Tag Archives: singularity
#431592 Reactive Content Will Get to Know You ...
The best storytellers react to their audience. They look for smiles, signs of awe, or boredom; they simultaneously and skillfully read both the story and their sitters. Kevin Brooks, a seasoned storyteller working for Motorola’s Human Interface Labs, explains, “As the storyteller begins, they must tune in to… the audience’s energy. Based on this energy, the storyteller will adjust their timing, their posture, their characterizations, and sometimes even the events of the story. There is a dialog between audience and storyteller.”
Shortly after I read the script to Melita, the latest virtual reality experience from Madrid-based immersive storytelling company Future Lighthouse, CEO Nicolas Alcalá explained to me that the piece is an example of “reactive content,” a concept he’s been working on since his days at Singularity University.
For the first time in history, we have access to technology that can merge the reactive and affective elements of oral storytelling with the affordances of digital media, weaving stunning visuals, rich soundtracks, and complex meta-narratives in a story arena that has the capability to know you more intimately than any conventional storyteller could.
It’s no understatement to say that the storytelling potential here is phenomenal.
In short, we can refer to content as reactive if it reads and reacts to users based on their body rhythms, emotions, preferences, and data points. Artificial intelligence is used to analyze users’ behavior or preferences to sculpt unique storylines and narratives, essentially allowing for a story that changes in real time based on who you are and how you feel.
The development of reactive content will allow those working in the industry to go one step further than simply translating the essence of oral storytelling into VR. Rather than having a narrative experience with a digital storyteller who can read you, reactive content has the potential to create an experience with a storyteller who knows you.
This means being able to subtly insert minor personal details that have a specific meaning to the viewer. When we talk to our friends we often use experiences we’ve shared in the past or knowledge of our audience to give our story as much resonance as possible. Targeting personal memories and aspects of our lives is a highly effective way to elicit emotions and aid in visualizing narratives. When you can do this with the addition of visuals, music, and characters—all lifted from someone’s past—you have the potential for overwhelmingly engaging and emotionally-charged content.
Future Lighthouse inform me that for now, reactive content will rely primarily on biometric feedback technology such as breathing, heartbeat, and eye tracking sensors. A simple example would be a story in which parts of the environment or soundscape change in sync with the user’s heartbeat and breathing, or characters who call you out for not paying attention.
The next step would be characters and situations that react to the user’s emotions, wherein algorithms analyze biometric information to make inferences about states of emotional arousal (“why are you so nervous?” etc.). Another example would be implementing the use of “arousal parameters,” where the audience can choose what level of “fear” they want from a VR horror story before algorithms modulate the experience using information from biometric feedback devices.
The company’s long-term goal is to gather research on storytelling conventions and produce a catalogue of story “wireframes.” This entails distilling the basic formula to different genres so they can then be fleshed out with visuals, character traits, and soundtracks that are tailored for individual users based on their deep data, preferences, and biometric information.
The development of reactive content will go hand in hand with a renewed exploration of diverging, dynamic storylines, and multi-narratives, a concept that hasn’t had much impact in the movie world thus far. In theory, the idea of having a story that changes and mutates is captivating largely because of our love affair with serendipity and unpredictability, a cultural condition theorist Arthur Kroker refers to as the “hypertextual imagination.” This feeling of stepping into the unknown with the possibility of deviation from the habitual translates as a comforting reminder that our own lives can take exciting and unexpected turns at any moment.
The inception of the concept into mainstream culture dates to the classic Choose Your Own Adventure book series that launched in the late 70s, which in its literary form had great success. However, filmic takes on the theme have made somewhat less of an impression. DVDs like I’m Your Man (1998) and Switching (2003) both use scene selection tools to determine the direction of the storyline.
A more recent example comes from Kino Industries, who claim to have developed the technology to allow filmmakers to produce interactive films in which viewers can use smartphones to quickly vote on which direction the narrative takes at numerous decision points throughout the film.
The main problem with diverging narrative films has been the stop-start nature of the interactive element: when I’m immersed in a story I don’t want to have to pick up a controller or remote to select what’s going to happen next. Every time the audience is given the option to take a new path (“press this button”, “vote on X, Y, Z”) the narrative— and immersion within that narrative—is temporarily halted, and it takes the mind a while to get back into this state of immersion.
