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There’s a mantra in healthcare right now to “drive patient engagement.” The idea is that informed and engaged patients play a crucial role in improving the quality of care our health system delivers. With the right information, these healthcare consumers will be more active participants in their care, select providers based on quality and value metrics, demand appropriate, high-quality, high-value services and choose treatment options wisely after a thorough process of shared decision-making.
Joseph Smith, MD. PhD (@JoeSmithMD), a cardiologist, cardiac electrophysiologist and engineer, is chief medical and science officer of theGary and Mary West Health Institute (@WestHealth), an independent, nonprofit medical research organization that works with healthcare providers and research institutions to create new, more effective ways of delivering care.
Via Technical Dr. Inc., Beeyond
How To Track Your Life With Apple Health14,2363David NieldProfileFollowDavid NieldFiled to: Apple HealthiOS Tuesday 11:30amShare to KinjaShare to FacebookShare to TwitterGo to permalink
Apple Health still has a long way to go. Some better data analysis would be welcome, for example, and there's no easy way to go back through your statistics, but it's a confident start and one that's currently more comprehensive than Google's comparable offering. If you're an iPhone user then it's automatic monitoring capabilities might just be enough to get you to take your health more seriously.
Via Alex Butler
Via ReactNow, Laurentiu Bogdan
Santé : la vidéo, média par excellence ?Posted on 04/12/2014 by Club Digital Santé wrote in Tribune de la semaine. It has 1 Comment.
Via Anne-Sophie Hardel, Emmanuelle Darsonval
Via Jean-Pierre Blanger
December 9, 2014 1:00 PM
Intel missed being the leader in mobile tech, but it doesn’t want to miss the wearables wave. So the company is investing heavily in components for wearables, and that strategy is integrated with the company’s larger mission of providing tech for the Internet of Things (IoT), or connected everyday objects.
Not only will Intel design components for wearables, but it’s also designing its own wearable devices and partnering with the fashion houses and retailers that will sell them. Those wearables will provide a stream of data to Intel’s Internet of Things infrastructure, which will analyze and make sense of the data so that you can get insight into your life, such as how much you need to exercise or sleep.
“We consider wearables to be personal IoT,” said Mike Bell, vice president and general manager of Intel’s New Devices Group, in a media briefing today in San Francisco. “They are an expression of yourself, and how you want to be seen. At the same time, we’re trying to make them smart and useful.”
Above: Intel’s Mike BellImage Credit: Intel
Intel expects there will be 50 billion IoT devices in the market by 2020, and 400 million of those will be wearables. Intel has built its IoT platform to support wearables as an end-to-end solution. The wearable app partner gets everything from chips to a dashboard portal that tells you how a device is being used.
Bell added, “Everything is getting smart. The number keeps going up and up every time you see these surveys. Wearables are a small part of it, but it is still a very significant number.”
Intel has created partnerships with watch maker Fossil, fashion brand Opening Ceremony, and eyeglass maker Luxxotica.
“These non-traditional players are realizing that these things are getting smart, technology is at the point where it can be embedded in their devices, and thankfully we struck partnerships with them to make the best wearables on the planet,” Bell said.
Intel is focusing on IoT markets such as consumer, industrial, and health care. And wearables fit within each of those segments. Earlier this year, Intel acquired Basis Science, the maker of a line of fitness and sleep-tracking smartwatches. Such devices will eventually be used to identify you, he said. Intel has also made a pair of headphones that records your heart rate and sends the data to the cloud.
“Wearable devices will provide an interesting way to interact with the world around them,” he said. “It could be an interesting way to do away with badges. Wearable devices could become a way to identify you.”
Bell said the market is divided into trackers for fitness under $200, smartwatches and bracelets at around $200 to $400, and then head-worn devices such as Google Glass at about $1,000.
“We think there has to be a blending of fashion and technology for this to really take off,” he said. “The best thing about Google Glass is that it has people talking about the concept,” such as what is acceptable when wearing a recording device in public.
