Deep learning. Largely ConvNets or ViTs trained with backprop
TECHNOLOGY
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China Releases Kimi K2.6 and DeepSeek V4 Open-Source Models
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china just double-dropped Kimi K2.6 and DeepSeek V4. Same week. Both rad. Both open-source. Gemini, GPT and Claude right now:
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Robotic Technology Scaling Requires Years Not Months
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Whilst robotic tech is accelerating fast, I still believe we are looking at years and not months away for successful and safe implementation and scaling. https://t.co/OcKD9pP90n
— AI (@DeepLearn007) 24 avril 2026Whilst robotic tech is accelerating fast, I still believe we are looking at years and not months away for successful and safe implementation and scaling.
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DeepSeek’s open-source strategy challenges US AI monopolies, alters AGI business models.
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I swear DeepSeek open-sourcing everything is some Sun-Tzu shit. America is trying to build trillion-dollar AI monopolies, and China is trying to make that impossible. If the secret recipe to AGI is (eventually) public, the business model changes. Valuations compress.
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Deep Learning ConvNets: Core AI Beyond Language Models
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The vast majority of them use deep learning, i.e. ConvNets trained with backprop. It's not LLMs but it is AI.
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Deep Learning Without LLMs: Neural Net Backpropagation Focus
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It's all based on neural net / deep learning trained with backprop.
But none of it uses LLMs, GPT, or other token-based generative architectures. -
MRI Technology Advancement Reduces Time and Cost
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I wasn't advocating the indiscriminate use of full-body MRIs, but the fact that the technology that makes this possible reduces the time and cost of an MRI exam is intrinsically a Good Thing.
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LLMs vs Vision Systems: Neural Nets and Architecture Differences
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Absolutely nothing. They both use neural nets and backprop. But LLMs are generative architectures trained on sequences of discrete symbols. Vision systems used in AEBS and other applications use ConvNets or ViTs trained to detect and classify from labelled samples.
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Exponential Growth is Actually Sigmoid Curves in Disguise
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People criticize me because they fail to see that every real-world process that looks like an exponential is really the initial segment of a sigmoid. Even processes that remain exponential for a while are actually a succession of small sigmoids, each corresponding to a major