Inconsiderate Universities

The following list the types of things that make a bad university.

• Never get back to your enquiries within 24-48 hrs
• Don't prioritize or who can't be bothered about students
• Really slow admin processes you are bound to be frustrated for the entire academic period, it really is not worth it
• Terrible housing if you are looking for on-campus living
• Lecturers have no office hours and don't go out of their way to make themselves accessible to students
• Don't sympathize or show any empathy for students
• High faculty turnover
• Consistently drop in rankings each year
• Low student satisfaction
• Antiquated systems and IT
• Unhelpful staff
• Make mistakes even on academic transcripts
• Grade incorrectly
• Hold biases and discriminate on students
• Staff take kickbacks and discriminate on the admissions process
• Very low graduation rates or very high acceptance rates
• No accreditation for their courses
• Struggle to get funding for research and teaching and dependent completely on intake of students
• Probationary status or where abuse/harassment/discrimination are rampant
• Cannot separate education from religion
• Lack student services
• Driven by profits over quality of education
• No scholarship or alumni progams
• Terrible teaching methods 
• Don't have any form of recognized research program or a poor publishing record
• Poor collaborative record with other established institutions both for teaching and research
• Poor health and safety record
• Poor international recognition
• Poor job prospects after graduation
• Unrecognized credential
• Poorly rated on government metrics for teaching and research quality
• Academic programs are not designed through standardized guidelines and recommendations
• Lacks diversity, equity, and inclusion in their processes
• Graduates from the university have little change in their prospects, and often lack ethics in how they conduct themselves in society
• the institution gives nothing back to society or the local community either via jobs, funding, charity, regeneration programs, or cultivating urban improvements
• the institution has misleading and complicated processes for admissions and in most cases done on purpose to cover up for biases and discrimination
• work experiences are asked for but not used for admissions
• trying to get students to take more prerequisites in order to make more money or the fact that they don't have that many advanced courses that are supposed to make the course interesting
• most of the courses that they advertise on open days are rarely available for students

Cryptography and Machine Learning

Cryptography and Machine Learning

What makes a sucky Employer

Increasingly, in a competitive world, many organizations are becoming the worst places to work for employees and the worst places to apply for candidates. The below sheds some light on questionable employer practices.

• Employers that stress diversity, equity, and inclusion but have very little reflecting that in their management team or recruitment practices
• Employers that hide behind the facade of cultural fit to discriminate on candidates and employees
• Employers that want people from top schools but have very low compensation packages
• Employers that want everything but don't want to offer anything in return
• Employers that expect their employees to lie and cheat to customers
• Employers that have disgruntled customers and bad customer service practices
• Employers that have cheap and terrible products to sell to customers or can't be bothered to understand the frustrations of the customer, identify the gaps in market, and take the necessary steps to improve their business model
• Employers that have terrible work life balance for employees
• Employers that treat their employees like disposable goods
• Employers that care more about where you went to school rather than what you did with your degree
• Employers that are pretentious and procrastinate with employees
• Employers that can't be bothered about candidates during the hiring process and display unprofessional practices
• Employers that have terrible record of dealing with abuse, harassment and discrimination in the workplace
• Employers that are unethical and lack the sense of social responsibility
• Employers during tough times will always look to job cuts as a point of cutting costs rather than reducing red tape between managers
• Employers that have poor incentives for training and career growth
• Employers that don't appreciate good employees and overcompensate bad employees
• Employers that have high turnover as a result of bad recruitment and cultural practices
• Employers that don't realize that it is a two way street between them and customers, employees, and candidates
• Employers that don't realize their brand value can drop in a second through bad social media reviews
• Employers that don't value customers, employees, and candidates will lose business in long run
• Employers that don't make themselves accessible to the public
• Employers that are out of touch with reality and the internal issues in the organization
• Employers that don't listen to customers, employees, and candidate feedback and put it into action to improve both the product, service, and the workplace
• Employers that display a total lack of empathy towards customers, employees, or candidates
• Employers that don't take the time to quality check their products before selling to customers
• Employers that don't care about the privacy rights of customers, employees, and candidates nor take the necessary steps of care to protect their personal data
• Employers that don't have an effective health and safety induction process
• Employers that are disorganized, badly managed, and hire clueless individuals
• Employers that have core values that includes teamwork, valuing people, and integrity while not practicing any of it in the workplace, especially if valuing people involves making them redundant at the drop of a hat or not valuing customer service
• Employers that mostly have bad reviews from their customers, candidates, and employees
• Employers that have biased and discriminatory processes for employee performance reviews for bonuses, increments and promotions
• Employers that don't provide support for maternity and paternity leave
• Employers that have dodgy benefits packages
• Employers that expect employees to work during holiday leave
• Employers that provide bonuses to their mgmt for mass layoffs
• Employers that pay huge pay packages to mgmt but are stingy when it comes to paying their employees
• Employers that don't have sustainability in their business model nor take initiatives to protect the environment
• Employers that don't provide days off for volunteering to employees 

