How Connected Life Sciences Devices Lead To Continuous Care

With connected medical devices, apps and data, life sciences organizations can bridge long-standing gaps in healthcare and deliver a more continuous care experience, says Brian Williams, Cognizant’s Chief Digital Officer for Life Sciences.

Global health systems have traditionally delivered services episodically, by focusing on acute, critical care rather than individual health and well-being. It should come as no surprise, then, that life sciences companies often deliver their solutions following that same model of care.

Sadly, this leads to gaps in data and service alignment, not to mention significant disconnects with the broader healthcare ecosystem. Consumer devices and wellness apps, for example, often exist within their own individual siloes — causing organizations to miss out on valuable data that could inform patient diagnosis, management and treatment.

This lack of orchestration produces sub-optimal outcomes at significant expense to providers, payers and patients alike. It is also at direct odds with patients’ increasing digital expectations when using medical devices and when taking drugs and therapies. Whether they are participating in a clinical trial, living with a chronic condition or recovering from a procedure, patients expect to be informed and cared for with seamless digital experiences on par with what they receive when shopping or banking online.

However, the emergence of integrated, connected devices, apps and data has opened new possibilities for treatments and clinical trials. This new level of connectivity helps bridge a longstanding gap in wellness: the disconnect between an individual’s everyday health behavior and their episodic healthcare. These experiences generate valuable data insights, creating new commercial opportunities and the promise of better patient outcomes.

The impact of life sciences connectivity

Drawing from our recent series on healthcare IoT, here are three stakeholder groups within the healthcare and life sciences ecosystem that stand to benefit greatly from this new level of connectivity and the more continuous, predictive and preventive care it enables.

  • Patients with chronic conditions. Chronic diseases are often accompanied by additional conditions, such as depression, that can impede effective treatment. Consequently, information about an individual’s behavioral health status has become increasingly important in treatment decisions, as has information about the individual’s relationships with the people around them.
  • Wearable IoT devices that monitor fitness and health conditions can pair with an ever-growing set of apps for health, wellness and nutrition monitoring. Over time, a baseline of physiological indicators such as an individual’s heart rate and blood pressure, as well as activity, diet and sleep patterns, will develop. When additional data from clinical encounters, including diagnostic imaging, lab tests, genomics, stress tests and physician notes, is integrated with that baseline, it increases the ability to predict how an individual may respond to any particular treatment.
  • Elderly patients. Quite often, the most effective tools for early detection of a developing condition in elderly patients are not implants or biometric monitors, but devices that monitor changes in activities of daily living (ADL).
  • For example, the onset of congestive heart failure can be detected through reduced use of the bed, as patients with trouble breathing when lying down switch to sleeping semi-upright in a recliner. Changes in toilet flushes, meanwhile, can detect a urinary tract infection or incipient dehydration. Moreover, while one in four Americans over 65 falls each year, only half tell their doctor.
  • Passive infrared motion detectors, pressure sensors in beds and chairs, sensors for CO2 concentration, sound (vibration) and video — anonymized as necessary for privacy — can all be used to first establish a baseline of normal variability, and then be applied to detect significant deviations from that baseline. This continuous and nearly invisible sensing can be surprisingly effective in assisting in care.
  • Hospital clinicians and support staff. Healthcare is increasingly a team enterprise — including not only physicians, nurses, allied health staff and technicians but also AI-enabled equipment. The point of care is also expanding, with shortened hospital stays and more care delivered in outpatient facilities and in-home settings.
  • Connected sensors enable every member of the team to access to real-time data relevant to their task. Smart hospitals with a real-time health system (RTHS) can leverage sensors to collect data widely, distill and analyze it — and then quickly distribute curated findings to users. When captured remotely, this eases the transition in care from the hospital to other settings, allowing a more continuous and participatory level of care that extends long past a patient’s physical stay in a healthcare facility.
  • An RTHS can improve operations, clinical tasks and patient experience. For example, providers that boost operational effectiveness typically rely on a wide range of IoT-enabled asset management solutions that locate mobile assets, monitor equipment operating conditions and track inventories of consumables, pharmaceuticals and medical devices. This optimizes equipment utilization, reduces waste, increases equipment uptime and ensures optimal inventories.
  • Once clinicians and support staff can view how long various steps take in their workflows, where delays occur and what patients experience as a result, they can then evolve solutions based on a combination of their intimate day-to-day knowledge and data on how that workflow interacts with or is used by other functions.

