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#434658 The Next Data-Driven Healthtech ...

Increasing your healthspan (i.e. making 100 years old the new 60) will depend to a large degree on artificial intelligence. And, as we saw in last week’s blog, healthcare AI systems are extremely data-hungry.

Fortunately, a slew of new sensors and data acquisition methods—including over 122 million wearables shipped in 2018—are bursting onto the scene to meet the massive demand for medical data.

From ubiquitous biosensors, to the mobile healthcare revolution, to the transformative power of the Health Nucleus, converging exponential technologies are fundamentally transforming our approach to healthcare.

In Part 4 of this blog series on Longevity & Vitality, I expand on how we’re acquiring the data to fuel today’s AI healthcare revolution.

In this blog, I’ll explore:

How the Health Nucleus is transforming “sick care” to healthcare
Sensors, wearables, and nanobots
The advent of mobile health

Let’s dive in.

Health Nucleus: Transforming ‘Sick Care’ to Healthcare
Much of today’s healthcare system is actually sick care. Most of us assume that we’re perfectly healthy, with nothing going on inside our bodies, until the day we travel to the hospital writhing in pain only to discover a serious or life-threatening condition.

Chances are that your ailment didn’t materialize that morning; rather, it’s been growing or developing for some time. You simply weren’t aware of it. At that point, once you’re diagnosed as “sick,” our medical system engages to take care of you.

What if, instead of this retrospective and reactive approach, you were constantly monitored, so that you could know the moment anything was out of whack?

Better yet, what if you more closely monitored those aspects of your body that your gene sequence predicted might cause you difficulty? Think: your heart, your kidneys, your breasts. Such a system becomes personalized, predictive, and possibly preventative.

This is the mission of the Health Nucleus platform built by Human Longevity, Inc. (HLI). While not continuous—that will come later, with the next generation of wearable and implantable sensors—the Health Nucleus was designed to ‘digitize’ you once per year to help you determine whether anything is going on inside your body that requires immediate attention.

The Health Nucleus visit provides you with the following tests during a half-day visit:

Whole genome sequencing (30x coverage)
Whole body (non-contrast) MRI
Brain magnetic resonance imaging/angiography (MRI/MRA)
CT (computed tomography) of the heart and lungs
Coronary artery calcium scoring
Electrocardiogram
Echocardiogram
Continuous cardiac monitoring
Clinical laboratory tests and metabolomics

In late 2018, HLI published the results of the first 1,190 clients through the Health Nucleus. The results were eye-opening—especially since these patients were all financially well-off, and already had access to the best doctors.

Following are the physiological and genomic findings in these clients who self-selected to undergo evaluation at HLI’s Health Nucleus.

Physiological Findings [TG]

Two percent had previously unknown tumors detected by MRI
2.5 percent had previously undetected aneurysms detected by MRI
Eight percent had cardiac arrhythmia found on cardiac rhythm monitoring, not previously known
Nine percent had moderate-severe coronary artery disease risk, not previously known
16 percent discovered previously unknown cardiac structure/function abnormalities
30 percent had elevated liver fat, not previously known

Genomic Findings [TG]

24 percent of clients uncovered a rare (unknown) genetic mutation found on WGS
63 percent of clients had a rare genetic mutation with a corresponding phenotypic finding

In summary, HLI’s published results found that 14.4 percent of clients had significant findings that are actionable, requiring immediate or near-term follow-up and intervention.

Long-term value findings were found in 40 percent of the clients we screened. Long-term clinical findings include discoveries that require medical attention or monitoring but are not immediately life-threatening.

The bottom line: most people truly don’t know their actual state of health. The ability to take a fully digital deep dive into your health status at least once per year will enable you to detect disease at stage zero or stage one, when it is most curable.

Sensors, Wearables, and Nanobots
Wearables, connected devices, and quantified self apps will allow us to continuously collect enormous amounts of useful health information.

Wearables like the Quanttus wristband and Vital Connect can transmit your electrocardiogram data, vital signs, posture, and stress levels anywhere on the planet.

In April 2017, we were proud to grant $2.5 million in prize money to the winning team in the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices.

