This article builds a simple additive model to explain how interview order influences graduate exam scores through reference bias and evaluator fatigue, analyzes their combined impact on candidates of different ability levels, and offers practical advice for applicants despite institutional safeguards.
A Reddit experiment showed that major LLMs consistently choose numbers between 7200 and 7500 when asked to pick a random value from 1 to 10000, revealing deterministic patterns rooted in training data bias, human heuristics, and serious security implications.
A new study reveals that large language models which excel on static medical exams dramatically lose accuracy when subjected to the Dynamic, Automatic, Systematic (DAS) red‑team framework, exposing serious weaknesses in robustness, privacy, bias, and hallucination, and urging the adoption of continuous adversarial evaluation for trustworthy clinical AI.
This talk explores how large AI models become overconfident, leading to bias and hallucinations, examines adversarial examples in vision and language, explains why data and algorithms cause these issues, and shows how reinforcement learning can teach models to admit uncertainty and align with human values.
This article explains the mathematical foundation of layer normalization, why it is needed for deep neural networks like Transformers, how scaling (γ) and bias (β) parameters restore important signal variations, and practical placement tips for stable training.
The article explores how people manipulate evaluation systems by redefining variables, adjusting weights, and shifting perspectives, turning losses into perceived wins, and reveals the psychological and statistical biases that create this illusion, urging more honest, multi‑dimensional, transparent modeling for genuine assessment.
Simpson’s Paradox shows how aggregated data can suggest one trend while each subgroup reveals the opposite, illustrated with hospital survival rates where overall A appears better than B, yet detailed analysis by severity flips the conclusion, highlighting the need to consider background variables in statistical interpretation.
This article examines how data can be misused or misinterpreted through real‑world case studies—ranging from breakfast myths and toothpaste advertising to contraceptive risks, crime statistics, judicial decisions, questionnaire bias, airline efficiency, and correlation‑causation confusion—offering practical guidelines to recognize and prevent invalid data analysis in the big‑data era.
In a two‑hour interview with Lex Fridman, OpenAI CEO Sam Altman admits AI could one day kill humans, reveals limited insight into GPT‑4’s training, discusses RLHF, data sources, bias, safety challenges, and the evolving non‑profit versus commercial direction of OpenAI.
Understanding why causal relationships matter reveals the limits of predictive machine learning, introduces counterfactual reasoning, explains potential outcomes, treatment effects, bias, and how to distinguish correlation from causation using simple examples like tablet distribution in schools.
Neglecting AI ethics and governance can expose companies to severe public‑relations crises, biased outcomes, regulatory penalties, unexplainable systems, and employee disengagement, ultimately threatening both societal trust and business sustainability.
This article explains why causal inference matters beyond prediction, introduces potential outcomes notation, demonstrates how bias separates correlation from causation, and outlines the conditions under which observed differences can be interpreted as true causal effects.
This article explains how biological neurons inspire artificial neural networks, describing neuron concepts, threshold firing, weighted inputs, bias, activation functions such as the step and sigmoid functions, and shows how these ideas are expressed mathematically and visualized with diagrams.
This article explores the evolution of human‑computer interaction from its early interface concepts through multimodal and intelligent interaction stages, highlighting historical milestones, the rise of AI‑driven smart devices, emerging challenges such as bias, transparency, and the quest for universal interaction methods.
The article explores how IBM Watson’s cognitive computing reveals human creative processes and common decision‑making biases such as the ambiguity effect, confirmation bias, and “not‑invented‑here” syndrome, showing how AI can help overcome these mental constraints.
