Grounded Theory Methodology For Knowledge Engineering: Fill & Download for Free

At good research universities, no such academic program exists.As of now, a good academic/degree program challenges you to develop 21st century skills, including skills for data science and intercultural competence, and reframes traditional world views and views of looking at problems and topics by teaching them about network science and computational thinking.To develop innovative solutions, one needs an academically rigorous background based on science, engineering, mathematics, and/or computer science.Trying to produce an incrementally innovative solution without using rigorous and sound methodologies for design, verification, testing, and validation is foolish, and arguably stupid.Trying to regularly come up with patentable, disruptive technologies without engineering knowledge is practically costly and nearly impossible.While start-up unicorns have started business without amazing, world-leading technologies, many good start-ups are created by former grad students and professors (especially at competitive EECS programs/departments) via technology transfer from their research deliverables. They do so by exploiting their knowledge and skills (grounded in sound theories) to create innovative products.Classic examples lie in markets and market segments in electronic design automation, processor architecture (or microarchitecture), hardware model checking, hardware security, and embedded/VLSI deep learning.

Sure. Become an academic.Or accept that your skills in programming may take longer to develop than the theory. That's normal for anyone who doesn't already have a grounding in programming, or isn't having skills uploaded to their brain Matrix-style.You could also look into product management, where understanding the theory is enough if you understand how to define a problem well without getting overly caught up in the solution. And if you have something of an entrepreneurial side to your character.But for that, you really need a good business head, which you can get either through experience, or doing a business course in conjunction with a computer related course.I did this - it was called 'Software Engineering' but it was 50% business stuff and history of methodologies etc. etc.Or become a product owner and translate those higher level objectives into well defined requirements.Or become a QA tester where you can use your knowledge of algorithms, boundary conditions etc to ensure a good coverage of test cases to fit the uses cases the developers are trying to implement.There's plenty of room in the industry for people who don't want to become coders.

