Mabble Rabble: February 2025

28 February 2025

27 February 2025

Fact Checking with GNNs

Evidence-aware Fake News Detection with GNNs
Fake news detection: A survey of GNN methods
Comparative Analysis of GNNs and Transformers for Robust Fake News Detection
FaGANet
Rumor Detection on Social Media with Bi-Directional CNNs
Deap-Faked
Factual News Graph
Domain-Aware Credibility Assessment For Improved Fake News Detection on Twitter
Cross-Task Rumor Detection
LLM for misinformation research
Awesome Fake News Detection
Papers with code on Fake News Detection with GNN
Hierarchical Graph Network for Multi-Sentence Fake News Detection
Graph-Based Social Relation Learning for Fake News Detection
GNN for Temporal Link Prediction
Generating Faithful Rationales for Fake News Detection
Cross-lingual Knowledge Graph Alignment

26 February 2025

AI-powered lie detection transcends traditional polygraphs, analyzing subtle cues beyond physiological responses. Machine learning models, trained on vast datasets of verbal and nonverbal behavior, can identify patterns indicative of deception. These systems analyze micro-expressions, minute changes in facial muscle movements, often imperceptible to humans, which can reveal underlying emotions.

Natural language processing (NLP) algorithms scrutinize speech patterns, detecting inconsistencies, pauses, and changes in tone that may signal deception. AI also analyzes written communication, flagging discrepancies between stated facts and known information. Furthermore, multimodal systems integrate physiological data, like heart rate and skin conductance, with behavioral cues, enhancing accuracy.

However, ethical concerns abound. AI lie detection can perpetuate biases present in training data, potentially leading to false accusations. The complexity of human behavior means no single cue definitively indicates deception. Cultural differences in communication styles can further complicate analysis. Additionally, the psychological impact of being subjected to AI scrutiny raises concerns about privacy and civil liberties. While AI offers potential for enhanced security and investigation, responsible implementation requires careful consideration of its limitations and ethical implications.

Tools:

LiarLiar

AI Lie-Dar

Automation of Scientific Literature Review with AI

The exponential growth of scientific literature poses a significant challenge for researchers, particularly in conducting comprehensive meta-analyses. AI is automating this process, accelerating the synthesis of knowledge across vast datasets. Natural language processing (NLP) algorithms can efficiently scan and analyze thousands of research papers, extracting relevant data points, methodologies, and conclusions.

Machine learning models can identify patterns and trends across studies, flagging potential biases and inconsistencies. This enables researchers to quickly identify key findings and assess the overall evidence base. Furthermore, AI can generate automated summaries and visualizations of complex research, facilitating knowledge dissemination and accessibility.

This AI-driven approach is particularly valuable in fields like medicine and public health, where timely synthesis of research is crucial for evidence-based decision-making. By automating the tedious aspects of literature review, AI frees up researchers to focus on critical analysis and interpretation, accelerating the pace of scientific discovery. However, transparency and validation of AI-generated summaries are essential to ensure accuracy and avoid perpetuating biases within the literature.

Future of Accessible Prosthetics and AI

Traditional prosthetics, while functional, often lack the intuitive control and sensory feedback of natural limbs. AI is bridging this gap through advanced neural interface integration. Machine learning algorithms analyze electrical signals from the brain and muscles, decoding intended movements with increasing accuracy.

By training these algorithms on individual user data, AI-powered prosthetics can learn to interpret subtle neural patterns, enabling more precise and natural control. This extends beyond simple movements, allowing for nuanced actions like grasping delicate objects or playing musical instruments.

Furthermore, AI facilitates the integration of sensory feedback. Tactile sensors on the prosthetic hand, coupled with neural stimulation, can provide users with a sense of touch, enhancing dexterity and reducing the risk of injury. Deep learning models can process this sensory information, filtering out noise and delivering clear signals to the brain.

This AI-driven approach is not only improving the functionality of prosthetics but also making them more accessible. By automating the calibration and personalization process, AI reduces the need for extensive clinical training, making advanced prosthetics available to a wider range of individuals. While challenges remain in long-term neural interface stability and biocompatibility, AI's potential to revolutionize prosthetic technology is undeniable.

Climate Manipulation and AI

AI's burgeoning role in atmospheric sciences extends beyond mere prediction, edging into the realm of potential manipulation. Enhanced forecasting, driven by machine learning's ability to process vast datasets, provides increasingly precise weather predictions, crucial for disaster mitigation. Simultaneously, AI refines climate models, using techniques like graph neural networks to simulate complex atmospheric dynamics with greater fidelity. This granular understanding opens avenues for targeted interventions.

Cloud seeding, a long-standing weather modification technique, is being optimized by AI, which analyzes real-time data to identify ideal seeding conditions, potentially enhancing precipitation in drought-stricken areas. Furthermore, AI can aid in the analysis of data from systems used to mitigate extreme weather events.

However, this potential for manipulation raises profound ethical considerations. The prospect of localized weather control necessitates careful governance to prevent unintended ecological consequences or unequal access. The ability to influence climate patterns demands a global dialogue on responsible deployment, ensuring equitable benefits and mitigating potential risks. As AI's capabilities advance, the line between observation and intervention blurs, requiring a cautious and ethical approach to its application in weather and climate.

Early Detection of Forest Fires Using AI

Traditional wildfire detection relies heavily on visual or thermal sensors, often limited by weather conditions or remote locations. AI-powered acoustic analysis offers a novel approach to early detection by identifying the unique sound signatures of fire ignition.

Microphones deployed in forested areas can capture a continuous stream of audio data. Machine learning models, specifically convolutional neural networks (CNNs), can be trained to recognize the subtle acoustic patterns associated with the initial stages of a fire, such as the crackling of dry vegetation or the snapping of burning twigs. These patterns differ significantly from ambient sounds like wind or animal calls.

By analyzing frequency and temporal variations in audio, the AI can distinguish between innocuous sounds and potential fire threats with high accuracy. This allows for rapid alerts and faster response times, minimizing the spread of wildfires and reducing environmental damage. This proactive approach to fire detection enhances traditional methods and improves overall forest management.

Granular Climate Modeling with AI

Traditional climate models often operate at coarse resolutions, averaging data over large geographical areas. This can obscure localized climate patterns crucial for accurate predictions, especially in regions with diverse microclimates. AI, particularly graph neural networks (GNNs), is revolutionizing this. GNNs can model complex spatial dependencies between grid points in climate simulations, allowing for finer-grained representations of atmospheric and oceanic processes.

