Elizabeth Ricker /author/elizabeth-ricker/ Fact-based, well-reasoned perspectives from around the world Sat, 27 Sep 2014 23:42:52 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 The Internet of Things: Toward a Two Trillion Dollar Industry /region/north_america/the-internet-of-things-toward-a-two-trillion-dollar-industry-99743/ /region/north_america/the-internet-of-things-toward-a-two-trillion-dollar-industry-99743/#comments Wed, 03 Sep 2014 10:33:55 +0000 http://www.fairobserver.com/?p=45001 Smart gadgets that help users in their daily lives need three things to succeed. Nine months ago, Google paid $3.2 billion for a little company called Nest. To put that sum in perspective: $3.2 billion is roughly equivalent to the GDP of the British Virgin Islands, American Samoa, and Greenland – combined. Why would Google… Continue reading The Internet of Things: Toward a Two Trillion Dollar Industry

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Smart gadgets that help users in their daily lives need three things to succeed.

Nine months ago, Google paid $3.2 billion for a little company called Nest. To put that sum in perspective: $3.2 billion is roughly equivalent to the GDP of the British Virgin Islands, American Samoa, and Greenland – combined. Why would pay that much for a thermostat and smoke alarm company? Perhaps because market research firms such as Gartner think the market for the of Things – in which Nest is included – will be worth over 600 times that () by 2020. Given this summer’s $555 million acquisition of , the “plug-and-play” wifi-enabled security camera, it looks like Google and Nest are starting to hatch their own Internet of Things.

Gadgets and Communication

The Internet of Things will succeed by taking three steps. First, when it makes things connect in ways that are easy for us to use; second, when it creates things that are deeply valuable; and third, when it makes the Things likeable.

Before we start prognosticating, though, what is the “Internet of Things”? It consists of gadgets and their communication with you, and it consists of the communication of those gadgets with each other. Consider Nest’s smoke and carbon monoxide detector, on June 18 for $99, down from its $129 launch price. It is specific, minimally annoying, and it provides information you can access anywhere. When you install the device in a room, it knows which room it is in, and you can always check what it is detecting on your smart phone app. Under normal conditions, it shows a green light and makes no sound. If some smoke is detected, a calm, human-sounding voice intones: “Heads up: There is smoke in the kitchen.”

If conditions worsen, the device flashes red and says: “Emergency: There is smoke in the kitchen.” Even if you are at the office, you will be alerted through your mobile app if there is a fire. Contrast this with the traditional smoke detector that constantly emits low beeps that keep you up at night and then deafens you with panicked cries if you burn the toast even slightly. In the latter scenario, you risk becoming a , since over half of all fatal house fires actually had disabled detectors in them. They were disabled because they were so annoying. Also, because traditional detectors have no mobile app, you could be two blocks away and be blissfully unaware that your house is burning down.

Although Google’s $3.2 billion would sound crazy to most, $99 seems reasonable for a product that could help keep your house fire-free.

Now, back to predictions. For the Internet of Things to take off, use of it will have to be effortless. We don’t need more remote controls than we have hands. In a world of smart phones, what you can pull out of your pocket is more valuable and weighs a fraction of what a pile of board games, a filing cabinet, a telephone, a notebook, and a stereo would. We have already replaced physical objects with apps. The next question is: Can we thoroughly digitize physical objects, too? ($299 on Amazon) comes close – you get a remote control house through your phone: control your music, lights, temperature, shades, even door locks. Again, all through your phone. Not all home devices are compatible with Revolv, though.

Smart Things Talk to Each Other

Liat Ben-Zur, Senior Director of Product Management and Software Strategy at Qualcomm, wants to make the Internet of Things even easier. At this year’s MIT Technology Review Digital Summit, quipped that we are still trapped in the era of “the Internet of Thing, not the Internet of Things.” Liat Ben-Zur’s solution is an open source project called . It “lets the compatible smart things around us recognize each other and share resources and information across brands, networks, and operating systems.”If it succeeds, smart Things will be easier to use, because they will talk to each other before they bother us.

How might a truly easy Internet of Things help? Take weight loss for an example. Many individuals become overwhelmed by the sheer overhead of tracking and analyzing everything they eat, every workout they do, and every pound they gain or lose. Sure, it’s great to have a smart weight scale, a wearable pedometer, a heart rate monitor, a smart plate and bottle tracking how much of each food and beverage type you consume. But each of these devices typically has its own app you must manage independently. Imagine how much better it would be if all of the relevant devices worked together to create a single, streamlined daily report. You might not give up on your weight loss plan so quickly. Progressing past the Internet of Thing stage to the Internet of Things stage could help you get healthier faster.

