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.

Bayesian Optimization

• BoTorch
• GPyTorch

AI Related Online Non-Degree Courses

There are a lot of online courses floating about. Some absolutely horrendous with typos, incomprehensible, outdated, questionable instructors, and sometimes incorrect theory. Sifting through all the barrage of nonsensical and poorly delivered courses can be a drag. Here are some that are CS, AI, Knowledge Graph, Data Science, and Network Science related courses that seem to fit the par on most of the core materials and theory coverage with some good practical understanding. In most cases. the MIT opencourseware courses are unmatched by other providers as they directly have archived coverage of the full taught course which seem to be extensive and thorough in nature.

MIT OpenCourseWare :

• Affective Computing
• Artificial Intelligence
• Artificial Intelligence and Machine Learning
• Advanced Natural Language Processing
• Automatic Speech Recognition
• Brain and Cognitive Sciences
• Commonsense Reasoning and Applications
• Computational Thinking in Data Science
• Computational Models of Discourse
• Cooperative Machines
• Conversational Computer Systems
• Blockchain and Money
• Brains, Minds, and Machines
• Minds and Machines
• Decision Making
• Ethics for Engineers in AI
• Game Theory
• Game Theory and Engineering Applications
• Game Theory for Strategic Advantage
• Human Brain
• Introduction to Algorithms
• Introduction to Network Models
• Introduction to Probability and Statistics
• Natural Language and Computational Representation of Knowledge
• Networks
• Networks, Complexity, and Applications
• Quantum Information Science1
• Quantum Information Science2
• Social Theory and Analysis
• Introduction to Machine Learning
• Machine Learning
• Introduction to Deep Learning
• Introduction to Computer Science and Programming in Python
• Time Series Analysis
• Distributed Algorithms
• Statistical Learning Theory and Applications
• Machine Vision
• Data Mining 
• Theory of Computation
• Mathematics of Big Data and Machine Learning
• Cognitive Robotics
• Fundamentals of Statistics
• Multivariate Calculus
• Calculus
• Information Theory
• Communicating with Data
• Computational Structures
• Advanced Complexity Theory
• Computer Systems Architecture
• Computer Systems Engineering
• Cryptocurrency Engineering and Design
• Database Systems
• Distributed Computer Systems Engineering
• Exploring Fairness in Machine Learning for International Development
• Introduction to Computational Thinking
• Knowledge-Based Application Systems
• Linear Algebra
• Mathematics for Computer Science
• Operating Systems Engineering
• Principles of Computer Systems
• Principles of Discrete Applied Mathematics
• Probabilistic Methods in Combinatorics
• Software Construction
• Theory of Probability
• Network Flows
• Parallel Computing
• Elements of Software Construction
• Algorithmic Biology

Stanford Online - Coursera :

• NLP with Deep Learning (Manning)
• Deep Learning (Ng)
• Machine Learning (Ng)
• Game Theory
• Probabilistic Graphical Models
• Algorithms
• Social and Economic Networks: Models and Analysis
• Information Retrieval and Web Search

OpenHPI :

• Linked Data Engineering
• Knowledge Graphs
• Knowledge Engineering with Semantic Web Technologies
• Semantic Web Technologies

Deeplearning.ai - Coursera :

• NLP
• GAN
• Machine Learning for Production
• Deep Learning
• Practical Data Science on AWS
• Build, Train, Deploy ML Pipelines Using Bert
• Optimize ML Models and Deploy Human-in-the-Loop Pipelines
• Analyze Datasets and Train ML Models using AutoML

Alberta - Coursera :

• Reinforcement Learning

Davis - Coursera :

• Computational Social Science

UIUC - Coursera :

• Cloud Computing
• Data Mining
• Accelerated Computer Science Fundamentals

UMich - Coursera :

• Recommender Systems
• Python - Python 3
• Python - Statistics with Python
• Python - Applied Data Science with Python
• Sports Performance Analytics

JHU - Coursera :

• GPU Programming
• Data Science
• Genomics Data Science

San Diego - Coursera :

• Big Data
• Data Structures and Algorithms
• Bioinformatics

Duke - Coursera :

• Cloud Computing at Scale

Buffalo - Coursera :

• Blockchain

Washington - Coursera :

• ML

CMU - Emeritus Executive Learning :

• NLP
• Deep Learning
• ML
• DevOps
• Data Structures
• AI
• Fundamentals of Software Engineering
• Computer Vision

UPenn - Coursera :

• Business Analytics

Finance - Coursera :

• Practical Guide to Trading - Interactive Brokers
• Investment and Portfolio Management - Rice
• ML and Reinforcement Learning in Finance - NYU

AWS :

