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Get paid to be a good human being? That's the future AI will deliver
A.I. will bring a series of social and financial changes, and it will force us to confront a problem we've been avoiding for much too long, says Joscha Bach.
Dr. Joscha Bach (MIT Media Lab and the Harvard Program for Evolutionary Dynamics) is an AI researcher who works and writes about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems. He is founder of the MicroPsi project, in which virtual agents are constructed and used in a computer model to discover and describe the interactions of emotion, motivation, and cognition of situated agents. Bach’s mission to build a model of the mind is the bedrock research in the creation of Strong AI, i.e. cognition on par with that of a human being. He is especially interested in the philosophy of AI and in the augmentation of the human mind.
Joscha Bach: I think the question of whether we should be afraid of strong A.I. taking over and squashing us like bugs because it doesn’t need us for the things that it’s doing is exactly the same question as if we should be afraid of big corporations taking over and squashing us like bugs. Because big corporations are already agents: they are already intelligent agents in some sense. They’re not sentient. They borrow humans right now for their decision making. But they do have goals of their own that are different from the goals of the humans that they employ. They usually live longer. They’re much more powerful than people. And it’s very hard for a person to do anything against a corporation.
Usually if you want to fight a corporation you have to become some major organization or corporation or nation state yourself. So in some sense the agency of an A.I. is going to be the agency of the system that builds it, that employs it. And of course most of the A.I.s that we are going to build will not be little Roombas that clean your floors, but it’s going to be very intelligent systems—corporations, for instance—that will perform exactly according to the logic of these systems. So if we want to have these systems built in such a way that they treat us nicely you have to start right now. And it seems to be a very hard problem to do so.
The job loss because of automation has several aspects. I think the most obvious thing that we should be seeing is: if our jobs can be done by machines, that’s a very, very good thing. It’s not a bug, it’s a feature.
If I don’t need to clean the street, if I don’t need to drive a car for other people, if I don’t need to work a cash register for other people, if I don’t need to pick goods in a big warehouse and put it into boxes, it’s an extremely good thing.
And the trouble that we have with this is that right now this mode of labor, that people sell their lifetime to some kind of corporation or employer, is not only the way that we are protected, it’s also the way we allocate resources. This is how we measure how much bread you deserve in this world. And I think this is something that we need to change.
Some people suggest that we need a Universal Basic Income. I think it might be good to be able to pay people to be good citizens, which means massive public employment. There are going to be many jobs that can only be done by people and these are those jobs where we are paid for being good, interesting people. For instance good teachers, good scientists, good philosophers, good thinkers, good social people, good nurses, for instance. Good people that raise children. Good people that build restaurants and theaters. Good people that make art. And for all these jobs people have enough productivity to make sure that enough bread comes on the table. The question is how we can distribute this.
There’s going to be much, much more productivity in our future. Actually we already have enough productivity to give everybody in the U.S. an extremely good life. And we haven’t fixed the problem of allocating it, how to distribute these things in the best possible way.
And this is something that we need to deal with in the future, and AI is going to accelerate this need, and I think by and large it might turn out to be a very good thing that we are forced to do this and to address this problem.
If the past is any evidence of the future it might be a very bumpy road, but who knows. Maybe when we are forced to understand that actually we live in an age of abundance it might turn out to be easier than we think.
Right now we are living in a world where we do certain things the way we’ve done them in the past decades—and sometimes in the past centuries—and we perceive that this is the way it “has” to be done. And we often don’t question these ways, so we might think, “If I do work at this particular factory and this is how I earn my bread, how can we keep that state? How can we prevent A.I. from making my job obsolete? How is it possible that I can keep up my standard of living and so on in this world?"
Maybe this is the wrong question to ask. Maybe the right question is: how can we reorganize societies so that I can do the things that I want to do most, that I think are useful to me and other people, that I really, really want to? Because there will be other ways that I can get my bread made and how I can get money or how I can get a roof over my head, that are going to be more awesome and abundant than the ways that we have now.
We are going to be able to build better cars in the future, better houses in the future, better roads or better ways of transporting people. We are going to have a cleaner environment, if we want to and if we might pull it off, because we have more productivity. And people can have better food, better healthcare and a better way of living. And it’s not because we give them jobs that require more work and require them to lean in harder. They need to work less. They need to sell less of their lifetime for things that they don’t want.
And that’s actually a very, very good thing. But it means that we have to change the way our current economy works which means we have to reinvent a lot of our labor market systems. We have to reinvent the way we distribute money and allocate resources in society. And when that happens people have a much better life than we can currently imagine.
To know whether or not we should fear A.I., we first have to understand how it will behave in the world. Cognitive scientist Joscha Bach believes A.I. has the potential to mistreat humans—but no worse than big corporations already do. The future won't filled with Roombas and anthropomorphized house-help robots, he says, so a physical threat is not the main concern. A.I. will take the form of intelligent systems that operate as corporations, and they will adopt the ethics of whatever company builds them. "If we want to have these systems built in such a way that they treat us nicely you have to start right now. And it seems to be a very hard problem to do so," Bach says. And yet he appears to be optimistic about society's other main A.I. fear: job automation. He frames it like this: if a job is you selling the best years of your life to a corporation, automating as many manual tasks as possible is really a release from that contract—but how will we afford to live, and what will we do with our days? Many think Universal Basic Income, but Bach sees it a little differently: mass public employment. Pay people to be good humans: good at teaching and at raising their children. Pay them to be good scientists, good philosophers, good architects and chefs — the things that make us most human. Job automation will also force us to confront one of our most difficult and uncomfortable problems: that we are living in an age of abundance, but fail to distribute resources so that everyone can live a decent life. "It might turn out to be a very good thing if you are forced... to address this problem," he says. Joscha Bach's latest book is Principles of Synthetic Intelligence.
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>
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.
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.
Vaccines find more success in development than any other kind of drug, but have been relatively neglected in recent decades.
Vaccines are more likely to get through clinical trials than any other type of drug — but have been given relatively little pharmaceutical industry support during the last two decades, according to a new study by MIT scholars.