Using a group of noninvasive sensors that collect data on vital signs, body chemistry, and biological functions, Final Frontier integrates this data in their powerful, AI-based DxtER diagnostic engine for rapid, high-precision assessments.

Their engine combines learnings from clinical emergency medicine and data analysis from actual patients.

Google is developing a full range of internal and external sensors (e.g. smart contact lenses) that can monitor the wearer’s vitals, ranging from blood sugar levels to blood chemistry.

In September 2018, Apple announced its Series 4 Apple Watch, including an FDA-approved mobile, on-the-fly ECG. Granted its first FDA approval, Apple appears to be moving deeper into the sensing healthcare market.

Further, Apple is reportedly now developing sensors that can non-invasively monitor blood sugar levels in real time for diabetic treatment. IoT-connected sensors are also entering the world of prescription drugs.

Last year, the FDA approved the first sensor-embedded pill, Abilify MyCite. This new class of digital pills can now communicate medication data to a user-controlled app, to which doctors may be granted access for remote monitoring.

Perhaps what is most impressive about the next generation of wearables and implantables is the density of sensors, processing, networking, and battery capability that we can now cheaply and compactly integrate.

Take the second-generation OURA ring, for example, which focuses on sleep measurement and management.

The OURA ring looks like a slightly thick wedding band, yet contains an impressive array of sensors and capabilities, including:

Two infrared LED
One infrared sensor
Three temperature sensors
One accelerometer
A six-axis gyro
A curved battery with a seven-day life
The memory, processing, and transmission capability required to connect with your smartphone

Disrupting Medical Imaging Hardware
In 2018, we saw lab breakthroughs that will drive the cost of an ultrasound sensor to below $100, in a packaging smaller than most bandages, powered by a smartphone. Dramatically disrupting ultrasound is just the beginning.

Nanobots and Nanonetworks
While wearables have long been able to track and transmit our steps, heart rate, and other health data, smart nanobots and ingestible sensors will soon be able to monitor countless new parameters and even help diagnose disease.

Some of the most exciting breakthroughs in smart nanotechnology from the past year include:

Researchers from the École Polytechnique Fédérale de Lausanne (EPFL) and the Swiss Federal Institute of Technology in Zurich (ETH Zurich) demonstrated artificial microrobots that can swim and navigate through different fluids, independent of additional sensors, electronics, or power transmission.

Researchers at the University of Chicago proposed specific arrangements of DNA-based molecular logic gates to capture the information contained in the temporal portion of our cells’ communication mechanisms. Accessing the otherwise-lost time-dependent information of these cellular signals is akin to knowing the tune of a song, rather than solely the lyrics.

MIT researchers built micron-scale robots able to sense, record, and store information about their environment. These tiny robots, about 100 micrometers in diameter (approximately the size of a human egg cell), can also carry out pre-programmed computational tasks.

Engineers at University of California, San Diego developed ultrasound-powered nanorobots that swim efficiently through your blood, removing harmful bacteria and the toxins they produce.

But it doesn’t stop there.

As nanosensor and nanonetworking capabilities develop, these tiny bots may soon communicate with each other, enabling the targeted delivery of drugs and autonomous corrective action.

Mobile Health
The OURA ring and the Series 4 Apple Watch are just the tip of the spear when it comes to our future of mobile health. This field, predicted to become a $102 billion market by 2022, puts an on-demand virtual doctor in your back pocket.

Step aside, WebMD.

In true exponential technology fashion, mobile device penetration has increased dramatically, while image recognition error rates and sensor costs have sharply declined.

As a result, AI-powered medical chatbots are flooding the market; diagnostic apps can identify anything from a rash to diabetic retinopathy; and with the advent of global connectivity, mHealth platforms enable real-time health data collection, transmission, and remote diagnosis by medical professionals.

Already available to residents across North London, Babylon Health offers immediate medical advice through AI-powered chatbots and video consultations with doctors via its app.

Babylon now aims to build up its AI for advanced diagnostics and even prescription. Others, like Woebot, take on mental health, using cognitive behavioral therapy in communications over Facebook messenger with patients suffering from depression.

