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#431427 Why the Best Healthcare Hacks Are the ...
Technology has the potential to solve some of our most intractable healthcare problems. In fact, it’s already doing so, with inventions getting us closer to a medical Tricorder, and progress toward 3D printed organs, and AIs that can do point-of-care diagnosis.
No doubt these applications of cutting-edge tech will continue to push the needle on progress in medicine, diagnosis, and treatment. But what if some of the healthcare hacks we need most aren’t high-tech at all?
According to Dr. Darshak Sanghavi, this is exactly the case. In a talk at Singularity University’s Exponential Medicine last week, Sanghavi told the audience, “We often think in extremely complex ways, but I think a lot of the improvements in health at scale can be done in an analog way.”
Sanghavi is the chief medical officer and senior vice president of translation at OptumLabs, and was previously director of preventive and population health at the Center for Medicare and Medicaid Innovation, where he oversaw the development of large pilot programs aimed at improving healthcare costs and quality.
“How can we improve health at scale, not for only a small number of people, but for entire populations?” Sanghavi asked. With programs that benefit a small group of people, he explained, what tends to happen is that the average health of a population improves, but the disparities across the group worsen.
“My mantra became, ‘The denominator is everybody,’” he said. He shared details of some low-tech but crucial fixes he believes could vastly benefit the US healthcare system.
1. Regulatory Hacking
Healthcare regulations are ultimately what drive many aspects of patient care, for better or worse. Worse because the mind-boggling complexity of regulations (exhibit A: the Affordable Care Act is reportedly about 20,000 pages long) can make it hard for people to get the care they need at a cost they can afford, but better because, as Sanghavi explained, tweaking these regulations in the right way can result in across-the-board improvements in a given population’s health.
An adjustment to Medicare hospitalization rules makes for a relevant example. The code was updated to state that if people who left the hospital were re-admitted within 30 days, that hospital had to pay a penalty. The result was hospitals taking more care to ensure patients were released not only in good health, but also with a solid understanding of what they had to do to take care of themselves going forward. “Here, arguably the writing of a few lines of regulatory code resulted in a remarkable decrease in 30-day re-admissions, and the savings of several billion dollars,” Sanghavi said.
2. Long-Term Focus
It’s easy to focus on healthcare hacks that have immediate, visible results—but what about fixes whose benefits take years to manifest? How can we motivate hospitals, regulators, and doctors to take action when they know they won’t see changes anytime soon?
“I call this the reality TV problem,” Sanghavi said. “Reality shows don’t really care about who’s the most talented recording artist—they care about getting the most viewers. That is exactly how we think about health care.”
Sanghavi’s team wanted to address this problem for heart attacks. They found they could reliably determine someone’s 10-year risk of having a heart attack based on a simple risk profile. Rather than monitoring patients’ cholesterol, blood pressure, weight, and other individual factors, the team took the average 10-year risk across entire provider panels, then made providers responsible for controlling those populations.
“Every percentage point you lower that risk, by hook or by crook, you get some people to stop smoking, you get some people on cholesterol medication. It’s patient-centered decision-making, and the provider then makes money. This is the world’s first predictive analytic model, at scale, that’s actually being paid for at scale,” he said.
3. Aligned Incentives
If hospitals are held accountable for the health of the communities they’re based in, those hospitals need to have the right incentives to follow through. “Hospitals have to spend money on community benefit, but linking that benefit to a meaningful population health metric can catalyze significant improvements,” Sanghavi said.
Darshak Sanghavi speaking at Singularity University’s 2017 Exponential Medicine Summit in San Diego, CA.
He used smoking cessation as an example. His team designed a program where hospitals were given a score (determined by the Centers for Disease Control and Prevention) based on the smoking rate in the counties where they’re located, then given monetary incentives to improve their score. Improving their score, in turn, resulted in better health for their communities, which meant fewer patients to treat for smoking-related health problems.
4. Social Determinants of Health
Social determinants of health include factors like housing, income, family, and food security. The answer to getting people to pay attention to these factors at scale, and creating aligned incentives, Sanghavi said, is “Very simple. We just have to measure it to start with, and measure it universally.”
His team was behind a $157 million pilot program called Accountable Health Communities that went live this year. The program requires all Medicare and Medicaid beneficiaries get screened for various social determinants of health. With all that data being collected, analysts can pinpoint local trends, then target funds to address the underlying problem, whether it’s job training, drug use, or nutritional education. “You’re then free to invest the dollars where they’re needed…this is how we can improve health at scale, with very simple changes in the incentive structures that are created,” he said.
