{"id":665,"date":"2019-11-09T17:29:13","date_gmt":"2019-11-09T17:29:13","guid":{"rendered":"https:\/\/datagradient.com\/?page_id=665"},"modified":"2020-03-05T09:53:39","modified_gmt":"2020-03-05T09:53:39","slug":"news","status":"publish","type":"page","link":"https:\/\/datasciencediscovery.com\/index.php\/news\/","title":{"rendered":"News"},"content":{"rendered":"\n<p>Catch up with the fast moving field of data science. Even for us it has been difficult to stay up to date with the latest news and developments in the industry. We are putting together some of the top sources such as MIT, NVIDIA, ARXIV and others. These articles are aggregated from well trusted sources in the community. We will continue to add more sources and refine the type of articles showcased here over time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Data Science<\/h2>\n\n\n\n<p>The recent developments in the field of data science and AI. Sources: Explosion.ai, Rasa, fast.ai, MIT, Berkley, Uber, IBM and Google AI Blog and other trusted sources.<\/p>\n\n\n<div class=\"feedzy-41b89415b773e2454856b435fe9feb4d feedzy-rss\"><div class=\"rss_header\"><h2><a href=\"\" class=\"rss_title\" rel=\"noopener\"><\/a> <span class=\"rss_description\"> <\/span><\/h2><\/div><ul><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/beta.ellf.ai\/\" target=\"_blank\" rel=\" noopener\" title=\"Beta test our new product for agentic NLP development\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/david-becker-V862kywlKkw-unsplash.jpg);\" title=\"Beta test our new product for agentic NLP development\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/david-becker-V862kywlKkw-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/beta.ellf.ai\/\" target=\"_blank\" rel=\" noopener\">Beta test our new product for agentic NLP development<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/beta.ellf.ai\" target=\"_blank\" title=\"beta.ellf.ai\">Explosion<\/a> on July 31, 2026 at 10:57 pm <\/small><p>We\u2019re looking for beta partners for Ellf: a platform and virtual assistant that makes your coding agent like Claude [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.mit.edu\/2026\/daniela-rus-receives-bavarian-minister-presidents-high-tech-prize-0730\" target=\"_blank\" rel=\" noopener\" title=\"Daniela Rus receives Bavarian Minister-President&#039;s High-Tech Prize\" style=\"width:150px; height:150px;\"><span class=\"fetched\" style=\"background-image:  url('https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__cover_image__original\/public\/images\/202607\/mit-csail-daniela-rus-bavarian-award-00.png?itok=9XnNV8-Q');\" title=\"Daniela Rus receives Bavarian Minister-President&#039;s High-Tech Prize\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/news.mit.edu\/sites\/default\/files\/styles\/news_article__cover_image__original\/public\/images\/202607\/mit-csail-daniela-rus-bavarian-award-00.png?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.mit.edu\/2026\/daniela-rus-receives-bavarian-minister-presidents-high-tech-prize-0730\" target=\"_blank\" rel=\" noopener\">Daniela Rus receives Bavarian Minister-President&#8217;s High-Tech Prize<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/news.mit.edu\" target=\"_blank\" title=\"news.mit.edu\">Rachel Gordon | Alex Shipps | MIT CSAIL<\/a> on July 30, 2026 at 9:00 pm <\/small><p>Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.mit.edu\/2026\/connecting-research-to-policy-on-capitol-hill-0730\" target=\"_blank\" rel=\" noopener\" title=\"Connecting research to policy on Capitol Hill\" style=\"width:150px; height:150px;\"><span class=\"fetched\" style=\"background-image:  url('https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__cover_image__original\/public\/images\/202607\/mit-spi-congressional-visit-26.jpg?itok=Vo00t4gt');\" title=\"Connecting research to policy on Capitol Hill\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/news.mit.edu\/sites\/default\/files\/styles\/news_article__cover_image__original\/public\/images\/202607\/mit-spi-congressional-visit-26.