TREK-NOLOGY: Tech Devices Predicted by Star Trek

For a generation of nerds (and I use that term affectionately, for they are my people) growing up in the late 60’s, 70’s, and 80’s, Star Trek was essential viewing.  Every week, Gene Roddenberry’s most famous creation brought us a vision of a hopeful future: a progressive, highly-evolved environment, where mankind has grown up and defeated racism, broken through social barriers, and explored the universe in the name of peace.  On the surface, the show was a sci-fi Space Western. However, it has always been about social issues and bold statements, cleverly hiding behind a veil of popcorn entertainment. While the complex social issues were lost on many of us young viewers, the cool gadgetry was not. Everyone in my neighborhood held toy pistols backward, pretending it was a classic Starfleet phaser. And who didn’t dream about using a Transporter to get somewhere in an instant?  It’s easy to see that modern examples of technology can trace their lineage back to Star Trek. One glance around the world of science and technology, and it’s easy to spot Star Trek’s unquestionable influence.  Here are ten modern technological marvels that we sometimes take for granted, but were predicted decades ago in Star Trek.

iPad    

When Steve Jobs took to the stage in 2007 and unveiled the iPad, he cemented Apple’s grip on technology must-have items.  There was a certain “cool factor” in using the iPad, which, like the iPhone that preceded it, had a slick interface, groovy apps, and flexibility as a work tool, toy, and multimedia device.  It was for many the neatest new item. Thing is, it wasn’t a new concept, with early examples in the original series (TOS in fan-speak), and especially later in The Next Generation, bearing a strong resemblance 20 years prior to Apple’s entry. 

Universal Translator

How do you travel across the cosmos and understand races from other worlds? Star Trek solved the problem of speaking to alien races across the universe by the Universal Translator trope.  Everyone carries a device capable of understanding an alien’s speech, and translating it for humans in real-time (I’m still curious as to why their lips don’t move out of sync like on Kung Fu Theater, but I digress).  The technology seemed light-years ahead in 1966, but is now a reality.  Both Google and Waverly Labs are working hard to disrupt the market first with real-time translation devices (Templeton, 2016), capable of allowing the wearer to communicate with native speakers in French and other languages. No word on how long we’ll have to wait until travelers can speak to a Klingon.

Replicator

This one might be a small stretch, but it is worth mentioning.  We may be a few years away from ordering earl grey tea, hot, but the ability to build small items with 3D printing technology is becoming more common, and cheaper than ever.  For a little as $267, budding entrepreneurs and generally bored artists can fabricate small items, normally out of high-impact polystyrene or other thermoplastic media (Hoffman, 2017).

Tricorder

 In the future, according to Star Trek, humans who were sick or injured were just a few minutes away from the best in high-tech treatment.  Starfleet physicians like Dr. McCoy and Dr. Crusher would quickly scan a patient and receive a diagnosis as well as an X-ray scan with a medical tricorder.  We may not have to wait for the 23^rd century for a similar device, thanks to Scanadu’s Scout health sensor.  While it is not nearly as advanced as something you might see on Star Trek, it does claim to provide accurate data on heart rate, core body temperature, blood pressure, and respiratory rate in a painless, efficient manner (Williams,2015).

Natural Language Processing

One optimistic feature seen since the inception of Star Trek is the ability to speak to a computer and retrieve information.  While efforts to achieve this have been underway since the early days of computer science, the greatest leap has occurred in the last few years.  Advances in language processing algorithms have made it easier for computers to grasp the very difficult lexical variations of how humans express themselves. Amazon Echo and Google’s Pixel are two competing examples of NLP research tools that employ keywords for activation (Bhartiya, 2017).  Instead of saying “Computer, dim the lights”, users say “Echo, dim the lights”.

Planet Scanners

A trope virtually invented by Star Trek is the ship-scans-the-planet trope, in which our intrepid crew scans for life signs, planetary conditions, or other details to move the story along.  The technology is no longer the stuff of science fiction, however.  Since the late 1980’s researchers have planned to use earth-based scanners to observe the NIR spectra of light reflected from asteroids to determine their composition, surface minerology, and lithology, and claim that the technology can be used to make the same observations of other planetary bodies (Burns, 1989).

Video Conferencing

As difficult as it is to imagine now, video conferencing has not been ubiquitous for long.  Video conferencing has been around since at least 1982 ("Video ConferencingTimeline," 2015), when Compression Labs commanded $1,000 per hour for the service.  The technology, while extremely pricey, was not a new idea, appearing on Star Trek from day one.

