Mind Over Subject Matter: Researchers Develop A Better Understanding of How Human Brains Manage So Much Information

"Synapse", Image by Allan Ajifo

“Synapse”, Image by Allan Ajifo

There is an old joke that goes something like this: What do you get for the man who has everything and then where would he put it all?¹ This often comes to mind whenever I have experienced the sensation of information overload caused by too much content presented from too many sources. Especially since the advent of the Web, almost everyone I know has also experienced the same overwhelming experience whenever the amount of information they are inundated with everyday seems increasingly difficult to parse, comprehend and retain.

The multitudes of screens, platforms, websites, newsfeeds, social media posts, emails, tweets, blogs, Post-Its, newsletters, videos, print publications of all types, just to name a few, are relentlessly updated and uploaded globally and 24/7. Nonetheless, for each of us on an individualized basis, a good deal of the substance conveyed by this quantum of bits and ocean of ink somehow still manages to stick somewhere in our brains.

So, how does the human brain accomplish this?

Less Than 1% of the Data

A recent advancement covered in a fascinating report on Phys.org on December 15, 2015 entitled Researchers Demonstrate How the Brain Can Handle So Much Data, by Tara La Bouff describes the latest research into how this happens. I will summarize and annotate this, and pose a few organic material-based questions of my own.

To begin, people learn to identify objects and variations of them rather quickly. For example, a letter of the alphabet, no matter the font or an individual regardless of their clothing and grooming, are always recognizable. We can also identify objects even if the view of them is quite limited. This neurological processing proceeds reliably and accurately moment-by-moment during our lives.

A recent discover by a team of researchers at Georgia Institute of Technology (Georgia Tech)² found that we can make such visual categorizations with less than 1% of the original data. Furthermore, they created and validated an algorithm “to explain human learning”. Their results can also be applied to “machine learning³, data analysis and computer vision4. The team’s full findings were published in the September 28, 2015 issue of Neural Computation in an article entitled Visual Categorization with Random Projection by Rosa I. Arriaga, David Rutter, Maya Cakmak and Santosh S. Vempala. (Dr. Cakmak is from the University of Washington, while the other three are from Georgia Tech.)

Dr. Vempala believes that the reason why humans can quickly make sense of the very complex and robust world is because, as he observes “It’s a computational problem”. His colleagues and team members examined “human performance in ‘random projection tests'”. These measure the degree to which we learn to identify an object. In their work, they showed their test subjects “original, abstract images” and then asked them if they could identify them once again although using a much smaller segment of the image. This led to one of their two principal discoveries that the test subjects required only 0.15% of the data to repeat their identifications.

Algorithmic Agility

In the next phase of their work, the researchers prepared and applied an algorithm to enable computers (running a simple neural network, software capable of imitating very basic human learning characteristics), to undertake the same tasks. These digital counterparts “performed as well as humans”. In turn, the results of this research provided new insight into human learning.

The team’s objective was to devise a “mathematical definition” of typical and non-typical inputs. Next, they wanted to “predict which data” would be the most challenging for the test subjects and computers to learn. As it turned out, they each performed with nearly equal results. Moreover, these results proved that “data will be the hardest to learn over time” can be predicted.

In testing their theory, the team prepared 3 different groups of abstract images of merely 150 pixels each. (See the Phys.org link above containing these images.) Next, they drew up “small sketches” of them. The full image was shown to the test subjects for 10 seconds. Next they were shown 16 of the random sketches. Dr. Vempala of the team was “surprised by how close the performance was” of the humans and the neural network.

While the researchers cannot yet say with certainty that “random projection”, such as was demonstrated in their work, happens within our brains, the results lend support that it might be a “plausible explanation” for this phenomenon.

My Questions

  • Might this research have any implications and/or applications in virtual reality and augment reality systems that rely on both human vision and processing large quantities of data to generate their virtual imagery? (These 13 Subway Fold posts cover a wide range of trends and applications in VR and AR.)
  • Might this research also have any implications and/or applications in medical imaging and interpretation since this science also relies on visual recognition and continual learning?
  • What other markets, professions, universities and consultancies might be able to turn these findings into new entrepreneurial and scientific opportunities?


