The Futuristic Visit To The Physician Is Here!

Source: IUVM TECH

The world is in the midst of a gigantic 'data revolution' right now.  What does that entail?  There is a massive collection of data going on around us at any given moment.  From the geographical location at which you are located to the subject matter which you are searching for using a search engine such as Google or Bing.  What benefits might arise out of such a revolution?  Many have been proposed.  The overall concept to understand why such benefits arise was written about in an article titled "The 7 Industries That Benefit Most From Big Data" on the website 'Smart Collective' in 2016:

Generally, the more data you have, the more specific and accurate insights you’ll be able to generate, which is why big data has become such a powerful tool (and buzzword) in recent years

More data points equals more accuracy.  Which promotes the largest benefit which serves as a feedback into all of the industries mentioned in the article above:  'artificial intelligence.' A pathway toward the benefits of artificial intelligence exists and is currently being taken by technology groups around the world.  On this pathway toward 'artificial intelligence' playing a larger role in our society has been the concept of 'deep learning':

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.[1][2][3]

Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.[4][5][6]

Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brains), which make them incompatible with neuroscience evidences.[7][8][9]

Again, the amount of data which serves as an 'input' will greatly affect the accuracy of the 'output' the intelligent (computation) answer given to us after the algorithm is executed.



How does all of this play out in the immediate future?



One example is the field of medicine.  During a recent break, I was perusing through Facebook and found the following video by an organization called 'Forward'.  The video below gives us a glimpse into technology which has already been developed and is out on the market right now.  A glimpse of a 'future visit to the doctor' for a health check up.  The video is less than 10 minutes in length and worth watching.

Wow right?  For those who are uninterested in viewing the entire video here are the basics:


1) No receptionist to check in with.  Just an equivalent of an iPad.

2) A Body scan with infrared radiation.  While placing a single hand on a sensor, blood oxygen levels, heart rate, metabolic rate, and a body composition -- fat tissue, muscle and bone tissue.

3) Up to 3 visits normally to the center per month.  Although, a patient may come and get a body scan anytime.  Remember, more data more accuracy.

4) A Body scan creates a 3D model of your body.

5) The Information gathered in the body scan is synced with exam room digital display (i.e. monitor, tv screen) at which time the patient can view results with a physician.  Additionally, the information is also synced with the mobile app on your smartphone.

6) Patient goes into exam room and discusses data.

7) Patient and physician review body scan data and blood work (all on the same day) to come up with a plan to achieve health goals which lead to better health.

8) Planning is individual based.  No two plans are equal.



Having the ability to reduce time or redundancy with having an on site laboratory (for blood work), a visit to the 'futuristic physician' provided by 'Forward' is greatly enhanced.  Plus, as pointed out in the video above, the language which enters into the patient file is the patient's own language -- which is recorded on the screen during discussion with physician.  Additionally, all data and discussion is forwarded to your mobile app for your personal viewing and stay plugged into continuously.  Awesome.  This is the future of medicine.



Conclusion...



The data revolution has the ability to transform our daily lives in many ways.  As shown in the video above, the access of big data in real-time is effective in treating an individual patient with the patient's own needs based on the data collected.  Additionally, that data is compiled with the ability (upon patient approval) to be used in conjunction with other patients for research and discovery purposes.



One main obstacle with the deluge of big data are the different file types in which the data exists currently.  In order to make meaningful discoveries using the big data files, the processing/algorithms need to be able to read the file format.  With the collection of data in this forma (a single format), this represents a solution to an existing problem.



Of course, that means still processing old data from decades in the past.  Although, after processing old data and combining it with new data compiled in the new format, powerful advances will be able to be made by mining the data for medical research and drug discovery purposes (to name just two).  The future of medicine is truly exciting and should be embraced by each of us.



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Parameters: Amazon Go Will Seek To Understand How You Feel About A Grocery Product?