Reactive content has the potential to resolve these issues by enabling passive interactivity—that is, input and output without having to pause and actively make decisions or engage with the hardware. This will result in diverging, dynamic narratives that will unfold seamlessly while being dependent on and unique to the specific user and their emotions. Passive interactivity will also remove the game feel that can often be a symptom of interactive experiences and put a viewer somewhere in the middle: still firmly ensconced in an interactive dynamic narrative, but in a much subtler way.
While reading the Melita script I was particularly struck by a scene in which the characters start to engage with the user and there’s a synchronicity between the user’s heartbeat and objects in the virtual world. As the narrative unwinds and the words of Melita’s character get more profound, parts of the landscape, which seemed to be flashing and pulsating at random, come together and start to mimic the user’s heartbeat.
In 2013, Jane Aspell of Anglia Ruskin University (UK) and Lukas Heydrich of the Swiss Federal Institute of Technology proved that a user’s sense of presence and identification with a virtual avatar could be dramatically increased by syncing the on-screen character with the heartbeat of the user. The relationship between bio-digital synchronicity, immersion, and emotional engagement is something that will surely have revolutionary narrative and storytelling potential.
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Posted in Human Robots
#431559 Drug Discovery AI to Scour a Universe of ...
On a dark night, away from city lights, the stars of the Milky Way can seem uncountable. Yet from any given location no more than 4,500 are visible to the naked eye. Meanwhile, our galaxy has 100–400 billion stars, and there are even more galaxies in the universe.
The numbers of the night sky are humbling. And they give us a deep perspective…on drugs.
Yes, this includes wow-the-stars-are-freaking-amazing-tonight drugs, but also the kinds of drugs that make us well again when we’re sick. The number of possible organic compounds with “drug-like” properties dwarfs the number of stars in the universe by over 30 orders of magnitude.
Next to this multiverse of possibility, the chemical configurations scientists have made into actual medicines are like the smattering of stars you’d glimpse downtown.
But for good reason.
Exploring all that potential drug-space is as humanly impossible as exploring all of physical space, and even if we could, most of what we’d find wouldn’t fit our purposes. Still, the idea that wonder drugs must surely lurk amid the multitudes is too tantalizing to ignore.
Which is why, Alex Zhavoronkov said at Singularity University’s Exponential Medicine in San Diego last week, we should use artificial intelligence to do more of the legwork and speed discovery. This, he said, could be one of the next big medical applications for AI.
Dogs, Diagnosis, and Drugs
Zhavoronkov is CEO of Insilico Medicine and CSO of the Biogerontology Research Foundation. Insilico is one of a number of AI startups aiming to accelerate drug discovery with AI.
In recent years, Zhavoronkov said, the now-famous machine learning technique, deep learning, has made progress on a number of fronts. Algorithms that can teach themselves to play games—like DeepMind’s AlphaGo Zero or Carnegie Mellon’s poker playing AI—are perhaps the most headline-grabbing of the bunch. But pattern recognition was the thing that kicked deep learning into overdrive early on, when machine learning algorithms went from struggling to tell dogs and cats apart to outperforming their peers and then their makers in quick succession.
[Watch this video for an AI update from Neil Jacobstein, chair of Artificial Intelligence and Robotics at Singularity University.]
In medicine, deep learning algorithms trained on databases of medical images can spot life-threatening disease with equal or greater accuracy than human professionals. There’s even speculation that AI, if we learn to trust it, could be invaluable in diagnosing disease. And, as Zhavoronkov noted, with more applications and a longer track record that trust is coming.
“Tesla is already putting cars on the street,” Zhavoronkov said. “Three-year, four-year-old technology is already carrying passengers from point A to point B, at 100 miles an hour, and one mistake and you’re dead. But people are trusting their lives to this technology.”
“So, why don’t we do it in pharma?”
Trial and Error and Try Again
AI wouldn’t drive the car in pharmaceutical research. It’d be an assistant that, when paired with a chemist or two, could fast-track discovery by screening more possibilities for better candidates.