Above: Intel’s view of wearablesImage Credit: Intel
Via Alex Butler
Thanks to incentives under the Affordable Care Act, more hospital executives are offering telemedicine technologies in hospitals-but reimbursement is still the primary hurdle, according to the 2014 Telemedicine Survey by Foley and Lardner LLP.posted: Wednesday 10th of December 2014 by Shiva Gopal Reddy
Via Philippe Marchal/Pharma Hub
Today's mobile apps are helping diabetics aggregate blood sugar and nutritional data from multiple platforms and devices and logging data into central portals accessible anywhere, according to Steve Robinson, general manager of the Cloud Platform Services Division for IBM.
The gains include everything from simplifying records and improving doctor-patient conversations to gaining a holistic view of a diabetic's health. Doctors can "crunch and analyze patient data at rapid speeds to help identify patterns and predict future health and treatment needs," he writes.
"Mobile apps can help diabetes sufferers get ahead of their symptoms and live healthier, more carefree lives," Robinson says.eBrief | Advice for Healthcare Organizations Seeking to Centralize Patient Records, Decommission Legacy Systems
Health systems are transforming their foundations and infrastructures to cut costs and improve care. In this eBook, hospital leaders share challenges and tools to make systemwide decisions that can help boost quality care and outcomes. Download today!
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Diabetes tools have ranged from providing smartphone coaching that is helping diabetics living in low to modest socioeconomic communities manage their disease and improving their health, to a wearable, automated bionic pancreas for continuous glucose monitor and a software algorithm, according to a study at the New England Journal of Medicine.
In addition, mobile monitoring of diabetic employees can save more than $3,000 a year in healthcare costs, half of the average annual medical insurance cost for workers diagnosed with diabetes.
Today's tools and cloud-based capabilities are reducing those costs while also driving innovation for disease management, Robinson says.
"Using cloud services, combined with the ease and convenience of mobile, new methods of managing this disease are being brought to patients around the world," he writes.
Via Celine Sportisse, DIRECT MEDICA
Parents in California who have children who get chronic ear infections will soon have a more convenient way to get their kids care.
The social media landscape is constantly evolving. Given the strong interest and comments received from our members, we have published an updated version of the map.
The proliferation of small and large communities is the result ofphysicians’ increasing need to share ideas and discuss clinical cases with colleagues in every part of the world.
The more the number of communities grow, the greater the need to create stronger niche communities, increasingly unfolding the landascape of physician communities. Trying to find some differentiating features in theaggregation trend of physician communities, we have identified 3 main features:
“Specialized” communities tend to be a smaller group and represent the long tail of physician communities, with a small but very specialized number of subscribers. In this type of aggregation the common feature is the professional specialty and consequently a common specific area of interest. In the radiology field, for example, there are many examples of specialized communities like Radrounds.com or Radiopolis.com.
Location specific communities
Location specific communities usually represent an aggregation of physicians that come from thesame country or speak the same language.
These kinds of communities are generally larger than the specialized ones, since they tend to include all physician specialities.
Usually physicians turn to location specific communities for two main reasons. The first is language, especially in Europe, where due to the multitude of different European languages, localized communities are proliferating quickly. The second is related to local roles and rules shared by physicians coming from the same country with regard to their medical or practice management issues.
Examples of localized communities are DocCheck in Germany and Doctors.net.uk in UK that represent the top European physician communities.
What is also interesting is the presence of physician communities in emerging markets. In China for example the dxy.cn community has 1,7 million members, of which 50% are physicians.
Trustworthy Provider based communities
The last (but not least) aggregation factor depends on the community provider's trustworthyness. Many physicians prefer to join communities related to scientific societies they belong to or trusted professional websites that they already consider relevant or reliable information sources. This explains the proliferation of physician communities within professional websites such as BMJ (doc2doc community) or related to medical association websites, such as CardioSource from the American College of Cardiology.
Usually these kinds of communities have a significant number of subscribers, largely also due to their existing physician databases.
The physician community landscape is continuously changing, but there is a trend towards growth of smaller communities, which are able to aggregate and keep active specialist interest groups. The true benchmark for measuring the quality and health of a community in this fragmented scenario will be to measure its social life - in order to understand how active each member really is, communicating, playing and sharing information and knowledge to create collective intelligence.