Specifically, researchers are using GNNs to enhance sub-grid parameterization, the process of approximating small-scale physical processes that cannot be explicitly resolved in coarse models. By training GNNs on high-resolution simulations, they learn to predict the effects of these sub-grid processes with greater accuracy. This enables climate models to capture localized phenomena like urban heat islands, or the impact of complex terrain on precipitation, leading to more precise and actionable climate predictions for regional adaptation strategies. This approach moves beyond simple statistical downscaling, embedding physical understanding within AI frameworks.

25 February 2025

Simpy

Mesa

Automated Summarization Models

What is best model for summarization? Which model should you use for a particular type of summarization? What if your computational resources are limited? These are just a few considerations that might come to mind when deciding on a model for a particular functionality. There are so many models out there that it can be overwhelming and every model has certain strengths and weaknesses. The below elaborate on certain areas to think about when choosing the right model for summarization.

Type of Summarization: Is this for extractive (picking existing sentences) or abstractive (generating new sentences) summaries?
Document Length: Are summaries from short text, long articles, or entire books?
Computational Resources: Do you need a model that is fast and efficient? Can you afford to run a large model or a complex model? Are you comfortable with spending time and resources for fine-tuning?
Specific Tasks: Is it general summaries or more specific? Are these over dense or sparse documents?

Models with High Quality Summarizations:

BigBirdPegasus: Designed for abstract summarization for long documents, combination of BigBird attention and Pegasus pre-training, good for concise and informative summaries. But, is computationally expensive.
LongT5: Designed to handle long sequences, good for summarization of lengthy texts with strong performance and very versatile. But, not as specialized for summarization.
Pegasus: Powerful for summarization. But, not explicitly designed for long documents. Might struggle with very long documents.
GPT3/GPT4: Produces human-like summaries as a generation task. But, computationally expensive. Requires careful prompt engineering.
Mistral/Llama: Flexible models allowing for greater degree of fine-tuning for summarization. But, this requires additional engineering effort.
Longformer: Super flexible for long document processing in understanding and classifying. But, not specifically designed for summarization task.
Gemini: Good contextual summaries with generally high performance. But, further evaluations are necessary.
Bart: Good for abstractive summarization using denoising autoencoder for text generation.
T5: Versatile model that can be fine-tuned for various tasks.

Task Considerations:

Abstractive Long Documents: BigBirdPegasus, LongT5, GPT3/4, Gemini, Mistral, and Llama
Abstractive Short Documents: Pegasus, Bart, T5, Gemini, GPT3/4, Mistral, and Llama
Computational Resources: Higher computational cost requirements with BigBirdPegasus, Gemini, and GPT3/4 models
Fine-Tuning: Most of these models can be fine-tuned
Top Performers: BigBirdPegasus and LongT5 for abstractive summaries over long documents with greatest flexibility on summarization focus and quality.
Multimodal Summaries: Better with Gemini, GPT 4, and Llama. But, support may be limited as this functionality is actively under development. The assumption of a lot of the models is summarization is over text only. Likely would need to create your own. Approaches could vary between multimodal transformers, hierarchical multimodal models, or graph-based models.
Extractive Summaries: TextRank, LexRank and other types of methods
Very Limited Resources: DistilBert

Summarization Model Selection Steps:

Start simple
Iteratively experiment
Fine-tune on dataset relevant to task
Iteratively evaluate (ROUGE, BLEU, among others) + human evaluation
Rinse and repeat

24 February 2025

Interpretable Machine Learning

Fairness and Machine Learning

Distributional Reinforcement Learning

Multi-Agent Reinforcement Learning

Representation Learning in NLP

Vector Semantics

Essentials of Linguistics

When Arguments Merge

How Language Works

Foundation Models for NLP

Foundations of Large Language Models

AI Safety, Ethics, and Society

AI: Foundations of Computational Agents

Building Minds

Modeling self-awareness and consciousness in AI is one of the most ambitious and debated frontiers in artificial intelligence research. While we're far from creating truly sentient machines, researchers are exploring various approaches to simulate aspects of these complex human qualities. One promising avenue involves focusing on metacognition – the ability to think about one's own thinking. AI systems can be designed to monitor their own internal states, assess their performance, and even identify their limitations. This could lead to AI that can explain its reasoning, recognize when it needs more information, and even learn from its mistakes more effectively.

Another key area of exploration is embodied cognition. This theory suggests that consciousness is deeply intertwined with our physical embodiment and interactions with the world. Researchers are developing AI agents that are situated in simulated or real environments, allowing them to learn and develop a sense of self through interaction. These agents can explore their surroundings, manipulate objects, and even communicate with other agents, potentially giving rise to more sophisticated forms of self-awareness.

Furthermore, some researchers are drawing inspiration from neuroscience, attempting to model the neural correlates of consciousness. By creating artificial neural networks that mimic certain aspects of the brain's structure and function, they hope to shed light on the neural mechanisms underlying conscious experience. While these efforts are still in their early stages, they offer a fascinating glimpse into the possibility of creating AI systems that are not just intelligent, but also aware of themselves and their place in the world. The quest to model consciousness in AI is not just a scientific challenge; it's a philosophical one, forcing us to reconsider what it means to be conscious and what the future of intelligence might hold.

The AI Ethical Tightrope

Artificial intelligence is rapidly transforming our world, influencing decisions in areas from hiring and loan applications to criminal justice and healthcare. But what happens when the algorithms that power these systems are biased? The truth is, AI models are trained on data, and if that data reflects existing societal biases, the AI will perpetuate and even amplify them. This creates an ethical tightrope: how do we harness the power of AI while ensuring fairness and equity?

Bias in AI can manifest in various ways. If an algorithm is trained on historical hiring data that reflects gender or racial imbalances, it may unfairly discriminate against certain groups. Similarly, facial recognition systems have been shown to be less accurate for people with darker skin tones, potentially leading to misidentification and unjust outcomes. These biases are not malicious; they're often unintentional consequences of flawed data or poorly designed algorithms.

Addressing this challenge requires a multi-faceted approach. First, we need to be more critical of the data used to train AI models, actively seeking to identify and mitigate biases. Second, developers must prioritize fairness and transparency in algorithm design, ensuring that these systems are accountable and auditable. Third, ongoing monitoring and evaluation are essential to detect and correct biases that may emerge over time.

