Western economics has experienced a lot of success over the years through technological advancements, globalization, and innovation. But, it has also faced challenges through credit crisis, natural disasters, pandemics, and other issues. Most western economies over the years have experienced higher living standards and reduced sense of poverty. Capitalism has provided a shift in focus towards the incentivized growth and success of the individual. However, this has also provided higher levels of inequality, environmental damage, stagnant wages, rising debt levels, and financial instability. Focus towards GDP growth has led to unsustainable practices and neglect of both the social and environmental well-being. It has also increased the gap in wealth and power in concentrations towards a few. This has been seen as a major flaw in many western economies. Although, western economic models are complex, increasingly they are showing failure in economic sustainability with a bleak future outlook. Politicians continue to display policy interests towards a wealthy few and neglect towards the greater population which has dire consequences. In many respects, western economies are showing a very heavy burden on the taxpayer with rising cost of living while also displaying irresponsibility for the outpouring of funds for financing foreign wars that have very negative global economic implications. The heightened sense of uncertainty has led to significant stagnation across the western economic frontiers.
16 November 2024
14 November 2024
Drawbacks of Bitcoin
Bitcoin has been gaining in popularity as of late. However, it has fundamental downsides that means diversification with minimizaton of risk is key. The below highlight some common drawbacks of Bitcoin investing.
- Extreme Volatility - Bitcoin is highly volatile with rapid fluctuations in market sentiment.
- Regulatory Uncertainty - Bitcoin has an uncertain trajectory with significant impact on value from regulatory changes that are still evolving.
- Security Risks - Bitcoin wallets and exchanges are targets for hackers. If you lose access to wallet or it is compromised you could lose your investment.
- Lack of Intrinsic Value - There is no intrinsic value. It is difficult to measure the asset value of Bitcoin in real terms. The value is derived from demand and speculation.
- Energy Consumption - Bitcoin is a drain to the environment with a growing energy footprint.
- Limited Acceptance - Bitcoin is not widely accepted form of payment and has limited use cases.
- Psychological Risks - Bitcoin is emotionally draining due to high price fluctuations.
- Tax Implications - Bitcoin investing comes with complex tax implications that vary by jurisdiction which means understanding your tax obligation can be tricky.
9 November 2024
AI Consciousness
- Consciousness for AI
- Artificial Consciousness and Human-Robot Interaction
- Do Machines Really Understand Meaning
- Consciousness in AI
- AI Consciousness is Inevitable
- A Case for AI Consciousness
- Is artificial consciousness achievable?
- Artificial consciousness
- AI Consciousness and Public Perceptions
- Conscious AI
- If consciousness is dynamically relevant, artificial intelligence isn't conscious
City and Management Simulation Games
- SimCity 4
- Cities: Skylines
- Anno 1800
- Tropico Series
- Ostriv
- TheoTown
- Surviving Mars
- Prison Architect
- Theme Park
First-Person Shooter Games
Modern Classics:
- Doom Eternal
- Halo: Infinite
- Titanfall 2
- Half-Life: Alyx
Competitive Shooters:
- Counter-Strike Global Offensive
- Valorant
- Overwatch 2
- Apex Legends
Others:
- Call of Duty
- Battlefield 2042
- Crysis
- Hunt: Showdown
- Deep Rock Galactic
War Simulation Games
Grand Strategy:
- Hearts of Iron IV
- Europa Universalis IV
- Crusader Kings III
Real-Time Strategy:
- Company of Heroes 2
- Men of War: Assault Squad 2
- Steel Division 2
- Command & Conquer
Tactical/Squad:
- Arma 3
- Squad
- Hell Let Loose
Historical:
- Total War: Warhammer III
- Mount & Blade II: Bannerlord
- Age of Empires IV
Others:
- War Thunder
- World of Tanks
- XCom2
Asian Animation
Donghua are animation forms made in China with diverse styles and genres from traditional to modern. These animations tend to have a more realistic look. Themes and storylines are influenced from Chinese mythology and history.