Via Plus91, Lionel Reichardt / le Pharmageek
Huge changes are ahead in healthcare. From the Affordable Care Act to new service models to advances in health and fitness technology, the field is definitely in a growth and change mode.
In a nutshell, doing health care better will involve using data better.
Via Celine Sportisse
More than five years ago, Apple sold consumers on mobile applications by telling them no matter what they want to do, “there’s an app for that.” The same couldn’t be said for healthcare providers and patients.
Last July, there were more than 1.5 billion apps in the iTunes and Google Play stores combined. Less than 2% of them—fewer than 28,000—were classified as medical, according to the publication iMedicalApps.
But thanks to FDA’s risk-based regulatory framework, announced in September 2013, and predictions that the market for mobile medical apps could grow to 26-billion users by 2017, more companies are starting to try their hand at mobile medical apps.
“2014 was the year of the app,” says Steve Wilcox, founder of Philadelphia design firm Design Science.
Consumer tech giants Apple, Google, and Microsoft grabbed headlines with platforms that enable more health and fitness app development, while several traditional medical device companies launched notable apps as well. One is Dexcom’s Follow, which is used in conjunction with a docking cradle to enable diabetics to share data from their continuous glucose monitors.
AliveCor also got FDA clearance for an algorithm to detect atrial fibrillation using its ECG smartphone attachment and app.
Via Celine Sportisse
While Novartis’ recent partnership with Google and its longtime relationship with Proteus have indicated that the pharma company has an interest in digital health, a page on the company’s website, added this summer, lays out its broad vision and explicit interest in mobile health specifically. The company even has a mobile health strategy lead, Michele Angelaccio, who holds the title of Associate Director US Mobile Health Strategy at Novartis Pharmaceuticals.
Wearables and the Internet of Things (IoT) may give the impression that it’s all about the sensors, hardware, communication middleware, network and data but the real value (and company valuation) is in insights. In this article, we explore artificial intelligence (AI) and machine learning that are becoming indispensable tools for insights, views on AI, and a practical playbook on how to make AI part of your organization’s core, defensible strategy.First Definitions
Before we proceed, let’s first define the terms. Otherwise, we risk commingling marketing terms like “Big Data” and not addressing the actual fields.
Artificial Intelligence: The field of artificial intelligence is the study and design of intelligent agents able to perform tasks that require human intelligence, such as visual perception, speech recognition, and decision-making. In order to pass the Turing test, intelligence must be able to reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills towards a common goal.
Machine Learning: The subfield of machine learning grew out of the effort of building artificial intelligence. Under the “learning” trait of AI, machine learning is the subfield that learns and adapts automatically through experience. It focuses on prediction, based on known properties learned from the training data. The origin of machine learning can be traced back to the development of neural network model and later to the decision tree method. Supervised and unsupervised learning algorithms are used to predict the outcome based on the data.
Data Mining: The field of data mining grew out of Knowledge Discovery in Databases (KDD), where data mining represents the analysis step of the KDD process. Data mining focuses on the discovery of previously unknown properties in the data. It originated from research on efficient algorithm for mining association rules in large databases, which then spurred other research on discovering patterns and more efficient mining algorithms. Machine learning and data mining overlap in many ways. Data mining uses many machine learning methods, but often with a slightly different goal in mind. The difference between machine learning and data mining is that in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge while in KDD the key task is the discovery of previously unknown knowledge. Unlike machine learning, in KDD, supervised methods cannot be used due to the unavailability of training data.Fear of AI
Though perhaps not explicitly stated, you will find that some at your work hold sci-fi views of AI that could hamper proactive exploration of AI and machine learning within your organization. AI, for some, bring images of HAL 9000 from A Space Odyssey or more recent films such as Her and The Machine.
Many futurists have speculated about the future of artificial intelligence that could rival or exceed human intelligence. One of those futurists is Ray Kurzweil, a recipient of the prestigious National Medal of Technology and Innovation honor.
In The Singularity is Near, Kurzweil elaborates on the singularity hypothesis. Kurzweil predicts that accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization in an event called the singularity. During this period, he predicts “human life will be irreversibly transformed” and humans will transcend the “limitations of our biological bodies and brain”.
