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#434637 AI Is Rapidly Augmenting Healthcare and ...

Posted on February 15, 2019 by Android

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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#433895 Sci-Fi Movies Are the Secret Weapon That ...

Posted on November 17, 2018 by Android

If there’s one line that stands the test of time in Steven Spielberg’s 1993 classic Jurassic Park, it’s probably Jeff Goldblum’s exclamation, “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”

Goldblum’s character, Dr. Ian Malcolm, was warning against the hubris of naively tinkering with dinosaur DNA in an effort to bring these extinct creatures back to life. Twenty-five years on, his words are taking on new relevance as a growing number of scientists and companies are grappling with how to tread the line between “could” and “should” in areas ranging from gene editing and real-world “de-extinction” to human augmentation, artificial intelligence and many others.

Despite growing concerns that powerful emerging technologies could lead to unexpected and wide-ranging consequences, innovators are struggling with how to develop beneficial new products while being socially responsible. Part of the answer could lie in watching more science fiction movies like Jurassic Park.

Hollywood Lessons in Societal Risks
I’ve long been interested in how innovators and others can better understand the increasingly complex landscape around the social risks and benefits associated with emerging technologies. Growing concerns over the impacts of tech on jobs, privacy, security and even the ability of people to live their lives without undue interference highlight the need for new thinking around how to innovate responsibly.

New ideas require creativity and imagination, and a willingness to see the world differently. And this is where science fiction movies can help.

Sci-fi flicks are, of course, notoriously unreliable when it comes to accurately depicting science and technology. But because their plots are often driven by the intertwined relationships between people and technology, they can be remarkably insightful in revealing social factors that affect successful and responsible innovation.

This is clearly seen in Jurassic Park. The movie provides a surprisingly good starting point for thinking about the pros and cons of modern-day genetic engineering and the growing interest in bringing extinct species back from the dead. But it also opens up conversations around the nature of complex systems that involve both people and technology, and the potential dangers of “permissionless” innovation that’s driven by power, wealth and a lack of accountability.

Similar insights emerge from a number of other movies, including Spielberg’s 2002 film “Minority Report”—which presaged a growing capacity for AI-enabled crime prediction and the ethical conundrums it’s raising—as well as the 2014 film Ex Machina.

As with Jurassic Park, Ex Machina centers around a wealthy and unaccountable entrepreneur who is supremely confident in his own abilities. In this case, the technology in question is artificial intelligence.

The movie tells a tale of an egotistical genius who creates a remarkable intelligent machine—but he lacks the awareness to recognize his limitations and the risks of what he’s doing. It also provides a chilling insight into potential dangers of creating machines that know us better than we know ourselves, while not being bound by human norms or values.

The result is a sobering reminder of how, without humility and a good dose of humanity, our innovations can come back to bite us.

The technologies in Jurassic Park, Minority Report, and Ex Machina lie beyond what is currently possible. Yet these films are often close enough to emerging trends that they help reveal the dangers of irresponsible, or simply naive, innovation. This is where these and other science fiction movies can help innovators better understand the social challenges they face and how to navigate them.

Real-World Problems Worked Out On-Screen
In a recent op-ed in the New York Times, journalist Kara Swisher asked, “Who will teach Silicon Valley to be ethical?” Prompted by a growing litany of socially questionable decisions amongst tech companies, Swisher suggests that many of them need to grow up and get serious about ethics. But ethics alone are rarely enough. It’s easy for good intentions to get swamped by fiscal pressures and mired in social realities.

Elon Musk has shown that brilliant tech innovators can take ethical missteps along the way. Image Credit:AP Photo/Chris Carlson
Technology companies increasingly need to find some way to break from business as usual if they are to become more responsible. High-profile cases involving companies like Facebook and Uber as well as Tesla’s Elon Musk have highlighted the social as well as the business dangers of operating without fully understanding the consequences of people-oriented actions.

Many more companies are struggling to create socially beneficial technologies and discovering that, without the necessary insights and tools, they risk blundering about in the dark.