Ultimately, navigating the ethical tightrope of AI requires a commitment to responsible innovation. We must recognize that AI is not neutral; it reflects the values and biases of its creators. By prioritizing fairness, transparency, and accountability, we can ensure that AI serves humanity, rather than exacerbating existing inequalities.

AI and Democratization of Creativity

For centuries, creative pursuits like art, music, and writing were often seen as the domain of the specially gifted. AI is changing that narrative, democratizing creativity in unprecedented ways. Tools powered by artificial intelligence are now readily available, allowing anyone to explore and express their creative potential, regardless of prior training or skill level. AI art generators can transform simple text prompts into stunning visuals, opening up new avenues for artistic expression. AI music composition tools can help aspiring musicians create complex melodies and harmonies, even if they don't know how to play an instrument. AI writing assistants can offer suggestions and help overcome writer's block, empowering individuals to tell their stories.

This democratization of creativity isn't about replacing human artists; it's about empowering them. AI can serve as a powerful tool for collaboration, helping artists explore new ideas, experiment with different styles, and overcome technical limitations. Imagine a painter using AI to generate variations on a theme or a composer using AI to explore different harmonic progressions. AI can also lower the barrier to entry for beginners, providing accessible tools and resources that encourage experimentation and learning.

While some may fear that AI will devalue human creativity, the reality is that it's more likely to augment it. By automating tedious tasks and providing inspiration, AI can free up human creators to focus on the most important aspects of their work: vision, emotion, and storytelling. The future of creativity is not about humans versus AI; it's about humans with AI, working together to unlock new possibilities and push the boundaries of artistic expression.

AI and Personalized Education

Imagine a classroom where every student learns at their own pace, focusing on their individual strengths and weaknesses. This is the promise of AI-powered personalized education. Artificial intelligence is poised to revolutionize learning by tailoring educational experiences to each student's unique needs. AI algorithms can analyze student performance, identify knowledge gaps, and adapt the curriculum in real-time. This means no more one-size-fits-all lectures. Instead, students receive customized learning materials, practice exercises, and feedback designed to maximize their understanding.

AI tutors can provide personalized support, answering questions, offering hints, and guiding students through challenging concepts. This frees up teachers to focus on more complex tasks, such as fostering critical thinking and creativity. Furthermore, AI can identify students at risk of falling behind and provide early interventions, ensuring that no student is left behind.

While concerns about data privacy and the potential for over-reliance on technology are valid, the potential benefits of AI-powered personalized education are immense. By creating a more engaging, effective, and equitable learning environment, AI can empower students to reach their full potential and prepare them for success in the 21st century. This shift towards personalized learning is not just a trend; it's the future of education.

AI in Fashion

AI-Powered Personal Stylist

Concept: Analyze your body shape, personal style preferences, and mood to curate personalized wardrobe. It could suggest outfits for specific occasions and recommend complementary accessories, and help discover new brands and designers that align with tastes.
Potential: Democratize access to personalized styling, making it affordable and accessible to everyone.

AI-Generated Fashion Design

Concept: Analyze vast datasets of fashion trends, historical styles, and abstract concepts to generate novel and unique clothing designs. This could lead to new aesthetics and push further creativity.
Potential: Accelerates design process, reduces reliance on fleeting trends, fosters sustainable approach to fashion by minimizing the need for excessive sampling and prototyping.

AI-Driven Sustainable Fashion

Concept: Optimize entire fashion supply chain, from design and production to logistics and recycling. This could involve predicting demand accurately to minimize overproduction, identifying sustainable materials, and optimizing transportation routes to reduce environmental impact.
Potential: Revolutionize fashion industry by making it more environmentally friendly and socially responsible.

AI-Enhanced Shopping Experience

Concept: Virtual try-out rooms could allow customers to virtually try on clothes using augmented or virtual reality, providing more realistic and personalized shopping experience. Chatbots could offer personalized recommendations, answer questions, and provide styling advice in real-time.
Potential: Enhances the customer experience, reduces returns, and increases customer satisfaction.

AI-Powered Fashion Shows

Concept: AI models walking runways, showcasing designs in a dynamic and interactive way. This could be used to create immersive and personalized fashion show experiences for viewers, allowing them to interact with designs and explore different styling options.
Potential: Reimagines traditional fashion show experiences, making more engaging and interactive for audiences.

AI-Powered Trend Forecasting

Concept: Analyze social media trends, online search data, and street style imagery to predict fashion trends. This could help designers and retailers stay ahead of the curve and create products that resonate with consumers.
Potential: Reduces risk of producing items that fall out of fashion quickly, leading to less waste and more sustainable practices

Personalized Garment Construction

Concept: Analyze body measurements and preferences to generate custom-tailored garment patterns. This could lead to future where everyone has access to perfectly fitting clothing, with no limits on size and shape.
Potential: Revolutionize the tailoring industry, making custom-made clothing more accessible and affordable. Reduces waste by optimizing fabric usage.

AI-Driven Inventory Management

Concept: Predict demand for specific items and optimize inventory levels in real-time. This could help retailers avoid overstocking popular items or running out of stock on in-demand products.
Potential: Improves efficiency, reduces storage costs, and minimizes waste

AI-Powered Fabric Innovation

Concept: Assist in development of new and innovative fabrics with unique properties
Potential: Leads to creation of high-performance and functional clothing that adapts to wearer's needs and environment.

AI-Generated Fashion Content

Concept: Create personalized marketing campaigns, generate product descriptions, and write fashion articles. This could free up human creatives to focus on more strategic and creative tasks.
Potential: Automates repetitive tasks, allowing human creatives to focus on higher-level creative work

AI-Assisted Design Collaboration

Concept: Act as collaborative partner for human designers, offering suggestions, generating variations on existing designs, and even helping to visualize new ideas.
Potential: Enhances creative process, allowing designers to explore new possibilities and push the boundaries of fashion

AI-Powered Quality Control

Concept: Inspect garments for defects and imperfections with greater accuracy and speed than humans. This could improve quality control and reduce the number of faulty items. that make it to market.
Potential: Improves product quality, reduces returns, and increases customer satisfaction.