Kurzweil claims that machines will pass the Turing AI test by 2029, and that around 2045, “the pace of change will be so astonishingly quick that we won’t be able to keep up, unless we enhance our own intelligence by merging with the intelligent machines we are creating”. He further claims that humans will be a hybrid of biological and non-biological intelligence that becomes increasingly dominated by its non-biological component. Kurzweil envisions nanobots inside our bodies that fight against infections and cancer, replace organs, and improve memory and cognitive abilities. Eventually our bodies will contain so much augmentation that we will be able to alter our “physical manifestation at will”.
The artificial general intelligence (AGI) or strong AI community, though varying widely in timeframe to reach singularity, are in consensus that it’s plausible, with most mainstream AI researchers doubting that progress will be rapid.
In regards to feasibility, Microsoft co-founder Paul Allen believes that such intelligence is unlikely in this century because it would require “unforeseeable and fundamentally unpredictable breakthroughs” and a “scientifically deep understanding of cognition”. Roboticist Alan Winfield claims the gap between modern computing and human-level artificial intelligence is “as wide as the gulf as that between current space flight and practical faster than light space flight”. Neuroscientist David J. Linden writes that, “Kurzweil is conflating biological data collection with biological insight”. He feels that data collection might be growing exponentially, but insight is increasing only linearly.
AGI raises difficult ethical questions and risks to civilization and humans. Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. He argues that “any sufficiently advanced benevolence may be indistinguishable from malevolence.” Humans should not assume machines or robots would treat us favorably, because there is no reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology. Hyper-intelligent AI may not necessarily decide to support the continued existence of mankind, and would be extremely difficult to stop.
Stephen Hawking believes that AI has a lot of promising things to offer for future, but not without possible dire consequences. He says that “success in creating AI would be the biggest event in human history,” and “unfortunately, it might also be the last”.
Even Elon Musk, the Tesla and SpaceX billionaire, tweeted recently that “We need to be super careful with AI. Potentially more dangerous than nukes. Hope we’re not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable.”What’s Reality: Strong AI vs. Weak AI
Before running for the hills, let’s pause for a reality check. It’s important that we don’t confuse AGI with subcomponents AI applications.
AGI or strong AI is defined as the intelligence of a machine that could successfully perform any intellectual task that a human being can. For practical wearables and IoT implementations, we are working with weak AI, which studies a specific problem solving or reasoning tasks and does not attempt to simulate the full range of human cognitive abilities. There are AI applications that exhibit capabilities such as visual perception, speech recognition, and decision-making, but none at human levels. The chasm from stitching subsystems, bottom-up to fully intelligent machines is galaxies wide.Practical Applications of AI and Machine Learning
From Apple’s Siri, Google Voice Search, Google Brain, Google Translate, Xbox, Netflix, IBM’s Watson, autonomous cars, email spam filtering to credit card fraud detection, AI has already infiltrated into nearly aspect of our daily lives… and our dependence on it is only growing.
So how can AI and machine learning be applied to wearables and the Internet of Things? Let’s walk through a few examples.Medical Diagnosis and Treatment: Lumiata
Lumiata’s machine graph is based on multi-dimensional probability distribution that contains 160 million data points from textbooks, journal articles, and public data sets to replicate and scale doctor’s knowledge for use by nurses to diagnose and treat illnesses. Add patient-specific data, effects of time and location to Lumiata’s massive data set, the machine learning system is able to generate a clinical model of a patient. In the future, clinically approved wearables can interface with Lumiata’s API to provide a constant feed of a patient’s physiological data, time and place for proactive monitoring and event triggers.
Google X recently announced that they are researching the use of nanoparticles. Released into the bloodstream via a swallowed pill, nanoparticles can proactively detect and diagnose diseases, cancers, impending heart attacks or strokes based on changes to the person’s biochemistry, at the molecular and cellular level. The patient can then use a wearable wristband to view readings of the nanoparticles. Google aims to use nanoparticles to clump around a cancerous cell or identify fatty plaques in the lining of blood vessels about to break free, potentially causing a heart attack or stroke.