For instance, earlier this year, researchers from Google and DeepMind published details of an artificial intelligence-enabled system that can lip-read far better than people. According to the paper’s authors, the technology has enormous potential to improve the lives of people who have trouble speaking aloud. Yet it doesn’t take much to imagine how this same technology could threaten the privacy and security of millions—especially when coupled with long-range surveillance cameras.

Developing technologies like this in socially responsible ways requires more than good intentions or simply establishing an ethics board. People need a sophisticated understanding of the often complex dynamic between technology and society. And while, as Mozilla’s Mitchell Baker suggests, scientists and technologists engaging with the humanities can be helpful, it’s not enough.

An Easy Way into a Serious Discipline
The “new formulation” of complementary skills Baker says innovators desperately need already exists in a thriving interdisciplinary community focused on socially responsible innovation. My home institution, the School for the Future of Innovation in Society at Arizona State University, is just one part of this.

Experts within this global community are actively exploring ways to translate good ideas into responsible practices. And this includes the need for creative insights into the social landscape around technology innovation, and the imagination to develop novel ways to navigate it.

People love to come together as a movie audience.Image credit: The National Archives UK, CC BY 4.0
Here is where science fiction movies become a powerful tool for guiding innovators, technology leaders and the companies where they work. Their fictional scenarios can reveal potential pitfalls and opportunities that can help steer real-world decisions toward socially beneficial and responsible outcomes, while avoiding unnecessary risks.

And science fiction movies bring people together. By their very nature, these films are social and educational levelers. Look at who’s watching and discussing the latest sci-fi blockbuster, and you’ll often find a diverse cross-section of society. The genre can help build bridges between people who know how science and technology work, and those who know what’s needed to ensure they work for the good of society.

This is the underlying theme in my new book Films from the Future: The Technology and Morality of Sci-Fi Movies. It’s written for anyone who’s curious about emerging trends in technology innovation and how they might potentially affect society. But it’s also written for innovators who want to do the right thing and just don’t know where to start.

Of course, science fiction films alone aren’t enough to ensure socially responsible innovation. But they can help reveal some profound societal challenges facing technology innovators and possible ways to navigate them. And what better way to learn how to innovate responsibly than to invite some friends round, open the popcorn and put on a movie?

It certainly beats being blindsided by risks that, with hindsight, could have been avoided.

Andrew Maynard, Director, Risk Innovation Lab, Arizona State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Posted in Human Robots

#431899 Darker Still: Black Mirror’s New ...

