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Hybrid Intelligence: Coupling AI and the Human Brain

Even our most imaginative expectations of AI are only primitive — but as neuroscience understands the brain more deeply, it will unlock the full potential of hybrid intelligence.

Edward Boyden: Humans and machines have been merging for thousands of years. Right now I’m wearing shoes, I have a microphone on my jacket, we all probably used our phones at least once today… And we communicate with the augmentation of all sorts of amplification and even translation technologies: You can speak into a machine, and it’ll translate the words you’re saying in nearly real time. 

So I think what might be different in the years to come is a matter of degree, not a matter of kind. One concept that I think is emerging is what I like to call the brain coprocessor, a device that intimately interacts with the brain. It can upload information to the brain and download information from it. Imagine that you could have a technology that could replace lost memories or augment decision making or boost attention or cognition. To do that though we have to understand how the brain works at a very deep level. 

Although over a third of a million patients have had brain implants or neural implants that stimulate the nervous system, so far they’ve operated in an open-loop fashion. That is, they drive activity in the brain, but not in a fully-responsive fashion. What we want to do is to have bi-directional communication to the brain: Can you read and write information continuously, and supply—maybe through coupling these interfaces to silicon computers— exactly the information the brain needs?

My hope is that over the next five to ten years we’re going to get deep insights thanks to our technologies into how brain circuits compute, and that will drive the design of these interfaces so that we can deliver information to the brain and record information from the brain using the natural language of the brain – reading and writing information in a way that augments, for example, the number of things you can hold in your mind at once. Or the ability to recall things nearly perfectly, which is, you know, not an ability so different from looking something up on a search engine on your phone, right?

So I think what’s going to happen is a continuation of this trend, and I think a lot of people like to talk about artificial intelligence right now. Artificial intelligence as it stands is based on a lot of concepts that go back many decades that build from some very simple observations about the brain.

What might A.I. do though, once we have incredibly deep insights into the nature of creativity and ethics (and other things that the human brain seems to be uniquely equipped for)? 

I think once we start to couple artificial intelligence to the brain that can really augment these uniquely human capabilities, it leads to a new era of what you might call “hybrid intelligence”.

So it won’t just be A.I. running away in some positive-feedback loop; It won’t be humans upgrading themselves in the absence of coupling to the world; But I think it will yield a new kind of symbiosis. And I think that’s probably the best possible path, and it’s also already (if you look at how people operate in the world) what seems to be one of the most prevalent models.

 

Edward Boyden is a Hertz Foundation Fellow and recipient of the prestigious Hertz Foundation Grant for graduate study in the applications of the physical, biological and engineering sciences. A professor of Biological Engineering and Brain and Cognitive Sciences at MIT, Edward Boyden explains how humanity is only at its infancy in merging with machines. His work is leading him towards the development of a "brain co-processor", a device that interacts intimately with the brain to upload and download information to and from it, augmenting human capabilities in memory storage, decision making, and cognition. The first step, however, is understanding the brain on a much deeper level. With the support of the Fannie and John Hertz Foundation, Ed Boyden pursued a PhD in neurosciences from Stanford University.


The Hertz Foundation mission is to provide unique financial and fellowship support to the nation's most remarkable PhD students in the hard sciences. Hertz Fellowships are among the most prestigious in the world, and the foundation has invested over $200 million in Hertz Fellows since 1963 (present value) and supported over 1,100 brilliant and creative young scientists, who have gone on to become Nobel laureates, high-ranking military personnel, astronauts, inventors, Silicon Valley leaders, and tenured university professors. For more information, visit hertzfoundation.org.

Does conscious AI deserve rights?

If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.

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A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

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Technology & Innovation
  • 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.
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Hints of the 4th dimension have been detected by physicists

What would it be like to experience the 4th dimension?

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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.

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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
Technology & Innovation
  • 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

nurse wrapping patient's arm

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.

Going forward

person leaning their head on another's shoulder

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.

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