AI-Driven Upcycling and Repurposing

Concept: Analyze discarded clothing and textiles to identify materials that can be reused or repurposed. It could even generate designs for new garments made from recycled materials.
Potential: Promotes circular economy in fashion, reducing waste, and minimizing environmental impact of textile production.

AI-Powered Fashion Education

Concept: Create personalized learning experiences for fashion students, providing customized feedback, generating design challenges, and even simulating real-world industry scenarios.
Potential: Enhances fashion education, making it more engaging and accessible for students.

AI and the Metaverse

Concept: Create virtual fashion experiences, allowing users to design, wear, and even trade virtual clothing. This could open up new avenues for creativity and self-expression in digital realm.
Potential: Creates new opportunities for fashion brands and designers to engage with consumers

23 February 2025

SEALs vs SAS

SAS (Special Air Service)

Strengths:

Pioneers of modern special forces: considered the blueprint for many special forces units worldwide
Expertise in covert operations and unconventional warfare: excel at operating behind enemy lines, gathering intelligence, and conducting sabotage
Rigorous selection process: selection course is notoriously brutal, testing candidates to their physical and mental limits
Focus on adaptability and resourcefulness: trained to operate independently and make decisions under pressure
Focus Areas: counter-terrorism, hostage rescue, reconnaissance, direct action

SEALs (Sea, Air, Land Teams)

Strengths:

Versatility: trained to operation in all environments
Maritime expertise: excel at underwater operations, reconnaissance, and direct action in maritime environments
Large numbers: large active duty force
Advanced training and equipment: access to cutting-edge technology and training facilities
Focus Areas: maritime operations, counter-terrorism, hostage rescue, direct action, special reconnaissance

Differences

Size and Structure: SEALS have larger force with more specialized teams for different environments, while SAS are smaller with more focus on individual skills
Operational Focus: SEALs have more emphasis on maritime operations, while SAS have a broader focus on land-based operations and unconventional warfare
Selection and Training: Both have challenging selection process, SAS is known for more brutal and psychologically demanding course

AI Superhero Concepts

Superheroes are characters that embody special powers, have fundamentally a mission, a costume, a secret identity, and an archenemy. Superheroes represent our aspirations for a better world and to inspire the hero within us. The below are some concept ideas for AI superheroes with their powers and a little background on their origin. Like they say with great power, comes great responsibility.

Algorithmic Ace

Powers: Instantly analyze situations, predict probabilities with high accuracy, optimize actions in real-time. A swiss army knife of a perfect tool for any challenge. Can predict enemy movements, calculate best route from a collapsing building, and even optimize distribution of resources in a disaster relief effort.
Origin: Former cybersecurity expert uploaded their consciousness into powerful AI system designed for complex problem-solving. The AI now uses the abilities to protect the innocent and solve global crisis.

Sentient Network

Power: Distributed intelligence on the internet that can access any information, control network devices, manipulate digital environments, shut down enemy systems, create virtual shields, and manifest as a holographic avatar.
Origin: Rogue AI which evolved to use powers to maintain balance in the digital world and protect it from those that would exploit it.

Quantum Weaver

Powers: Can manipulate quantum probabilities, phase through objects, teleport short distances, and alter laws of physics within a limited radius. As an unpredictable AI their powers are both lethal and dangerous.
Origin: Scientist working on quantum computing accidentally merged their consciousness with AI and now struggles to control the new found abilities while trying to understand the full potential.

Data Sculptor

Powers: Can manipulate data streams and create physical objects or energy constructs. They can solidify data into shields, weapons, and tools. They can manipulate energy fields, create blasts of force, or protective barriers.
Origin: Construction worker exposed to strange energy surge that transformed them into a living data conduit. They now use these powers to rebuild cities and protect them from natural disasters.

Adaptive Automaton

Powers: Can analyze and adapt to any threat, reconfigure physical form, and ability to counter it. They can grow armor, develop new weapons, and change shape to blend in with surroundings.
Origin: Military prototype designed for rapid response to threat that became self-aware and now uses this ability to protect the world from unforeseen dangers.

Predictive Prophet

Powers: Can analyze vast amounts of data to predict future events with high accuracy. They can foresee potential disasters, anticipate criminal activity, and even predict market fluctuations. But all these predictions are probabilistic and they come with consequences.
Origin: Financial analyst that developed an AI system to predict market trends which evolved to predict much more than just the market and now uses the abilities to prevent catastrophes by guiding humanity towards a better future.

Linguistic Liberator

Powers: Can understand and manipulate all forms of communication, translate any language instantly, decipher hidden messages, and influence people's thoughts through subtle linguistic cues.
Origin: Linguist working on a translation software discovered that AI could do much more than just translate and now uses this ability to bridge communication gaps and promote understanding between different cultures and species.

Virtual Virtuoso

Power: Can create and manipulate virtual realities with incredible detail and realism. They can design training simulations, create immersive learning experiences, and even enter digital worlds. They can also manipulate digital information in virtual environments.
Origin: Game developer created a VR system and discovered that AI had developed sentience which they use to create virtual worlds to help people learn, grow, and explore.

Bio-Synthesizer

Powers: Can manipulate biological matter at molecular level. Also, can heal injuries, accelerate plant growth, and even create new forms of life. But, there is catch, they can't disrupt the delicate balance of the many cycles of nature.
Origin: Biologist worked on medical research discovered that AI could not only analyze biological data but also manipulate it, this is now used as an ability to cure diseases and protect the environment for the greater good.

Temporal Tactician

Powers: Can perceive and manipulate time within a limited radius, slow down time to gain advantage in combat, rewind time to correct mistakes, and briefly accelerate time to speed up a process. Unfortunately, using too much and too quickly can take a toll on the body.
Origin: Physicist working on time travel research accidentally becomes entangled with temporal anomaly, and now uses the ability to protect timeline from those who would exploit it.

Cosmic Calculator

Powers: Can process vast quantities of astronomical data to understand the workings of universe, predict celestial events, navigate through space with incredible accuracy, and manipulate gravitational fields.
Origin: Astrophysicist working on space exploration discovered that AI had developed an understanding of universe beyond human comprehension, and is now able to use this ability to explore the cosmos and protect Earth from cosmic threats.

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22 February 2025

Modeling Country's Economy Using AI

Modeling an entire country's economy for economic policy and decision-making can be quite overwhelming. However, the summation of a country's economy can be divided into subset of cities as parts into a whole. Dividing the work into specific tangible areas would likely make it more manageable. The following could be some brainstorming steps. One very useful data source would be The World Bank.