5. ‘Securitizing’ Public Health
Sanghavi’s final point tied back to his discussion of aligning incentives. As misguided as it may seem, the reality is that financial incentives can make a huge difference in healthcare outcomes, from both a patient and a provider perspective.
Sanghavi’s team did an experiment in which they created outcome benchmarks for three major health problems that exist across geographically diverse areas: smoking, adolescent pregnancy, and binge drinking. The team proposed measuring the baseline of these issues then creating what they called a social impact bond. If communities were able to lower their frequency of these conditions by a given percent within a stated period of time, they’d get paid for it.
“What that did was essentially say, ‘you have a buyer for this outcome if you can achieve it,’” Sanghavi said. “And you can try to get there in any way you like.” The program is currently in CMS clearance.
AI and Robots Not Required
Using robots to perform surgery and artificial intelligence to diagnose disease will undoubtedly benefit doctors and patients around the US and the world. But Sanghavi’s talk made it clear that our healthcare system needs much more than this, and that improving population health on a large scale is really a low-tech project—one involving more regulatory and financial innovation than technological innovation.
“The things that get measured are the things that get changed,” he said. “If we choose the right outcomes to predict long-term benefit, and we pay for those outcomes, that’s the way to make progress.”
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#431412 3 Dangerous Ideas From Ray Kurzweil
Recently, I interviewed my friend Ray Kurzweil at the Googleplex for a 90-minute webinar on disruptive and dangerous ideas, a prelude to my fireside chat with Ray at Abundance 360 this January.
Ray is my friend and cofounder and chancellor of Singularity University. He is also an XPRIZE trustee, a director of engineering at Google, and one of the best predictors of our exponential future.
It’s my pleasure to share with you three compelling ideas that came from our conversation.
1. The nation-state will soon be irrelevant.
Historically, we humans don’t like change. We like waking up in the morning and knowing that the world is the same as the night before.
That’s one reason why government institutions exist: to stabilize society.
But how will this change in 20 or 30 years? What role will stabilizing institutions play in a world of continuous, accelerating change?
“Institutions stick around, but they change their role in our lives,” Ray explained. “They already have. The nation-state is not as profound as it was. Religion used to direct every aspect of your life, minute to minute. It’s still important in some ways, but it’s much less important, much less pervasive. [It] plays a much smaller role in most people’s lives than it did, and the same is true for governments.”
Ray continues: “We are fantastically interconnected already. Nation-states are not islands anymore. So we’re already much more of a global community. The generation growing up today really feels like world citizens much more than ever before, because they’re talking to people all over the world, and it’s not a novelty.”
I’ve previously shared my belief that national borders have become extremely porous, with ideas, people, capital, and technology rapidly flowing between nations. In decades past, your cultural identity was tied to your birthplace. In the decades ahead, your identify is more a function of many other external factors. If you love space, you’ll be connected with fellow space-cadets around the globe more than you’ll be tied to someone born next door.
2. We’ll hit longevity escape velocity before we realize we’ve hit it.
Ray and I share a passion for extending the healthy human lifespan.
I frequently discuss Ray’s concept of “longevity escape velocity”—the point at which, for every year that you’re alive, science is able to extend your life for more than a year.
Scientists are continually extending the human lifespan, helping us cure heart disease, cancer, and eventually, neurodegenerative disease. This will keep accelerating as technology improves.
During my discussion with Ray, I asked him when he expects we’ll reach “escape velocity…”
His answer? “I predict it’s likely just another 10 to 12 years before the general public will hit longevity escape velocity.”
“At that point, biotechnology is going to have taken over medicine,” Ray added. “The next decade is going to be a profound revolution.”
From there, Ray predicts that nanorobots will “basically finish the job of the immune system,” with the ability to seek and destroy cancerous cells and repair damaged organs.
As we head into this sci-fi-like future, your most important job for the next 15 years is to stay alive. “Wear your seatbelt until we get the self-driving cars going,” Ray jokes.
The implications to society will be profound. While the scarcity-minded in government will react saying, “Social Security will be destroyed,” the more abundance-minded will realize that extending a person’s productive earning life space from 65 to 75 or 85 years old would be a massive boon to GDP.
3. Technology will help us define and actualize human freedoms.
The third dangerous idea from my conversation with Ray is about how technology will enhance our humanity, not detract from it.
You may have heard critics complain that technology is making us less human and increasingly disconnected.
Ray and I share a slightly different viewpoint: that technology enables us to tap into the very essence of what it means to be human.
“I don’t think humans even have to be biological,” explained Ray. “I think humans are the species that changes who we are.”
Ray argues that this began when humans developed the earliest technologies—fire and stone tools. These tools gave people new capabilities and became extensions of our physical bodies.