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.mit.edu\/2026\/connecting-research-to-policy-on-capitol-hill-0730\" target=\"_blank\" rel=\" noopener\">Connecting research to policy on Capitol Hill<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/news.mit.edu\" target=\"_blank\" title=\"news.mit.edu\">Science Policy Initiative<\/a> on July 30, 2026 at 8:35 pm <\/small><p>MIT students and postdocs discussed science funding and research with policymakers in Washington during the MIT Science [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.mit.edu\/2026\/how-an-mit-database-evolved-into-global-standard-data-sharing-0729\" target=\"_blank\" rel=\" noopener\" title=\"How a medical database developed at MIT evolved into a global standard of data-sharing\" style=\"width:150px; height:150px;\"><span class=\"fetched\" style=\"background-image:  url('https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__cover_image__original\/public\/images\/202607\/mit-RogerMark-GeorgeMoody.jpg?itok=57wqdl_A');\" title=\"How a medical database developed at MIT evolved into a global standard of data-sharing\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/news.mit.edu\/sites\/default\/files\/styles\/news_article__cover_image__original\/public\/images\/202607\/mit-RogerMark-GeorgeMoody.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.mit.edu\/2026\/how-an-mit-database-evolved-into-global-standard-data-sharing-0729\" target=\"_blank\" rel=\" noopener\">How a medical database developed at MIT evolved into a global standard of data-sharing<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/news.mit.edu\" target=\"_blank\" title=\"news.mit.edu\">Emma Foehringer Merchant | Institute for Medical Engineering and Science<\/a> on July 29, 2026 at 2:00 pm <\/small><p>The visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"http:\/\/bair.berkeley.edu\/blog\/2026\/07\/29\/cuda-to-mlx-k-search\/\" target=\"_blank\" rel=\" noopener\" title=\"From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon\" style=\"width:150px; height:150px;\"><span class=\"fetched\" style=\"background-image:  url('https:\/\/bair.berkeley.edu\/static\/blog\/cuda-to-mlx-k-search\/cover.svg');\" title=\"From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/bair.berkeley.edu\/static\/blog\/cuda-to-mlx-k-search\/cover.svg\"><\/a><\/div><span class=\"title\"><a href=\"http:\/\/bair.berkeley.edu\/blog\/2026\/07\/29\/cuda-to-mlx-k-search\/\" target=\"_blank\" rel=\" noopener\">From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon<\/a><\/span><div class=\"rss_content\" style=\"\"><small>on July 29, 2026 at 9:00 am <\/small><p>Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into [&hellip;]<\/p><\/div><\/li><\/ul> <\/div><style type=\"text\/css\" media=\"all\">.feedzy-rss .rss_item .rss_image{float:left;position:relative;border:none;text-decoration:none;max-width:100%}.feedzy-rss .rss_item .rss_image span{display:inline-block;position:absolute;width:100%;height:100%;background-position:50%;background-size:cover}.feedzy-rss .rss_item .rss_image{margin:.3em 1em 0 0;content-visibility:auto}.feedzy-rss ul{list-style:none}.feedzy-rss ul li{display:inline-block}<\/style>\n\n\n<h2 class=\"wp-block-heading\">News<\/h2>\n\n\n<div class=\"feedzy-dc85bee917210dab29fbdd80a4ed6007 feedzy-rss\"><div class=\"rss_header\"><h2><a href=\"\" class=\"rss_title\" rel=\"noopener\"><\/a> <span