Machine learning can be applied to learn to diagnose diseases and changes in biochemistry in the bloodstream through the movement of nanoparticles, as unattached nanoparticles would move differently in a magnetic field from those clumped around, for instance, a cancer cell.Preventive Health: Entopsis
Another early molecular diagnosis startup is Entopsis, a medical diagnosis platform that can screen for medical conditions using nano-engineering and machine learning. Entopsis is able to detect patterns and biomarkers that point to specific conditions by analyzing the protein composition of biofluids.
After incubating a biofluid sample using their Nanoscale Unbiased TExtured Capture (NUTeC) process to capture molecules, Entopsis applies its signature analysis based on machine learning algorithms to analyze the molecular signature on a NUTeC glass. The scanned signatures are then uploaded to the cloud to run signature comparison against others in the database to find similar profiles.
Entopsis desires to give consumers direct access to NUTeC dishes to collect biofluids and send it in for molecular analysis. Could NUTeC glasses be equipped with IoT sensors to optically scan and transmit signature data to the cloud remotely?Body Movements: Atlas Wearables
Atlas Wearables is a fitness band plus intelligence platform, powered by the Motion Genome Project database of movements. Aside from measuring heart rate and calculating the calories burned, Atlas’ claim to fame is their machine learning algorithms that automatically classifies your exercise routine in 3D vector, being able to decipher the difference between push-ups and triangle pushups. Exercise detection is just the beginning. In speaking with co-founder Peter Li, the startup’s aspiration is to bring “intelligence into body language and movements”. Machine learning algorithms and datasets can be extended to understand how you are walking, sitting, moving or interacting with others, that can give clues about your mood, physical reaction, energy level and even context.Emotion Measurement: BrandEmotions
BrandEmotions solves the problem of quantifying consumers’ emotions. Sentiment analysis and surveys provide positive/ negative or stacked ranked results but brands still can’t classify nor measure the emotions of their consumers. BrandEmotions enables brands to measure how consumers feel about their brand experience, from retail, live events, movies, hotels, cruises, amusement parks to advertising. BrandEmotions, a product of Amyx+McKinsey, visualizes the emotional reaction of participants to brand engagement, allowing brands to optimize the brand experience, increase brand loyalty and accurately target products and services at the right time. BrandEmotions’s emotion sensing, machine learning platform measures physiological data captured through a broad range of wearable devices and Internet of Things connected devices to translate data into emotional classifications and intensity using its proprietary EmotionIQ methodology.Medication Compliance: Vitality
Vitality, acquired by NANTHEALTH, addresses the billion dollar medication adherence market with an Internet-connected pill cap called the GlowCap that blinks and sounds when it’s time to take medication. Vitality’s compliance-enhancing system tries to change patient behavior through a combination of feedback, reminders, education, and incentives to improve patient’s medication adherence. The GlowCap provides real-time data to caregivers, such as when medication has been removed or a dose skipped. Patients can reorder by simply pushing a button on the cap.
Speaking on a panel at the Wearables + Things 2014 conference, Dr. Yan Chow, the former Medical Director of Kaiser Permanente’s IT Innovation group, stated that drug compliance is a complex, multi-layered issue. “It’s not simply that patients forget to take their medication. Some patients disregard advice from doctors and family members for irrational reasons,” asserts Dr. Chow. Blinking pill bottles may not be enough. IoT startups need to understand at a deeper level how to overcome stubborn resistance to drug compliance, whether that’s through reminders, education, gamification, or something else.Farming: ENORASIS and SCRI-MINDS
The third year of record drought in the farmland of the San Joaquin Valley in California is forcing growers to rely almost entirely on well water and farmers are worried that groundwater will run out. California produces more than 90 percent of the broccoli grown in the United States and just about every other fruits and vegetables. No other state can match California’s output per acre. Hence, the drought condition in California affects national agricultural output and commodity prices that we personally feel at the grocery store. So how can IoT and machine learning help?
The ENORASIS project uses a network of sensors in the fields to determine how much water to give their crops through subsurface drip and micro-irrigation systems. The sensors collect environmental and soil conditions such as soil humidity, temperature, sunshine, wind speed, rainfall and the water valves to quantify water already added to the fields. ENORASIS combines weather forecast and sensor data about the farm’s crops to create a detailed daily irrigation plan that best suits the needs of each crop. The model also includes crop yield data and energy and water costs, helping farmers decide whether extra irrigation will increase yields profitably or cause a loss.