1.  I was unable to definitively source this online but I recall that I may have heard it from the comedian Steven Wright. Please let me know if you are aware of its origin. 

2.  For the work of Georgia’s Tech’s startup incubator see the Subway Fold post entitled Flashpoint Presents Its “Demo Day” in New York on April 16, 2015.

3.   These six Subway Fold posts cover a range of trends and developments in machine learning.

4.   Computer vision was recently taken up in an October 14, 2015 Subway Fold post entitled Visionary Developments: Bionic Eyes and Mechanized Rides Derived from Dragonflies.

Flashpoint Presents Its “Demo Day” in New York on April 16, 2015


“Fishbowl Jump”, Image by Kay Kim

Flashpoint is a startup accelerator at Georgia Tech in Atlanta, Georgia. Using a well-defined program, they apply engineering-based methods to nurture new and scalable companies with the intention of addressing unmet demands in the marketplace.

I had the great pleasure of attending their Demo Day presentation in New York, yesterday on April 16, 2015, at the outstanding SUNY Global Center. It was indeed inspiring to see such smart and creative new companies providing innovative products and services. A metric ton of thanks to everyone involved in this special event.

This was part of a series of Flashpoint Demo Days currently touring a number of cities across the US. They are showcasing eight of their current startups, each of whom is seeking additional funding from investors. At this event, one representative from each gave a concise five-to-ten minute speech to the audience about the particulars of their ventures.

The proceeding began with a talk by Professor Merrick Furst of the Georgia Tech College of Computing. He gave a compelling explanation of Flashpoint’s guiding principles, methodologies and results. Among other things, he shared his perspectives about starting up a new firm, including two imperative elements: “Authentic demand” needed in the market and “authentic innovation” provided by startups to meet it. I believe that the audience, many of whom are involved in startup financing, learned much from him.

Next, in the order that they appeared, one speaker for each of the following Flashpoint startups addressed the audience:

  • GalliumGroup (and on @datawhipper):   A data-driven service to assist in warranty analysis, claims efficiency and payment optimization for heavy equipment manufacturers in construction, transportation and farming.
  • DecisionIQ (and on @DecisionIQ):  A data management, analytics and decision-support firm whose software platform spots potential equipment failure well in advance of its occurrence in the health, energy and transportation industries. The application presented was for industrial turbine engines.
  • Generation DyNAmics (@generationDNA): A firm working on creating an environment where everyone (ideally 90%), in a specific geographical area receives genomic screening in order to halt the spread of preventable genetic diseases.
  • Visit:  A firm establishing a platform to enable customers to meet with actual creators of limited edition handmade goods such as food and clothing. For example, a small producer of gin attached labels to his bottles encouraging his buyers to meet with him for a tasting.
  • Acivilate (and on @acivilate): A firm seeking to change the management and coordination of the delivery of public and social services. For example, parolees need help with their documents, housing and employment. Acivilate prepares a master case file for these purposes which is owned by the client.
  • Vault (and on @VaultStemCell): A firm that provides “a medical concierge service” to gather and store a client’s own stem cells if and when they might be needed for regular medical procedures or else in the future to treat illnesses. An example of a target market is professional and amateur athletes.
  • GetLawyer (and on @getlawyer):  A firm aiming to assist in “clearing the legal markets” by using its software to match and deliver clemency eligibility cases to the appropriate attorneys in order to provide support for such filings for  prisoners (often first-timers), being held non-violent charges.
  • Florence Healthcare (and on @FlorenceHCare): A firm providing a data management software solution (including  collection, processing and sharing), for clinical trials of pharmaceuticals under development.

All of the presenters greatly impressed me with their visions, passions and sincerity for the objectives and potentials of their startups. I highly recommend clicking through on the above links to view at the full details of their endeavors.

Bravo! and the very  best of good luck and good fortune to all of them as they move forward.