class=\"rss_description\"> <\/span><\/h2><\/div><ul><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMingFBVV95cUxPSUlDMmh0M0ptVy1EWjQyZlVlR09CZktFWEVYYndROVZvRENKRjMxU21LUUU2Ty1fOFlXVzZQRlZ3Slp5VURFU2ZZcE1ja3I0VVVUR3piRW90SmpjY1ljQWs1QUxGQVRqYzlmelFvTUJZd0xuTkJrdlVnNzRuZFAtWEUtWVM2MXRrQVJXYlZjZDR2NnEwSWVsc0V5NDZRUQ?oc=5\" target=\"_blank\" rel=\" noopener\" title=\"FACEIT adds new machine-learning layer to combat rising AI cheats - Dust2.us\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg);\" title=\"FACEIT adds new machine-learning layer to combat rising AI cheats - Dust2.us\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMingFBVV95cUxPSUlDMmh0M0ptVy1EWjQyZlVlR09CZktFWEVYYndROVZvRENKRjMxU21LUUU2Ty1fOFlXVzZQRlZ3Slp5VURFU2ZZcE1ja3I0VVVUR3piRW90SmpjY1ljQWs1QUxGQVRqYzlmelFvTUJZd0xuTkJrdlVnNzRuZFAtWEUtWVM2MXRrQVJXYlZjZDR2NnEwSWVsc0V5NDZRUQ?oc=5\" target=\"_blank\" rel=\" noopener\">FACEIT adds new machine-learning layer to combat rising AI cheats &#8211; Dust2.us<\/a><\/span><div class=\"rss_content\" style=\"\"><small>on July 31, 2026 at 6:17 pm <\/small><p>FACEIT adds new machine-learning layer to combat rising AI cheats\u00a0\u00a0Dust2.us<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMib0FVX3lxTE1LWjNVTGZ3b0JsbzNQbXh5TzIzSVpiVWFFZ2pzTDgtYUdLajI3VXdxNGxCa3U2NHBwNHZyOUZ4VnNwSHNWZFpxamthX09NSVhNR3FNb1BaV1hSWG1SZWxNMkFYSU9VcVFGY2E0c00zRQ?oc=5\" target=\"_blank\" rel=\" noopener\" title=\"Decoding animal communication with AI - Harvard University\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg);\" title=\"Decoding animal communication with AI - Harvard University\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMib0FVX3lxTE1LWjNVTGZ3b0JsbzNQbXh5TzIzSVpiVWFFZ2pzTDgtYUdLajI3VXdxNGxCa3U2NHBwNHZyOUZ4VnNwSHNWZFpxamthX09NSVhNR3FNb1BaV1hSWG1SZWxNMkFYSU9VcVFGY2E0c00zRQ?oc=5\" target=\"_blank\" rel=\" noopener\">Decoding animal communication with AI &#8211; Harvard University<\/a><\/span><div class=\"rss_content\" style=\"\"><small>on July 31, 2026 at 4:40 pm <\/small><p>Decoding animal communication with AI\u00a0\u00a0Harvard University<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMidEFVX3lxTE0tV0xfWU1XNjlGTF92LXBZT2I3clVrX2h0czM5ZmU3dFBRYWVtZnRiaDhONDZPZ053UjgzUWk4UFg0STBmVVlWSDItZ0xRZGozcVFwOW1hU0pRWHVjYUdvcFpoWjQtZzFRcU93bnpYSldLNW9z?oc=5\" target=\"_blank\" rel=\" noopener\" title=\"CEO of OpenAI says we&#039;re &#039;in the singularity&#039; with AI: Is he right? - Tech Xplore\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg);\" title=\"CEO of OpenAI says we&#039;re &#039;in the singularity&#039; with AI: Is he right? - Tech Xplore\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMidEFVX3lxTE0tV0xfWU1XNjlGTF92LXBZT2I3clVrX2h0czM5ZmU3dFBRYWVtZnRiaDhONDZPZ053UjgzUWk4UFg0STBmVVlWSDItZ0xRZGozcVFwOW1hU0pRWHVjYUdvcFpoWjQtZzFRcU93bnpYSldLNW9z?oc=5\" target=\"_blank\" rel=\" noopener\">CEO of OpenAI says we&#8217;re &#8216;in the singularity&#8217; with AI: Is he right? &#8211; Tech Xplore<\/a><\/span><div class=\"rss_content\" style=\"\"><small>on July 31, 2026 at 11:40 am <\/small><p>CEO of OpenAI says we&#8217;re &#8216;in the singularity&#8217; with AI: Is he right?\u00a0\u00a0Tech Xplore<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.google.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?oc=5\" target=\"_blank\" rel=\" noopener\" title=\"Google study finds workplace AI acting more as assistant than replacement - Business Standard\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg);\" title=\"Google study finds workplace AI acting more as assistant than replacement - Business Standard\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.google.