There’s plenty of room to make things more efficient, according to Zhavoronkov.
Drug discovery is arduous and expensive. Chemists sift tens of thousands of candidate compounds for the most promising to synthesize. Of these, a handful will go on to further research, fewer will make it to human clinical trials, and a fraction of those will be approved.
The whole process can take many years and cost hundreds of millions of dollars.
This is a big data problem if ever there was one, and deep learning thrives on big data. Early applications have shown their worth unearthing subtle patterns in huge training databases. Although drug-makers already use software to sift compounds, such software requires explicit rules written by chemists. AI’s allure is its ability to learn and improve on its own.
“There are two strategies for AI-driven innovation in pharma to ensure you get better molecules and much faster approvals,” Zhavoronkov said. “One is looking for the needle in the haystack, and another one is creating a new needle.”
To find the needle in the haystack, algorithms are trained on large databases of molecules. Then they go looking for molecules with attractive properties. But creating a new needle? That’s a possibility enabled by the generative adversarial networks Zhavoronkov specializes in.
Such algorithms pit two neural networks against each other. One generates meaningful output while the other judges whether this output is true or false, Zhavoronkov said. Together, the networks generate new objects like text, images, or in this case, molecular structures.
“We started employing this particular technology to make deep neural networks imagine new molecules, to make it perfect right from the start. So, to come up with really perfect needles,” Zhavoronkov said. “[You] can essentially go to this [generative adversarial network] and ask it to create molecules that inhibit protein X at concentration Y, with the highest viability, specific characteristics, and minimal side effects.”
Zhavoronkov believes AI can find or fabricate more needles from the array of molecular possibilities, freeing human chemists to focus on synthesizing only the most promising. If it works, he hopes we can increase hits, minimize misses, and generally speed the process up.
Proof’s in the Pudding
Insilico isn’t alone on its drug-discovery quest, nor is it a brand new area of interest.
Last year, a Harvard group published a paper on an AI that similarly suggests drug candidates. The software trained on 250,000 drug-like molecules and used its experience to generate new molecules that blended existing drugs and made suggestions based on desired properties.
An MIT Technology Review article on the subject highlighted a few of the challenges such systems may still face. The results returned aren’t always meaningful or easy to synthesize in the lab, and the quality of these results, as always, is only as good as the data dined upon.
Stanford chemistry professor and Andreesen Horowitz partner, Vijay Pande, said that images, speech, and text—three of the areas deep learning’s made quick strides in—have better, cleaner data. Chemical data, on the other hand, is still being optimized for deep learning. Also, while there are public databases, much data still lives behind closed doors at private companies.
To overcome the challenges and prove their worth, Zhavoronkov said, his company is very focused on validating the tech. But this year, skepticism in the pharmaceutical industry seems to be easing into interest and investment.
AI drug discovery startup Exscientia inked a deal with Sanofi for $280 million and GlaxoSmithKline for $42 million. Insilico is also partnering with GlaxoSmithKline, and Numerate is working with Takeda Pharmaceutical. Even Google may jump in. According to an article in Nature outlining the field, the firm’s deep learning project, Google Brain, is growing its biosciences team, and industry watchers wouldn’t be surprised to see them target drug discovery.
With AI and the hardware running it advancing rapidly, the greatest potential may yet be ahead. Perhaps, one day, all 1060 molecules in drug-space will be at our disposal. “You should take all the data you have, build n new models, and search as much of that 1060 as possible” before every decision you make, Brandon Allgood, CTO at Numerate, told Nature.
Today’s projects need to live up to their promises, of course, but Zhavoronkov believes AI will have a big impact in the coming years, and now’s the time to integrate it. “If you are working for a pharma company, and you’re still thinking, ‘Okay, where is the proof?’ Once there is a proof, and once you can see it to believe it—it’s going to be too late,” he said.
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#431356 Humanoid Robot Tête-à-tête
“Sophia” and “Han” discuss the future of humanity with Hanson Robotics’ Ben Goertzel.
Posted in Human Robots
#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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