Something about the way we relate to machines changes when they exhibit a sense of humor. If your smoke alarm cracked jokes from time to time, might you be less likely to unplug it if it annoyed you with its occasional false alarm?

Beyond ease of use, the Internet of Things will succeed when it poses more value than it raises concerns. Security cameras (Dropcam) provide some peace of mind, but they also provide a portal for companies to spy on us in our own homes. In contrast, energy and health pose fewer immediate privacy concerns but still offer tremendous value. We spend around our monthly energy bills on heating, cooling, and lighting, so carefully controlling the temperature and enabling light bulbs to turn off when we are not present makes both financial and environmental sense. Nest’s thermostat already addresses this nicely, as do a handful of other products.

Some health startups aim to ease dieting in deep ways. Although the Fitbit products, Nike’s FuelBand, and Jawbone’s Up have the attention of many fitness tracking fanatics, newer companies target more intuitively valuable applications. For instance, is a physical plate that helps you to follow your desired diet by communicating with your smart phone and visibly highlighting – on the plate itself – the exact portion that you should devote to meat, vegetables, carbohydrates, and dessert for your current meal. tracks the type and calories contained in each drink you consume throughout the day via a physical bottle and an iPhone app.

Other startups are providing valuable, low-cost medical diagnostics. The potential replacements for prohibitively expensive and cumbersome medical devices include an ECG monitor by and an ultrasound device by . Both are mobile and available for fractions of the usual in-clinic price (only $200 for Alivecor’s monitor). Finally, Google’s promises to help control diabetes by detecting glucose levels and communicating its findings via RFID to nearby devices. These are terrific applications, but it is hard to imagine developing an emotional attachment to your contact lenses.

Cuteness Saves Lives

Finally, the Internet of Things must be likable. Who says computers shouldn’t have a sense of humor? Consider the dry and responsive wit of Jarvis (the Artificial Intelligence in Iron Man’s armor):

Jarvis: The [paint] render is complete.

Tony Stark: A little ostentatious, don’t you think?

Jarvis: What was I thinking? You’re usually so discreet.

Tony Stark: [gazes at 1930s hotrod] Tell you what. Throw a little hotrod red in there.

Jarvis: Yes, that should help you keep a low profile.

Sure, that’s just fiction. But how far have we come in real life? Queue the stand-up comedian, Data. Data is a wise-cracking little robot built by MIT and Carnegie Mellon trained roboticist Heather Knight. Data actually responds to the audience’s laughter and adapts which jokes he tells, just like a human comedian would. The following is excerpted from the :

Data: The Swiss have an interesting army. Five hundred years without a war. Pretty lucky for them. Ever see that little Swiss Army knife they have to fight with? “Come on buddy, I have the toe clippers right here. You get past me, the guy behind me has a spoon.”

[Laughter from the audience ensues. Perhaps emboldened by his success ragging on the Swiss, Data moves onto New Jersey hunters]

Data: A couple of New Jersey hunters are out in the woods. One of them falls to the ground. He does not seem to be breathing. The other guy whips out his cell phone and calls 911. He gasps to the operator, “My friend is dead. What can I do?” The operator says, “Just take it easy. I can help. First, let’s make sure he’s dead.” There is a silence, and the operator hears a shot. The guy’s voice comes back on the line, “Okay, now what?”

[Laughter and applause ensue; Data analyzes and prioritizes this joke in his database]

A few weeks ago, a supposedly Artificially Intelligent chatbot dubbed Eugene successfully fooled 33% of a human panel into thinking that he was real after conversing with him online. Although overblown claims of human-like intelligence have been made and refuted over and over (Ray Kurzweil’s to Eugene), this particular chatbot’s pseudo-humor is memorable. The panelists were told he was a 13-year-old Ukrainian boy, and in this interchange, he demonstrates what might rightly be interpreted as the sarcasm or hedging of a teenaged boy.

Human: I live in the capital of the United States. Do you know where that is?

Eugene: Every nerd knows that the capital of The United States is Washington.

[later in the chat]

Human: If I have two marbles in a bowl and I add two more how many marbles are in the bowl now?

Eugene: Not too many. But I can’t tell you the exact number, I forgot it.

Something about the way we relate to machines changes when they exhibit a sense of humor. If your smoke alarm cracked jokes from time to time, might you be less likely to unplug it if it annoyed you with its occasional false alarm? If so, its cuteness might just save your life.

