from the world's big
Coal: Cleaner than Solar?
A leading venture capitalist, Vinod Khosla is the co-founder of Daisy Systems and founding Chief Executive Officer of Sun Microsystems. Khosla pioneered open systems and commercial RISC processors. He became a general partner of the venture capital firm Kleiner, Perkins, Caufield & Byers in 1986 and has mentored many entrepreneurs in building technology-based businesses. In 2004, he started his own firm, Khosla Ventures. He is an advocate of clean energy and supported the campaign to pass California's Proposition 87. Born in 1955 in India, Khosla was determined to pursue technology as a career since his early teens. Khosla was educated at the IIT Delhi, Carnegie Mellon University and the Stanford Graduate School of Business.
Vinod Khosla: Thanks Peter. I’m a big believer that in the end, economic gravity always rules. And that environmentalists, by at large, trying to convert the rest of the world into environmentalists are going the wrong way. We need to take environmentalists and turn them into what I call “pragmentalists”. That understand the role of economics, economic gravity in large social adoption of new energy sources, technologies, everything else.
Having said that, I actually believe all the people, all the gurus, the experts, and we can talk about expert forecasts, they are probably almost always wrong and random. We can come back to that; there is statistical data to support it.
Peter Voser: I don’t need them, I believe you.
Vinod Khosla: But those of you who are interested read a book called, “Expert Political Judgment,” by Professor Tetlock, a 20-year study of 80,000 forecasts; a very rigorous study. But we start believing these 20, 30, and 50 year forecasts from experts. We need to abandon them and instead of exporting the past into these future forecasts, we need to invent that future. We will along with that future, I am on record saying, I can’t imagine oil being more than $30 a barrel by 2030. And the reason is very simple, it will have to compete it’s way down to compete with biofuels and cost of production of biofuels is the marginal cost of rent on land, and if you look at fundamental economics, that’s the number it will drive to in real terms, real dollars. There is no question in my mind that we will have technologies that meet unsubsidized market comparativeness against fossil fuels that are 100 percent renewable. Maybe better than 100 percent, which is where my comment where coal can in fact be cleaner than solar. That’s based on the technology we feel is waiting for commercialization that reduces the lifecycle of carbon production of power generation from coal to more than 100 percent. So, solar can only do 100 percent reduction in carbon emissions, this technology, by displacing products that could otherwise produce carbon can get up to 200 percent reduction.
I’ll be happy to go into it in more detail, but the main point is this. We have to bet on innovation and technology. And technology that’s radical and different and get our best talent working on this technology. And I would submit that five years ago, there wasn’t a PhD student at MIT or Cal Tech, or Stanford, at least that I knew of, that was interested in working on energy recent. All the best minds went to biotechnology, nanotechnology, computer science, semi-conductor devices, not in energy research. And that has changed, and that’s why the future will be different and why innovation will reach this point of unsubsidized market competitiveness. Because of that, I use my favorite phrase, these technologies will meet Chindia price; the price at which India and China will adapt them without regulation. I believe we are far closer to that point than anybody realizes.
Tom Stewart: Bureaucratic survival. Never put a date in the same sentence. So, you’re not a bureaucrat, so I’m going to ask you to break that rule. You said these technologies will meet the Chindia price, when?
Vinod Khosla: So, I believe carbon sequestration today for many carbon point emitters, many coal plants. I’m not saying most, I’m not saying all. Many coal sources can be carbon negative, or carbon zero, in the next year or two without a price on carbon because they turn carbon dioxide in this case into useful building products that can be sold. And so carbon become, not a problem, but a feed stock. In the case of oil, I would say the same thing. I believe in the next year or so, renewable oil sources can compete unsubsidized with crude oil. I also believe –
Tom Stewart: The biofuels and things like that –
Vinod Khosla: Biofuels, right. I also believe on the consumption side of this, unlike and I wrote a blog that got me in a lot of trouble where I wrote a blog about two years ago, which said, “Prius is more green wash than green.” And it is more green wash than green. It is a completely uneconomic technology and in the McKenzie study came out, hybridization of cause came out as the single most expensive way to reduce carbon in dollars of costs per ton of carbon reduced. I believe it was about $100 per ton. And so we are picking technologies because the environmentalists love them, that is pleasing to the political appetite and certain political communities, and that’s the wrong way to go.
On the efficiency side. We’re working on engines that can have 50% more efficiency without an increase in costs of internal combustion engines. And I believe those are near commercial today. It’s pretty easy to do a light bulb. LED light bulb, and we will be introducing that within the next year, that’s cheap enough, that means below $10, where it pays for itself within the first 12 months.
Tom Stewart: You’re saying it’s across three of the four technologies you actually said within the next year or 18 months, we reach a tipping point, and the fourth you didn’t –
Vinod Khosla: I would say all four will be commercial next year at retail, and I’ll add a fifth which is a major energy consumer, I think air conditioning that is cheaper than today’s air conditioning and 75 percent less energy consumption. That’s most of the consumption, lighting, HVAC, transportation fuels.
Sun Microsystems co-founder Vinod Khosla can’t imagine oil being more than $30 a barrel by 2030.
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.