Source: China Brands

Technology has served many different functions in our society.  Among the most important recently are the algorithms which correct themselves while directing people around the world.  Yes, I am talking about the residents of the world who use 'GoogleMaps'.  Over time, the algorithm seeks to improve the accuracy by self assessment.  What? Yes, the algorithm updates and assesses itself after every use.  Amazing.  Back in January in Seattle, Amazon opened up a store without cashier type check out stands.  Yes, without check out stands.  I have been sitting on this short post for quite a while for no good reason.  Although, with the greater use of digital tracking of our preferences, the subject is worth highlighting.

Do I Really Love That Food?

Back in January, an article in 'The New York Times' titled "Inside Amazon Go, a Store of the Future"

But the technology that is also inside, mostly tucked away out of sight, enables a shopping experience like no other. There are no cashiers or registers anywhere. Shoppers leave the store through those same gates, without pausing to pull out a credit card. Their Amazon account automatically gets charged for what they take out the door.

There are no shopping carts or baskets inside Amazon Go. Since the checkout process is automated, what would be the point of them anyway? Instead, customers put items directly into the shopping bag they’ll walk out with.

Every time customers grab an item off a shelf, Amazon says the product is automatically put into the shopping cart of their online account. If customers put the item back on the shelf, Amazon removes it from their virtual basket.  

The only sign of the technology that makes this possible floats above the store shelves — arrays of small cameras, hundreds of them throughout the store. Amazon won’t say much about how the system works, other than to say it involves sophisticated computer vision and machine learning software. Translation: Amazon’s technology can see and identify every item in the store, without attaching a special chip to every can of soup and bag of trail mix.  

Before the above excerpt can be explored more, the differences between a traditional grocery store and the new store offered by Amazon should be briefly highlighted.  Grocery stores with the option of 'cashier assisted' checkout are nothing new.  Stores ranging from Ralphs to Home Depot (or Lowes) have all incorporated the 'checker' less option.  What is new is the option without a 'check out stand' altogether.  To test your ability of paying attention to the potential impact of opening a store such as that which has been open for over a few months now, there are a few questions which a school teacher came up with in "teacher has come up with questions" from 'The New York Times' shown below:

1. What type of convenience store opened in Seattle on Jan. 22?

2. What details make the Amazon Go store different from a traditional grocery store?

3. What is noticeable about the photos in the article? What do they show about the new store?

4. How are items paid for in the Amazon Go store, and what is eliminated in the process?

5. What does Amazon say about the role of cashiers and potential loss of jobs with the new system? 

6. Why does the author say the experience feels like shoplifting, and what happened when he attempted to shoplift a four-pack of vanilla soda?

The above questions represent a good exercise in critical thinking for the article under scrutiny about the new grocery stores.   You may be wondering why I am bringing this up now when the stores have been open for the last few months.  The reason is that there is a larger change at hand with this new technology.  Amazon is looking to expand the information extracted about each customer by introducing new technology.  The grocery store is just one.



Inside the grocery store are a large amount of cameras which are tracking movements.  Not to scare you in any way, this is for the main purpose of tracking purchases.  Although, the amount of time that each customer stands in front of a given product is being recorded along with the customers who simply walk by and pay no attention toward a given item.  This technology is being extended into algorithms which are embedded into the 'Kindle' by Amazon.



I accidentally misplaced the reference (the name of the podcast/episode) which described the shift in Amazon's strategy to gather more information out of their readers Kindle usage.  Including tracking how long each reader stays on a page and if the reader returns to a section with a given phrase or story.  This information will inevitably help Amazon sell better books by adjusting the plot to tailor the customers exact needs.  Scary?  Possibly.

Conclusion...

The changes proposed or being sought by Amazon are interesting and potentially frightening.  As the Virtual Reality pioneer -- computer scientist -- Jaron Lanier implied in his book titled "Who Owns The Future?" -- nothing is for free in Silicon Valley.  Meaning, any discount or free technology is accompanied by a lengthy 'legal disclaimer' which is basically saying that the information collected on this device belongs to Amazon or any other technology company.