Another project is the SCRI-MINDS project, a research collective comprised of scholars from the University of Maryland Center for Environmental Science, Carnegie Mellon University Robotics Institute, Colorado State University, Cornell University and the University of Georgia that applies wireless sensor networks and environmental modeling to conserve irrigation water for nurseries and greenhouses.
Over time, researchers can amass a rich dataset of geographic-specific irrigation, weather, environment, soil, and crop yield data by plant and tree varieties that machine learning algorithms can use to determine the best crops to plant for the next farming cycle.Robotics: Petronics
Petronics recently launched and funded Mousr on Kickstarter, the first robotic mouse that can see and react to a cat’s movements, bringing to life the Tom & Jerry chase for your cats. Equipped with a 360 degree camera, motion sensing technologies, and Bluetooth, Mousr responds to motion and external forces to escape a cat’s paw. According to co-founder David Jun, next on their product roadmap is “to develop AI algorithms that will help Mousr to outsmart a cat every time”. Petronics introduces robotics into the home in an affordable, gamified way that opens the door for other startups to pursue robotic AI dogs and eventually child-sized AI robots with machine learning-based movements and natural language process capabilities. This is validated by JIBO, the world’s first family robot, pioneered by a social robotics MIT professor.
Other noteable AI-based home robotics include Anki Drive and WowWee. Anki Drive combines virtual car racing games with physical RC cars powered by artificial intelligence. WowWee’s MiP self-balancing robot with GestureSense technology not only responds to hand gestures but is aware of its surroundings.
AI and machine learning techniques are being actively applied in many areas:Affective computing (Affectiva)Bioinformatics (Classifying biological sequences, clustering biological entities)Brain-machine interfaces (Emotiv)Brain neurons connections (EyeWire -crowdsourced)Cheminformatics (Thomson Reuters Systems Biology)Classifying DNA sequencesComputational advertising (Microsoft, Yahoo)Computational finance (Algorithmic trading, quantitative investing, high-frequency trading)Computer vision and object detection (Dropbox/ KBVT, Occipital, Google+ photo)Facial recognition (Emotient)Fraud detection (First Data, Fiserv)Game playing (The Last of Us, Halo, Sim City)Information retrieval & search engines (Google, Yahoo, Bing)Inputted facts (IBM Watson)Optical character recognition (Google Docs)Machine perception (Computer vision, machine hearing, and machine touch)Market segmentation (IBM)Medical diagnosis (Entopsis, Google X nanoparticles)Natural language processing (Google Translate, IBM Watson — Fluid, MD Buyline, Welltok, Healthline, Elance)Protein prediction (Noble Research Lab)Recommender systems (Amazon, Netflix)Robot locomotion (Honda ASIMO)Sentiment analysis (Twitter, Google Prediction API, AlchemyAPI, BeyondVerbal)Speech and handwriting recognition (Google Translate)Text categorization (Gmail, Outlook)Playbook for Integrating AI As a Core Business Strategy
1. Get clarity on the business problem that you are trying to solve. As a senior executive, you shouldn’t be trying to figure out which AI or machine learning approach to apply but rather to determine what are the actionable insights that will make your offering defensible in the marketplace. For Google it meant better search results.
2. Familiarize yourself so that you can competently evangelize the value of AI within your organization. Remember, you’re not trying to become an AI expert but to have a reasonable understanding of AI and machine learning concepts. For an introductory overview, consider these resources:Artificial Intelligence: A Modern Approach (3rd Edition) by Stuart Russell and Peter NorvigMachine Learning by Thomas M. Mitchell
For a cliff note version, refer to AI co-founder John McCarthy’s article.
For more in-depth, search for specialized books that cover machine learning, natural language processing, robotics, computer vision, neuroscience, probabilistic reasoning/ programming, logic, bioinformatics, etc.
Another great resource is the Association for the Advancement of Artificial Intelligence (AAAI) non-profit scientific society.
3. Recognize challenges and limitations. As alluded in the beginning of the article, we are far from reaching strong AI. That means that business expectations have to recognize the limitations of AI applications.