Wow! This is a loaded question, not to mention the sub-questions in the OP's description!Regarding the main question, I think the first ordinary response is: because that's all we have to go on. What means other than observation do we have to learn about our world? Sure, as suggested in sub-question (c), there is "pure" reason, such as the stuff of "pure" math and logic. Such reason, however, fails to be informative without application. And what do we have to apply it to other than the phenomena of our senses, of course! This is the sort of view presented by common sense realism, such as that of G.E. Moore and John Searle. To them, the application of Cartesian methodological skepticism to something they view as primary as sense phenomena is so removed from common sense that it doesn't make sense. Of course I have a hand! Here it is! And here's another!Many philosophers find their kind of reasoning dismissive hand-waving, not really addressing the deeper philosophical issues. It's not that Cartesian methodological skepticism has to be the start, or that infallibility needs to be the goal, but that there is something deeper driving our search for knowledge. Yes, we can be deceived by our senses as noted by Socrates 2500 years ago. So it doesn't make sense to trust all our senses all the time, especially when it really matters. You certainly don't want to board a rocket to the moon if it was built by engineers who took all their sense experience at face value, as common sense realists (and earlier positivists) would seem to suggest. Throughout the history of the empirical sciences, we have been refining our methods to reduce unreliable sense experiences. If you see something strange in the sky, and your friend is standing next to you, likely you'll point to the thing and ask them if they see it too to verify that it is not just you. Shared experience is one of the foundations of empirical knowledge. And on it, we use forms of demonstration ranging from pointing to an object in the sky to rigidly controlled lab experiments. Our sense experience is not completely subjective and unreliable, contrary to skepticism and one interpretation of the OP's question. But it's also not completely objective, as suggested by common sense realism. We've developed methods to help distinguish which experiences are reliable and which are not. But what do we base those on? How did we develop those methods and how do we develop future methods?On these questions, I don't find the classical rationalism or empiricism very useful. I tend to take the more pragmatic approach. What counts as knowledge depends on whether it is useful. Neither raw sense data nor its formal organization makes it useful. What makes it useful to me depends on what I want to use it for. Knowledge is a tool, just like any technology. If what I want is to leave my home, knowledge of how doors work is very useful and informative. Knowledge of the mass of the moon is not. Similarly, what counts as reliable knowledge depends on what we want to use it for. A hammer is a reliable tool for driving nails, but not so reliable for trimming toe nails. Just like any technology, we need the right kind of knowledge for the desired application. A hermit monk living in a cave, perhaps, might not have much use for knowledge of sensory experience. He might be looking for some inner experience instead. But for most of us, we really want to navigate well through the world and have good relationships with one another. In order to get knowledge of sensory phenomena, we consult sensory experience and pragmatically attribute reliability to the methods which get us closer to what we desire. The monk might be more interested in spiritual applications, and so it is more pragmatic for them to look to inner experiences for methods which reliably satisfy them.Is empirical evidence a reliable indicator of "truth", given that some sense experience is unreliable?This is where the question starts to get really loaded. Notice in my description of pragmatism that I say nothing about "truth". A pragmatist doesn't really need to worry about "truth". If some belief reliably works to do what it is meant to do, what does it matter how close or far it is from approximating "the truth"? Who cares whether it corresponds to some "objective reality" or not? And who cares what things like "truth" and "objective reality" really are? All that is essential to a pragmatic epistemology is justified belief where the justification is the utility of the belief for the person or persons. "Truth" might be seen as an archaic legacy from an epistemology which hoped for incorrigible beliefs.Most philosophers, however, pragmatists or otherwise, want some sort of theory of truth. There are quite a number of different theories, and they might have different applications. I tend to view truth as semantic, governing the coherence of the meaning of a belief, with the ideal being something like an analytic proposition. But when a belief involves a belief about empirical phenomena, then it might be more useful to shift to a correspondence or referential theory, something like the supervenience of a topographical map on a geography. And when it involves a process of reasoning, such as an inference, consistency might be the most useful view of truth. The notion of "truth" is so ubiquitous, I suspect it has different meanings in different contexts depending on its application. But as I said, my fallback position is that truth is one of many possible semantic values, and of a special preservational kind of semantic values.So for me, empirical evidence has nothing to do with truth. It is not a reliable means to truth. Well formed definitions are the means to truth, which, when involving demonstrables, requires a means to reference publicly observable phenomena. To count as knowledge, a justified belief must be true only in as much as is required to understand the meaning of the belief. It's hard to justify a belief, pragmatically or otherwise, if the belief itself is hopelessly ambiguous and ill-formed. If I understand the reverse of the gas and brake pedals, I can't be said to be a very good driver, even if by sheer luck I always get them right. I might understand what I want to do, and I might even get it done, but I couldn't be said to understand what I was doing.Subquestionsa. Are conflicting experiential perceptions possible? How can they be resolved? What are the implications regarding knowing what is true?For sure. Car sickness is an example of conflicting perceptions at the same time. Your ears are telling your brain you are doing one thing while your eyes are telling you another. I tend to close my eyes if I'm feeling extremely motion sick, or otherwise try to stare as far as I can down the road. It's strange though that I never get motion sick if I am the one driving.In science, I think Khun presented an excellent answer to how knowledge progresses with multiple inconsistent data sets. I tend to think that that is how we process inductive knowledge as well. Observations, along with reasoning, form beliefs. It's not so much the observations which are contradictory, but the assumptions we make about them. Different models have different applications. Add to that that we sometimes have conflicting desires, so it is not clear which model we should apply. We're perfectly capable of segmenting belief sets. And we're perfectly capable of maintaining contradictory beliefs simultaneously. If we weren't, counterfactuals and reductio would not be possible. When it comes to an overarching world view, we might do something like maximize the coherency of the largest number of beliefs, segmenting the ones which don't fit into the temporary bin. That set is probably extremely dynamic as we go through life and experience a wide range of phenomena, as well as other people's beliefs. I would suggest that your brain is already working on your behalf to resolve incoherence. And if you wanted to model it, I would suggest a paraconsistent inductive logic.b. How do we know we're not "brains in vats"?Huh? As a pragmatist, the question for me is whether this is a useful model or not. In what application would it be better for me to understand myself as a brain in a vat? I fail to imagine any. So it comes across as a ridiculous hypothesis to me. I find it tends to be more constructive to trust our common sense experience until it proves otherwise. The brain in the vat thought experiment comes from Cartesian methodological doubt. It's a modernization of Descartes' malicious demon. I reject Cartesian methodological doubt on pragmatic grounds as being less constructive than positivism until demonstrated otherwise. I tend to give my experience the benefit of the doubt until something doesn't go as well as I had hoped.c. Is reasoning somehow more "true" than evidence, being based on axiomatic principles (e.g. identity, excluded middle, non-contradiction) rather than on subjective perception? Or is reasoning itself somehow grounded in empiricism?Neither and both. Reasoning is more truth preserving when it is well defined, but you don't get any useful information from it until you populate the variables with observables. Observables aren't very useful if they aren't connected by reasoning. Reasoning is the form of beliefs, but observables are the substance. They need to be coordinated correctly to get useful results. The classical rationalism versus empiricism debate has pretty much faded away similar to the nature-nurture debate. Both are required and the interesting and useful questions are how they work together to form knowledge.