From episodic to continuous care

Too often, the life sciences industry has delivered a one-size-fits-all approach to clinical trials and patient care that may not represent real-life, individual situations — situations that require tailored engagement that wrap therapies and interventions in end-to-end, digital solutions.

This can and should change. Device connectivity and access to data are impacting every aspect of healthcare and life sciences, moving the industry away from acute, episodic care, to a system that is more participatory and predictive.

For example, a patient may be walking a mere 24 hours after a typical hip surgery and could be discharged from the hospital a day or two after the procedure. However, that episodic care experience belies a much longer recovery and rehabilitation period spanning weeks or months.While that care experience today takes place largely outside the purview of the orthopedic surgeon, better device connectivity can enable patient monitoring — and even patient services — to be extended well beyond the length of the initial hospital visit.

Rather than relying on spotty reporting from physical therapists or the patients themselves, an orthopedist can continuously and seamlessly track a patient’s progress, and then decide when and how to intervene if things aren’t going as expected. Zimmer’s mymobility application, which supports patient engagement and monitoring outside the hospital following surgery, is a good example of what this looks like in practice.

A fully orchestrated ecosystem

Sensors and instrumentation — and the hundreds of APIs that connect them — can provide accurate and timely data about many parameters of the human condition. When this is all properly orchestrated, we can better understand how diseases progress and how bodies respond to various interventions.

That’s the intent behind our alliance with Philips and its HealthSuite Digital Platform, which is built on AWS and designed to simplify and standardize device connectivity, data access, identity management, and structured and unstructured data management within a high-trust, HIPAA and GDPR-compliant environment.

We believe that life sciences companies can derive true value from this influx of new data. Not only can the resulting insights inform new services, drugs and therapies and inspire new models of continuous engagement; they can also improve adherence to treatment and patient health.

To learn more, visit the Life Sciences section of our website.

Brian is Cognizant’s Chief Digital Officer for Life Sciences and is responsible for designing digitally enabled solutions to facilitate care access and delivery. He is also the Global Life Sciences Consulting

Source: How Connected Life Sciences Devices Lead To Continuous Care

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Related Contents:

M. Birkholz; A. Mai; C. Wenger; C. Meliani; R. Scholz (2016). “Technology modules from micro- and nano-electronics for the life sciences”. WIREs Nanomed. Nanobiotech. 8 (3): 355–377. doi:10.1002/wnan.1367. PMID 26391194

“What is Biomonitoring?” (PDF). American Chemistry Council. Archived from the origin(2005-04-08). Natural Fibers, Biopolymers, and Biocomposites. CRC Press. ISBN 978-0-203-50820-6.

National Human Genome Research Institute (2010-11-08). “FAQ About Genetic and Genomic Science”

If You Love Staying Up Late and Sleeping In, Doing Otherwise Might Actually Hurt Your Health

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Night owls might get a rap for staying up too late watching Netflix or getting lost in meme spirals on the web, but it’s not all fun and games. Study after study shows the later you sleep and rise, the more likely you are to develop some serious health complications.

A 2018 paper by researchers from Northwestern University and the University of Surrey in the UK doubles down on the findings that night owls are more likely to suffer from a host of different diseases and disorders—diabetes, mental illnesses, neurological problems, gastrointestinal issues, and heart disease, to name a few. It also concludes, for the first time, that night owls had a 10 percent increased risk of dying (in the time period used in the study) compared to those who are early to rise and early to sleep (a.k.a. larks).

“I think it’s really important to get this message out to people who are night owls,” says lead author Kristen Knutson, an associate professor of neurology at Northwestern’s Feinberg School of Medicine. “There may be some compelling consequences associated with these habits, and they might need to be more vigilant in maintaining a healthier lifestyle.”