Phd Incompetence

Academia is very different from the practical world. In academia, foundational skills are measured through exams and rudimentary coursework. While in practical world, organizations want people that can do the job under uncertainty, huge amounts of complexity, efficiently process noisy data, adaptability to change, and a requirement for following best practices. At some point in a person's career, work experience far surpasses academics both educationally and in problem solving. The below points highlight the typical patterns of behavior that Phd individuals display in the work place and academia:

• Will want to command authority simply because they hold a Phd which may not even be in a technical field and totally unrelated to the job
• Will measure someone's skills by the amount of degrees they hold and where they got them from
• Will expect others to help them with 80% of their job
• Will struggle to convert theory into practice 
• Will take years to do something that could be done in months by an engineer
• Will reject perfectly good candidates simply because they don't hold a Phd like they do or went to a certain university like they did
• Will advertise for roles where job titles are miss-aligned to job descriptions to miss-lead candidates
• Will be enamored by the amount of papers they have published even though many of them are either plagiarized, summary synthesis of other papers, lack technical depth, incorrect in plausible theory, or are simply too unimportant to hold any real value to an organization
• Will ask really dumb questions to engineers and then expect them to sound deep like asking what is unit testing
• Will reject the very same people with experience in academic admissions to universities while lacking the same skills themselves
• Will try to take credit for other people's work 
• Will have questionable basic foundational skills for the job, but will call themselves as experts anyway
• Will have multiple published papers that have different topical headings but basically the same content
• Will spend majority of their time hacking through things but will expect others to think they are following a scientific method or process
• Will usually have no clue themselves as to what they are doing
• Will introduce biases into their data just because they can and no one will question them for it, they will then produce such flawed and biased results and expect everyone to call them experts
• Will have no real accountability for their work output in organizations
• Will likely make terrible leaders or managers of people
• Will struggle to teach others the very same concepts that they call themselves experts in because they don't fully understand the concepts either
• Will make silly reasons as to why they need to hire more people on the team, even when it is really part of their own job to know how to do
• Will expect anyone that doesn't hold a Phd does not know what they are talking about
• Will likely patronize and discriminate on non-Phd people in the workplace
• Will reject people if they hold a difference of opinion to themselves
• Will likely be very insecure and take offense if their skills or work is criticized or is taken into question purely because they assume they are experts because they have a Phd, eventhough that Phd could be in cartoonism
• Will likely add more cost to organization for all the help they will need to be able to do their job, and the job that they do amounts to very little in quantifiable value
• Will likely leave a forgettable mark in organization especially as most of their work would already be done by others 
• Will tend to hire people that will not be a threat to them or highlight their deficiencies in the workplace 
• Will likely not know how to read a CV/resume
• Will reject approaches that are outside of their own discipline, knowledge, or comfort zone
• Will not be adaptable to change outside of what they already know
• Will likely hamper organizational productivity and efficiencies in work output and provide incorrect recommendations to management
• Will produce incorrect models which then influences flawed strategic decisions within organizations
• Will likely only have questionable academic skills with jupyter notebooks, matlab, or tools that will require huge amount of effort to refactor and productionize
• Will likely be expected to teach what they can't put into a practical implementation
• Will likely not have basic skills in data structures and software engineering or the background of things that they will look for in admissions applications to universities
• Will focus on theory all day long but be clueless about how to apply any of it in practice
• Will likely lack fundamental work experience to put them in a position of seniority to call them experts in organizations
• Will likely struggle to take such things as uncertainty, noisy data, biases, complexity, and context into account within their work output and likely expect someone else to sort out for them
• Will likely for all the things they call themselves an expert in, they will expect someone else to do for them
• Will likely be the weakest link in organizations expected to be an expert in an area, but a master of none when it comes to practical implementations
• Will likely be unprofessional when interacting with non-Phd individuals and not value their time
• Will get easy access to funding for their research projects but the work output will not be sufficient to justify the cost, and at times a lot of that funding may be deceptively used to fund other projects in other research teams or faculties
• Will require an awful lot of mentoring to be able to do even simple tasks, making you wonder how in the world they could be called experts or have achieved a Phd
• Will act like a grasshopper with the absence of legs in the workplace
• Will use overly complicated methods rather than approach it first with the simplest solution
• Will use academic approaches to solve non-academic problems in most cases the approach they use will be inefficient and unworkable for prime time production