In some cases automated learning systems need to be combined with hand-coded knowledge to produce better results.
Depending on the subfield, some systems cannot reach a high degree of accuracy without human assistance, such as in the case of recognizing images. In those cases, a crowdsourcing approach like the Amazon Mechanical Turk, reCAPCHA, and EyeWire helps refine the model further through human input.
A real challenge is data integration, integrating across different data sets. Google’s Alon Halevy notes that “no matter how much you speed up the computers or the way you put computers together, the real issues are at the data level.” The relationship between the different schemas must be understood before the data in all those tables can be integrated. Additionally, companies are shifting to using both SQL and NoSQL, structured or unstructured relational database, formats for data storage depending on the application.
4. Partner with AI research institutions. MIT (CSAIL), Stanford (SAIL), Carnegie Mellon, UC Berkeley, University of Toronto, University of Washington, to name just a few, are the world’s most renowned institutions for AI research. Leverage their expertise by partnering with them on your next AI project.
5. Hire the right talent. To do it right, your organization has to commit to building the right team. For instance, adding to Google’s already deep AI bench, Google hired futurist Ray Kurzweil in 2012 as the Director of Engineering to oversee Google’s most forward-leaning ideas.
Because the AI field is interdisciplinary that crosses computer science, mathematics, psychology, linguistics, philosophy and neuroscience, your resource matrix has to be multi-disciplinary.
In some cases, it might make more sense to hire a consultancy with the expertise in AI rather than to build from ground up.
6. Start experimenting. Get your team to start experimenting with open source code and libraries on GitHub and other sources.Actionable Intelligence
It’s easy to get caught up on the wearable and the Internet of Things sensors, hardware and communication protocols but the key differentiator to your solution will be the actionable intelligence that it derives from data.
Spend the time to seek the truth about AI and appropriate the fundamental knowledge. Equipped with a powerful arsenal, you will then be ready to craft a defensible business strategy. In turn, you have the potential to create a higher valuation firm that will lead the competitive pack. Start applying the scale and power of AI today.
Scott Amyx is founder and CEO of the wearables digital agency Amyx+McKinsey.
Via Tictrac, Aequolab
New York-based health insurer startup Oscar Health is teaming up with Misfit Wearables to get more of its 16,000 members moving. As part of the deal, each Oscar member will get a free Misfit Flash tracker and the opportunity to earn up to $20 a month in Amazon.com credit by meeting step goals.
The partnership means Misfit gets exposure for its newest wearable on a few thousand New York wrists, while Oscar hopefully lowers the healthcare costs of its members and continue to differentiate itself as an innovative player in the insurance market.
The two companies will integrate their mobile apps as well. Oscar members will be able to see the step and activity data from the Misfit Flash on Misfit’s app, but will have to transmit that data to Oscar’s mobile app in order to get paid. Members will earn one dollar per day that they meet their personalized fitness goals for a maximum of $20 per month.
It’s not unusual for a health insurer to introduce a wellness incentive program based on wearable activity tracking — programs like Humana Vitality have been around for a number of years. But buying devices for every one of its members is something Oscar can do more easily than its larger competitors, because it has comparatively few members (and $150 million in the bank).
More to the point, Oscar might be uniquely positioned for an endeavor like this because of the startup’s tight focus on positive user experience and sticky consumer engagement. At a recent Boston event, Oscar co-founder Kevin Nazemi made a point of sharing some numbers along those lines.
“If you took the list of the feature set we have and you put it against a major carrier, they checked a lot of the boxes, to be fair,” he said. “But then ask them what percent of the people visit your website or use those tools. And I can tell you proudly that over 90 percent of our members have a log-in. Over 70 percent have filled out a detailed health risk assessment. Because we didn’t frame it that way. We framed it around making the user experience customized, the way Facebook would.”
The Misfit Flash, a less expensive, plastic iteration of the Misfit Shine, debuted in September and like the Shine operates on a coin cell battery and does not require charging. It automatically tracks steps, calories burned, distance, sleep quality and duration, cycling, and swimming. It can be worn in a number of ways: around the wrist or clipped to pants, a shirt, shoes, a lapel, or attached to a keychain.
Via Celine Sportisse