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 Some absolutely horrendous with typos, incomprehensible, outdated, questionable instructors, and sometimes incorrect theory.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

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