Anime are animation forms made in Japan with distinctive styles, colors and diverse stories. These animations tend to have a more exaggerated look. Themes and storylines are very diverse with explorations into fantasy, scifi, and slice-of-life.
Aeni are animation forms made in Korea with similarities often shared with Anime and Donghua across various styles and genres with distinctive western variations. These animations tend to have a more realistic look. Themes and storylines are more realistic with grounded stories.
In many respects, different animation variations may share cross influences in both themes, storylines, styles, and genres. Asian animation is evolving and diverse with huge options.
These categories may include further sub-genres and styles, such as:
- Mecha which includes robots, war, technology, and humanity.
- Magical Girl which includes girls that transform into magical fighters against evil.
- Slice-of-Life which covers everyday lives of ordinary individuals which include themes like friendship and family.
- Historical which includes ancient periods covering real-world events.
- Fantasy which focuses on magic and myth within a fantastical world.
- Scifi which focuses on futuristic and technologically advanced experiences.
- Wuxia which focuses on martial arts with a mix of fantasy.
- Xianxia which focuses on Chinese mythology and folklore with immorality and cultivation.
- Xuanhuan which focuses on magic and supernatural powers.
- Manhua which covers Chinese comics
- Manhwa which covers Korean comics
- Manga which covers Japanese comics
- Comedy which covers humor and satire
- Cyberpunk which covers futuristic dystopian themes.
- Guohuaxianxia which covers Chinese mythology and folklore.
- Psychological which covers dark and disturbing psychological themes.
- Idol which covers idols and their fans.
- Music which covers musical performances.
- Isekai which covers fantastical characters that get transported to other worlds.
- Sports which covers sports competitions.
- Martial Arts which covers martial arts combats.
7 November 2024
1 November 2024
GenAI, AGI, and Superintelligence
Artificial Intelligence has come along way. However, AI still faces a significant challenge and several roadblocks. They can assist us in mundane tasks but not completely replace humans in many tasks that require complex learning and adaptability. The below highlight the primary three areas of AI advancement stages. We are currently in the first GenAI stage of enlightenment. There is still a long way to go before AI becomes a real competitive contender to a human and even to surpass those human limits.
Generative AI (GenAI):
- Focus: Content generation in audio, video, image, code, and text in a specific application
- Capabilities: Trained on huge datasets to generate near human-replicated content
- Current State: Advanced use cases across industry for various applications
Artificial General Intelligence (AGI):
- Focus: Human-level intelligence for generalizable tasks
- Capabilities: Ability to understand and learn any human task
- Current State: Not yet achievable with ongoing research progress
Superintelligence:
- Focus: Ability to go beyond human intelligence
- Capabilities: Able to do things beyond human comprehension and abilities
- Current State: Not yet achievable, still in theoretical stage
31 October 2024
Java vs Go vs C vs C++
The below highlight the key areas where the various programming languages are used and their summarized characteristic differences.