• Amazon Web Services - Learning and Implementing AWS Solution - Udemy
• Amazon Web Services - Zero to Hero - Udemy
• AWS Fundamentals: Migrating to the Cloud - Coursera
• Cloud Computing with Amazon Web Services - Udemy
• AWS Certified Solutions Architect Associate - Udemy
• Hands-On Machine Learning with AWS and NVidia - Coursera
• Introduction to Designing Data Lakes on AWS - Coursera

Nanodegrees - Udacity :

• AI, Data Science, Programming, Cloud Computing Specialisms

Shap

Shap

Neosemantics

Neosemantics

ML is not the answer to Advanced AI

Machine learning by its very nature is built on statistics. If we are to advance AI we have to think beyond machine learning. Humans rarely ever use statistics in the every day life and still have advanced mechanisms to learn through experiences. In fact, those experiences are also retained in memory to form new patterns of learning. Everyday as humans we form associations and relationships with the things around us as we form new experiences. Machine learning on the other hand still requires a lot of training data and that data has to be balanced between variance and bias. Transfer learning on unseen and untrained data is still a challenge. Architectures of deep learning can be formed into very complex and sophisticated structures. However, this complexity is unsustainable when compared to the prohibitive cost and the returns achieved in the process. AI is still very narrow and focused. Any general AI will require thinking beyond the standard concepts of probabilities in statistics. In fact, AI is not just about machine learning but almost eighty percent of the field is based on computer science concepts. The only way to really approach Advanced AI is to take inspiration from the human mind and brain and build models that are highly complex and yet cheaper to put together as building blocks of conceptualization in a hybrid system. Such systems may even be sub-divided into sub-systems just like the organs of the human body and parts of the brain. A natural progression of AI is to combine knowledge representation and reasoning with probabilistic methods to provide such metaphors of adaptability in generalizable learning. Probabilities is not the answer to understanding emotions or other generalizable forms of human learning which lead to brittle and ridged models not to mention a significant margin of error. Machine learning does not provide assurances for key AI functions, which in most cases blurs the lines between what a machine is able to comprehend as a false sense of articulation. For AI to be truely autonomous and live among humans the learning process not only needs to take ethics into account but also be able to reduce such margins of error on its own through the learning process. Increasingly, reinforcement learning methods are being used that do not require huge amounts of training data. However, even in this process learning can be initially slow and also lead to incorrect training in feedback loops which can be disastrous for critical environments like medicine or autonomous driving. An interpretable representation of knowledge is needed to define context as well as some form of logical reasoning constructs. Going further, a long-term and short-term retention of memory through every iterative process of learning is necessary in order to learn from mistakes and past experiences. It may be plausible to assume that to mimic the nervous system one could use more of the statistical thinking to replicate the concepts of impulse and the human senses. Advancement of AI then becomes a joint effort of advancing hardware as well as software. Hardware may even take the form of naturally-inspired computation to enhance the level of coding of information. AI still has a long way to go yet to be regarded as a sentinent being that can cohabitate, live safely among humans, or even to surpass into superintelligence.

University Application Processes

Most universities have application deadlines. However, these deadlines are often not reflective of a fair process. One should never wait until the last deadline date. One should not wait until the priority deadline either. Apply within the first or two weeks of the application process opening date to stand a chance of acceptance. International applicants should be even more cautious of such deadlines as they will often face a more formidable task. The task is further compounded by limited quota of application acceptance rates where one is not only competing against local applicants but also other international applicants for a place. The pool of applicants is likely to be even higher and where places are severely limited. Every university is likely to be selective. However, some are highly competitive than others. In fact, strong calibre students often get rejected. And, sometimes mediocre students get accepted. And, in many cases it matters if the academic staff already know the student in some way through an open day or some networking social. Sometimes, it is who one knows that matters in the recommendations. Contrary to what one might think, the admissions committee do not care about applicants in the slightest. The entire education system in most countries is built on making money. The application fee is a money making machine that can be multiplied by the number of semesters or quarters in an academic year of entry by the total number applications received, especially as it is non-refundable. In fact, the more applications are made each year and the less acceptances are allocated the better it is for the university rankings as a highly competitive institution. Many universities promote open days and application deadlines especially from applicants that they know will not gain acceptance. Some applicants are given preferential treatments to fast-track their application process with guaranteed acceptance. International applicants are usually sidelined as last of the pile to get reviewed and acceptance and rejection notices are provided all at once. International applicants are the single biggest money source for universities as they pay almost double in tuition fees. As not to deter potential international applicants the universities may even embellish on their acceptance rates. Each year new applicants apply and the pool of applications is different. Some years can be more competitive than others which means it is a stroke of luck given whatever is in the bag of applicants. There tends to be a greater demand for fall applications because the intake is higher which also means greater degree of competition for places. However, as less people apply in spring the chances could increase to some degree. Even though, in most terms the quotas are roughly the same across academic terms. In fact, in most cases the odds are stacked against most international applicants from the start of their application. Admissions coordinators may even filter out many from just a cursory glance. For many, it can be a heartbreak. And, for others it is a right chore going through the whole application process. Many that get rejected have likely been honest on their applications. The few that do get accepted may have even embellished on their essays or recommendation letters. There is no denying that some applicants lie on their extracurricular and essay sections of applications especially the ones that get accepted. The only piece of evidence that one knows that has not been lied about are academic credentials and test scores. And, many times in the technical structure of resumes. However, one can find on various crowdsourcing sites the very people that got accepted having had their essays written by someone else. The almost dodgy process of admissions is even more apparent at top schools who give preferential treatment to wealthy individuals that are able to provide donations in return for admissions. Such donations could be provided in multiple forms and usually mean enhanced credibility, ambition, contacts, and networking. Every year applications grow and acceptance rates reduce across universities providing for a challenging atmosphere for people trying to gain entry into higher education. While unethical processes abound at universities and where educational systems are increasingly focused at maximizing their financial returns at the expense of quality education offered to students. Even though, the student is supposed to be treated as the customer and the university as the service, in reality this is hardly ever the case. Once the applicant enrolls at a university it becomes the start of a whole range of rules and regulations like a prison. One is reminded of the song another brick in the wall from pink floyd. From the point of making the application to the time one is enrolled at the institution it becomes a two-way game. If one plays their cards right they can excel at it and ride through the storm undeterred. 

