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  • Tony Dobaj

Data vs Knowledge

No matter what you do in these modern times, technology is an indispensable fact of daily life. It teases us with vast potential, but like a heartless vixen, many times lets us down. When it does this, it seems the underlying reason falls in two distinct categories – there’s either too much data, or too little data. Let’s examine the first one first.


Google’s search engine is an extraordinary piece of technology that has transformed our relationship with data. Imagine attempting to make sense of the WWW without it or something like it. It would be like going into the Library of Congress without the Dewey Decimal System (or something like it). But as extraordinary it is, data scientists can only do so much with such volumes of almost completely unstructured data. In this environment, keywords become the currency (literally and figuratively) of knowledge, and knowledge is the cake whereas information is just the ingredients. At the first level of cognitive abstraction, Google does a great job of baking the cake. But at deeper levels of reasoning, Google confuses confectioner’s sugar with raw sugar, and one gets something entirely unexpected, and cookies are delivered to the birthday party instead of cake.


It’s not completely Google’s fault. Without formalizing the relationships between keywords (that is, giving them context), the word “Venus” could mean several very different things (a statue, a tennis phenom, or a planet to name a few). Teasing out context is something that the human brain does very well, computers not so much. The holy grail of AI is not just making making sense of data – humans do that pretty well. It’s making sense of huge stores of data, because processing speed (in this context) is not one of evolution’s highest achievements.


Knowledge, not search, is Google’s bread and butter, and lots of resources are being applied to ensure the knowledge mine keeps producing when the mine shaft keeps getting filled with useless rubble. So what’s this about not enough data?


So far I’ve been writing about our alternative, that is, digital reality. However (my son’s addiction to video games notwithstanding) we don’t live in a “matrix” – our primary reality is still analog. The song on the radio that we hear with our ears is continuous, not discrete. That wonderful odor coming from the kitchen is not being delivered in bytes. These are purely analog encounters which is the only way evolution has afforded us to interact with the world around us.


These two undeniably mandatory aspects of our lives are a round peg and a square hole. Until the robot apocalypse, or we figure out how to install an Ethernet connector straight into our brain, this is something we need to deal with. And this, in my view, is the number one concern of “frictionless” (also known as “ubiquitous” or “seamless”) computing. That is, bridging the gap between these two realities in a way that takes full advantage of both. In a clear and present personal example, it is absolutely absurd to me that I still need to write CSS code in a language that the computer understands (digital reality) to satisfy the needs of my visual processing system (analog reality). Where are you, Hal?


Gadgettronix’s mission statement is all about exploiting new sensor technologies to bridge the gap between our primary reality and alternate reality. We need both, and we intend to just keep whittling away at the corners of the square peg.


Here is a link to a Udacity video that explains it very well.

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