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?oc=5\" target=\"_blank\" rel=\" noopener\">Google study finds workplace AI acting more as assistant than replacement &#8211; Business Standard<\/a><\/span><div class=\"rss_content\" style=\"\"><small>on July 31, 2026 at 10:42 am <\/small><p>Google study finds workplace AI acting more as assistant than replacement\u00a0\u00a0Business Standard<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMiywFBVV95cUxQMDRya1B2aGsxNUVoTEM2V3c3eVdJUVhncWFnUVJtYlB5X2oxODJpLWtVTS1CRENxSTRhaC1pT29vOFZzcmpiRm9jWjFFRzdGWkVGcmdmQ2JESVJEV0dfc3kzQlpuVnU2emxrMzZkRkxURG9sTkhQa0lZZHlHdWtMWGVGTU1KNlYyY0lEeFozUmFKTlF3NDFlZG1aODdfbVNiNXFvN2N0R0t3YzBTclRSZzlCT09Da3h6NEZVbVlTNnFKQU9GeXV2cVBHWQ?oc=5\" target=\"_blank\" rel=\" noopener\" title=\"Staff Machine Learning Engineer, Animation - Singapore Efficiency Team - Riot Games\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg);\" title=\"Staff Machine Learning Engineer, Animation - Singapore Efficiency Team - Riot Games\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/sharon-pittaway-N7FtpkC_P7o-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMiywFBVV95cUxQMDRya1B2aGsxNUVoTEM2V3c3eVdJUVhncWFnUVJtYlB5X2oxODJpLWtVTS1CRENxSTRhaC1pT29vOFZzcmpiRm9jWjFFRzdGWkVGcmdmQ2JESVJEV0dfc3kzQlpuVnU2emxrMzZkRkxURG9sTkhQa0lZZHlHdWtMWGVGTU1KNlYyY0lEeFozUmFKTlF3NDFlZG1aODdfbVNiNXFvN2N0R0t3YzBTclRSZzlCT09Da3h6NEZVbVlTNnFKQU9GeXV2cVBHWQ?oc=5\" target=\"_blank\" rel=\" noopener\">Staff Machine Learning Engineer, Animation &#8211; Singapore Efficiency Team &#8211; Riot Games<\/a><\/span><div class=\"rss_content\" style=\"\"><small>on July 31, 2026 at 10:18 am <\/small><p>Staff Machine Learning Engineer, Animation &#8211; Singapore Efficiency Team\u00a0\u00a0Riot Games<\/p><\/div><\/li><\/ul> <\/div><style type=\"text\/css\" media=\"all\">.feedzy-rss .rss_item .rss_image{float:left;position:relative;border:none;text-decoration:none;max-width:100%}.feedzy-rss .rss_item .rss_image span{display:inline-block;position:absolute;width:100%;height:100%;background-position:50%;background-size:cover}.feedzy-rss .rss_item .rss_image{margin:.3em 1em 0 0;content-visibility:auto}.feedzy-rss ul{list-style:none}.feedzy-rss ul li{display:inline-block}<\/style>\n\n\n<h3 class=\"wp-block-heading\">White Papers<\/h3>\n\n\n\n<p>Latest research and developments in the field of machine learning and deep learning. Sources: ARXIV, Papers with Code and others<\/p>\n\n\n<div class=\"feedzy-dd6de31578e58d857e2d6426937f7e31 feedzy-rss\"><div class=\"rss_header\"><h2><a href=\"\" class=\"rss_title\" rel=\"noopener\"><\/a> <span class=\"rss_description\"> <\/span><\/h2><\/div><ul><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/arxiv.org\/abs\/2607.27138\" target=\"_blank\" rel=\" noopener\" title=\"DLAM: Distributional Latent Actions with Temporal Constraints\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg);\" title=\"DLAM: Distributional Latent Actions with Temporal Constraints\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/arxiv.org\/abs\/2607.27138\" target=\"_blank\" rel=\" noopener\">DLAM: Distributional Latent Actions with Temporal Constraints<\/a><\/span><div class=\"rss_content\" style=\"\"><p>arXiv:2607.27138v1 Announce Type: cross \nAbstract: Vision-language-action (VLA) models remain constrained by scarce [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/arxiv.org\/abs\/2606.22916\" target=\"_blank\" rel=\" noopener\" title=\"Intent-Governed Tool Authorization for AI Agents\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg);\" title=\"Intent-Governed Tool Authorization for AI Agents\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/arxiv.org\/abs\/2606.22916\" target=\"_blank\" rel=\" noopener\">Intent-Governed Tool Authorization for AI Agents<\/a><\/span><div class=\"rss_content\" style=\"\"><p>arXiv:2606.22916v3 