Why Pure Theoretical Degrees Are Useless

Theoretical degrees are utterly useless in the practical world. However, they may be useful for teaching. Reason being they have zero element for practical reasoning. If one can't apply theory into practice then what is the point of such degrees. Math degrees teach concepts, they provide the formula and they provide the problem. In physics, they provide the problem and they provide the formula to solve for that problem. Such degrees amplify little in application. When such people enter the practical world, they need to be taught how to do literally everything. One wonders where they forgot to think along the way of attaining such a theoretical degree in being able to apply themselves. In real world, one has to find the problem then find a way to solve for that problem, and this is the case ninety nine percent of the time. The only way one can combine such excessive theory is to add an element of engineering to it. In biology, chemistry, and other such courses the degree is transformed into application for medicine, pharmacology and life science disciplines. Any degree that only provides an element of theory is pointless, as it is only good for academic purposes. In practical world one has to be taught how to apply such theory into practice to be productive and useful in society. Increasingly, universities are failing to combine theory with practice, because they aim to meet numbers for educational measures and indicators so as to achieve more academic funding. The biggest mistake employers can make in the IT world is recruit graduates with purely theoretical backgrounds to do practical AI work. Don't hire a math, physics, or statistics graduate to build a machine learning model they will require a lot of mentoring and training. Majority of practical and theoretical AI work requires a computer science background where such material is formally taught in the degree course.

Data Journalism

Data Journalism

Why Pure Probabilistic Solutions Are Bad

In Data Science, there is a tendency to focus on machine learning models that are inherently based on statistical outcomes that are essentially probabilistic in nature. However, to test these models one uses an evaluation method which is also seeped in statistics. Then to apply further analysis on explainability and interpretability one again uses a statistical method. What this turns into is a vicious cycle of using statistics to explain statistics with an uncertainty of outcomes. At some point, one will need to incorporate certainty to gain confidence in the models being derived for a business case. Essentially, knowledge graphs serve multiple cases towards increasing certainty into the models but also for providing logical semantics that can be derived through constructive machine-driven inference. Logic, through relative inference, can give a definite answer while the machine learning model can at most provide a degree of confidence score to which something holds or doesn't hold with a total lack of care for contextual semantics. A machine learning model rarely provides a guaranteed solution as it is based on targets of approximations and error. Hence, why there is a tendency to measure bias and variance in training, testing, validation data. The evaluation is also relatively based on approximations of false positives, false negatives, true positives, and true negatives. Logical methods can be formally tested. A machine learning model can at most be subjectively evaluated with a degree of bias. Invariably, at any iterative time slice a pure statistically derived model will always be overfitted to the data to some degree. Statistics derive ridged models that don't lend themselves to providing definite guarantees in a highly uncertain world. Invariably, the use of statistics is to simplify the problem into mathematical terms that a human can both understand, solve, and constructively communicate. Hence, why there is such a huge statistical bias in academia as it tends to be traditionally a very conservative domain of processing thoughts and reasoning over concepts as a critical evaluation method within the research community. One can say that such a suboptimal solution may be sufficiently good enough? But, is it really good enough? One can always provide garbage data and train the model to provide garbage output. In fact, all the while the statistical model never really understands the semantics of the data to correct itself. Even the aspect of transfer learning in a purely statistical model is derived in a probabilistic manner. The most a statistically derived model can do is pick up on the patterns. But, the semantic interpretability of such data patterns is still yet to be determined for guarantees of certainty in fact it is presumably lost in translation. Even the state of the art model is fairly subjective. Evaluations that only look at best-cost analysis in terms of higher accuracy are flawed. If someone says their model is 90% accurate, one should question in terms of what? And, what happens to the other 10% that they failed to account for in their calculations which is something of an error that the person pipelining the work will have to take into account. Invariably, such a model will likely then have to be re-evaluated in terms of average-cost and worst-cost which is likely going to mean an increase in variable error between 5% and 15%. The average-cost is likely to lie somewhere in 10% and the worst-cost somewhere near 15%. So, 90% of time, in production the idealized performance accuracy of the model would be 90% - 10% = 80% on average-cost, plus/minus 5% on the best-cost, and anywhere between minus 10% and 15% on the worst-cost. This implies that 5% of time the model will perform on best-cost, 5% of time on worst-cost basis, but 90% of time on average-cost where the idealized predictive accuracy, when taken into account the full extent of the error would be 80%. Even though, this is still fairly subjective, at least idealized metric where it takes into account the environment factors improves on the certainty. This is because in most cases a model is built under the assumptions of perfect conditions without taking into account the complexity and uncertainty which would be present in production environment. There is also the need to be mindful, sensible, and rational with the use of the accuracy paradox. One can conclude here, that a hybrid solution of combining probabilistic and logic approach would be the best alternative for reaching a model generalization case of sufficient certainty to tackle the adaptability mechanism for process control as well as to capture the complexity and uncertainty domains of the world.

