June 2026
April 2026
January 2026
- Jan 16, 2026TechnologyUnderstanding AI and Machine Learning FundamentalsAlphaGo's move 37 against Lee Sedol in 2016 looked like a mistake to professional commentators, then proved brilliant: it came from millions of self-play games rather than programmed strategy. How AI, machine learning and deep learning nest inside one another, how models actually learn by measuring error and adjusting parameters through gradient descent, the difference between supervised, unsupervised and reinforcement learning, and the limitations that make outputs fail.
- Jan 16, 2026TechnologyAI Limitations and Failure ModesSix structural limits of current AI, from pattern matching without understanding to the absence of causal reasoning, paired with the failure modes to test for: adversarial examples, distribution shift, spurious correlations, and feedback loops. Case studies include Buolamwini and Gebru's Gender Shades benchmark, which found commercial facial recognition error rates reaching 34.7 percent for darker-skinned women against under 1 percent for lighter-skinned men.
- Jan 12, 2026TechnologyTraining AI Models ExplainedTraining is one loop repeated billions of times: forward pass, loss calculation, backpropagation, parameter update. No rules are written, only weights adjusted. The counterintuitive part is how much data those weights need. DeepMind's 2022 Chinchilla work trained 400 models and found the compute-optimal ratio is roughly 20 tokens per parameter, so Chinchilla at 70 billion parameters and 1.4 trillion tokens beat Gopher at 280 billion on the same compute budget.
June 2025
January 2025
- Jan 29, 2025TechnologyDeep Learning: Unraveling AI's Layered Learning ProcessThat 2012 margin was far beyond normal year-over-year progress and it came from two consumer GPUs, not a supercomputer. The deeper idea is hierarchy: early layers find edges and gradients, middle layers assemble them into eyes and textures, later layers carry semantic concepts, with nobody specifying the intermediate steps. That is what removes the feature engineering traditional machine learning requires, at the cost of far more data and much less interpretability.
- Jan 17, 2025TechnologyArtificial Intelligence: Understanding Its Core FunctionsThe system invented plausible case names, docket numbers and verbatim quotes from opinions never written, and it was not malfunctioning. It was predicting likely text, with no mechanism to tell fact from confident fabrication. McCarthy coined the term at the 1956 Dartmouth workshop, and everything built since is narrow AI, strong inside its training distribution and unreliable outside it. General and superintelligence remain hypotheses.
- Jan 5, 2025TechnologyWhat Is a Neural Network: How AI Learns From DataIn 2012 AlexNet, an eight-layer network built by Alex Krizhevsky with Geoffrey Hinton and Ilya Sutskever, cut the ImageNet top-5 error rate to 15.3 percent against 26.2 percent for the runner-up, retiring decades of manual feature engineering. Covers weights and biases, backpropagation, how CNNs, RNNs and transformers differ, and the limits: on the ARC-AGI reasoning benchmark humans averaged 85 percent, the best systems 34.
November 2024
June 2024
March 2024
August 2023
July 2023
June 2023
May 2023
- May 15, 2023Work SkillsAI's Influence on Data Science: Key Changes AheadWhat generative AI actually changed in data science work. Text classification that once meant TF-IDF plus a trained classifier over days of work is now prompt engineering and an API call measured in hours, and RAG pipelines have replaced search-plus-manual-reading for document questions. AutoML genuinely speeds up baselines on clean tabular data, but it cannot frame the problem, will not detect data leakage or biased sampling, and handles text, images and graphs poorly.
- May 8, 2023Work SkillsData Scientist Career Path: IC Progression and SpecializationsTreating senior as a terminal level is a legitimate planning decision rather than a failure, and the page argues management is not the default definition of success, since staff and principal IC tracks pay comparably with more technical freedom. The mid-level shift that unlocks progression is asking a stakeholder what decision they are trying to make instead of what analysis they want.
February 2023
- Feb 22, 2023TechnologyData Science Explained: Skills and Career InsightsRogati's hierarchy of needs explains it, since reliable collection, pipelines and defined metrics have to exist before machine learning on top of them means anything. A McKinsey survey found just 18 percent of organizations pursuing these initiatives had reached widespread adoption generating significant value, with data quality the most cited barrier at 58 percent, ahead of talent at 47 percent.
- Feb 5, 2023TechnologyDefining AGI: Insights into Artificial General IntelligenceLegg and Hutter measure it as goal achievement across many environments, OpenAI frames it as outperforming humans at most economically valuable work, and Marcus and Davis argue current architectures lack the compositional reasoning it requires. The survey evidence moved though: the AI Impacts median for a 50 percent chance shifted from about 2059 to the 2047 to 2053 range after GPT-4, with disagreement still wide.
October 2022
September 2022
- Sep 12, 2022TechnologyWhat Is Transfer Learning in AI: How Models Reuse KnowledgeYosinski and colleagues showed at NeurIPS 2014 why it works: the first layers of a network trained on ImageNet are essentially universal, middle layers transfer moderately, and only the final layers are strongly task-specific. That layer-by-layer result underpins everything from fine-tuning a ResNet on a few thousand medical scans to LoRA, which injects low-rank matrices into a frozen model and cuts trainable parameters by a factor of 10,000 while keeping most of the gain.
- Sep 11, 2022TechnologyAnalyzing the Overfitting Problem in Machine LearningWhat overfitting is and how to recognize it. A model overfits when it learns the training data too precisely, capturing noise and random variation specific to that dataset rather than generalizable rules, and the signature is a gap between training and test performance: 98 percent accuracy on training data against 62 percent on test data is almost certainly overfitting. The analogy is a student who memorized past exams rather than learning the subject.
- Sep 10, 2022TechnologyWhat Is the History of Artificial IntelligenceMcCulloch and Pitts model a neuron in 1943, Dartmouth names the field in 1956, and the 1973 Lighthill report dismisses the results as toy-domain work, triggering the first funding winter. Expert systems bring real money next, with XCON saving Digital Equipment an estimated 40 million dollars a year by 1986, then collapse on brittleness. In 2012 AlexNet wins ImageNet with a 15.3 percent top-5 error rate against 26.2 percent for the runner-up, and consensus shifts.
August 2022
- Aug 15, 2022TechnologyWhat Is the Turing Test and Why It Still MattersTuring replaced "Can machines think?" with a game: if a text-only interrogator cannot reliably tell machine from human, treat the machine as intelligent. Its weakness is that it rewards impersonation. Eugene Goostman cleared the 30 percent threshold in 2014 by role-playing a 13-year-old boy, lowering judges' expectations. Harder benchmarks like ARC-AGI, where the best systems reach 30 to 50 percent against about 85 percent for humans, probe what it ignores.
- Aug 1, 2022TechnologyUnderstanding the Principal Hierarchy Problem in AI SafetyWhat AI alignment research is trying to solve. Beyond the value alignment problem of specifying human values in optimizable form sits the principal hierarchy problem: how a system should resolve conflicting instructions from developers, operators and users. Goodhart's law is central, since a measure that becomes a target ceases to be a good measure, and Victoria Krakovna at DeepMind maintains a documented list of specification gaming cases.
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