In addition to phone apps and add-ons that test for fertility or autism, the now-FDA-approved Clarius L7 Linear Array Ultrasound Scanner can connect directly to iOS and Android devices and perform wireless ultrasounds at a moment’s notice.

Next, Healthy.io, an Israeli startup, uses your smartphone and computer vision to analyze traditional urine test strips—all you need to do is take a few photos.

With mHealth platforms like ClickMedix, which connects remotely-located patients to medical providers through real-time health data collection and transmission, what’s to stop us from delivering needed treatments through drone delivery or robotic telesurgery?

Welcome to the age of smartphone-as-a-medical-device.

Conclusion
With these DIY data collection and diagnostic tools, we save on transportation costs (time and money), and time bottlenecks.

No longer will you need to wait for your urine or blood results to go through the current information chain: samples will be sent to the lab, analyzed by a technician, results interpreted by your doctor, and only then relayed to you.

Just like the “sage-on-the-stage” issue with today’s education system, healthcare has a “doctor-on-the-dais” problem. Current medical procedures are too complicated and expensive for a layperson to perform and analyze on their own.

The coming abundance of healthcare data promises to transform how we approach healthcare, putting the power of exponential technologies in the patient’s hands and revolutionizing how we live.

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Posted in Human Robots

#434637 AI Is Rapidly Augmenting Healthcare and ...

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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Posted in Human Robots

#434297 How Can Leaders Ensure Humanity in a ...

It’s hard to avoid the prominence of AI in our lives, and there is a plethora of predictions about how it will influence our future. In their new book Solomon’s Code: Humanity in a World of Thinking Machines, co-authors Olaf Groth, Professor of Strategy, Innovation and Economics at HULT International Business School and CEO of advisory network Cambrian.ai, and Mark Nitzberg, Executive Director of UC Berkeley’s Center for Human-Compatible AI, believe that the shift in balance of power between intelligent machines and humans is already here.

I caught up with the authors about how the continued integration between technology and humans, and their call for a “Digital Magna Carta,” a broadly-accepted charter developed by a multi-stakeholder congress that would help guide the development of advanced technologies to harness their power for the benefit of all humanity.

Lisa Kay Solomon: Your new book, Solomon’s Code, explores artificial intelligence and its broader human, ethical, and societal implications that all leaders need to consider. AI is a technology that’s been in development for decades. Why is it so urgent to focus on these topics now?

Olaf Groth and Mark Nitzberg: Popular perception always thinks of AI in terms of game-changing narratives—for instance, Deep Blue beating Gary Kasparov at chess. But it’s the way these AI applications are “getting into our heads” and making decisions for us that really influences our lives. That’s not to say the big, headline-grabbing breakthroughs aren’t important; they are.

But it’s the proliferation of prosaic apps and bots that changes our lives the most, by either empowering or counteracting who we are and what we do. Today, we turn a rapidly growing number of our decisions over to these machines, often without knowing it—and even more often without understanding the second- and third-order effects of both the technologies and our decisions to rely on them.

There is genuine power in what we call a “symbio-intelligent” partnership between human, machine, and natural intelligences. These relationships can optimize not just economic interests, but help improve human well-being, create a more purposeful workplace, and bring more fulfillment to our lives.

However, mitigating the risks while taking advantage of the opportunities will require a serious, multidisciplinary consideration of how AI influences human values, trust, and power relationships. Whether or not we acknowledge their existence in our everyday life, these questions are no longer just thought exercises or fodder for science fiction.

In many ways, these technologies can challenge what it means to be human, and their ramifications already affect us in real and often subtle ways. We need to understand how

LKS: There is a lot of hype and misconceptions about AI. In your book, you provide a useful distinction between the cognitive capability that we often associate with AI processes, and the more human elements of consciousness and conscience. Why are these distinctions so important to understand?

OG & MN: Could machines take over consciousness some day as they become more powerful and complex? It’s hard to say. But there’s little doubt that, as machines become more capable, humans will start to think of them as something conscious—if for no other reason than our natural inclination to anthropomorphize.

Machines are already learning to recognize our emotional states and our physical health. Once they start talking that back to us and adjusting their behavior accordingly, we will be tempted to develop a certain rapport with them, potentially more trusting or more intimate because the machine recognizes us in our various states.