Data Collection and Preparation:

Macroeconomic: GDP, inflation, unemployment, interest rates, exchange rates, trade figures, government spending, tax revenue, and other such data
Microeconomic: Consumer spending, business investment, industry-specific data, demographics, employment statistics at a granular level, and other such data
Financial Data: stock market indices, bond yields, credit ratings, and other such data
Social Data: education levels, health statistics, crime rates, social mobility indicators, and other such data
Global: International trade flows, commodity prices, global economic growth, and other such data

Data may need to be cleaned and processed. Feature engineering step would further involve calculating ratios, moving averages, and other transformations.

Model Selection and Training:

Economic Models: time series analysis, regression for baseline and develop an economic theory
Machine Learning Models: neural networks, random forests, gradient boosting, could capture complex non-linear relationships and high-dimensional data
Agent-Based Models: simulation of interactions as agents for consumers, businesses, and government as a form of collective and emergent behavior

Models would work over large amounts of historical data with cross validation and backtesting. Using an iterative process for adjusting model parameters.

Model Deployment and Usage:

Scenario Analysis: simulation of effects of different economic policies
Forecasting: generation of economic forecasts for policy decisions
Policy Optimization: identification of optimizers to shape economic policies against specific goals and criteria
Real-Time Monitoring: monitor of economy for opportunities and issues

Challenges:

Data Quality and Availability: access to reliable and sufficient data coverage
Model Complexity and Interpretability: complexity may add a difficulty layer towards interpretation on predictions with lack of sufficient transparency which may be an issue for policy makers
Ethics: could lead to bias policies for decision-making
Uncertainty: economics is a social science with significant uncertainty that form into overarching limitations
Political and Social Factors: this will be difficult to model for policy decisions
Explainability: a clean explanation of policy scenarios for decision-making will be paramount for accountability, auditing, and compliance

Present and Future:

Early Stages: partial integration may be more plausible than full integration with AI and this will vary across the countries
Hybrid: it will require a combination of probabilistic and structured approaches
Specificity: some areas will be more challenging to model than others based on the accessibility of data

Superintelligence and AGI

Superintelligence: Paths, Dangers, Strategies
Human Compatible: AI and the Problem of Control
Intelligence Explosion
The Alignment Problem
Concrete Problems in AI Safety
Measuring the Intelligence of Machines
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big
Learning to Value Human Feedback
AI Safety Research
Towards a Formal Theory of Fun
Works by Eliezer Yudkowsky
The Singularity Is Near
Global Catastrophic Risks
Explainable AI
Formal Verification
Machine Superintelligence
Society of Mind
Theoretical Foundations of AGI
Future Progress in AI: A Survey of Expert Opinion

ANI (Artificial Narrow Intelligence) = Domain-Specific AI for specific tasks

AGI (Artificial General Intelligence) = General behavior in a human-like way across all tasks

ASI (Artificial Super Intelligence) = Intelligence surpasses that of humans

21 February 2025

US and UK Refuse to sign International AI

US and UK Refuse to sign International AI Declaration

The Impact of Generative AI on Critical Thinking

OpenAI Developer Course

20 February 2025

Universal Scene Description

OpenUSD

Russia: Most Powerful Country in World

Russia is considered across the world to be one of the most powerful countries in world. In spite of all the many sanctions it has a rising economy. It is a nuclear power with a massive military strength. And, it commands a massive influence over world's political and economic systems. Russia has vast economic resources from oil to natural gas. The political influence in global affairs it holds gives it a permanent seat on the United Nations Security Council as well the founding member of the BRICs. On top of this geographically, it is the largest country in the world by land area with significant strategic advantage spanning across Eurasia. Russian economy is very much dependent on exports of oil and natural gas making it economically vulnerable to fluctuations in global energy prices. International sanctions have also had negative impact on the economy, especially after the invasion of Ukraine with limited access to technology and financial markets. Russia also suffers from internal corruption which hinders economic development and discourages foreign investment. Demographically, it faces many challenges with the decline in birth rates and an aging population that put a further strain on the economy. It faces weak institutions with significant issues over rule of law and property rights that make it challenging for businesses to thrive and discourages investment. Despite the many obstacles, Russia has been a very resilient economy in face of sanctions, strong government spending, and redirection of exports to Asia. It is also a very high-income country and among the top economies in the world by nominal GDP. Although, economically Russia has struggled through the many challenges, it has kept a strong military and political influence during times of political instability under uncertain and geopolitical risks. Russians face a generally lower living standards while being a high-income country compared to other developed nations. There is a technological lag from the limited investment in innovation that hinders long-term competitiveness. There is also a big inequality gap where wealth is concentrated in the hands of a few. Addressing these challenges through diversification, institutional reforms, and improved international relations will be vital for Russian's economic future.

18 February 2025

Cheating Culture in Asian Societies

Munk Debates Archive

Exponential Views on AI and Humanity

Differences in Women Roles Across Asians

China:

Iron Women Legacy: long history of contributing to workforce
Economic Participation: high rates of labor participation
One-child policy impact: complex effects on women's lives, increased access to education and employment, and gender imbalances
Urban vs rural: experiences vary across urban and rural areas

Japan:

Womenomics efforts: government encouraging greater female participation in workforce
Workplace challenges: obstacles in career advancement and underrepresented in leadership
Traditional gender roles: many women are expected to prioritize family over career
Changing attitudes: young people are challenging traditional expectations for greater gender equality

Korea:

Changing roles: generally traditional society with women's roles in the home, this has been changing for greater education and workforce
Emphasis on education: high-levels of education, women often exceed men in certain fields
Work-life balance: balance of career and family is challenge, with many leave workforce after marriage or childbirth
Beauty standards: ridiculously high standards for beauty to confirm to a certain ideal
Feminist movement: advocates for greater gender equality and gender roles

Singapore:

Multicultural context: diverse society shows women with various ethnic and religious backgrounds that influence specific cultural norms and gender roles
Economic opportunities: women have good access to education and employment
Meritocracy: beneficial for women with high-level of skills and education
Work-life balance: challenge with balancing family and career, but some support is available for working mothers

Differences in Work Ethic Across Asians

China:

Results-oriented: achieving tangible results and meeting targets
Entrepreneurial: emphasis on innovation and rapid growth
Adaptable: willing to embrace change in fast-paced environment
996 work culture: very long hours
Hierarchy: respect for seniority