Published in Chronobiology International, the paper analyzed 433,268 individuals who participated in the UK Biobank, a massive cohort study run from 2006 to 2010 aimed at investigating the role of genetic predisposition and environmental contributions to disease prevalence. Those participants were asked questions related to their chronotype, or preferred time and duration of sleeping during a 24-hour day. Participants identified as “definitely a morning person,” “more a morning person than evening person,” “more an evening than a morning person,” or “definitely an evening person.”

The researchers found that about 10,000 subjects died in the six-and-a-half years that followed the end of the Biobank study, and the ones who were “definite evening types” had a 10 percent increased risk of perishing compared to “definite morning types.” This number, the researchers say, was found after controlling for age, gender, ethnicity, and prior health problems.

That sounds scary, sure—but there are a few limitations worth considering. For one, says Knutson, “we weren’t able to pinpoint and find out why night owls were more likely to die sooner,” so the direct cause of mortality is unknown, creating some murkiness as to what extent night owl lifestyles influenced those deaths.

“We think,” says Knutson, “it is at least partly due to our biological clocks. We think the problem is that the night owls are forced to live in a more ‘lark’ world, where you have to get up early for work and start the day than their internal clocks want to. So it’s a mismatch between the internal clock and the external world, and it’s a problem in the long run.”

The mismatch Knutson is referring to has to do with circadian rhythms, the biological processes that govern the body over the course of the 24-hour day. Circadian rhythms determine sleep patterns, energy levels, hormones, and body temperature—basically all the most important things. “There are ideal or optimal times for certain things to occur,” says Knutson.

Messing with your preferred sleep schedule can drastically disrupt your circadian rhythms, which in turn can have severe, negative effects on your health. We’re all feeling the effects of this, to some extent, no matter when we like to go to sleep; research indicates that modern humans are sleeping poorly thanks to artificial light, warmer temperatures, and stress, and scientists are working to understand what kind of impact this has on our health. Studies on extreme cases—shift workers and people like ER doctors and firefighters who regularly stay up all night—suggest the downsides can be quite dire.

Unfortunately, the Biobank data only indicated whether someone identified as a morning or evening person, not whether they had a sleep schedule that suited their chronotype. “We know what their preferred time to sleep is, but we have no idea what they were actually doing on a day-to-day basis,” says Knutson. That’s a question she hopes to address in subsequent studies.

Moreover, the data is limited to just British participants, most of whom were caucasians of Irish or English descent. It’s likely the results would be similar for other populations in the Western world, but they could also be substantially different for night owls elsewhere.

To some extent, you’re stuck with the chronotype you’re born with. Genes play a significant role in governing your internal clock, so if you’re naturally attuned to sleeping at 3:00 a.m. and waking up at 11:00 a.m., your best bet would be to find a career and lifestyle where this is okay.

But there are certain actions individuals could take to minimize the difference between their internal clock and their external life. In a perfect world, Knutson notes, employers could be more cognizant and allow employees to pick a work schedule that offers a good compromise between everyone’s needs. People can also shift their sleep and wake hours a little earlier to minimize discord, but they would need to do so gradually, and maintain that shift consistently. Lapsing into night owl habits on the weekends or on vacation is out of the question.

Of course, being a creature of the night isn’t all bad. Other studies have shown that the whole morning versus night person debate is really more of a proxy battle between organized and meticulous, or being expressive and imaginative: day-dwellers might be more focused on achieving goals and paying attention to details, but all-nighters tend to be more creative and open to new experiences. If you’re a night owl, don’t be too rash to think you should change yourself. Maybe you just need a career that harnesses your artistic side—and lets you sleep in a little.

By: Neel V. Patel

Source: Pocket

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Critics:

A night owl, evening person or simply owl, is a person who tends to stay up until late at night, or the early hours of the morning. Night owls who are involuntarily unable to fall asleep for several hours after a normal time may have delayed sleep phase disorder.