Bias, Slant, and Spin

• How to spot types of media bias
• Bias, Slant, and Spin

Hydroponics Fruits, Herbs and Veggies

Agrotech industry is a fairly active area of research for AI. The setup requires indoor soil, lighting and AI to make it happen. The below list includes most common fruits, herbs, and vegetables grown in hydroponics:

• Cucumber
• Tomato
• Lettuce
• Strawberry
• Basil
• Coriander
• Spring Onion
• Pepper
• Spinach
• Blueberry
• Radish
• Kale
• Green Bean
• Chive
• Mint
• Cabbage
• Carrot
• Cauliflower
• Celery
• Beet
• Chard
• Broccoli
• Corn
• Eggplant
• Leek
• Sage
• Melon
• Onion
• Pea
• Zucchini
• Squash
• Parsley
• Oregano
• Rosemary
• Thyme
• Chamomile
• Dill
• Lavender
• Chicory
• Fennel
• Mustard Cress
• Lemon Balm
• Watercress
• Stevia
• Peppermint
• Tarragon
• Bok Choy

Thorny Weeds of Data Science

In industry, the area of data science is a bit like navigating through a large field of thorny weeds. There are just too many people pitching themselves as experts, that don't understand what they are doing. Many of them with Phd backgrounds who have the complete inability to translate theory into practice. The field is a breeding ground of insecure people in teams with pure academic backgrounds. For everything, they require help, additional resources, adding to the frustration of their peers but also to the management who have to support their work with unnecessary inefficiencies and extensive funding. The patterns of recruitment or lack thereof seem to be typical across many organizations. Often these false hopes of hiring Phd individuals to lead research work in business seems to stem from clueless investors who neither have the interest nor the sense to understand the practical aspects of the field. Interview rounds for candidates becomes a foregone conclusion of misplaced, inept, and aberrant hiring. As a result, the entire organization becomes guilty of compounding issues in hiring incapable, pretentious, and arrogant individuals, lacking basic common sense, who may apparently have steller academic credentials. In many cases, it is questionable on the merit of a Phd qualification, where much of it may have been gained via crowdsourcing platforms or even via the extensive help of the academic advisor for the thesis write-up. Most things that a Phd caliber individual can do, a non-Phd individual can do it better and translate it into a product. If a Phd individual cannot convert theory into practice then what is the point of such a hire? Considering the fact, only one percent of the population has a Phd, is it any wonder why organizations are so ill-informed about calling it a skills shortage when there isn't any to begin with and should really focus on correcting their idealized job requirements. Invariably, organizations learn the hard way when projects fail to deliver and there is no tangible return on investment from a Phd hire. Is this an evidence of a failure of the education system, of the entire technology industry, or perhaps both. Flawed online training courses, mentoring, and certification courses further amplify this ineffective practice. Bad code and models, just breeds bad products to the end-user which ultimately effects investment returns from lack of business performance where targets need to be offset through additional end-user support plans. It still stands to reason, that for any company, people are the biggest asset. Hiring the right people and looking beyond the fold of their credentials is paramount. In fact, hiring astute generalists is far more important than hiring specialists in the long-term. A specialist in a certain area, at a given point in time, is likely to be an outdated specialist in the short-term to long-term cycle of work. Organizations that function as businesses need to strategize their game plan and forecast for the future, which may just be a few quarterly cycles ahead of time. The quickest way to failed startup is to increase costs by hiring Phds and increasing head count of staff to support their work. One needs to wonder why hire so many people to support someone with a Phd unless they are practically incompetent. Academics invariably cannot translate into practice which impacts delivery cycles of work. Phds as a result become the weakest link in many cases, inhibiting and hampering the cycle of productivity and innovation both for the short-term as well as long-term growth of an organization.

Awesome Semantic Web

Awesome Semantic Web

Why Microsoft Products Are Terrible

• Tight coupling approach to products and services
• Documentation bias towards own products and services
• This only works on windows
• Plagiarism and stolen code from other vendors
• Security risks and software glitches
• Business model built on stolen products, services, ideas, and code
• Market hijacking
• Consolidation rather than any significant innovation
• Windows copied from Mac
• DOS copied from DEC
• Bing copied from Google
• Explorer copied from Netscape
• All windows versions come with design flaws
• Lack of separation between OS and application
• Unrepairable codebase
• Waste of resources
• The dreadful blue screen of death
• Unreliable as cloud servers
• Trying to do everything but master of none
• Terrible network management
• Terrible at internet related services and products
• Enjoys copying other competitors
• Lots of security vulnerabilities
• Forced sales targets for substandard products and services
• Marketing gimmicks that breed lies and failed promises
• Buying open source solutions to kill off the competition
• Doesn't support open source community
• Works on the vulnerabilities of ignorant customers
• Ease of use can be subjectivity and at the detriment to lack of quality
• Ignorant users are happy users
• Forcing updates and patch releases for security failures in quality
• Bad practices and foul play
• Forcing users to use windows instead of linux or mac
• Vendor lock-in and further use of the cloud to apply the same methodologies
• Business as usual with anti-trust
• Rigged tests and distorted reality
• Bogus accusations
• Censorship
• Limited memory protection and memory management
• Insufficient process management
• No code sharing
• No real separation between user-level and kernel-level
• No real separation between kernel-level code types
• No maintenance mode
• No version control on DLL
• A weak security model
• Very basic multi-user support
• Lacks separation of management between OS, user, and application data  
• Does not properly follow protocol standards
• Code follows bad programming practices
• Anti-competitive practices to block open source innovation and technological progress