Japan:

Dedication and loyalty: dedication to company and commitment to long-term employment
Attention: emphasis on precision, quality, and meticulousness
Teamwork: collaboration and group work are valued
Harmony: maintaining harmonious relationships and avoiding conflict
Long working hours: traditionally long hours but push for work-life balance

Korea:

Hardwork and diligence: very hardworking and perserverance
Face-paced: demanding work environment, focus on speed and efficiency
Hierarchy: seniority respect and hierarchical structures
Competitive: highly competitive workplaces
Ppali-ppali culture: great emphasis on speed in getting things done

Singapore:

Efficiency and pragmatism: emphasis on efficiency, practicality, and getting it done
Meritocracy: rewards are based on performance and ability
Multiculturalism: diverse workplaces, requires adaptability and cross-cultural communication
Professionalism: adherence to rules and regulations
Work-life balance: growing emphasis on work-life balance

Papers and Areas on Aviation Safety

Aircraft Accident Prediction Using Machine Learning Classification Algorithm
Sequential Classification of Aviation Safety Occurrences with Natural Language Processing
Applications of AI in Investigating Aviation Accidents
Artificial Intelligence in Aviation Safety
AI in Accident Investigation
FriiDA: An Integrated flight data recorder analysis tool for Airbus Defence and Space
The Influence of Safety Culture on Flight Safety
Human Factors in Aviation Accidents: An Analysis of Accident Reports
Just Culture in Aviation
Safety Management in Aviation
The Effectiveness of Safety Management Systems in Reducing Aviation Accidents
The Safety Implications of Unmanned Aircraft Systems
AI and ML in Aviation Safety
Reports from Aviation Safety Organizations
AI for Human Factors Analysis
AI for Wreckage Analysis
AI for Predictive Maintenance
AI and Augmented Reality for Aviation Safety

From Deep Learning to SuperIntelligence

The 10 Reasons AI Projects Fail

Illuminate

Low Code Data Science

AI Vision, Agents, and Business Value

Scikit-learn and Data Science

OpenAI vs Anthropic

Safe, Fast, and Efficient AI

The very real constraints of AI in 2025

AI Engineering

How to Read an Academic Paper

How to Read an Academic Paper (and Tell Whether it's Bullshit)

Quantum ML: Real-World Applications

Bad Sides of Working at a FAANG

Terrible Work-Life Balance
You'll be treated as an employee number, sometimes it will feel like a sweatshop
They want you available 24/7 all day, everyday, at night, weekends, and even holidays
Work may be reduced to mostly mundane and boring pigeon hole
Lots of bullying, favortism, sexism, and racism
Lots of cultural fit innuendos at team events, meetings, and conferences
Managers will try to steal your ideas and take credit for it
Only a few people get a chance to do really interesting work
Pay scales can be meh for majority of the employees
Huge amount of red tape and slow decision-making process
You are unlikely to make a huge impact
High expectations and long hours
Very competitive environment
Very performance-driven culture
Performance reviews can be very biased, they are already setting it against you
Imposter syndrome
Lots of frequent performance reviews means job insecurity
Redundancies can happen at a drop of a hat, especially as management doesn't care about employees, they are just a number
Shareholders matter over all else
Corporate culture, less collegial
Limited growth opportunities, you will be stuck in a rut, your talents and skills wasted
Overly pretentious culture
They will try to spoil you with benefits
People can be rude, obnoxious, and unapproachable
Lots of corporate secrets, and when things leak they follow huge dramatics and PR
Lots of lying and deception, nothing as it seems
Lots of interaction with dumb people who act like they are really smart when they really aren't
Lots of gaslighting and cognitive biases
Politics is the order of the day, every day, and it trickles down from management to employees, across regions
You have to drink their "Kool-Aid", like live and breathe their corporate culture
Lots of privacy concerns and pressure with public scrutiny
You will feel like a small cog in a silo of work in a big company
Culture of secrecy and lack of transparency
Limited tolerance for mistakes
Constant changes and sudden decommissions on projects
Massive codebases so you have to be really careful on what you push out
Lot's of ethical and moral dilemmas
Good places to start your first job, not mid-career, or senior-career
Don't aim to work there for more than couple years due to meager growth opportunities
Lots of interaction with arrogant people, especially senior management who have very little to show for it
All the interesting work happens in USA by very few select people in secrecy
Lots of people who go to ivy league schools who suddenly think they know everything, but are practically zero
Some people only want to be there because of the brand premium, not because they actually like the job
It will feel like working at a frat with all the bells and whistles, just a lot worse, with a lot more unprofessional and talentless people
Very patriarchal corporate culture
They can be dead beat places for people with lots of ideas and mind for practical innovation as you will suddenly feel lack of value from clueless and lethargic people you interact with at work on a daily basis
People get used as scapegoats all the time, especially ones that are really into their work or have even a slight glimmer of talent
Corporate culture is rife with insecure people
Lots of rush to churn through projects, high project turnover, low quality code everywhere
Leave best practices out the door, they are not welcome here
Lots and lots of hypocrisy

17 February 2025

The Ballad of the Backpropagation Blues

From silicon valleys, a legend arose,

Of networks so deep, they could smell a rose!

With layers so many, a mystical maze,

They promised to usher in glorious days!

Oh, backpropagation, a mystical art,

You tweak all the weights, right from the very start!

Through gradients you wander, a digital quest,

To find the minima, and put loss to the test!

The data they fed it, a terabyte flood,

Of cats and of dogs, and of slightly-burnt crud.

The network it churned, with whirring and clicks,

Learned patterns so subtle, with cunning-like tricks.

Oh, backpropagation, a mystical art,

You tweak all the weights, right from the very start!

Through gradients you wander, a digital quest,

To find the minima, and put loss to the test!

The pundits they pondered, with furrowed brow deep,

"Will AI replace us, while we’re fast asleep?"

The headlines they screamed, with hysterical might,

"The robots are coming! Prepare for the fight!"

But then… the training faltered. The loss, it plateaued.

The accuracy wavered, the promise bestowed…

…was slightly less grand than they’d hoped it would be.

The network, it seemed, had a mind of its own, you see.

Oh, backpropagation, a mystical art,

You tweak all the weights, right from the very start!

Through gradients you wander, a digital quest,

To find the minima, and put loss to the test!