At its base level, technology is the means by which we change our environment and change ourselves. This will continue, even as the technologies themselves evolve.
“People say, ‘Well, do I really want to become part machine?’ You’re not even going to notice it,” Ray says, “because it’s going to be a sensible thing to do at each point.”
Today, we take medicine to fight disease and maintain good health and would likely consider it irresponsible if someone refused to take a proven, life-saving medicine.
In the future, this will still happen—except the medicine might have nanobots that can target disease or will also improve your memory so you can recall things more easily.
And because this new medicine works so well for so many, public perception will change. Eventually, it will become the norm… as ubiquitous as penicillin and ibuprofen are today.
In this way, ingesting nanorobots, uploading your brain to the cloud, and using devices like smart contact lenses can help humans become, well, better at being human.
Ray sums it up: “We are the species that changes who we are to become smarter and more profound, more beautiful, more creative, more musical, funnier, sexier.”
Speaking of sexuality and beauty, Ray also sees technology expanding these concepts. “In virtual reality, you can be someone else. Right now, actually changing your gender in real reality is a pretty significant, profound process, but you could do it in virtual reality much more easily and you can be someone else. A couple could become each other and discover their relationship from the other’s perspective.”
In the 2030s, when Ray predicts sensor-laden nanorobots will be able to go inside the nervous system, virtual or augmented reality will become exceptionally realistic, enabling us to “be someone else and have other kinds of experiences.”
Why Dangerous Ideas Matter
Why is it so important to discuss dangerous ideas?
I often say that the day before something is a breakthrough, it’s a crazy idea.
By consuming and considering a steady diet of “crazy ideas,” you train yourself to think bigger and bolder, a critical requirement for making impact.
As humans, we are linear and scarcity-minded.
As entrepreneurs, we must think exponentially and abundantly.
At the end of the day, the formula for a true breakthrough is equal to “having a crazy idea” you believe in, plus the passion to pursue that idea against all naysayers and obstacles.
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#431392 What AI Can Now Do Is Remarkable—But ...
Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
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#431385 Here’s How to Get to Conscious ...
“We cannot be conscious of what we are not conscious of.” – Julian Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind
Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland’s 2015 masterpiece, isn’t Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it’s his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind.
Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
Hollywood producers aren’t the only people stumped. As machine intelligence barrels forward at breakneck speed—not only exceeding human performance on games such as DOTA and Go, but doing so without the need for human expertise—the question has once more entered the scientific mainstream.
Are machines on the verge of consciousness?
This week, in a review published in the prestigious journal Science, cognitive scientists Drs. Stanislas Dehaene, Hakwan Lau and Sid Kouider of the Collège de France, University of California, Los Angeles and PSL Research University, respectively, argue: not yet, but there is a clear path forward.
The reason? Consciousness is “resolutely computational,” the authors say, in that it results from specific types of information processing, made possible by the hardware of the brain.
There is no magic juice, no extra spark—in fact, an experiential component (“what is it like to be conscious?”) isn’t even necessary to implement consciousness.
If consciousness results purely from the computations within our three-pound organ, then endowing machines with a similar quality is just a matter of translating biology to code.
Much like the way current powerful machine learning techniques heavily borrow from neurobiology, the authors write, we may be able to achieve artificial consciousness by studying the structures in our own brains that generate consciousness and implementing those insights as computer algorithms.
From Brain to Bot
Without doubt, the field of AI has greatly benefited from insights into our own minds, both in form and function.
For example, deep neural networks, the architecture of algorithms that underlie AlphaGo’s breathtaking sweep against its human competitors, are loosely based on the multi-layered biological neural networks that our brain cells self-organize into.
Reinforcement learning, a type of “training” that teaches AIs to learn from millions of examples, has roots in a centuries-old technique familiar to anyone with a dog: if it moves toward the right response (or result), give a reward; otherwise ask it to try again.
In this sense, translating the architecture of human consciousness to machines seems like a no-brainer towards artificial consciousness. There’s just one big problem.
“Nobody in AI is working on building conscious machines because we just have nothing to go on. We just don’t have a clue about what to do,” said Dr. Stuart Russell, the author of Artificial Intelligence: A Modern Approach in a 2015 interview with Science.
Multilayered consciousness
The hard part, long before we can consider coding machine consciousness, is figuring out what consciousness actually is.
To Dehaene and colleagues, consciousness is a multilayered construct with two “dimensions:” C1, the information readily in mind, and C2, the ability to obtain and monitor information about oneself. Both are essential to consciousness, but one can exist without the other.