At the same time, Jaron Lanier states that in order to get around such an inevitable problem, a new system will have to arise -- something akin to 'micro-payments'.  If the user is unwilling to pay the 'micro-payment' then a short commercial might need to be watched by the user to access the 'free service'.  Ultimately, the technology offered by Amazon might not be terrible given that the time needed to search for an interesting book for a person will be reduced as A.I. algorithms become more intelligent.



In the end, the technology depends on a choice by the consumer (you and I).  Are we willing to give up our information for a "free service"?  Do we really understand what data is being collected by theses technology companies?  Do we really care what data is being collected?  These questions will have to be answered in the future as technology rapidly advances in data collection over time.



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The NFL Is Collecting Big Data?

Currently, there is a data revolution occurring in the world.  Recent articles in professional journals often highlight the need for science based data degrees.  The hope is to have data scientists migrate often away from the field of science into more lucrative jobs crunching numbers to determine how to increase the number of "likes" and the sort.  Add to that craziness, and you get big organizations like the National Football league joining the party of 'big data' collection.  The question is the following:



What is the NFL going to do with the 'big data' collected?



In order to find out a few of the possibilities, one must continue to read below.  The possibilities are endless, although, the initial reasons are restrictive.

What Is Big Data In Football?

When I read the title of the article on the website "Scientific Computing" titled "The NFL Joins The Data Revolution in Sports", the first question that came to mind is:



What data are they collecting that is not already being collected?



I was confused by the title of the article, since, one would think that a huge organization like the NFL would already have an enormous amount of data.  Think about the gambling industry across the world and their profits on sports.  One would imagine that big data has played a significant role already in generating an enormous amount of profits from big data.  Evidently not.  Hard to believe.



According to the article mentioned, the NFL is just entering the field of "Big Data":

In some potentially game-changing news for the way we understand professional football, the National Football League began the 2016 preseason by placing tracking sensors in its footballs for the first time. The chips are also in balls used in Thursday night games.

Over the past decade, we’ve seen an explosion in data analytics in sports, particularly on the professional level. Technological advances in cameras and sensors have allowed teams, media and fans to gain insight into a bunch of previously gray areas of sport performance, such as the National Basketball Association’s use of SportVU to track every bit of player and ball movement on the floor.

The concept of integrating numbers and analysis into scouting, training and coaching isn’t new. But access to powerful hardware and software has greatly increased the quality and quantity of available data. A nearly insatiable appetite for data on sports has created a sports analytics market that is set to grow from the millions to the multiple billions of dollars over the next few years.

 The amount of data generated during each game would be enormous.  By keeping the sensor limited to the football and possibly the sidelines, the data generated would be reduced too.  Although, with a reduction of data flowing in from the game, the accuracy of the plays suffer too.  The author mentions that the next step would be to incorporate sensors into the players 'shoulder pads' - which would increase the data stream coming in.



Overall, the practice would be transformative to the entire industry.  I wonder how that would change the challenges that referees face during the game.  Currently, during a challenge, the play is reviewed on a closed circuit screen available to the referee and officials only.  With the rise of sensors, now the game can be analyzed by each team in real time.  Although, the technology is not distributed in real time yet.


Any avenue of improvement that the coaching staff can incorporate into the teams training regimen would be greatly sought after.  Currently, teams are exploring both game simulators and drone coverage of their practices to improve overall flow.  The incorporation of data from the NFL offers two great aspects of improvement:

Ideally, data from ball trackers or shoulder pad trackers could serve two purposes for the NFL. First, it can help teams understand player movement and the flow of play more completely, providing coaches a greater understanding on how players are physically performing during plays, and allowing for input from coaches to players on how to fix their technique to increase efficiency or limit exposure to injury, possibly leading to more efficient training and practice.