Google Glass, one of the first consumer wearable headset computers, seems to be aiming at easy to use (just slide it onto your face), potentially deeply valuable (current apps range from exercise to social media), but heading toward fun (voice activation aims it in the right direction). Even if you are not ready to pay $1,500 for Google’s computer headset, you can still begin creating your own Internet of Things at home.

Using a tool by startup company Integreight, the Ěýallows you to manipulate the sensors that come with your smart phone (using its microphone, camera, GPS, etc.) and connect them to a microcontroller, such as an Arduino. Amr Saleh, Integreight’s CEO, reported that in less than ten minutes, one meditation expert with minimal software/hardware experience was able to create a health app to reduce anxiety. It sensed stress in the user’s voice and sent out a positive, affirming message via text. If the person continued to exhibit stress, a 20 second meditation audio clip played.

As you build, remember the three key ingredients for success: Your Internet of Things will need to provide real value and be easy to use. Don’t forget to make it likable, too – for its sake and for yours.

*[If you’d like to interact with some hilariously bad chatbots, click .]

The views expressedĚýin this article are the author’s own and do not necessarily reflectĚý51łÔąĎ’sĚýeditorial policy.

Ěý/ĚýĚý

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Neuroinformatics Heads to School: The Future of Personalized Learning /region/north_america/neuroinformatics-heads-school-future-personalized-learning/ /region/north_america/neuroinformatics-heads-school-future-personalized-learning/#comments Thu, 05 Jan 2012 00:34:41 +0000 The future is already here – it's just not evenly distributed.

—William Gibson, quoted in The Economist, December 4, 2003

In America, we love personalization: personal computers, personal trainers, and more recently, personalized medicine. Because we especially prize individuality, the ground is fertile for developing services that cater to individualized preferences and needs. Personalized education is no exception; we have a growing drive to individualize and maximize how each student learns. Because the parallels between personalized medicine and personalized education are enlightening, let’s start with personalized medicine.

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The future is already here – it’s just not evenly distributed.

—William Gibson, quoted in The Economist, December 4, 2003

In America, we love personalization: personal computers, personal trainers, and more recently, personalized medicine. Because we especially prize individuality, the ground is fertile for developing services that cater to individualized preferences and needs. Personalized education is no exception; we have a growing drive to individualize and maximize how each student learns. Because the parallels between personalized medicine and personalized education are enlightening, let’s start with personalized medicine.

First of all, what is personalized medicine? Imagine for a moment that instead of taking medicine designed for the general population, you took therapy specifically catering to your genetic profile. Your doctor looked at your DNA, measured certain protein levels, and your doctor divined what drugs would work best for you. So far, measuring proteins in the body, such as and , have successfully provided the basis for that had better outcomes than treatments designed for a more general population of cancer patients. More than just for cancer, however, personalized medicine promises to use individuals’ genetic and other biological information to provide medical care with fewer side effects and higher rates of success. Thus, genetic and other biological information leads to the medical intervention in personalized medicine.

In its , personalized education does not take a biological perspective. Instead, success stories in personalized education typically consist of software programs that are designed to provide the best amount, type, and rate of information for individual students’ cognitive, emotional, and social styles of learning. In order to do this, personalized educational systems typically draw from the insights of computer science, education, and behavioral-level psychology. Even one of the leaders in the field that has , Carnegie Learning, does not employ neuroscience. Neuroscience covers phenomena at a cellular or molecular level as opposed to cognitive science, which does not limit itself to models based in biology. Researchers at Carnegie Mellon University developed Carnegie Learning’s software program using a computer algorithm that adapts to the learner and responsively provides content based on the learner’s needs. Their software already enables over half a million students across the United States to learn mathematics . But what if they could take things a step farther?

Imagine using general neuroscience, not just cognitive science, to aid education. Just as an improved knowledge of DNA and proteins promises to revolutionize medicine, and alike predict that brain science will revolutionize education in the near future. With the frustration with current educational methods building, much of the necessary societal infrastructures of interest and funding are slowly lining up. What would this revolution ultimately look like? Whereas in personalized medicine, a doctor would measure your protein levels and look at your DNA in order to prescribe you the optimal treatment, in personalized education, a teacher would assess your neural profile in order to prescribe you the optimal curriculum. Now, this sounds good in theory, but how far away is the science in practice?