Bluetooth

In early TOS episodes, it was not unusual to see a rogue robotic entity come along and steal data from computers, change settings, or sabotage systems through a wireless connection.  While it must have seemed like sorcery in 1966, and an easy plot device, this could be construed as an early depiction of Bluetooth technology, decades before it became standard fare with wireless headphone enthusiasts.  In fact, Uhura’s earpiece is another prime example of wireless audio, rendered via Bluetooth-style technology. 

Hypospray

According to Star Trek’s optimistic future, painful needles will be a thing of the past.  If a character needed to be inoculated after a brush with an alien disease, a quick shot of Bones’s hypospray would do the trick.  No longer the stuff of science fiction, hypospray technology, or painless injection without breaking the skin, has actually been available since 2012 (Humphries, 2012).

Augmented Vision Glasses

We take our vision for granted. Patients who are legally blind struggle with the most basic tasks.  In the future, perhaps that will no longer be the case.  In Star Trek: The Next Generation, Geordi La Forge, a blind character, saved the day many times, partly due to his extraordinary vision thanks to his hi-tech visor.  His character showed that even people who were disabled in some way can be an integral part of the crew, thanks to innovative tools.  The concept has inspired developers who have introduced eSight, a visor that corrects genetically malformed retinas in patients with no central vision (Fox News, 2015).  The tool is helping people who have been blind since birth to see for the first time.  Similarly, Oxford Scientists created Smart Specs, a visor that captures real-time imagery and heightens the contrast, assisting legally blind users in everyday life (Matchar,2015). Plus, they just look insanely cool.

                                       

Conclusion

For many, the show impacted the career choices of many an astronaut, pilot, or scientist. I myself have been fortunate enough to study engineering and computer science, inspired in no small part by Trek.  The show has impacted our culture in profound ways, perhaps more so than any science fiction franchise.  For most of us die-hard fans, the optimism of the series lives on.  Perhaps in some small way, our modern hi-tech tools can help usher in an era of peace depicted in classic Trek, and take us where no gadgets have gone before.

About the author:

Dax Bradley is an author with non-fiction books on movies and technology, and can be found at Amazon.com and Indyplanet.com. Visit his Facebook page on B-movies and say hi!

C++ Tips: Two Approaches to Find an Average of Numbers

by Dax Bradley

On occasion, I will drop notes on programming some basic code here and there.  In this entry, we examine how to use C++ to prompt the user to enter a set of positive integers and average them up.  The code is pretty straightforward, when you have a fixed number of integers pre-determined.  For example, you might know you have 20 numbers to add up and average out. 

However, it would be more useful to be able to specify how many numbers you need to add up and discover the average for.  Here are two methods.  The first is not using Dynamic Memory Allocation, and the 2nd one does use it.  I'll explain the difference.

Dynamic memory allocation is crucial if there is a chance you could enter a null value, such as a negative number by mistake, or possibly that you use so much memory with your data that you crash the program.  The second example below will address that, with a few comments thrown in for good measure (Denoted by the /*...*/ symbols).

Find the Average of n Integers 

(without Dynamic Memory Allocation)

#include <iostream>

using namespace std;

int main()

{

   int n, count;

   float sum, avg;

   sum = 0;

   cout << "How many numbers?  ";

   cin >> n;

   int a;

   for (count=1; count<=n; count++) {

        cout << "Enter Number:  ";

        cin >> a;

        sum = sum + a;

   }

   cout << "The sum is " << sum << endl;

   avg = sum / n;

   cout << "The average is  " << avg << endl;

   return 0;

 }

Online compiler: C Shell

And finally, here is another version with a little more code in it, and some comments that highlight the need for pointers and other tidbits...

Find the Average of n Integers 

(with Dynamic Memory Allocation)

#include<stdio.h>

#include<stdlib.h>

#include <string.h>

int main ()

{

int sum=0, avg, i;

int how_many;

int *p;

printf ("Enter number of values you want to input: ");

scanf ("%d",&how_many);

/* This next block uses malloc dynamic memory pointer to make sure the pointer value is not null*/

p = (int *)malloc(how_many * 4);

if (p == NULL)   
      

{

printf(" Out of memory!\n");

printf ("Press any key to close.");

exit(0);

}

for (i=0;i<how_many;i++)

{ 

     /*printf returns the number of characters written or a negative value if an error occurs*/

printf ("Enter value of %dth integer: ",i+1);

     /*scanf returns data items successfully assigned a value, like the opposite of printf*/

scanf ("%d",&p[i]);

     /* %d is a format specifier that stands for Signed Decimal integers */

sum = (sum + p[i]);

}

     avg = (sum / how_many);

     printf ("Sum = %d\n\n",sum);

     printf ("Average = %d\n\n",avg);

     free (p);

/* where malloc allocates memory, free needs to be at the end to free up the memory */

     printf ("press any key to close.");

}

Did I get anything wrong? See something you'd do differently? Drop me a note, leave a comment in the comment section, and let me know. Let's do some coding!