Second, the data can be used by the league’s media partners, and perhaps its fans, to further explain the game to audiences, particularly on television. By tracking player movement digitally, clearer representations of what makes individual football plays succeed (or fail) can be provided. These data also allow media to break down individual physical accomplishments, such as extraordinary bursts of speed by wide receivers.

The NFL’s plan to release tracking data within 24 hours of a game’s end points to a future in the league where hard data on player and ball movement are integrated into the daily strategic calculations of each coaching staff. This will likely create a rush to innovation within NFL coaching, as each staff grapples with what will likely be a huge amount of data every week, trying to come up with best practices and analytical methods for evaluating and using that data constructively.

Of course, generating a tremendous amount of data means that the NFL along with individual teams that participate need to have the technological infrastructure (computing power, data scientists, etc.) to make meaningful use of the data coming into the organization.  This requires both technology and scientists to handle that technology in a fruitful manner.



That means scientists will be taken away from professional fields in which they were trained to contribute.  Is this good?

NFL Data Science Improves Science Indirectly

There are a tremendous amount of scientists who are interested in sports.  At least, that is my impression after going through the university system in a science driven field -- through an advanced degree program.  The prospect of losing a scientist to the NFL organization at first sight might seem unethical.  Scientists should stick within their field (discipline) right?



Not necessarily.  There might be many benefits by losing data scientist to the NFL.  First, the scientist working for the NFL will inevitably have a appropriate infrastructure to handle the large amounts of data coming in.  In science, funding is scarce and often sought out among many research groups.


I have always maintained that in order to improve the funding for science, we need the entertainment industry and the sports industry to get involved (financially and technologically) to boost the ability of science.  Why?  Not all great ideas come from working on science problems in science.


What do I mean by this last statement?


A famous story about the world famous physicist Albert Einstein revolves around generating his best ideas while shaving.  Successful people will often tell stories of ideas which have been generated about their business while performing outside work or tasks.  The shower or shaving are just two.


Additionally, while performing a job outside a given field, a scientist may gain insight into the problems within their field.  This methodology is sometimes referred to as "thinking outside the box."  By tackling problems associated with dealing with large data sets like players in a game, other problems might be tackled using different algorithms.  Can you think of any?  I can.



One such problem is tracking people in real time in a city and finding potential threats (WM -- chemical and biological weapons, etc.).  Sifting through the data to find meaningful answers might improve the governments ability to sift through data to find a threat.  Although, the funding opportunities to develop an algorithm or simulation might be too costly on part of the city.  Therefore, having organizations such as the sports organizations tackling the data regarding player movement within a given region (on field inside a stadium) will inevitably improve our ability to detect a threat.


As most of us know, the entertainment industry is rich in funding and not at a loss for funding such interesting projects.  Alternatively, new algorithms will be made (which are proprietary for the NFL) to tackle the issue of analyzing real-time data.  But the inherent thinking or structure of mining the data is what is critical.  After that is known, then an algorithm could be changed to achieve that specific problem.  This prospect offers a great future to science and society in the future.

Conclusion...

The correlations which will arise as a result of data mining real-time player information have yet to be realized.  By the descriptions in the cited article above, we are just at the tip of the iceberg in terms of finding relationships within such data sets.  Additionally, no one knows the benefit or adverse effect the data mining will have on both the gaming (gambling industry) and the NFL organization.



Hopefully, out of such data mining algorithms, safer players (with less injuries, etc.) will result.  Science will inevitably benefit out of the data mining processes that are developed.  I have no doubt about that.  Scientists are interested in sports and already use the industry to approach problems in science.  Even if progress is made on the initial thought process of how to find correlations in the data, I believe that meaningful results will arise from the exercise.  Initial findings suggest that this is the case.  Although, as I mentioned, we are just at the 'tip of the iceberg' in the process.  Stay tuned!



Until next time, Have a great day!