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 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.
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 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.
June 2024
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 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.
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