Progress So Far: Gathering Neural Data

In the analogy of personalized medicine as it relates to personalized education, what is personalized education’s equivalent of DNA and proteins? The answer may lie in a field at the intersection of neuroscience and computer science called neuroinformatics.

The neuroinformatics community was represented at the Society for Neuroscience conference in Washington, DC earlier this fall (which was attended by an astounding 31,000 people). Over the past few decades, technological advances in neuroimaging and increased data collection have resulted in so much data (especially from neuroimaging tools like fMRI) that some have claimed that neuroscience is a field that is data rich but theory poor.

Neuroinformatics tackles the mass of data by applying the tools of computer science to collecting, analyzing, and modeling the data. What kind of data does this encompass? Generally, it runs the gamut from molecular all the way up to behavioral levels. It maps and aims to visualize what regions of the brain connect, what differences exist between healthy and diseased brains, and (perhaps most relevantly) how the human brain functions under different circumstances (e.g., participants performing different tasks in fMRI machines).

A science coordinator from the Human Connectome project, Dr. Jennifer Elam, explained that with the help of a generous grant from the National Institute of Health, the project is taking the scans of 1200 healthy people’s brains and mapping their complete functional and structural connectivity. This could provide the basis for incredible insight into normal brain functioning. Ultimately, the project aims to determine how the parts of a human brain are interconnected and how their activities are interrelated under a variety of different experimental conditions.

Although not explicitly designed with applications for education in mind, this kind of information may ultimately provide the basis for the neuro-cognitive profiles needed to design biologically-based personalized education. If we know a student’s neuro-cognitive profile, we may be able to provide biologically-based interventions. Just as personalized medicine strives to prevent the negative side-effects of one-size-fits-all treatment, a neuroscience-based personalized education could prevent the negative effects of a one-size-fits-all education.

What kind of interventions might come out of such a paradigm shift? Already, the development of so-called smart drugs aim to improve professionals’ and students’ ability to and , brain-machine interfaces have hooked into patients’ brains enabling locked-in patients to . Optogenetics has enabled neuroscientists to in mouse brains involved in motivation and learning—although it will be awhile before this is applied to humans. that enable neuroscientists to read, with surprising accuracy, the stimuli patients lying in an fMRI scanner may be looking at. Neurofeedback, a brain-based form of biofeedback, has been used to .

These advancements represent windows into how neuroscience-based personalized learning may be implemented in the future. Perhaps smart drugs will be added to the stack of multi-vitamins children eat with their breakfast cereal. Perhaps executive education courses will provide some future version of the iPhone’s Siri in brain-machine interface form; all users would have to do is think “email the client” and it would be done. Perhaps optogenetics will enable students to learn concepts more quickly by activating the right neurons to fire at the right time in order to execute a task optimally. Perhaps fMRI “mind-reading” will enable educational technologists to design personalized, just-in-time curriculum. Perhaps neurofeedback will be integrated with adaptive learning systems in order to create extremely personalized curricula. Many questions remain about who should use these tools and have access to these tools. Should they only be used to help eliminate disorders or should they also be used to move baseline functioning from normal to superior? Ultimately, if personalized education is to follow a similar path as personalized medicine, much neuroinformatics research remains to be done. To a society in love with personalizing the world to fit our individual needs, what could be more useful than to study the very seat of our individuality: the brain. And in so doing, we may very well facilitate the act of study itself.

I am grateful to the following people and organizations: many thanks to Jorge Conde at Knome, Inc and Atul Singh of 51łÔąĎ for making my attendance at the Society for Neuroscience conference possible, a big thanks to Jennifer Elam from the Human Connectome Project for thoughtfully answering my questions, and a major thanks to Leif Gibb, Ogi Ogas, Alex Rivest, Jehan deFonseka, my dad, George Ricker, and Barbara Lam, for all their thoughtful feedback, constructive criticism, and creative brainstorming throughout the writing process.

The views expressed in this article are the author’s own and do not necessarily reflect 51łÔąĎ’s editorial policy.

Ěý

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Improvers, Not Losers: Plasticity and Positive Feedback for the American Educational Mindset /region/north_america/improvers-not-losers-plasticity-and-positive-feedback-american-educational-mindset/ /region/north_america/improvers-not-losers-plasticity-and-positive-feedback-american-educational-mindset/#respond Thu, 10 Nov 2011 21:31:22 +0000 Educational achievement depends on a “growth” mindset and not a “fixed” mindset, and neuroscience tells us that hard work is a fundamental driver of achievement.

