered space-time. But here, physicists mean a spatial dimension beyond the normal three, not a parallel universe, as such dimensions are mistaken for in popular sci-fi shows.
An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.
- 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
- Early treatment is available but there's been no way to tell who needs it.
- Using clinical data already being collected, machine learning can identify who's at risk.
The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.
In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.
That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.
70 data points and machine learning
Image source: Creators Collective/Unsplash
Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:
"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."
The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.
Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."
Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.
Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.
On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.
Image source: Külli Kittus/Unsplash
Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."
"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.
The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.