Posted on January 10, 2018 by Android

The key difference between science fiction and fantasy is that science fiction is entirely possible because of its grounding in scientific facts, while fantasy is not. This is where Black Mirror is both an entertaining and terrifying work of science fiction. Created by Charlie Brooker, the anthological series tells cautionary tales of emerging technology that could one day be an integral part of our everyday lives.
While watching the often alarming episodes, one can’t help but recognize the eerie similarities to some of the tech tools that are already abundant in our lives today. In fact, many previous Black Mirror predictions are already becoming reality.
The latest season of Black Mirror was arguably darker than ever. This time, Brooker seemed to focus on the ethical implications of one particular area: neurotechnology.
Emerging Neurotechnology
Warning: The remainder of this article may contain spoilers from Season 4 of Black Mirror.
Most of the storylines from season four revolve around neurotechnology and brain-machine interfaces. They are based in a world where people have the power to upload their consciousness onto machines, have fully immersive experiences in virtual reality, merge their minds with other minds, record others’ memories, and even track what others are thinking, feeling, and doing.
How can all this ever be possible? Well, these capabilities are already being developed by pioneers and researchers globally. Early last year, Elon Musk unveiled Neuralink, a company whose goal is to merge the human mind with AI through a neural lace. We’ve already connected two brains via the internet, allowing one brain to communicate with another. Various research teams have been able to develop mechanisms for “reading minds” or reconstructing memories of individuals via devices. The list goes on.
With many of the technologies we see in Black Mirror it’s not a question of if, but when. Futurist Ray Kurzweil has predicted that by the 2030s we will be able to upload our consciousness onto the cloud via nanobots that will “provide full-immersion virtual reality from within the nervous system, provide direct brain-to-brain communication over the internet, and otherwise greatly expand human intelligence.” While other experts continue to challenge Kurzweil on the exact year we’ll accomplish this feat, with the current exponential growth of our technological capabilities, we’re on track to get there eventually.
Ethical Questions
As always, technology is only half the conversation. Equally fascinating are the many ethical and moral questions this topic raises.
For instance, with the increasing convergence of artificial intelligence and virtual reality, we have to ask ourselves if our morality from the physical world transfers equally into the virtual world. The first episode of season four, USS Calister, tells the story of a VR pioneer, Robert Daley, who creates breakthrough AI and VR to satisfy his personal frustrations and sexual urges. He uses the DNA of his coworkers (and their children) to re-create them digitally in his virtual world, to which he escapes to torture them, while they continue to be indifferent in the “real” world.
Audiences are left asking themselves: should what happens in the digital world be considered any less “real” than the physical world? How do we know if the individuals in the virtual world (who are ultimately based on algorithms) have true feelings or sentiments? Have they been developed to exhibit characteristics associated with suffering, or can they really feel suffering? Fascinatingly, these questions point to the hard problem of consciousness—the question of if, why, and how a given physical process generates the specific experience it does—which remains a major mystery in neuroscience.
Towards the end of USS Calister, the hostages of Daley’s virtual world attempt to escape through suicide, by committing an act that will delete the code that allows them to exist. This raises yet another mind-boggling ethical question: if we “delete” code that signifies a digital being, should that be considered murder (or suicide, in this case)? Why shouldn’t it? When we murder someone we are, in essence, taking away their capacity to live and to be, without their consent. By unplugging a self-aware AI, wouldn’t we be violating its basic right to live in the same why? Does AI, as code, even have rights?
Brain implants can also have a radical impact on our self-identity and how we define the word “I”. In the episode Black Museum, instead of witnessing just one horror, we get a series of scares in little segments. One of those segments tells the story of a father who attempts to reincarnate the mother of his child by uploading her consciousness into his mind and allowing her to live in his head (essentially giving him multiple personality disorder). In this way, she can experience special moments with their son.
With “no privacy for him, and no agency for her” the good intention slowly goes very wrong. This story raises a critical question: should we be allowed to upload consciousness into limited bodies? Even more, if we are to upload our minds into “the cloud,” at what point do we lose our individuality to become one collective being?
These questions can form the basis of hours of debate, but we’re just getting started. There are no right or wrong answers with many of these moral dilemmas, but we need to start having such discussions.
The Downside of Dystopian Sci-Fi
Like last season’s San Junipero, one episode of the series, Hang the DJ, had an uplifting ending. Yet the overwhelming majority of the stories in Black Mirror continue to focus on the darkest side of human nature, feeding into the pre-existing paranoia of the general public. There is certainly some value in this; it’s important to be aware of the dangers of technology. After all, what better way to explore these dangers before they occur than through speculative fiction?
A big takeaway from every tale told in the series is that the greatest threat to humanity does not come from technology, but from ourselves. Technology itself is not inherently good or evil; it all comes down to how we choose to use it as a society. So for those of you who are techno-paranoid, beware, for it’s not the technology you should fear, but the humans who get their hands on it.
While we can paint negative visions for the future, though, it is also important to paint positive ones. The kind of visions we set for ourselves have the power to inspire and motivate generations. Many people are inherently pessimistic when thinking about the future, and that pessimism in turn can shape their contributions to humanity.
While utopia may not exist, the future of our species could and should be one of solving global challenges, abundance, prosperity, liberation, and cosmic transcendence. Now that would be a thrilling episode to watch.
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Posted in Human Robots

#430855 Why Education Is the Hardest Sector of ...