Beneficial AI

The six stages of AI alignment towards human values:

• The agent does what is instructed by a person
• The agent does what is intended by a person
• The agent does what human behaviors suggest they prefer to do
• The agent does what a rational and informed human wants it to do
• The agent does what is objectively in a person's best interests
• The agent does what is moral as defined by individuals or society

Newspapers

Newspapers

Nova Annotation Tool

Nova Annotation Tool

Hudi

Hudi

Women in AI Initiatives

Some fields have very few women in them. So, there are initiatives to get more women involved. But, there are also some fields where there are few men in them where no one bothers to highlight this disparity through any initiatives. Eventhough, initiatives may have the right positive intent, they often are flawed by overlooking interests and create very limited impact. Maybe, the reason why women are in so few in number in a certain field is because they find the area of occupation a bit boring. For example, there are virtually no female bricklayers in construction industry, whereas the field of psychology is dominated by women. This would imply that opportunities are there but the distribution of where women prefer to work is based on their own choices and interests. In a society where women have significant rights they can choose to be in whatever field they want - nothing is holding them back. So, such initiatives in long-run will not create any dramatic change in the statistical distribution of women in AI. In fact, ten years from now women will still be guided by whatever they find interesting. There are no significant initiatives to get more men in women dominated fields either like in the nursing profession. In many cases, the choices in what field to study is often guided by the opportunities of financial rewards and their environmental circumstances. It is up to the individual to decide between interests and financial rewards of any given occupation as an opportunity cost. Perhaps, one reason why women are so few in certain fields is because they tend to think with their emotions and have preference for the easiest way out like choosing softer subjects instead of hard sciences. And, as one knows emotions are explained in evolutionary theory. Whatever it may be, the gender disparity may just be down to the different needs and wants of individuals that tends to create the statistical distribution of a collective in society and any form of initiative will not necessarily change that mindset.

How To Read A CV 101 For Recruiters

Recruiters and HR representatives are one of the most clueless people in any organization. When it comes to hiring processes they don't appear to know the most fundamental aspect of reviewing applications: knowing how to read a CV. This is a big issue as perfectly good candidates get rejected. And, the ones that do come through the filters are normally not a good match for the role. The primary reasons for this are highlighted below:

• Recruiters sift through CVs without understanding the context, they only read through job titles or whatever was the last job title of the candidate.
• Many don't even have the time to read through hundreds of CVs so they only scan their way picking out keywords or even how many times they are mentioned.
• From the hundreds of CVs they have they might review the first five to ten CVs on the pile, put another five to ten on hold, and reject the rest without even reading through them.
• Sometimes they already have their preferred candidate pool who they go to first rather than bothering to sift through the entire pile of new candidates.
• Sometimes they might reject the candidate because they don't answer their call on first instance so they assume that the candidate is only passively looking.
• They may even favor candidates that are already in a role and looking for a change, compared to the ones that are actively looking and not in a current role.
• On other occasions they just can't make sense of the CV so they reject it.
• On other occasions if the CV does not contain certain keywords they reject it, ignoring the fact that the same keyword could have been used in alternative synonym forms.
• Sometimes they will reject the candidate simply because they are racist and make the assumption that they want a typical white person for the job as a safe bet or the fact they stereotypically assume that a non-white won't have the skills to do the job. In fact, in many cases they might even label it as a cultural fit issue.
• In many cases, it also boils down to the fact they don't have the necessary understanding and skills of the domain that is being recruited for to be able to review the applications.
• Sometimes, it is the case that the role was only advertised to meet compliance but that direct applicants are automatically rejected in favor of agency supplied candidates.
• On other occasions, it could be the fact that they might have had a bad experience in past with the candidate and decided to blacklist them for future roles.
• Or, it could simply be that the role never really existed and was merely a marketing gimmick to showcase that the organization has alot of work on the go.
• Sometimes they might like the look of the CV but just not like the candidate personality, the way they come across in-person or on the phone.
• In some organizations, a CV is not even looked at and a separate scoring grid matrix may be used just as in public sector jobs and if this has been filled out by a recruiter then there is likely to be some discrepancies.
• Sometimes the recruiter may ask the candidate to custom tailor the CV for the job, which usually means they either don't think it is a match based on missing keywords or don't have a clue of what they are even looking for in their application screening process. 

The way to get around this hurdle is for organizations to use smarter more intelligent tools that can provide better context-specific matching of candidates, measure of their associated risk selections, and to resolve for biases in such screening processes. And, fundamentally to remove the human-in-the-loop biases as much as possible. However, this process should not stop here but also be extended to review of interview processes as on many cases the interviewer can also bring their own sense of biases. There also needs to be a way of picking out fake job ads especially ones that don't quite make sense where the job titles don't match the job description or they simply don't exist. There is an endless process of improvements that could be achieved through AI in the human resources and recruitment sectors. However, in many cases this starts out with identifying the right places were AI can make the greatest impact. Perhaps, sending HR and recruiters on a 101 course in how to read a CV could be the first point of training. It is understandable that a recruiter may not have the technical background to know about all the domain skills in every specific type of job that they recruit for an organization. However, there are various tips in reading that can be used to make the job effective such as: 5W1H question/answering, SQ3R, using a tool that uses probabilistic NLP models and knowledge graph to bring context and aspects of match/filter functions to light.

Flawed English Language

The english language is flawed and inherently embedded in historical racism and sexism. This often stems from the cultural normalization of words and phrases that don't quite work in the modern day society. In some cases, the words have evolved in the pronunciation and spelling. While in other cases they have been linguistically defined and created over time. It is surprising how feminists don't take issue with changing significant aspects of the language. The following are some examples of words, phrases, and their usage that should really change and provide some uniqueness to genders as well as the ways in which race is contextualized as part of every day speech.

Words like mankind, manipulate, mansplaining, etc - that have the word man in them

Words like womansplaining - that have the word man in them

Female and Woman - that have the word male and man embedded in them

Blacklist and Whitelist - that make it obvious that black is bad, white is good

The Dog That Stepped On A Bee

This story is about a victim and an abuser. For some odd reason, society expects the man to always play the role of abuser, while the woman is expected to play the role of the victim. However, in modern day society women neither want to be seen as victims nor do they want men to be chivalrous. They want the legal system to always side with women whether it were wrong or right - to throw away the burden of proof. Whatever happened to the concept of innocent until proven guilty is a typical question one asks when the biased media is seen jumping to conclusions. Since when has the "metoo" movement just been about women? The clash of genders seems like women are all too confused about the definition of equality. Or, is it the fact they want to play the victim card when it suits them? Is the "metoo" movement more about "metoohatemen" movement? Equality is when one sees things beyond the biases of gender stereotypes. However, with equality of opportunity also comes equal levels of consequences for punishment and responsibility for actions. There should be no cause of special treatment for women just because they are women. There should be no drum down special treatment for abusers just because they are women and have softer hands or that they are smaller in stature than men. In the trial for Depp vs Heard, we see what society would define as a type of anomaly of reversed gender roles between victim and abuser. In fact, this trial gives a confident voice to male victims of domestic violence who are often laughed off by women. And, here in lies the truth. To seek the truth one must look beyond the biases and through the spectrum of evidence. Only then can one seek justice and equality. Women should acknowledge when they are wrong and learn to take responsibility for their actions just like men. When one takes an oath, they should uphold that oath to their testimony. Over the years we have seen a shift in the legal system that has gradually sided with women over men in matters of divorce, domestic violence, harassment, abuse, abortion, child custody, and discrimination. But, this only implies that no justice is truly blind. Are our legal courts becoming increasingly biased in their sentences and accountability for justice? Perhaps, it begs one to ponder on the philosophical question of whether justice is a vice or a virtue.