The opposite of a night owl is an early bird – a lark as opposed to an owl – which is someone who tends to begin sleeping at a time that is considered early and also wakes early. Researchers traditionally use the terms morningness and eveningness[1] for the two chronotypes or diurnality and nocturnality in animal behavior. In several countries, especially in Scandinavia, early birds are called A-people and night owls are called B-people.

The tendency to be a night owl exists on a spectrum, with most people being typical, some people having a small or moderate tendency to be a night owl, and a few having an extreme tendency to be a night owl.[13] An individual’s own tendency can change over time and is influenced by multiple factors, including:

  • a genetic predisposition, which can cause the tendency to run in families,
  • the person’s age, with teenagers and young adults tending to be night owls more than young children or elderly people, and
  • the environment the person lives in, except for the patterns of light they are exposed to through seasonal changes as well as through lifestyle (such as spending the day indoors and using electric lights in the evening).[13]

The genetic make-up of the circadian timing system underpins the difference between early and late chronotypes, or early birds and night owls.[14] While it has been suggested that circadian rhythms may change over time, including dramatic changes that turn a morning lark to a night owl or vice versa,[15][16] evidence for familial patterns of early or late waking would seem to contradict this, and individual changes are likely on a smaller scale.[17]

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References:

Teaching Diversity And Inclusion To The Billions Of Intelligent Systems Making Autonomous Decisions

Engineers Meeting in Robotic Research Laboratory

The idea that diversity and inclusion should be core drivers of the new economy and the emerging global society are mostly understood at a human level. The more people who are part of one system, being offered the same opportunities regardless of their gender, race, ethnic origin, and many other diverse variables, the higher the tide rises for everybody. But even with that intrinsic understanding of the idea that diversity and inclusion will generate a different and better world, significant barriers still exist to making this human truth a practical reality in our daily lives.

The power of all genders, all races, and all languages can change the world. Even each of these pieces, though, has its blind spots if taken as a standalone viewpoint — in effect, by seeing the world through a single lens, that lens can act as a deep barrier. Imagine how much is lost with only a single way of seeing, thinking, learning, and maybe even applying those learnings.

Digital companies talk about the power of the individual or the customer to be the center of the service. Yet how can we build around individuals without recognizing and servicing the unique combinations of needs or opinions that diverse thinking and actions entail? McKinsey has, since 2014, leaned into the idea of measuring diversity and inclusivity as a driver of business value creation. The intent is to show every year that companies that live and deliver diverse and inclusive strategies outperform their industry peers.

The gap (between diverse and inclusive leaders and the poorest performers) has gotten bigger year by year, growing from 33% percent in 2018 to 36% in 2019. Even with clear and longitudinal data, we still struggle against many inherent biases to accept and act on the fact that diversity and inclusion widen the lens for viewing ideas, thinking, processes, and customers in an increasingly global market.

The world will get more diverse over time. By 2044 it is projected that over half of Americans will belong to a minority group. We will in effect be a collection of diversities, with one in five of us not being born in the USA but living here. Multiply this American future by the nuances of each of the 195 countries in the world and together we will be the largest collection of diversities the planet has ever seen.

Now imagine a world of not just 7 billion people, but 40 billion devices computing, connecting, sensing, predicting, and running autonomously in an intelligent systems world. PwC estimated that 70% of all global GDP growth between 2020 and 2030 will come from this machine economy (AI, robotics, IoT devices).

U.S. GDP is expected to grow $10T between now and 2030. If 70% of that is from these machines sensing, predicting, computing, and connecting on the intelligent edge, then that is a $7T economy. Will these machines be more capable than humankind has been to think about diversity and inclusion in the way they work with data, humans, and other machines?

These devices don’t have a McKinsey to explain to them where and how inclusion and diversity will drive a better result. They make decisions in milliseconds based on the programming instructions they receive, and they learn as they execute their many, often complex and intelligent, tasks.

