{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"Scientific AI Isn’t a Scaling Problem. It’s a Data-and-Reasoning Problem.","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/f1aa0343\"></iframe>","width":"100%","height":180,"duration":1035,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/scientific-ai-isnt-a-scaling-problem-its-a-data-and-reasoning-problem.\n             Innovator-VL argues scale isn’t destiny. With ~5M curated examples, it matches bigger models—reproducibly. \n            Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #ai, #innovator-vl, #ai-for-scientific-discovery, #multimodal-llm, #scientific-ai, #ai-data-and-reasoning, #ai-reasoning-capabilities, #ai-reasoning-problem,  and more.\n            \n            \n            This story was written by: @aimodels44. Learn more about this writer by checking @aimodels44's about page,\n            and for more stories, please visit hackernoon.com.\n            \n                \n                \n                Innovator-VL argues scale isn’t destiny: with ~5M curated examples, native-resolution vision tokens, and RL-for-reasoning, it matches bigger models—reproducibly.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}