We mostly think of data as something we put in into the systems using electronic forms. Human operators pour in data all around the world. There is also data about data, which is what mostly computers do with their invisible algorithms, and then there is data generated by machines equipped with sensors that measure all sorts of parameters. Referred to as the Internet of Things, this network of devices collects data incessantly streaming them into large databases. This data stream is about to explode, dwarfing the data collected by human operators.
Take the healthcare for example. Traditionally, the data collection occurs at healthcare facilities. You go there, a friendly nurse plugs into you a device that measures your blood, your heart, whatever is needed to help doctors produce a diagnostic. Once you are done, the data stream stops. On exceptional occasions you are given a device to carry with you for data sampling. As the sensors become cheaper and smaller, when it comes to data collection the healthcare industry starts to blur the boundaries between the inside and the outside of their facilities. Carrying monitoring devices will become normal creating a huge amount of data.
Everything that touches our lives will be equipped with sensors: the house, the office, the cars, the roads, you name it. With IPv6, the number of connectable devices is practically unlimited. Someone calculated that 1038 IP addresses will be available. Imagine how that world will look like.
WiTricity, a company that invented a system of charging a battery wirelessly, is considering the idea of powering the cars through mini generators planted into the road. This is an incredible idea. Cars flowing (driverlessly?) through the highway absorbing power from the road as they roll smoothly to their destination will be in constant dialogue with a huge network of small devices designed to identify them, and measure the energy consumption and other parameters. This is a data flood alright.
Who or what is going to handle all this data? Who/what is going to make sense out of all this? Forget about privacy – that will go away anyway – there is no place to hide, but handling this data will be a huge challenge.
On one hand we will use analytical tools to examine data and make decisions. This is a slow process that suits us, humans, to have time to figure out things. On the other hand you need faster decision systems that will respond to situations. Large financial institutions in US are already going through a huge redesign of their organisations by replacing human traders with ultra-fast machines that could execute optimised trades at lightning speed. We will have that adopted in healthcare, in transportation and other areas.
A good question is what are the system design principles we need to adopt in a world of fast computers and of an infinity of networked devices? Do we need to learn completely new skills that allow us to handle the increased cognitive load and to interact with computer systems in radically different ways, skills that are not taught in schools or elsewhere? At the moment, there is a growing disconnect between a schooling system obsessed with assessing numeracy and literacy skills and the transformed world in which we live in. Perhaps the technical system design needs to be merged into a social system design so that we don’t rely on highly skilled analysts and machines for making decisions, but integrate the computer network within higher order social networks.
While trying to explore the principles of creation of knowledge, I wanted to run an overview of the epistemology as a way of understanding knowledge from a philosophical perspective. As result of this mini-exercise am writing a brief overview with a few comments, followed by my brief personal critique of epistemology.
What Is Epistemology?
Philosophy as a thinking system likes to explore the universe through “rational investigation of the truths and principles of being, knowledge or conduct” (www.dictionary.com). Where does epistemology fit?
According to Peter D. Klein “epistemology is concerned with the nature, sources and limits of knowledge”. This is where the trouble starts. What is knowledge? According to some knowledge should include objective forms, others think that in the context of epistemology, knowledge is only about beliefs that something is true as opposed to knowledge about how to do things.
Epistemology ventures into areas where precise measurement is impossible. This is why there are many definitions of epistemology and heated debates have been going on for centuries.
Propositional Knowledge Epistemology
The focus of epistemology on knowledge analysed on the basis of beliefs and truth takes this philosophy out of the natural philosophy branch, where I would have preferred it to be. In my view this limits the influence of the advances of science on the development of epistemology simply because subjective knowledge is impossible to measure. The claim of the traditional epistemology is that the quality of the reasons for our beliefs determines the conversion of beliefs into knowledge. This approach is called the normative epistemology, and is supported by theories of justification. Another tradition, the naturalized epistemology, claims that the conditions in which the beliefs are acquired determine the truthfulness of the beliefs.