How these machines learn to think (constantly) are driven by rules set by humans and by other machines that were in part or wholly programmed by humans. How can the right behaviors be instilled in these intelligent systems? Think of two basic dynamics we must pay attention to in an increasingly intelligent systems world:

Human experiences drive diversity and inclusive design

Learning — and applying — how to be aware of the needs of diverse groups has more value than ever before. This acquired knowledge will act as the codex for how we program the devices that live and work with us globally by 2030 and beyond. There is a narrow time window in which to take our own personal experiences and the experience of others around us into account in the design and programming process for intelligent systems that will manage autonomous vehicles, medical devices, and manufacturing environments where cobots will be working alongside humans.

All machines might look and behave in the same way, but the humans around them do not, so what machine biases will exist in the intelligent systems world? Understanding how to design and program for inclusive and diverse thinking without bias means intelligent systems need to have a progressive learning ability (e.g., machine learning and digital feedback loops), as well as mission-critical capacities that mean they can safely and securely function around humans who may look, sound, move, or think differently from those whom the machines have been designed or operated around.

Machines will be diverse too and will need to be inclusive of each other

Once we live in an intelligent systems world, we will need intelligent systems to recognize each other in near instant time. These systems might be doing completely different tasks, but they might need to share data, space, or compute capacity in milliseconds. Knowing when, where, and how to have that network effect in an intelligent systems world (for example, consider autonomous vehicles) requires a capacity for inclusiveness and maybe even a clear comprehension about the power of diverse data sets from different devices to create value far greater than the sum of all parts.

Nurturing that capacity to create systems for a diversity of design and operations, as well as for an inclusiveness to allow constant learning, is a challenge that will be essential in an intelligent systems world.

We will not be able to make the right world for these intelligent systems and all that they can bring to humanity if we do not design, operate, and build them to be inclusive, diverse, and without bias in how they operate. While not suggesting that there should be a soul to an intelligent system, we should recognize that the moment in our own human world to encourage as much diversity and inclusion in our thinking is right now, and how well we do it will have major consequences for how we teach our intelligent systems to thrive in a world dominated with a diversity of machines and humans.

PRESIDENT AND CEO

With more than 25 years of experience driving digital innovation and growth at technology companies, Kevin Dallas is responsible for all aspects of the Wind River business globally. He joined Wind River from Microsoft, where he most recently served as the corporate vice president for cloud and AI business development. At Microsoft, he led a team creating partnerships that enable the digital transformation of customers and partners across a range of industries including: connected/autonomous vehicles, industrial IoT, discrete manufacturing, retail, financial services, media and entertainment, and healthcare.

Prior to joining Microsoft in 1996, he held roles at NVIDIA Corporation and National Semiconductor (now Texas Instruments Inc.) in the U.S., Europe, and the Middle East in roles that included microprocessor design, systems engineering, product management, and end-to-end business leadership. He currently serves as a director on the board of Align Technology, Inc. He holds a B.S.c. degree in electrical and electronic engineering from Staffordshire University, Stoke-on-Trent, Staffordshire, England.

Source: Teaching Diversity And Inclusion To The Billions Of Intelligent Systems Making Autonomous Decisions

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Related Links:

Understood.Org -Loneliness Can Impact Kids Who Learn and Think Differently

Beyond Differences – Consequences of Social Isolation

Vox – America’s Loneliness Epidemic and Coronavirus Pandemic Together

New York Times – Learning Pods

CDC.Gov – Parent Checklist

Tyler Clementi Foundation’s Cybersafety Guide

Connecticut Children’s – Mindfulness Exercises for Kids

Beyond Tolerance

Teaching Tolerance

National Seed Project Curriculum

Everyday Feminism: What is Heteronormativity?

https://www.weareteachers.com/mirrors-and-windows/

The Link Between Bioelectricity and Consciousness

Life seems to be tied to bioelectricity at every level. The late electrophysiologist and surgeon Robert Becker spent decades researching the role of the body’s electric fields in development, wound healing, and limb regrowth. His 1985 book, The Body Electric: Electromagnetism and the Foundation of Life, was a fascinating deep dive into how the body is electric through and through—despite our inability to see or sense these fields with our unaided senses. But Becker’s work was far from complete.