American fourth and eighth graders’ national math scores are in. The numbers are sobering: 8th graders’ scores improved by three points and 4th graders’ scores improved by merely a point over the last 4 years. In international tests for math and science proficiency, American students have been close to the bottom of the rankings amongst developed countries for quite a while. This point was rubbed in when The New York Times detailed the plight of Indian teenagers who apply to top American universities as their safety schools because they are afraid that they might not gain acceptance into competitive Indian Institutes of Technology.

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Educational achievement depends on a “growth” mindset and not a “fixed” mindset, and neuroscience tells us that hard work is a fundamental driver of achievement.

American fourth and eighth graders’ national math scores are in. The numbers are sobering: 8th graders’ scores improved by three points and 4th graders’ scores improved by merely a point over the last 4 years. In international tests for math and science proficiency, American students have been close to the bottom of the rankings amongst developed countries for quite a while. This point was rubbed in when The New York Times detailed the plight of Indian teenagers who apply to top American universities as their safety schools because they are afraid that they might not gain acceptance into competitive Indian Institutes of Technology.

How should American educators react to this situation? Or should they look at the numbers, decide the rest of the world has won, give up, and just stop trying?

In “Mindset”, a book written in 2006 by Stanford psychologist Carol Dweck, a deceptively simple premise is set forth: people relate to their own potential in two ways – either with a “fixed” or a “growth” mindset.

A fixed mindset means that you believe that ability is a fixed quantity and that key tests define what you can do in lifeĚý– the SAT, IQ tests, grades, etc. If you’re a winner, you don’t have to work hard. If you work hard, that’s because you’re a loser. Naturally, you avoid challenge or fake ease so that you don’t risk appearing to be working hard. There is little mobility between the two states: you are either a winner or a loser, period.

In the growth mindset, you care less about winning and more about improvement. You always believe that you can improve, and you believe that improvement requires work. You have the conviction that with purposeful practice, discipline, and hard work you will improve. Interestingly, this mindset accurately predicts the reality of super achievers in many fields. It is estimated that it takes an enormous amount of hard work and persistence, approximately 10,000 hours, to achieve mastery in any field. It is most likely that these super achievers had some initial talent but it was hard work that led them to their success. If they had seen their need to work hard as evidence that they were losers, they would never have achieved greatness.

In a longitudinal study of middle schoolers, Dweck found that students praised for their effort rather than for their innate ability tended to be less afraid of challenge, tended to work harder, and tended to do better academically, even controlling for initial academic ranking. Just as in the case of the career superstars, it was the investment of time and effort by these middle schoolers that led to their success.

Dweck highlighted two exceptional teachers, Marva Collins and Jaime Escalante, as individuals that epitomized the growth mindset. Both of them beat the odds completely. They worked with extremely disadvantaged students to achieve dramatic improvements. Jaime Escalante’s students beat out all of the country’s best public school students on national math and science tests. Marva Collins’ students went on to law, medical, and academic careers; one little girl who had been labeled as mentally retarded graduated summa cum laude from college. The common trait both these teachers instilled in their students was a strong work ethic.

All of the above clearly sounds good and rather common sense so you might ask if there is any science to it. The answer strangely enough is yes. Two scientific concepts underpin Dweck’s premise: plasticity and feedback.

Plasticity is a broad term, referring to the brain’s ability to enact cellular and ultimately larger-scale changes that lead to cortical remapping due to experiences of learning, memory, and even brain damage. Experiences lead to incredible changes to the overall physical structure and functional organization of the brain. Neuroscientists have repeatedly demonstrated this in rats, mice, and humans using functional and structural neuroimaging and electrophysiology. The age-old notion that if you try harder you will change is therefore rooted in hard science.

Feedback in a biological context means to increase or decrease a response. If the desired response is student effort, a positive feedback loop would increase student effort, whereas a negative feedback loop would decrease it. The theory here is that the growth mindset leads to a positive feedback loop, and the fixed mindset leads to a negative feedback loop.

Last year, Doug Lemov’s “Teach Like a Champion” project set out to capture hundreds of exceptional teachers, younger versions of Jaime Escalante and Marva Collins, across the country on video and ultimately in book form. The intention was to capture best teaching practices and disseminate them amongst the country’s teachers.

American math teachers across the country need to get inspired by books like “Mindset”, concepts like plasticity and feedback, and projects like “Teach Like a Champion”. There is no better way to pull our nation’s educational system from its current rut than to dig in, utilize helpful tools, and look forward to the reward that comes from working hard and growing. Those, after all, are classic American values.

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