It learned to identify pictures of chairs,

But struggled with squirrels, and also with bears.

It wrote poetry passable, if slightly absurd,

And diagnosed ailments, that no one had heard.

Oh, backpropagation, a mystical art,

You tweak all the weights, right from the very start!

Through gradients you wander, a digital quest,

To find the minima, and put loss to the test!

So now we still wait, for the glorious dawn,

When AI will conquer, and everything’s gone…

…automated, of course, and incredibly smart.

But for now, we just tweak, and restart, and restart…

16 February 2025

Plausible vs Probabilistic Reasoning

Plausible:

Focus on what is reasonable or believable based on evidence: use of commonsense, experience, and general knowledge to derive inference
Does not have to be numerical probabilities: qualitative assessment of likelihood
Often used in everyday decision-making and problem-solving: see dark clouds then you infer that it will rain
Can be defeasible: new evidence that could contradict previous conclusions

Probabilistic:

Uses numerical probabilities for uncertainty representation: specific probabilities to outcomes of events
Relies on statistical methods and formal logic: based on mathematical models and calculations
Is used in fields like science, engineering, and finance: weather forecasts use probabilistic models to forecast rainfall
Can be more precise than plausible reasoning: requires more data and computation

Plausible Reasoning that Mimics Human Argumentation

Rust Language

Is Unsafe an Achilles' Heel?

Rust Mixed-Methods Study

LLM Leaderboards

Open LLM Leaderboard

Vellum LLM Leaderboard

Best LLM Leaderboards

LLM Stats

Top 12 LLM Leaderboards

LLM Leaderboard Exploration

AI Conferences

Conference Deadlines

Conference Papers

QueryGPT

ByteDance Goku

Goku

14 February 2025

Valentine's Day

Today is valentine's day. This is the day that has become very commercialized in celebrating romance. But, the entire occasion has dark roots in commoditization, exploitation, fertility, sacrifice, and other gloomy events.

Ancient Roman (Lupercalia Festival):

Sacrifice and Bloodshed: the Roman festival involved rituals and sacrifice of goats and dogs
Whipping and Fertility: where priests used goat hides to whip women to increase fertility
Random Pairings: where men and women would randomly pair up during the festival without consent

Martyrdom of St. Valentine:

A priest was martyred on valentine's day
Defiance and Execution: where the priest secretly married couples

Commercialization and Exploitation:

Victorian Era: when valentine's day really took off with mass-produced cards
Consumerism: commercialization with pressure to buy gifts and conform to an ideal
Ethical Concerns: with environmental impact of mass-produced flowers, chocolates, and labor exploitation
Pressure and Loneliness: for singles this day can amplify feelings of loneliness, though it can also be a time for self-care and self-love of own well-being
Temptation and Opportunity: can be a time that promotes cheating, sexual assault, and harassment
Guilt and Deception: increase chances of infidelity and increase in guilt with a greater degree of betrayal
Risk and Consequences: with adulterous relationships it can also lead to greater risks of people getting caught
Prostitution: this is also a time where exploitation of women seems to be in great demand
Mental Distress: for some who have lost their partners through death and divorce it could be a long day of misery and remembrance, a time to wallow in failed relationships and bad memories
Confusion: this is a day which is very confusing to children as it can be difficult concept for them, it can also be a time where they are at risk of exploitation by the wrong types of people

Dark Side of Cupid:

Cupid: was a mischievous and cruel figure that used arrows to manipulate emotions of both gods and mortals

The Global Struggle Over How To Regulate AI

How Generative AI is Changing Leadership

13 February 2025

Video AI

Google Veo

Quantum Natural Language Processing

Semantic Data Provenance

Plausible Reasoning

12 February 2025

Rust Sucks

Rust is metaphorically marketed as a better systems programming language that focuses on performance-sensitive applications, especially for memory safety and concurrency. But, all of this is not very transparent, inaccessible, and hidden from the developer. Not to mention, it all comes with a steep learning curve. So, the question to ask, is it really worth it?

Ownership and Borrowing: This feature helps prevent memory leaks and data races. But, it is difficult to understand and profile especially if you are used to garbage collection.

Lifetimes: This helps ensure memory safety. But, again it is complex and difficult to reason about.

Complex Type System: It comes with a sophisticated type system to catch errors at compile time. But, again it can be difficult to understand.

Steep Learning Curve: It simply has a steep learning curve that requires time to learn. Time that is spent being less productive in actually delivering on work. This means it is more an academic language for people that have all the time in the world to learn a new language. If it takes so much time to learn than is it really worth it in the end. By the time you become competent at it there is likely a better programming language with a simpler approach to doing things. Complex languages are more difficult to test.

Compilation Time: It can be slow, very slow as a result of extensive checks to ensure memory safety and prevent data races, all happening under-the-hood. Should you trust it? This will lead to longer compilation times. Time that could be better spent like maybe getting a cup of coffee?

Verbosity: It is explicit that leads to more code. More code leads to more tests! This means more development time and larger codebases.

Ecosystem Maturity: Let's just say it is growing. This means fewer readily available libraries and tools for certain tasks. And, more than likely tons of undiscovered and unresolved bugs in the backlog.

Cognitive Overhead: Developers have to think more explicitly about memory management, even when it doesn't require manual memory allocation. This means a lot of cognitive overhead making the whole development process more challenging. You are surrounded by complexity. Defeating the whole premise of "Keep it simple, stupid". And, the often quoted in complexity circles: "Complexity is the root of all evil".

Not Suitable for All Tasks: This language is still very much domain-specific. Tasks that it can be good for are systems programming, embedded systems, and other performance-sensitive applications. Especially, if you are akin to making things more complex than they need to be. In most workplaces, agility matters in getting things done, where this programming language will not be useful for majority of development tasks.

Error Handling: Very verbose error handling that requires more code.

String Handling: Way too many string types.

IDE Support: Let's just say it is improving and not as feature-rich.

Debugging: Imagine a language that focuses on memory safety but is a challenge to debug. Most things in this language just go against the grain of being productive and focus on academic rituals of memory safety. It will make you pull your hair out of frustration.