Announce Type: replace \nAbstract: Tool-using AI agents commonly operate under integration [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/arxiv.org\/abs\/2606.04752\" target=\"_blank\" rel=\" noopener\" title=\"An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg);\" title=\"An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/arxiv.org\/abs\/2606.04752\" target=\"_blank\" rel=\" noopener\">An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers<\/a><\/span><div class=\"rss_content\" style=\"\"><p>arXiv:2606.04752v3 Announce Type: replace-cross \nAbstract: Transformers consuming multi-channel scalar signals must [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/arxiv.org\/abs\/2607.22663\" target=\"_blank\" rel=\" noopener\" title=\"Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg);\" title=\"Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/arxiv.org\/abs\/2607.22663\" target=\"_blank\" rel=\" noopener\">Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models<\/a><\/span><div class=\"rss_content\" style=\"\"><p>arXiv:2607.22663v2 Announce Type: replace \nAbstract: Block diffusion is the dominant approach for scaling discrete [&hellip;]<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"width:150px; height:150px;\"><a href=\"https:\/\/arxiv.org\/abs\/2603.06194\" target=\"_blank\" rel=\" noopener\" title=\"MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue\" style=\"width:150px; height:150px;\"><span class=\"default\" style=\"background-image:url(https:\/\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg);\" title=\"MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue\"><\/span><amp-img width=\"150\" height=\"150\" src=\"https:\/\/i0.wp.com\/datasciencediscovery.com\/wp-content\/uploads\/2019\/11\/lysander-yuen-wk833OrQLJE-unsplash.jpg?resize=150%2C150&#038;ssl=1\" data-recalc-dims=\"1\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/arxiv.org\/abs\/2603.06194\" target=\"_blank\" rel=\" noopener\">MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue<\/a><\/span><div class=\"rss_content\" style=\"\"><p>arXiv:2603.06194v3 Announce Type: replace-cross \nAbstract: Reinforcement learning (RL) for large language models (LLMs) [&hellip;]<\/p><\/div><\/li><\/ul> <\/div><style type=\"text\/css\" media=\"all\">.feedzy-rss .rss_item .rss_image{float:left;position:relative;border:none;text-decoration:none;max-width:100%}.feedzy-rss .rss_item .rss_image span{display:inline-block;position:absolute;width:100%;height:100%;background-position:50%;background-size:cover}.feedzy-rss .rss_item .rss_image{margin:.3em 1em 0 0;content-visibility:auto}.feedzy-rss ul{list-style:none}.feedzy-rss ul li{display:inline-block}<\/style>","protected":false},"excerpt":{"rendered":"<p>Catch up with the fast moving field of data science. Even for us it has been difficult to stay up to date with the latest news and developments in the industry. We are putting together some of the top sources such as MIT, NVIDIA, ARXIV and others. These articles are aggregated from well trusted sources [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_mi_skip_tracking":false,"spay_email":""},"_links":{"self":[{"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/pages\/665"}],"collection":[{"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/comments?post=665"}],"version-history":[{"count":11,"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/pages\/665\/revisions"}],"predecessor-version":[{"id":1011,"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/pages\/665\/revisions\/1011"}],"wp:attachment":[{"href":"https:\/\/datasciencediscovery.com\/index.php\/wp-json\/wp\/v2\/media?parent=665"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}