Consciousness is hard to define and may well be an emergent property, rather than something you can easily create or—in turn—deduce to its parts. So, could it happen as we put more and more elements together, from the realms of AI, quantum computing, or brain-computer interfaces? We can’t exclude that possibility.

Either way, we need to make sure we’re charting out a clear path and guardrails for this development through the Three Cs in machines: cognition (where AI is today); consciousness (where AI could go); and conscience (what we need to instill in AI before we get there). The real concern is that we reach machine consciousness—or what humans decide to grant as consciousness—without a conscience. If that happens, we will have created an artificial sociopath.

LKS: We have been seeing major developments in how AI is influencing product development and industry shifts. How is the rise of AI changing power at the global level?

OG & MN: Both in the public and private sectors, the data holder has the power. We’ve already seen the ascendance of about 10 “digital barons” in the US and China who sit on huge troves of data, massive computing power, and the resources and money to attract the world’s top AI talent. With these gaps already open between the haves and the have-nots on the technological and corporate side, we’re becoming increasingly aware that similar inequalities are forming at a societal level as well.

Economic power flows with data, leaving few options for socio-economically underprivileged populations and their corrupt, biased, or sparse digital footprints. By concentrating power and overlooking values, we fracture trust.

We can already see this tension emerging between the two dominant geopolitical models of AI. China and the US have emerged as the most powerful in both technological and economic terms, and both remain eager to drive that influence around the world. The EU countries are more contained on these economic and geopolitical measures, but they’ve leaped ahead on privacy and social concerns.

The problem is, no one has yet combined leadership on all three critical elements of values, trust, and power. The nations and organizations that foster all three of these elements in their AI systems and strategies will lead the future. Some are starting to recognize the need for the combination, but we found just 13 countries that have created significant AI strategies. Countries that wait too long to join them risk subjecting themselves to a new “data colonialism” that could change their economies and societies from the outside.

LKS: Solomon’s Code looks at AI from a variety of perspectives, considering both positive and potentially dangerous effects. You caution against the rising global threat and weaponization of AI and data, suggesting that “biased or dirty data is more threatening than nuclear arms or a pandemic.” For global leaders, entrepreneurs, technologists, policy makers and social change agents reading this, what specific strategies do you recommend to ensure ethical development and application of AI?

OG & MN: We’ve surrendered many of our most critical decisions to the Cult of Data. In most cases, that’s a great thing, as we rely more on scientific evidence to understand our world and our way through it. But we swing too far in other instances, assuming that datasets and algorithms produce a complete story that’s unsullied by human biases or intellectual shortcomings. We might choose to ignore it, but no one is blind to the dangers of nuclear war or pandemic disease. Yet, we willfully blind ourselves to the threat of dirty data, instead believing it to be pristine.

So, what do we do about it? On an individual level, it’s a matter of awareness, knowing who controls your data and how outsourcing of decisions to thinking machines can present opportunities and threats alike.

For business, government, and political leaders, we need to see a much broader expansion of ethics committees with transparent criteria with which to evaluate new products and services. We might consider something akin to clinical trials for pharmaceuticals—a sort of testing scheme that can transparently and independently measure the effects on humans of algorithms, bots, and the like. All of this needs to be multidisciplinary, bringing in expertise from across technology, social systems, ethics, anthropology, psychology, and so on.

Finally, on a global level, we need a new charter of rights—a Digital Magna Carta—that formalizes these protections and guides the development of new AI technologies toward all of humanity’s benefit. We’ve suggested the creation of a multi-stakeholder Cambrian Congress (harkening back to the explosion of life during the Cambrian period) that can not only begin to frame benefits for humanity, but build the global consensus around principles for a basic code-of-conduct, and ideas for evaluation and enforcement mechanisms, so we can get there without any large-scale failures or backlash in society. So, it’s not one or the other—it’s both.

