Why is it that those who try to divide us by ethnicity seem to contribute the least to our country?
| Blog | https://mothcloud.com/ |
Michael Gbadebo
@michaelgbadebo
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Aspire 2 inspire b4 u expire. Family / Tech / Innovation / Entrepreneurship / Politics / F1 / Cri-Thinking / Justice / Education / Sow & Nurture. A Polymath.
In Nigeria? Make your voice count at https://yourvoice.ng.
https://mothcloud.com/micropost/nigerias-public-opinion-poll-and-survey/
Omitting any discussion of rejoining the EU in the Beveridge Report for the Economy suggests that Keir Starmer and the Labour Party are not serious about driving economic growth or reducing the cost of living.
If the UK is to move forward, we need to return to the EU table, reconsider our relationship with the US, and crucially, stop allowing figures like Nigel Farage to dominate the national political conversation.
https://mothcloud.com/britain-at-a-crossroads-reclaiming-growth-influence-and-global-relevance/
https://www.theguardian.com/politics/2026/mar/20/ministers-blueprint-economic-overhaul-fears-cost-of-living-hand-election-far-right
https://mothcloud.com/micropost/beveridge-report-for-the-economy/
Britain at a crossroads: Reclaiming growth, influence, and global relevance
Britain stands at a defining moment in its modern history. Once recognised as a nation that shaped global trade, diplomacy, and innovation, Britain now faces an uncomfortable question: are we still looking forward, or are we becoming trapped in nostalgia and reactive policymaking? Across the world, the pace of technological, economic, and geopolitical change is ... Read more
Openverse is a tool that allows openly licensed and public domain works to be discovered and used by everyone.
Openverse searches across more than 800 million images and audio tracks from open APIs and the Common Crawl dataset. We aggregate works from multiple public repositories, and facilitate reuse through features like one-click attribution.
https://openverse.org/
https://mothcloud.com/micropost/openverse-has-more-than-800-million-images-audio-tracks/
Encyclopedia Britannica (owner of Merriam-Webster dictionary) have filed a lawsuit against OpenAI, alleging in its complaint that the AI giant has committed "massive copyright infringement" using their content to train ChatGPT.
One to watch.
https://techcrunch.com/2026/03/16/merriam-webster-openai-encyclopedia-brittanica-lawsuit/
https://mothcloud.com/micropost/dictionary-sues-openai-chatgpt-for-copyright/
Happy Mother's Day to all the mums. You are the heart of beautiful. Thank you for everything you do.
Deepfakes are getting scary good. AI can now create videos, images, and voices that look and sound real, and most people can’t reliably tell the difference anymore.
That is a big problem for things like scams, misinformation, and trust online.
We can not just rely on our eyes and ears anymore. Tech companies, governments, public education, and new tools that verify where content comes from will all be needed to keep the internet trustworthy.
https://www.ft.com/content/dd95e880-241a-4fd6-9f83-b49176b005e7
https://mothcloud.com/micropost/ai-generated-deepfake-videos-images-and-voices/
My definition of Art: “Anything declared art is art, provided the declaration is supported by a coherent explanation that others can understand or engage with.”
Language models (aka LLMs) are known to produce overconfident, plausible falsehoods, which diminish their utility and trustworthiness. This error mode is known as “hallucination,” though it differs fundamentally from the human perceptual experience. Despite significant progress, hallucinations continue to plague the field, and are still present in the latest models.
An interesting read.
https://arxiv.org/abs/2509.04664
https://mothcloud.com/micropost/why-language-models-hallucinate/
Why Language Models Hallucinate
Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such "hallucinations" persist even in state-of-the-art systems and undermine trust. We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging uncertainty, and we analyze the statistical causes of hallucinations in the modern training pipeline. Hallucinations need not be mysterious -- they originate simply as errors in binary classification. If incorrect statements cannot be distinguished from facts, then hallucinations in pretrained language models will arise through natural statistical pressures. We then argue that hallucinations persist due to the way most evaluations are graded -- language models are optimized to be good test-takers, and guessing when uncertain improves test performance. This "epidemic" of penalizing uncertain responses can only be addressed through a socio-technical mitigation: modifying the scoring of existing benchmarks that are misaligned but dominate leaderboards, rather than introducing additional hallucination evaluations. This change may steer the field toward more trustworthy AI systems.
I don't understand the obsession with learning Latin, after all, it's just another language. I understand that there are interesting and useful texts written in Latin, but that’s true of many other languages too.
https://mothcloud.com/micropost/whats-the-obsession-with-learning-latin/