Bayes' Theorem in Technology

by Dax Bradley

The Bayesian model for statistical analysis is highly popular in settings that require predictive strength to succeed.  Industries are numerous, from academic areas such as astronomy, to industrial applications to include telecommunications and pharmaceutical companies (May, 2014).  In the telecom sector, the method has been employed for some time.  Akamai Technologies in Cambridge uses Bayesian methods to better understand network latencies, internet traffic, and to identify clusters of users (Galkowski, 2014).

The computational complexity of the Bayesian model would suggest that primarily technology-based efforts would embrace this form of analysis (Edelsbrunner, 2014).  Google’s robotic cars, and Microsoft’s own early spam filters stood on the strength of Bayes theorem (McGrayne, 2011).  Google’s strength lies not only in the high level of technical prowess, which is obvious, but also by a factor not apparent: the large amount of data to draw from.

The Bayes approach relies heavily on what is known already to make a successful prediction on what is likely to happen (Russell & Norvig, 2010).  Google translate uses Bayes Theorem in the translation services, by drawing on a large pool of translation data already completed by humans (King, 2015).  So, rather than undergoing large amounts of translation algorithms, the software instead draws from experience, samples the data, and predicts the highest certainty.  The method has been applied by Google not only in translation and mapping, but also in controlling driverless cars. 

The tech giant claims that their autonomous vehicles have logged as many as 140,000 miles along the Pacific Coastal highway, all without incident (Matyszczyk, 2011).

It is easy to see how Bayesian methods are highly prized in the technology sector.  The approach is also critical in health technology assessment.  While no formal definition of Bayesian tools in healthcare technology has been established, proponents believe that it provides for conclusions in a form most appropriate for decisions specific to patients and decisions affecting policy.  That is based upon the following conclusions (Spiegelhalter, Myles, & Jones, 1999, para. 4):

• Data are interpreted from a study in the light of external evidence and judgement
• The form in which conclusions are drawn naturally contributes to decisions
• Prior plausibility of hypotheses is taken into account
• Skepticism about large treatment effects are formally expressed and cautiously interpreted
• Use of Bayesian methods in healthcare technology assessment are to be pursued carefully; guidelines, software, and critically evaluated case studies are needed

References

Edelsbrunner, P. (2014, November 17). Bayesian statistics: What is it and why do we need it? [Blog post]. Retrieved from JEPS Bulletin: http://blog.efpsa.org/2014/11/17/bayesian-statistics-what-is-it-and-why-do-we-need-it-2/

Galkowski, J. (2014, October 17). How companies use Bayesian methods [Online forum comment]. Retrieved from http://andrewgelman.com/2014/10/17/companies-use-bayesian-methods/

King, M. (2015, July 20). Bayes’ theorem and what we do [Blog post]. Retrieved from https://www.whitehatsec.com/blog/bayes-theorem-and-what-we-do/

Matyszczyk, C. (2011, March 4). Google’s self-driving car goes all Dale Earnhardt. Cnet. Retrieved from https://www.cnet.com/news/googles-self-driving-car-goes-all-dale-earnhardt/

May, J. (2014, October 17). How do companies use Bayesian methods? [Online forum comment]. Retrieved from http://andrewgelman.com/2014/10/17/companies-use-bayesian-methods/

McGrayne, S. (2011). Why Bayes rules: The history of a formula that drives modern life. Retrieved January 19, 2017, from https://www.scientificamerican.com/article/why-bayes-rules/

Russell, S., & Norvig, P. (2010). Artificial Intelligence: A modern approach (3rd ed.). Boston, MA: Pearson.

Spiegelhalter, D., Myles, J., & Jones, D. (1999, August 21). An introduction to bayesian methods in health technology assessment. The BMJ, 319(1), 7208. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1116393/

Bayesian Model Challenges

By Dax Bradley

Possible disadvantages to Bayesian learning methods can be significant.  For one thing, the information gleaned from the effort could be theoretically infeasible.  The act of specifying a prior datum is challenging.  Generally, it is necessary to specify a real number for every setting in the real-world parameters (Langford, 2005).  One potential solution is to acquire languages that allow more compact specification of priors.  The task of learning a new language may be prohibitive.  Another solution is to simply lie.  In other words, create a specification that is not accurate.  As deceitful as this sounds, it is not meant that way; in fact, it has been shown to be effective (Langford, 2005).