Ex-Googlers

It seems googlers are taught to practice arrogance as if their entire life goal was to be part of Google. In fact, working for such a large company does have its disadvantages. And, when they turn ex-googlers they become even more arrogant. So, why did they leave? Is this because they are insecure that they left the company or because they want to impress on the fact that they once were there as well? But, one thing both ex-googlers and googlers share is the fact that they find any one that did not work at Google as beneath them. This sort of sordid culture of arrogance and conceit is unlikely to be found in other companies like Amazon, Facebook, Microsoft, Apple, or even at Netflix. Perhaps, this is the way Google's internal culture brews and transforms the mentality of their employees into spoilt individuals. Either way, it projects a toxic culture. One can already see glimmers of toxicity in the way the organization forces other clients to use their cloud infrastructure which compared to AWS has a significantly long road ahead in playing catchup. Is it any wonder that ex-googlers struggle to find much success outside of Google as if to permanently lose their sense of what it means to be human. One can only anticipate that such a large company over time can only get bigger, and this perpetuated sense of complacency within Google is only bound to get worse, can only eventually lead to a corporate demise like a climactic fall of bricks.

Capsule Networks

Capsule Networks

Atticus Project

Atticus Project 

Atticus Github

Biased Practices of University Admissions

International students to USA universities from the beginning of their application submission are at a disadvantage with competition for seats that grows every year and may even be different from one term to another depending on the applicant pool. However, the biased practices don't stop there but they begin from the screening process. International students are expected stronger backgrounds, better GPA compared to local students, better essays where their english is evaluated more stringently even a grammar or spelling mistake is an issue, and many go through an FBI database profile check. Yes, there is racial profiling involved here. And, if the program is affiliated with defense or military then one can be sure to be scrutinized for religious affiliations which becomes apparent based on the application profile from such things as place of birth, name, and from essays. First amendment constitution rights don't hold any weight on an international student application screening process and affirmative action is only for namesake. And, no controls on the application for diversity, equity, and inclusion. One thing to note is that on most USA institutions the research assistants usually have very similar backgrounds. They are either locals, europeans, or asians with predominately alternative religious or non-religious affiliations. In public universities they are required to comply with anti-discrimination laws. However, in private universities this may not be the case. And, often these laws are abused by individuals under the covers of paperwork and cultural biases where it is extremely difficult to apply transparency and accountability. In fact, many admissions processes don't even provide any feedback to applicants. The academic admissions process is very much subjective. This is equally why universities need to resolve biases by introducing AI into the system of screening applications. And, not only accepting or rejecting applications but providing the right sense of constructive feedback that can help them in their future endeavors whether that be a re-application in the future, an acceptance at the university, or beyond.  The following are some suggestions on how an application could be evaluated via AI:

• Key/Value extractions in JSON form from applications, then store in a database
• Key/Value extractions in JSON form from resume, then store in a database
• Key/Value extraction in JSON form from academic transcript, then store in database
• Automated essay scoring using NLP methods
• Automated resume scoring using NLP methods
• Basic distribution curves of GPA in pool of applicants
• Basic distribution curves of standardized test scores of applicants
• Identify outlier class attributes using unsupervised clustering methods
• Building a bayesian model for uncertainty reasoning for causal inference, this could be in the form of a factor graph to identify whether this applicant will :
• struggle to maintain a 3.0 GPA, 
• whether they are likely to accept if given an offer, 
• whether resume experiences can be considered in lieu of lower GPA,
• the degree of course rigor for which they attained a high GPA,
• the overlap of courses in the alternative degree attained,
• the likelihood of meeting the prerequisites,
• whether they are likely to dropout part way through the course,
• whether they were top of their class in their respective peer group given the percentile,
• the degree of their intents, interests, experience, goals, maturity, and peers match to the program
• Produce a Knowledge Graph representation of the applicant profile that could be queried for 5W1H question/answering.
• Identify the key classification classes that every applicant must have to build a diversified pool of offers - this could be a combination of supervised regression and unsupervised clustering methods
• A graph-machine learning based recommendations system that ranks the candidates in order of priority taking into account outliers
• Build an affective computing model using symbolic reasoning to identify intents and interests then feed that back into the recommendations process
• Apply a fraud detection mechanism to identify fake application documents
• Apply a lie detection mechanism to identify whether the applicant actually did these extracurricular activities like volunteering, whether they actually did face such hardships, or whether they did have these work experiences
• Apply a financial evaluation of applicants to see whether they will be able to financially cope with the tuition and fee payments or will they struggle significantly, then look to evaluate recommendations for financial aid if they meet such criteria.
• Build a feedback loop mechanism to enhance the application structure in the way they are worded to analyze for biases and build an explainability model
• Build a criteria model for fairness then apply such transparency and accountability measures for errors in the feedback loop
• Build a constructive feedback loop for applicants identifying the class attributes that held them back and what they could do to improve their chances of future acceptance, although this may vary based on any given applicant pool for the term, at least it could be evaluated from historical trends. Most rejection letters are worded in a standard form which is unhelpful to applicants.
• For Phd applicants this may also have a separate step to evaluate the strengths of the department vs the interests of the applicant and whether an appropriate advisor could be provided.
• References could further be assessed with NLP methods of extraction, and as a feedback loop into the recommendations, question/answering, causal inference, financial evaluation, fraud, deception, quantifying biases, and other forms of assessment criteria
• Identify similarity trends between applicants using nearest-neighbor methods
• Identify regression method for whether applicants meet the relevant prerequisites
• Build a chatbot that assists applicants through the pre-application and post-application process. The objective should be of converting the applicant into a potential student and treating the individual as a customer. This chatbot should then take into account customer relationship management and affective computing for sentiments and emotions.
• Anonymization and masking of potential key attributes that could pose as an underlining bias in the decision like place of birth or name.
• Localization of applications if a threshold needs to be maintained between international, in-state, and out-of-state applicant offers.
• If there are publications they could be checked for citation scores and validated for theory correctness and coverage
• If there are any awards won they could be verified and validated
• Identify a key set of behavorial attributes that may be needed for a successful student on the course then measure against those set targets
• Making sure that correlation does not imply causation
• Convert non-refundable fees into refundable retainer fees so when an applicant is declined the application fee is refunded. Why penalize applicants that got declined, in fact it will only push more applicants to apply that would boost the ranking of the university. Why get applicants to apply if it is obvious they will be rejected at the expense of boosting university rankings. Only accept application fees from those that you have accepted to the university and let the fee go someway towards the tuition and fees.

*This does not include international credential discrepancies where different languages and grading systems would need to be translated. A separate algorithmic process would need to be defined for such functional use cases of international applications.