One scientist who has taken up Becker’s line of inquiry is Michael Levin. He got hooked on the subject after he read The Body Electric. Levin has been working on “cracking the bioelectric code,” as a 2013 paper of his put it, ever since. “Evolution,” Levin has said, “really did discover how good the biophysics of electricity is for computing and processing information in non-neural tissues,” the many thousands of cell types that make up the body, our word for trillions of cells cooperating. “It’s really hard to define what’s special about neurons,” he told me. “Almost all cells do the things neurons do, just more slowly.”

How do disarranged cells and organs intuit what do to?

His team at Tufts University develops new molecular-genetic and conceptual tools to probe large-scale information processing in regeneration, embryo development, and cancer suppression—all mediated by bioelectric fields in varying degrees. This work involves examining, for example, how frogs, which normally don’t regenerate whole limbs (like salamanders do) can regrow limbs, repair their brains and spinal cords, or normalize tumors with the help of “electroceuticals” (a pun based on “pharmaceuticals”).

These are therapies that target the bioelectric circuits of tumors instead of, or together with, chemical-based therapies. Bioelectric fields are, in other words, more powerful than we have suspected and perform many surprising roles in the human body and all animal bodies.

Nature seems to have figured out that electric fields, similar to the role they play in human-created machines, can power a wide array of processes essential to life. Perhaps even consciousness itself. A veritable army of neuroscientists and electrophysiologists around the world are developing steadily deeper insights into the degree that electric and magnetic fields—“brainwaves” or “neural oscillations”—seem to reveal key aspects of consciousness.

The prevailing view for some time now has been that the brain’s bioelectric fields, which are electrical and magnetic fields produced at various physical scales, are an interesting side effect—or epiphenomenon—of the brains’ activity, but not necessarily relevant to the functioning of consciousness itself.

A number of thinkers are suggesting now, instead, that these fields may in fact be the main game in town when it comes to explaining consciousness. In a 2013 paper, philosopher Mostyn Jones reviewed various field theories of consciousness, still a minority school of thought in the field but growing.

If that approach is right, it is likely that the body’s bioelectric fields are also, more generally, associated in some manner with some kind of consciousness at various levels. Levin provided some support for this notion when I asked him about the potential for consciousness, in at least some rudimentary form, in the body’s electric fields.

“There are very few fundamental differences between neural networks and other tissues of bioelectrically communicating cells,” he said in an email. “If you think that consciousness in the brain is somehow a consequence of the brain’s electrical activity, then there’s no principled reason to assume that non-neural electric networks won’t underlie some primitive, basal (ancient) form of nonverbal consciousness.”

This way of thinking opens up exciting possibilities. It recognizes that there is perhaps some intelligence (and, to some thinkers, maybe even consciousness) in all of the body’s bioelectric fields, which are efficient sources of information transfer and even a kind of computation. In his work, Levin pieces together how these fields can contain information that guides growth and regeneration.

He sometimes describes these guiding forces as “morphogenetic fields.” It may sound like a mystical notion, but it’s quite physical and real, backed up by hard data. This information, Levin said, can be stored in multicellular electric fields “in a way that is likely very similar to how behavioral memories—of seeing a specific shape for example—are stored in a neuronal network.”

Take the case of a frog. “To become frogs, tadpoles have to rearrange their faces during metamorphosis,” Levin said. “It used to be thought that these movements were hardcoded, but our ‘Picasso’ tadpoles—which have all the organs in the wrong places—showed otherwise.” The apparent know-how that these bioelectric fields demonstrate, in terms of growing normal frogs in very un-normal circumstances, is uncanny. “Amazingly, they still largely became normal frogs!”

How do disarranged cells and organs intuit what do to? Levin, and the renowned philosopher and cognitive scientist Daniel Dennet, recently tackled this question in a rather provocatively titled article, “Cognition All the Way Down.” Something like thinking, they argue, isn’t just something we do in our heads that requires brains.