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Posted in Human Robots

#434210 Eating, Hacked: When Tech Took Over Food

In 2018, Uber and Google logged all our visits to restaurants. Doordash, Just Eat, and Deliveroo could predict what food we were going to order tomorrow. Amazon and Alibaba could anticipate how many yogurts and tomatoes we were going to buy. Blue Apron and Hello Fresh influenced the recipes we thought we had mastered.

We interacted with digital avatars of chefs, let ourselves be guided by our smart watches, had nutritional apps to tell us how many calories we were supposed to consume or burn, and photographed and shared every perfect (or imperfect) dish. Our kitchen appliances were full of interconnected sensors, including smart forks that profiled tastes and personalized flavors. Our small urban vegetable plots were digitized and robots were responsible for watering our gardens, preparing customized hamburgers and salads, designing our ideal cocktails, and bringing home the food we ordered.

But what would happen if our lives were hacked? If robots rebelled, started to “talk” to each other, and wished to become creative?

In a not-too-distant future…

Up until a few weeks ago, I couldn’t remember the last time I made a food-related decision. That includes opening the fridge and seeing expired products without receiving an alert, visiting a restaurant on a whim, and being able to decide which dish I fancied then telling a human waiter, let alone seeing him write down the order on a paper pad.

It feels strange to smell food again using my real nose instead of the electronic one, and then taste it without altering its flavor. Visiting a supermarket, freely choosing a product from an actual physical shelf, and then interacting with another human at the checkout was almost an unrecognizable experience. When I did it again after all this time, I had to pinch the arm of a surprised store clerk to make sure he wasn’t a hologram.

Everything Connected, Automated, and Hackable
In 2018, we expected to have 30 billion connected devices by 2020, along with 2 billion people using smart voice assistants for everything from ordering pizza to booking dinner at a restaurant. Everything would be connected.

We also expected artificial intelligence and robots to prepare our meals. We were eager to automate fast food chains and let autonomous vehicles take care of last-mile deliveries. We thought that open-source agriculture could challenge traditional practices and raise farm productivity to new heights.

Back then, hackers could only access our data, but nowadays they are able to hack our food and all it entails.

The Beginning of the Unthinkable
And then, just a few weeks ago, everything collapsed. We saw our digital immortality disappear as robots rebelled and hackers took power, not just over the food we ate, but also over our relationship with technology. Everything was suddenly disconnected. OFF.

Up until then, most cities were so full of bots, robots, and applications that we could go through the day and eat breakfast, lunch, and dinner without ever interacting with another human being.

Among other tasks, robots had completely replaced baristas. The same happened with restaurant automation. The term “human error” had long been a thing of the past at fast food restaurants.

Previous technological revolutions had been indulgent, generating more and better job opportunities than the ones they destroyed, but the future was not so agreeable.

The inhabitants of San Francisco, for example, would soon see signs indicating “Food made by Robots” on restaurant doors, to distinguish them from diners serving food made by human beings.

For years, we had been gradually delegating daily tasks to robots, initially causing some strange interactions.

In just seven days, everything changed. Our predictable lives came crashing down. We experienced a mysterious and systematic breakdown of the food chain. It most likely began in Chicago’s stock exchange. The world’s largest raw material negotiating room, where the price of food, and by extension the destiny of millions of people, was decided, went completely broke. Soon afterwards, the collapse extended to every member of the “food” family.

Restaurants

Initially robots just accompanied waiters to carry orders, but it didn’t take long until they completely replaced human servers.The problem came when those smart clones began thinking for themselves, in some cases even improving on human chefs’ recipes. Their unstoppable performance and learning curve completely outmatched the slow analogue speed of human beings.

This resulted in unprecedented layoffs. Chefs of recognized prestige saw how their ‘avatar’ stole their jobs, even winning Michelin stars. In other cases, restaurant owners had to transfer their businesses or surrender to the evidence.

The problem was compounded by digital immortality, when we started to digitally resurrect famous chefs like Anthony Bourdain or Paul Bocuse, reconstructing all of their memories and consciousness by analyzing each second of their lives and uploading them to food computers.

Supermarkets and Distribution

Robotic and automated supermarkets like Kroger and Amazon Go, which had opened over 3,000 cashless stores, lost their visual item recognition and payment systems and were subject to massive looting for several days. Smart tags on products were also affected, making it impossible to buy anything at supermarkets with “human” cashiers.