Java
- Syntax: More verbose, object-oriented
- Concurrency: Thread-based
- Memory Management: Garbage Collection
- Ecosystem: Mature, extensive libraries and frameworks
- Performance: Generally slower startup time, but good performance at runtime
- Learning Curve: Steeper learning curve
- Use Cases: Enterprise Application, Android Development, Big Data and Data Science
- Syntax: Concise, more procedural
- Concurrency: Goroutines and channels
- Memory Management: Garbage collection
- Ecosystem: Growing, but less mature than Java
- Performance: Faster compilation and runtime
- Learning Curve: Easier to learn
- Use Cases: Microservices Architecture, Cloud-Native Applications, Network Programming and Systems Programming, High-Performance Applications
- Syntax: Low-level, procedural
- Concurrency: Threads
- Memory Management: Manual
- Ecosystem: Smaller ecosystem, but focused on system-level programming
- Performance: High performance, low-level control
- Learning Curve: Steep learning curve
- Use Cases: Systems Programming, Embedded Systems, Operating Systems
- Syntax: Complex, object-oriented
- Concurrency: Threads
- Memory Management: Manual
- Ecosystem: Large, complex ecosystem
- Performance: High performance, fine-grained control
- Learning Curve: Steep learning curve
- Use Cases: High-Performance Applications, Game Development, Scientific Computing
22 October 2024
19 October 2024
Data Entry
One of the simplest jobs in adminstration is of a data entry. The primary skills required are accuracy to detail and typing skills. Hiring an irresponsible person can mean applications get rejected because of sticky finger errors. Applications can imply anything that require a form. This can range from loans, bank account forms, immigration/visa forms, job forms, vetting forms, insurance claims, health forms, academic forms, and other applications. Incorrect entry of customer details can mean non-compliance to GDPR as it is all about storage and processing of correct personal information. It also means perfectly good applications get rejected, with bad service, and a very frustrated customer. Even replacing human data entry with AI can further compound the issue with an element of complexity if the model has high false-positives and false-negatives. The below outline some examples where system and human errors lead to incorrect processing, storage, and ultimately rejection of applications:
- When proof of documents have numbers in different currencies and the system says they don't match
- When a number is called out as incorrect even after providing proof of documentation with the correct number on it, even worse if the same proof is provided multiple times
- Making assumptions about what the customer meant rather than what is actually on the form
- Incorrectly inputting the correct details from the form
- Giving the customer the runaround to provide documentation which has already been provided multiple times
- Unable to use basic common sense when processing customer data forms
- Flagging a customer based on an incorrect data input then blaming the customer for it
- Incorrectly inputting the form data while half asleep
- Losing the customer form and expecting the customer to fill out another form
- Asking for the same information over and over without bothering to read the document
- Confusing one application form with another
- Rejecting an application by incorrectly mixing up details across two separate application forms
- Providing customer with reasons to decline the application that relate to another customer
- Sharing one customer details with another customer in the feedback
- Trying to correct the spelling of someone's name as if the customer would not know how to spell their own name
- Inputting month before the day, and day before the month without bothering to check for correctness between the form and the system
- Leaving out critical information from the form
- Badly worded forms get filled incorrectly which means lots of declined applications
- Some of the most irresponsible and negligent are credit reference agencies who don't even have the basic systems in place to properly resolve, update, verify, validate, store, and process customer data accurately. Individuals as members of public have to chase up for errors and corrections on simple things like names and addresses which shows how credit reference agencies fail in their most basic role of proactively practicing due care for the protection of customer data.
W3C
W3C has been a long standing consortium for the development and support of web standards. However, in many ways it has also been a hinderance to the community and the uptake of standards in development.
Benefits:
- A way for community to come together, collaborate, and research on improving web standardization efforts
- Shape the future of how web is used
- Connect with thought leaders across the world
- Develop consistency, accessibility, compatibility across the community
- The community members tend to be academic and often very arrogant in their selective interaction and collaboration
- Community members tend to be racist and discriminatory
- Practical compliance and ethics often seems as an afterthought
- A disconnect between academic members vs industry members
- A disconnect between cultural differences across the spectrum of web standards
- Majority of academic members are biased and lack basic ethics
- Lots of favoritism for selective academic members especially for specific sponsored members
- Processes, tools, and methods are antiquated
- Standardization efforts are slow moving and lack basic practical insights from industry
- Collaboration and communication is often discriminatory in nature, don't be suprised if the person on the other end assumes you are a clueless buffoon, showcasing an unapproachable attitude of a lot of members within the community
- Egotistic and arrogant members ruins collaborations which leads to a dwindling community of active members
- A lot of the W3C standards are not in favor anymore in industry or are outdated
- Web standardization efforts is not progressing fast enough to keep up with the ML/DL community
18 October 2024
ChatGPT vs Gemini
ChatGPT and Gemini are both widely popular Large Language Model chatbots that have gained significant interest in the AI community. ChatGPT was built by OpenAI. And, Gemini was built by Google. However, both respectively have their strengths and weaknesses. Both model chatbots provide a free playground as well as paid options.