It’s a process even individual cells themselves, and not requiring any kind of brain, also take part in. To the biologists who see this as a cavalier form of anthropomorphization, Levin and Dennet say, “Chill out.” It’s useful to anthropomorphize many different kinds of life, to see in their parts and processes a variety of teleological experience. “Ever since the cybernetics advances of the 1940s and ’50s, engineers have had a robust, practical science of mechanisms with purpose and goal-directedness—without mysticism,” they write. “We suggest that biologists catch up.”

With respect to purposes and teleology (goal-directed behavior), they make their key point clear: “We think that this commendable scientific caution has gone too far, putting biologists into a straitjacket.”

A promising route for better understanding may be found, they write, in “thinking of parts of organisms as agents, detecting opportunities and trying to accomplish missions.” This is “risky, but the payoff in insight can be large.” For Levin, at least, bioelectric fields are key mechanisms for this kind of collective decision-making. These fields connect cells and tissues together, allowing, along with synaptic connections, for rapid information exchange, not only with immediate neighbors but distant ones as well.

These communication channels are involved in the emergence of cancer, which means that, according to Levin, they can potentially be useful in curing some forms of cancer. “You can [use bioelectric fields to] induce full-on metastatic melanoma—a kind of skin cancer—in perfectly normal animals with no carcinogens or nasty chemicals that break DNA,” he said. You can also use these same fields “to normalize existing tumors or prevent them from forming.” He’s currently moving this work to human clinical models.

The importance of bioelectric fields is all about connection, information, and computation. These ingredients equal cognition for Levin and Dennett, which is, for them, a continuum of complexity that has developed over a billion years of biological evolution. It’s not an all or nothing kind of thing but a spectrum—one that plays a role in development, evolution, cancer, and in the workings of consciousness itself.

By: Tom Hunt

Tam Hunt is a philosopher, a practicing lawyer, and writer. He is the author of two books on the philosophy of consciousness: Eco, Ego, Eros: Essays in Philosophy, Spirituality, and Science and Mind, World, God: Science and Spirit in the 21st Century.

Source: The Link Between Bioelectricity and Consciousness

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Our bodies rely on an ultrafast nervous system to send impulses very quickly and it all starts with a special cell called the neuron. In this episode, Patrick will explain how these cells tell your body what to do. » Subscribe to Seeker! http://bit.ly/subscribeseeker » Watch more Human! http://bit.ly/HUMANplaylist » Visit our shop at http://shop.seeker.com
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Big Ethical Questions about the Future of AI

Artificial intelligence is already changing the way we live our daily lives and interact with machines. From optimizing supply chains to chatting with Amazon Alexa, artificial intelligence already has a profound impact on our society and economy. Over the coming years, that impact will only grow as the capabilities and applications of AI continue to expand.

AI promises to make our lives easier and more connected than ever. However, there are serious ethical considerations to any technology that affects society so profoundly. This is especially true in the case of designing and creating intelligence that humans will interact with and trust. Experts have warned about the serious ethical dangers involved in developing AI too quickly or without proper forethought. These are the top issues keeping AI researchers up at night.

Bias: Is AI fair

Bias is a well-established facet of AI (or of human intelligence, for that matter). AI takes on the biases of the dataset it learns from. This means that if researchers train an AI on data that are skewed for race, gender, education, wealth, or any other point of bias, the AI will learn that bias. For instance, an artificial intelligence application used to predict future criminals in the United States showed higher risk scores and recommended harsher actions for black people than white based on the racial bias in America’s criminal incarceration data.

Of course, the challenge with AI training is there’s no such thing as a perfect dataset. There will always be under- and overrepresentation in any sample. These are not problems that can be addressed quickly. Mitigating bias in training data and providing equal treatment from AI is a major key to developing ethical artificial intelligence.

Liability: Who is responsible for AI?

Last month when an Uber autonomous vehicle killed a pedestrian, it raised many ethical questions. Chief among them is “Who is responsible, and who’s to blame when something goes wrong?” One could blame the developer who wrote the code, the sensor hardware manufacturer, Uber itself, the Uber supervisor sitting in the car, or the pedestrian for crossing outside a crosswalk.