Another known disadvantage to Bayesian modeling lies in the analysis of causality.  There is a great challenge in analyzing the conditional probability links between various nodes.  Even with physical causal models and known variables, the computational burden is great, since the task equates to inverting a finite-element model (Sankararaman, 2015).  This is especially daunting if the analysis revolves around an acyclic model.  The assumption of independence among variables implies no latent cofounding variable in Bayesian modeling; however, often there are acyclic latent cofounding variables which can force the model to be seriously biased (Shimizu & Bollen, 2014).

Prior choice problems present challenges in the Bayesian methodology.  Another scenario in which researchers using the model is in astronomy.  Scientists have attempted to predict the number of extra-solar planets in this way.  Improper priors are generally disallowed here, so other strategies have been suggested, such as Intrinsic Bayes, Fractional Bayes, and Expected posterior factors (Jeffreys, 2006, p. 11).  The potential defeat of using such methods is that data may be inadvertently duplicated, so allowances must be made.

Many forms of industry employ the Bayesian model of statistical probability.  In drug development, the approach is widely regarded for its accuracy and natural interpretation.  It does, however, demand no small degree of planning.  The flexibility of this method creates an environment prone to potential computational errors.  Without proper planning, the various outcomes of intense computation may be unreliable, prompting some teams to opt instead for a frequentist model (Gupta, 2012).

References

Gupta, S. (2012, June). Use of Bayesian statistics in drug development: Advantages and challenges. International Journal of Applied & Basic Medical Research, 2(1), 3-6. http://dx.doi.org/Use of Bayesian statistics in drug development: Advantages and challenges

Jeffreys, W. (2006). Current challenges in Bayesian model choice: Comments [Lecture notes]. Retrieved from Department of Statistics University of Vermont: http://astrostatistics.psu.edu/scma4/Jefferys.pdf

Langford, J. (2005, April 23). Advantages and disadvantages to Bayesian learning [Discussion group comment]. Retrieved from http://hunch.net/?p=65
Sankararaman, S. (2015). What are the limitations of Bayesian Networks? Retrieved January 18, 2017, from https://www.quora.com/What-are-the-limitations-of-Bayesian-Networks

Shimizu, S., & Bollen, K. (2014, August). Bayesian Estimation of Causal Direction in Acyclic Structural Equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15(1), 2629-2652. Retrieved from http://jmlr.org/papers/volume15/shimizu14a/shimizu14a.pdf

Bayesian Theorem - Advantages

by Dax Bradley

Creating complex models from complex data can prove to be an arduous task, even in a system of computers.  Using even existing data, a computing system must perform many calculations, often at the expense of vast resources.  It can be difficult to express a problem mathematically, particularly using highly details models available through direct or indirect complex mechanisms.  It would be no small challenge for a computing tool to interpolate shapes and behavior from raw video footage, providing a prohibitively large amount of variability and noise (Hertzmann, 2004, p. 5).

Bayesian reasoning provides a methodology for many data-modeling problems.  By searching for logic in uncertainty, Bayesian methods provide a unified approach that on the surface is like human logic.  Bayesian logic provides three main areas of interest:

1. Principled modeling of uncertainty

2. General purpose models for unstructured data

3. Effective algorithms for data fitting and analysis under uncertainty

Courtesy: CBS

There are advantages to Bayes learning method.  One area is in interpolation.  When faced with a challenge in engineering, there is always a balancing act to determine whether the time and resources a human would consume vs. a computing system.  With the formulation of an engineering system, a world model must be built, along with a “controller” for that environment.  Bayesian methods interpolate this to the extreme because the Bayesian prior can be a delta function on one model of the world (Barak, 2005).

The Bayesian method also obeys the likelihood principle.  In a case where two proportional likelihood functions come from two distinct samples for , then all inferences about both groups will be similar (Jones & Huddleson, 2015).  It also provides interpretable answers in a convenient setting for a wide range of models.  This includes hierarchical models as well as missing-data problems.  Less training data is also required; in fact, lower training data is preferred with the Bayes naïve approach (Chen,  2011, para. 3).  A naïve Bayes classifier will converge more efficiently than discriminative models such as logistic regression.

References

Barak, B. (2005). Advantages and disadvantages of Bayesian learning. Retrieved January 17, 2017, from http://hunch.net/?p=65

Chen, E. ( 2011, April 27). Choosing a machine learning classifier [Blog post]. Retrieved from http://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier

Hertzmann, A. (2004). Introduction to Bayesian learning [Lecture notes]. Retrieved from https://www.dgp.toronto.edu/~hertzman/ibl2004/notes.pdf

Jones, A., & Huddleson, E. (2015). Bayesian analysis: Advantages and disadvantages. Retrieved January 17, 2017, from https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_introbayes_sect006.htm