Skip to main content

On reality and labels

Our reality is based on categorizations spun from our minds.  For a thing to be defined by science, it needs an objective identity.  Something that makes it irrefutably unique.  What makes a thing unique apart from the thing itself, are the words and symbols used to categorize it.  But those words and symbols themselves can only be defined by other words and symbols.  And reality would have it that everything is unique, for no parts are shared.  And yet we insist of putting labels on things, and wonder why they don't fit.

Popular posts from this blog

the Natural Language App, part 1

Introduction Natural Language Processing (or NLP) is the art of taking human written language (or indeed human spoken language) and analyzing it to use it in some form or fashion.  Advances in natural language processing have made it possible to embed human language understanding in software applications.  Things as personal assistants and bots are now common-place.  The next step is a more integrated approach, the nl-app.  An nl-app is architecturally different and has other architectural concerns, but that is for part 2 of this article. Before we start discussing this, we'll take a small detour through existing solutions and why I think there is a difference. Personal assistants have been a series of new devices like Alexa, Echo, Google-home, Siri, Bixby and a few others.  These are stand-alone devices, usually with their own application API.  There is great potential for such devices to interface with the Internet of Things (IoT), ordering onlin...

SimSage

Design of an Interactive A.I. for help desks, and the Internet of things Sean Wilson and I started a semantic search company over a decade ago.  This started my foray into  intelligent systems, big data, and artificial intelligence. We left this company after eight years of hard  work. This company is still operational today and doing well. I always felt that there was something missing from a search only solution.  First I tried to make the  search more intelligent. I tried many different approaches.  Focused on getting better Word Sense  Disambiguation (WSD) using neural networks. WSD can be thought of as being able to tell ambiguous  usage of words apart.  “Jaguar”, are we talking about the car or the animal? “bank”, did they mean a  financial institution or the side of a river? This can usually be resolved from the immediate or larger  context of whatever it is you’re looking at. This only led to better in...