Say you’re driving a car and the low fuel light comes on. Here, the perception of the fuel-tank light is C1—a mental representation that we can play with: we notice it, act upon it (refill the gas tank) and recall and speak about it at a later date (“I ran out of gas in the boonies!”).
“The first meaning we want to separate (from consciousness) is the notion of global availability,” explains Dehaene in an interview with Science. When you’re conscious of a word, your whole brain is aware of it, in a sense that you can use the information across modalities, he adds.
But C1 is not just a “mental sketchpad.” It represents an entire architecture that allows the brain to draw multiple modalities of information from our senses or from memories of related events, for example.
Unlike subconscious processing, which often relies on specific “modules” competent at a defined set of tasks, C1 is a global workspace that allows the brain to integrate information, decide on an action, and follow through until the end.
Like The Hunger Games, what we call “conscious” is whatever representation, at one point in time, wins the competition to access this mental workspace. The winners are shared among different brain computation circuits and are kept in the spotlight for the duration of decision-making to guide behavior.
Because of these features, C1 consciousness is highly stable and global—all related brain circuits are triggered, the authors explain.
For a complex machine such as an intelligent car, C1 is a first step towards addressing an impending problem, such as a low fuel light. In this example, the light itself is a type of subconscious signal: when it flashes, all of the other processes in the machine remain uninformed, and the car—even if equipped with state-of-the-art visual processing networks—passes by gas stations without hesitation.
With C1 in place, the fuel tank would alert the car computer (allowing the light to enter the car’s “conscious mind”), which in turn checks the built-in GPS to search for the next gas station.
“We think in a machine this would translate into a system that takes information out of whatever processing module it’s encapsulated in, and make it available to any of the other processing modules so they can use the information,” says Dehaene. “It’s a first sense of consciousness.”
Meta-cognition
In a way, C1 reflects the mind’s capacity to access outside information. C2 goes introspective.
The authors define the second facet of consciousness, C2, as “meta-cognition:” reflecting on whether you know or perceive something, or whether you just made an error (“I think I may have filled my tank at the last gas station, but I forgot to keep a receipt to make sure”). This dimension reflects the link between consciousness and sense of self.
C2 is the level of consciousness that allows you to feel more or less confident about a decision when making a choice. In computational terms, it’s an algorithm that spews out the probability that a decision (or computation) is correct, even if it’s often experienced as a “gut feeling.”
C2 also has its claws in memory and curiosity. These self-monitoring algorithms allow us to know what we know or don’t know—so-called “meta-memory,” responsible for that feeling of having something at the tip of your tongue. Monitoring what we know (or don’t know) is particularly important for children, says Dehaene.
“Young children absolutely need to monitor what they know in order to…inquire and become curious and learn more,” he explains.
The two aspects of consciousness synergize to our benefit: C1 pulls relevant information into our mental workspace (while discarding other “probable” ideas or solutions), while C2 helps with long-term reflection on whether the conscious thought led to a helpful response.
Going back to the low fuel light example, C1 allows the car to solve the problem in the moment—these algorithms globalize the information, so that the car becomes aware of the problem.
But to solve the problem, the car would need a “catalog of its cognitive abilities”—a self-awareness of what resources it has readily available, for example, a GPS map of gas stations.
“A car with this sort of self-knowledge is what we call having C2,” says Dehaene. Because the signal is globally available and because it’s being monitored in a way that the machine is looking at itself, the car would care about the low gas light and behave like humans do—lower fuel consumption and find a gas station.
“Most present-day machine learning systems are devoid of any self-monitoring,” the authors note.
But their theory seems to be on the right track. The few examples whereby a self-monitoring system was implemented—either within the structure of the algorithm or as a separate network—the AI has generated “internal models that are meta-cognitive in nature, making it possible for an agent to develop a (limited, implicit, practical) understanding of itself.”
Towards conscious machines
Would a machine endowed with C1 and C2 behave as if it were conscious? Very likely: a smartcar would “know” that it’s seeing something, express confidence in it, report it to others, and find the best solutions for problems. If its self-monitoring mechanisms break down, it may also suffer “hallucinations” or even experience visual illusions similar to humans.
Thanks to C1 it would be able to use the information it has and use it flexibly, and because of C2 it would know the limit of what it knows, says Dehaene. “I think (the machine) would be conscious,” and not just merely appearing so to humans.
If you’re left with a feeling that consciousness is far more than global information sharing and self-monitoring, you’re not alone.
“Such a purely functional definition of consciousness may leave some readers unsatisfied,” the authors acknowledge.
“But we’re trying to take a radical stance, maybe simplifying the problem. Consciousness is a functional property, and when we keep adding functions to machines, at some point these properties will characterize what we mean by consciousness,” Dehaene concludes.
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