Developing AI will have errors, long-term changes, and unforeseen consequences of the technology. Since AI is so complex, determining liability isn’t trivial. This is especially true when AI has serious implications on human lives, like piloting vehicles, determining prison sentences, or automating university admissions. These decisions will affect real people for the rest of their lives. On one hand, AI may be able to handle these situations more safely and efficiently than humans. On the other hand, it’s unrealistic to expect AI will never make a mistake. Should we write that off as the cost of switching to AI systems, or should we prosecute AI developers when their models inevitably make mistakes?

Security: How do we protect access to AI from bad actors?

As AI becomes more powerful across our society, it will also become more dangerous as a weapon. It’s possible to imagine a scary scenario where a bad actor takes over the AI model that controls a city’s water supply, power grid, or traffic signals. More scary is the militarization of AI, where robots learn to fight and drones can fly themselves into combat.

Cybersecurity will become more important than ever. Controlling access to the power of AI is a huge challenge and a difficult tightrope to walk. We shouldn’t centralise the benefits of AI, but we also don’t want the dangers of AI to spread. This becomes especially challenging in the coming years as AI becomes more intelligent and faster than our brains by an order of magnitude.

Human Interaction: Will we stop talking to one another?

An interesting ethical dilemma of AI is the decline in human interaction. Now more than any time in history it’s possible to entertain yourself at home, alone. Online shopping means you don’t ever have to go out if you don’t want to.

While most of us still have a social life, the amount of in-person interactions we have has diminished. Now, we’re content to maintain relationships via text messages and Facebook posts. In the future, AI could be a better friend to you than your closest friends. It could learn what you like and tell you what you want to hear. Many have worried that this digitization (and perhaps eventual replacement) of human relationships is sacrificing an essential, social part of our humanity.

Employment: Is AI getting rid of jobs?

This is a concern that repeatedly appears in the press. It’s true that AI will be able to do some of today’s jobs better than humans. Inevitably, those people will lose their jobs, and it will take a major societal initiative to retrain those employees for new work. However, it’s likely that AI will replace jobs that were boring, menial, or unfulfilling. Individuals will be able to spend their time on more creative pursuits, and higher-level tasks. While jobs will go away, AI will also create new markets, industries, and jobs for future generations.

Wealth Inequality: Who benefits from AI?

The companies who are spending the most on AI development today are companies that have a lot of money to spend. A major ethical concern is AI will only serve to centralizecoro wealth further. If an employer can lay off workers and replace them with unpaid AI, then it can generate the same amount of profit without the need to pay for employees.

Machines will create wealth more than ever in the economy of the future. Governments and corporations should start thinking now about how we redistribute that wealth so that everyone can participate in the AI-powered economy.

Power & Control: Who decides how to deploy AI?

Along with the centralization of wealth comes the centralization of power and control. The companies that control AI will have tremendous influence over how our society thinks and acts each day. Regulating the development and operation of AI applications will be critical for governments and consumers. Just as we’ve recently seen Facebook get in trouble for the influence its technology and advertising has had on society, we might also see AI regulations that codify equal opportunity for everyone and consumer data privacy.

Robot Rights: Can AI suffer?

A more conceptual ethical concern is whether AI can or should have rights. As a piece of computer code, it’s tempting to think that artificially intelligent systems can’t have feelings. You can get angry with Siri or Alexa without hurting their feelings. However, it’s clear that consciousness and intelligence operate on a system of reward and aversion. As artificially intelligent machines become smarter than us, we’ll want them to be our partners, not our enemies. Codifying humane treatment of machines could play a big role in that.

Ethics in AI in the coming years

Artificial intelligence is one of the most promising technological innovations in human history. It could help us solve a myriad of technical, economic, and societal problems. However, it will also come with serious drawbacks and ethical challenges. It’s important that experts and consumers alike be mindful of these questions, as they’ll determine the success and fairness of AI over the coming years.

By: By Steve Kilpatrick
Co-Founder & Director
Artificial Intelligence & Machine Learning

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