This tradition has two views about the structure of reasons: foundationalism and coherentism. The foundationalism reminds me of the Anglo-Saxon common law. Rulings can be made based on precedent rulings which have been gathered throughout the history of application of the law in territories under the crown for many, many centuries. Some of the rulings are unique and they can form the basis of a new ruling for a case that could occur in the future. When they occur and the reference to the precedent is accepted as being similar, the court can rule without having to repeat the previous process. Thus, according to this view, beliefs can be based on other beliefs which have been proven true in the past, therefore they don’t have to be justified and thus together they form the basis of the aggregating belief and deem it true. In other words if the new belief X is based on A deemed true, then X is true. Of course, it gets complicated when deciding if a belief is true when more than one hypothesises are available.
The basic beliefs can be of several types: empiric (Hume and Locke), rational intuition (Descartes, Leibniz and Spinoza), innate (Kant, Plato), or conversational contextual.
Coherentism by contrary, states that a belief is true if multiple beliefs are inferred for its justification. But this is not very helpful either. Gettier formulated a scenario (Gettier’s problem) where the assumptions might be true, but the inferred belief is not necessarily true. Gettier’s example of Jones and Smith when they apply for a job and Smith is making a deduction in which he concludes “the person who has ten cents in his pocket will get the job” which proves in the end to be false, although the hypothesis are true, seems to be focused on semantics rather than facts. When Smith said “the person with 10cents in his pocket will get the job” he was thinking of Jones. Thus, the actual belief was that Jones will get the job because he knew he has 10 cents in his pocket. Gettier tries to prove the point by solely focusing on the last sentence that went through Smith’s mind, not on the actual belief. The experiment clearly makes no connection whatsoever between the 10 cents and the job allocation, hence the hypothesis is false anyway.
Also called naturalistic epistemology, this tradition describes the knowledge as produced in natural circumstances and beliefs are considered true based on conditions verified using methods, results and theories specific to empirical sciences. This type of epistemology tends to rely on cognitive psychology and its empirical methods to determine the quality of conditions in which the knowledge is acquired. Quine, a naturalistic epistemologist, considers epistemology as part of psychology, while Thomas Kuhn thinks the social sciences should be applied to epistemology. This approach would solve the Gettier’s problem by qualifying the source of knowledge as not entirely reliable. Mind you, this is not bullet proof because the method cannot be applied to what you don’t know. Smith didn’t know he does not have 10 cents in his pocket, so his statement sounds true.
The fundamental issue I have with the proposition offered by epistemology, that knowledge is about beliefs and justification as an indication of truth, is that it is entirely subjective (even the empirical methods ultimately attempt to “guess” the quality of the subjective thought) and limited to human interpretation and mental storage of knowledge.
With the development of computers and large network systems the idea that knowledge is limited to the human brain and defined by individual beliefs is unsatisfactory. There are two major weak points in the traditional epistemology: knowledge can be stored outside the human brain and used as a repository which is accessible on a need by need basis or through gradual discovery and that knowledge could be distributed across large number of people and shared as common source of knowledge.
The first issue is a bit surprising. Epistemology seems to be stuck in a debate that has only marginally changed since Plato, based around a discourse focused on beliefs as mysterious forms of reflection of the external environment or as outcomes that result from internal mental processes. At a time when information was an inexistent concept and everything was mechanical, far more obvious and easier to recognise than thoughts, the fascination with the mind’s perceptions and deductions was understandable. But know this approach is outdated in my view because it does not recognise the possibility of knowledge created by and with computer systems in vast networks.
The second objection has to do with the lack of recognising the socially created knowledge as something that is acquired by large social group through an iterative process of sharing, collaboration and collective action. The role of social networks is ignored completely, thus missing the opportunity to explore the creation of knowledge at a higher order and implications of availability of knowledge across large populations and geographical areas, including the whole planet.
The Meaning of knowing has shifted from being able to remember and repeat information to being able to find and use it
– Herbert Simon, Nobel Laureate