Posted on August 13, 2017 by Android

We’ve all heard the warning cries: automation will disrupt entire industries and put millions of people out of jobs. In fact, up to 45 percent of existing jobs can be automated using current technology.
However, this may not necessarily apply to the education sector. After a detailed analysis of more than 2,000-plus work activities for more than 800 occupations, a report by McKinsey & Co states that of all the sectors examined, “…the technical feasibility of automation is lowest in education.”
There is no doubt that technological trends will have a powerful impact on global education, both by improving the overall learning experience and by increasing global access to education. Massive open online courses (MOOCs), chatbot tutors, and AI-powered lesson plans are just a few examples of the digital transformation in global education. But will robots and artificial intelligence ever fully replace teachers?
The Most Difficult Sector to Automate
While various tasks revolving around education—like administrative tasks or facilities maintenance—are open to automation, teaching itself is not.
Effective education involves more than just transfer of information from a teacher to a student. Good teaching requires complex social interactions and adaptation to the individual student’s learning needs. An effective teacher is not just responsive to each student’s strengths and weaknesses, but is also empathetic towards the student’s state of mind. It’s about maximizing human potential.
Furthermore, students don’t just rely on effective teachers to teach them the course material, but also as a source of life guidance and career mentorship. Deep and meaningful human interaction is crucial and is something that is very difficult, if not impossible, to automate.
Automating teaching is an example of a task that would require artificial general intelligence (as opposed to narrow or specific intelligence). In other words, this is the kind of task that would require an AI that understands natural human language, can be empathetic towards emotions, plan, strategize and make impactful decisions under unpredictable circumstances.
This would be the kind of machine that can do anything a human can do, and it doesn’t exist—at least, not yet.
We’re Getting There
Let’s not forget how quickly AI is evolving. Just because it’s difficult to fully automate teaching, it doesn’t mean the world’s leading AI experts aren’t trying.
Meet Jill Watson, the teaching assistant from Georgia Institute of Technology. Watson isn’t your average TA. She’s an IBM-powered artificial intelligence that is being implemented in universities around the world. Watson is able to answer students’ questions with 97 percent certainty.
Technologies like this also have applications in grading and providing feedback. Some AI algorithms are being trained and refined to perform automatic essay scoring. One project has achieved a 0.945 correlation with human graders.
All of this will have a remarkable impact on online education as we know it and dramatically increase online student retention rates.

Any student with a smartphone can access a wealth of information and free courses from universities around the world. MOOCs have allowed valuable courses to become available to millions of students. But at the moment, not all participants can receive customized feedback for their work. Currently, this is limited by manpower, but in the future that may not be the case.
What chatbots like Jill Watson allow is the opportunity for hundreds of thousands, if not millions, of students to have their work reviewed and all their questions answered at a minimal cost.
AI algorithms also have a significant role to play in personalization of education. Every student is unique and has a different set of strengths and weaknesses. Data analysis can be used to improve individual student results, assess each student’s strengths and weaknesses, and create mass-customized programs. Algorithms can analyze student data and consequently make flexible programs that adapt to the learner based on real-time feedback. According to the McKinsey Global Institute, all of this data in education could unlock between $900 billion and $1.2 trillion in global economic value.
Beyond Automated Teaching
It’s important to recognize that technological automation alone won’t fix the many issues in our global education system today. Dominated by outdated curricula, standardized tests, and an emphasis on short-term knowledge, many experts are calling for a transformation of how we teach.
It is not enough to simply automate the process. We can have a completely digital learning experience that continues to focus on outdated skills and fails to prepare students for the future. In other words, we must not only be innovative with our automation capabilities, but also with educational content, strategy, and policies.
Are we equipping students with the most important survival skills? Are we inspiring young minds to create a better future? Are we meeting the unique learning needs of each and every student? There’s no point automating and digitizing a system that is already flawed. We need to ensure the system that is being digitized is itself being transformed for the better.
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Posted in Human Robots