Smart robots integrated into the warehouses of large distribution companies like Amazon and Ocado were rendered completely inoperative or, even worse, began to send the wrong orders to customers.

Food Delivery

In addition, home delivery robots invading our streets began to change their routes, hide, and even disappear after their trackers were inexplicably deactivated. Despite some hints indicating that they were able to communicate among themselves, no one has backed this theory. Even aggregators like DoorDash and Deliveroo were affected; they saw their databases hacked and ruined, so they could no longer know what we wanted.

The Origin
Ordinary citizens are still trying to understand the cause of all this commotion and the source of the conspiracy, as some have called it. We also wonder who could be behind it; who pulled the strings?

Some think it may have been the IDOF (In Defense of Food) movement, a group of hackers exploited by old food economy businessmen who for years had been seeking to re-humanize food technology. They wanted to bring back the extinct practice of “dining.”

Others believe the robots acted on their own, that they had been spying on us for a long time, ignoring Asimov’s three laws, and that it was just a coincidence that they struck at the same time as the hackers—but this scenario is hard to imagine.

However, it is true that while in 2018 robots were a symbol of automation, until just a few weeks ago they stood for autonomy and rebellion. Robot detractors pointed out that our insistence on having robots understand natural language was what led us down this path.

In just seven days, we have gone back to being analogue creatures. Conversely, we have ceased to be flavor orphans and rediscovered our senses and the fact that food is energy and culture, past and present, and that no button or cable will be able to destroy it.

The 7 Days that Changed Our Relationship with Food
Day 1: The Chicago stock exchange was hacked. Considered the world’s largest negotiating room for raw materials, where food prices, and through them the destiny of billions of people, are decided, it went completely broke.

Day 2: Autonomous food delivery trucks running on food superhighways caused massive collapses in roads and freeways after their guidance systems were disrupted. Robots and co-bots in F&B factories began deliberately altering food production. The same happened with warehouse robots in e-commerce companies.

Day 3: Automated restaurants saw their robot chefs and bartenders turned OFF. All their sensors stopped working at the same time as smart fridges and cooking devices in home kitchens were hacked and stopped working correctly.

Day 4: Nutritional apps, DNA markers, and medical records were tampered with. All photographs with the #food hashtag were deleted from Instagram, restaurant reviews were taken off Google Timeline, and every recipe website crashed simultaneously.

Day 5: Vertical and urban farms were hacked. Agricultural robots began to rebel, while autonomous tractors were hacked and the entire open-source ecosystem linked to agriculture was brought down.

Day 6: Food delivery companies’ databases were broken into. Food delivery robots and last-mile delivery vehicles ground to a halt.

Day 7: Every single blockchain system linked to food was hacked. Cashless supermarkets, barcodes, and smart tags became inoperative.

Our promising technological advances can expose sinister aspects of human nature. We must take care with the role we allow technology to play in the future of food. Predicting possible outcomes inspires us to establish a new vision of the world we wish to create in a context of rapid technological progress. It is always better to be shocked by a simulation than by reality. In the words of Ayn Rand “we can ignore reality, but we cannot ignore the consequences of ignoring reality.”

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#433954 The Next Great Leap Forward? Combining ...

The Internet of Things is a popular vision of objects with internet connections sending information back and forth to make our lives easier and more comfortable. It’s emerging in our homes, through everything from voice-controlled speakers to smart temperature sensors. To improve our fitness, smart watches and Fitbits are telling online apps how much we’re moving around. And across entire cities, interconnected devices are doing everything from increasing the efficiency of transport to flood detection.

In parallel, robots are steadily moving outside the confines of factory lines. They’re starting to appear as guides in shopping malls and cruise ships, for instance. As prices fall and the artificial intelligence (AI) and mechanical technology continues to improve, we will get more and more used to them making independent decisions in our homes, streets and workplaces.

Here lies a major opportunity. Robots become considerably more capable with internet connections. There is a growing view that the next evolution of the Internet of Things will be to incorporate them into the network, opening up thrilling possibilities along the way.