Architecture and Training:
Gemini was trained on a broader and more extensive dataset that allows for more flexibility across modalities for understanding and generation with more complex reasoning for problem-solving.
ChatGPT has been trained on standard text data for more creative generation of content. This is likely to evolve further across the different versions to include multiple modalities.
Advantages and Disadvantages:
Gemini provides better coding ability, smoother conversation flow, enhanced understanding of complex concepts, and more problem-solving ability. While it sort of lacks on the creative front. It is also newer relatively in comparison to ChatGPT.
ChatGPT provides better creative skills for generation that require less understanding and complex reasoning. It has also iterated and evolved over time in different versions.
Use Cases:
Use Gemini when you want a code assistance and more granular reasoning with content. Use ChatGPT when you want to enhance your creative prowess. However, one common caveat across both chatbots is the questionable ethics, bias, and privacy controls. To err on side of caution, don't share personal data with the chatbots.
ChatGPT
16 October 2024
Schema.org
- Enables search engines to better understand content on sites that rank higher on search results
- Improve click-through rates and organically increase traffic on site
- Provide more flexibility and context to how sites appear in search results
- Increase user search relevancy
- Improve strategy for content and context
- Improve user experience
- Flexibility on markup from microdata, rdfa, and jsonld
- Provides a meta vocabulary to define the context of the site
- Extract who, what, when, why from sites
- Unfriendly schema.org community for suggestions, feedback, and improvements
- Submitting new changes or schemas is slow and often fraught with frustration
- Terrible and difficult to navigate schema.org site as the information is cluttered
- Community is not very open and unwelcoming to new users
- No real reasoning and significant effort towards web of data queryability
- Community is discriminatory towards user suggestions, submissions, and approval process
- Very opinionated and closed community which makes it unconstructive
- Huge Google bias with often rude and arrogant community members
- Markup often is buggy, flawed, and inflexible to community changes
- Process is fraught with trial and error
- Difficult to develop a strategy around the markup
- Difficult to implement at scale with larger websites
- Maintaining markup is a challenge
- It is subjective and questionable whether the markup significantly improves discoverability
- Limited tools that support and provide insights into the markup
- Inflexible schema.org developer community makes the standard inaccessible, inextensible, and unmaintainable
- Unclear documentation on the schema.org website
- The markup is still very limited in context and scope especially for larger websites
- Lacks sufficient domain coverage as a markup
13 October 2024
Netflix Terrible Recommendations
Netflix is all hype. The quality of recommendations and personalization is terrible and lacks variety of content. The entire flow of rating system looks flawed. And, when you refresh the browser the same content that you thumbs down on reappears at the very top. The infinite scrolling is annoying. The algorithm does not personalize to watch habits of a user. Majority of the recommendations seem to be new and irregular.
Netflix has an inaccurate and insufficient data collection gathering process which leaves an incomplete dataset for a recommendation model. The model is neither sufficiently able to gather how you use the platform nor how you don't use it while ignoring user interests and intents.
Netflix algorithms try to measure correlation but not sufficiently causation. It is not able to answer "why". This is in fact the whole point of a recommendation algorithm to utilize insights in order to make deeper contextual decisions on personalization to match items to users.
Netflix algorithms lack sufficient reasoning skills to understand the habits of the user to provide better recommendations in respect of context, intents, and interests. The connections drawn between two points of data seem blurred. This may be as a result of tastes that are not fixed but tend to change. Unfortunately, there is also lack of filters for the user to provide additional data. This could include preferences as part of user profile. The fundamental filtering attributes of grouping items with the user in context seems to be missing for quality recommendations. There is also little to no common sense in the recommendations. It often seems like the user will be trapped in a bubble of sorts. Also, one would assume that they would also recommend their own produced content to recuperate production costs through the platform and take benefit from user data. Furthermore, this may even provide insights on future production projects.
Finally, there is a significant lack of content variety on the platform. A huge bias towards Indian content over other regional content. The content library needs a complete reboot with more flexibility on user preference filters. And, an increase in frequency of new content to be able to sustainably compete with other streaming platform providers.