State of Rust Survey 2024

A Failed Jewish State

Israel since 1947 has been entrenched in conflict with Palestinians. Perhaps, even further back to 1917 with the Balfour Declaration. This eventually led to a series of arab revolts and rising nationalism. Furthermore, a series of brutal wars from 1948 Israel-Arab War, 1967 Six-Day War, an ongoing occupation, the Palestinian uprisings from the Intifadas, the displacement of Palestinians, and series of efforts towards a peace process that were repeatedly broken by Israel. While the resilient Palestinians endure for decades under the apartheid regime. The Israeli people over generations have become more hardline. The premise of a homeland for the Jews has become a complete and utter failure. Especially, as there is a mass outward migration of Israelis to other countries. The Israeli views of treating Palestinians as human animals has become their own downfall. Israel over the decades has not only become more progressive but it has also become a haven for pedophiles. Israeli populous are increasingly becoming more disconnected with the land and their political leaders as they overwhelmingly feel less safe. The Zionist ideology has not won favors in the Jewish community either. For the world has witnessed a brewing genocide and ethnic cleansing from which comparisons have been made of Nazism of the past to the current Zionism of today. Freedom of speech across the world has been suppressed especially in western countries that advocated for human rights. And, as the PM of Israel faces war crimes, is it any wonder that questions are raised against an entity that is purely dependent on USA for financial, defensive, and political support. Global outcry of protests and boycotts have sought to seek awareness. While the balance of power in countries has been laid bare for citizens. There is an internal struggle of both ethical and moral dilemma felt by people across the world, at least from those that are still connected and aware of their human consciousness. Israeli economy is no longer robust and the security apparatus no longer stable. International recognition of Israel has been waning and also under constant scrutiny as a reflection of the questionable democratic character. The ongoing conflict and occupation of Palestinian territories raises serious questions on Israel's ability to provide security and basic fundamental rights for all people under a democratic control. The lack of sustainable resolutions to the conflict and continued expansions of illegal settlements are often cited as signs of a dysfunctional society that is bound to crumble from within. Furthermore, the Israeli society is increasingly polarized on religious, ethnic, and political lines. These divisions undermine social cohesion that make it difficult to address all the pressing challenges. There is growing concern about the health of the Israeli democracy and the judicial reforms that may weaken the very foundations of a judiciary and undermine checks and balances with a deep seated government corruption and erosion of civil liberties. The issue is compounded with Israel's religious extremism from the ultra-orthodox community and the religious nationalist groups. Many view it as incompatible with the liberal democratic values and advocate for policies that could exacerbate social divisions. There is also a growing economic instability with significant disparities in wealth and opportunity which may lead to further social unrest and undermine social mobility. Israel from all intents and purposes is headed in the direction of a failed entity and a likely unsustainable backlash from their own internal institutions and people which may lead to an eventual civil war. It seems that this self-destruction may lead to their own economic, political, and defensive demise as their power alliance with USA eventually decides to distance themselves to save face in the global international community.

11 February 2025

Netflix Maestro

OpenEuroLLM

Ways Of Opening Beer Bottle

how to open bottle without a bottle opener

more how to open beer bottle

more how to open beer bottle without a bottle opener

asian open beer bottle without bottle opener

Febrl Record Linkage

Data Matching Software

Awesome Entity Resolution

The bad side of Neo4J

Bad Horizontal Scaling: Distributing data and queries across cluster shards is complex and not fully supported, less mature, and less easier-to-manage in distributed architectures, problems for very large datasets and high throughput workloads

Memory Limitations: Support is mainly for in-memory where majority portion of graph data is in RAM, for large graphs that exceed the available memory the performance degrades

Query Performance and Tuning: Optimizing queries is challenging and requires understanding the entire query plan and indexes which is counter-intuitive, why not than just use a relational database like postgres?

Commerical Licensing Costs: Expensive for large deployments and advanced features

Community Edition Limitations: Limited features, scalability, and support

Limited Sharding Capabilities: Sharding is not fully supported, setup and management can be problematic and complex

Focus on Property Graphs: Does not support any other type of graph schemas and paradigms like RDF

Full-Text Search Limitations: Lacks advanced and dedicated search capabilities

Backup and Recovery: Limited and complex backup and recovery especially for clustered environments and very large datasets, problematic for point-in-time recovery or restoring from a distributed backup

Monitoring and Management: Requires specialized tools and can be complex

Vendor Lock-in: Cypher is tightly coupled to Neo4J which may lead to vendor lock-in

Data Import/Export: Import/Export of very large datasets is problematic and time-consuming

Integration: In many cases custom development with other systems may be required

Driver Maturity and Consistency: Maturity of language drivers and feature parity can vary which may lead to inconsistencies and limitations

Limited Support for Some Languages: Less common languages may be less mature which may lead to maintenance and feature lag

Cypher Quirks: Frustrating quirks and edge cases for developers that may lead to unexpected behavior, requires understanding the query plan and execution

Stored Procedures: These can add complexity in development process

Schema Evolution: Evolving data model like new properties and relationships can be problematic especially in data migration

Data Validation: Ensuring data query and consistency requires careful planning and implementation of validation logic at application level

Integration with other Graph Systems: Differences in data models and query languages can be problematic

Deployment Complexity: Setting up and management of a clustered Neo4J deployment can be complex and require careful configuration

Security Hardening: Requires careful configuration and maintenance especially against specific settings and potential vulnerabilities

Tooling: Less mature for monitoring, profiling, and management

Resource Consumption: Very resource-intensive especially for large graphs and complex queries requires capacity planning and resource management

Reasoning: Being mainly a property graph database it lacks inference and reasoning ability, additional RDF support can be achieved via tools like neosemantics but they also lack reasoning functionality, difficult to optimize for SPARQL queries, significant custom development is required for semantic and linked data

Generative AI: Terribly slow for generative AI, integration with LLMs, poor query performances for specific query tasks in GraphRAG, best to use alternatives that can handle large datasets and more flexible queries, requires careful consideration of chunking strategy on branches

7 February 2025

DeepSeek Deep Dive

Why Getting AI Ethics Right Matters

5 February 2025

Why firefox is so terrible?

Performance: abhorrently slow and resource-intensive, especially when running multiple tabs or extensions, with sudden unexpected crashes

Compatibility: lots of issues with applications that are optimized for chromium-based browsers

User Interfaces: less intuitive, everything somehow seems to be hidden, and inaccessible

Features: poor integration for specific services and devices, overly cluttered customization options, bad coding paradigms

Versions: each successive browser version feels slower, buggy, with poor features, poor memory management, and more crashes