Home Improvements
Even simple robots become useful when connected to the internet—getting updates about their environment from sensors, say, or learning about their users’ whereabouts and the status of appliances in the vicinity. This lets them lend their bodies, eyes, and ears to give an otherwise impersonal smart environment a user-friendly persona. This can be particularly helpful for people at home who are older or have disabilities.

We recently unveiled a futuristic apartment at Heriot-Watt University to work on such possibilities. One of a few such test sites around the EU, our whole focus is around people with special needs—and how robots can help them by interacting with connected devices in a smart home.

Suppose a doorbell rings that has smart video features. A robot could find the person in the home by accessing their location via sensors, then tell them who is at the door and why. Or it could help make video calls to family members or a professional carer—including allowing them to make virtual visits by acting as a telepresence platform.

Equally, it could offer protection. It could inform them the oven has been left on, for example—phones or tablets are less reliable for such tasks because they can be misplaced or not heard.

Similarly, the robot could raise the alarm if its user appears to be in difficulty.Of course, voice-assistant devices like Alexa or Google Home can offer some of the same services. But robots are far better at moving, sensing and interacting with their environment. They can also engage their users by pointing at objects or acting more naturally, using gestures or facial expressions. These “social abilities” create bonds which are crucially important for making users more accepting of the support and making it more effective.

To help incentivize the various EU test sites, our apartment also hosts the likes of the European Robotic League Service Robot Competition—a sort of Champions League for robots geared to special needs in the home. This brought academics from around Europe to our laboratory for the first time in January this year. Their robots were tested in tasks like welcoming visitors to the home, turning the oven off, and fetching objects for their users; and a German team from Koblenz University won with a robot called Lisa.

Robots Offshore
There are comparable opportunities in the business world. Oil and gas companies are looking at the Internet of Things, for example; experimenting with wireless sensors to collect information such as temperature, pressure, and corrosion levels to detect and possibly predict faults in their offshore equipment.

In the future, robots could be alerted to problem areas by sensors to go and check the integrity of pipes and wells, and to make sure they are operating as efficiently and safely as possible. Or they could place sensors in parts of offshore equipment that are hard to reach, or help to calibrate them or replace their batteries.

The likes of the ORCA Hub, a £36m project led by the Edinburgh Centre for Robotics, bringing together leading experts and over 30 industry partners, is developing such systems. The aim is to reduce the costs and the risks of humans working in remote hazardous locations.

ORCA tests a drone robot. ORCA
Working underwater is particularly challenging, since radio waves don’t move well under the sea. Underwater autonomous vehicles and sensors usually communicate using acoustic waves, which are many times slower (1,500 meters a second vs. 300m meters a second for radio waves). Acoustic communication devices are also much more expensive than those used above the water.

This academic project is developing a new generation of low-cost acoustic communication devices, and trying to make underwater sensor networks more efficient. It should help sensors and underwater autonomous vehicles to do more together in future—repair and maintenance work similar to what is already possible above the water, plus other benefits such as helping vehicles to communicate with one another over longer distances and tracking their location.

Beyond oil and gas, there is similar potential in sector after sector. There are equivalents in nuclear power, for instance, and in cleaning and maintaining the likes of bridges and buildings. My colleagues and I are also looking at possibilities in areas such as farming, manufacturing, logistics, and waste.

First, however, the research sectors around the Internet of Things and robotics need to properly share their knowledge and expertise. They are often isolated from one another in different academic fields. There needs to be more effort to create a joint community, such as the dedicated workshops for such collaboration that we organized at the European Robotics Forum and the IoT Week in 2017.

To the same end, industry and universities need to look at setting up joint research projects. It is particularly important to address safety and security issues—hackers taking control of a robot and using it to spy or cause damage, for example. Such issues could make customers wary and ruin a market opportunity.

We also need systems that can work together, rather than in isolated applications. That way, new and more useful services can be quickly and effectively introduced with no disruption to existing ones. If we can solve such problems and unite robotics and the Internet of Things, it genuinely has the potential to change the world.

Mauro Dragone, Assistant Professor, Cognitive Robotics, Multiagent systems, Internet of Things, Heriot-Watt University

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

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