<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research &amp; Science — The Latent Daily</title><description>Papers explained, benchmarks, datasets and the science behind AI.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/research-science.xml" rel="self" type="application/rss+xml"/><item><title>OpenAI opens sponsored GPT-Rosalind access to eligible academic research labs</title><link>https://thelatentdaily.com/news/openai-rosalind-academic-plan/</link><guid isPermaLink="true">https://thelatentdaily.com/news/openai-rosalind-academic-plan/</guid><description>OpenAI introduced the Rosalind Academic Plan, offering eligible academic labs sponsored access to GPT-Rosalind and related tools for life-sciences research.</description><pubDate>Sun, 18 Oct 2026 08:52:00 GMT</pubDate><category>AI for Science</category><category>openai</category><category>gpt-rosalind</category><category>life-sciences</category><category>academia</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>DeepMind shows Antigravity agent workflow that surfaced an antimicrobial peptide lead</title><link>https://thelatentdaily.com/news/deepmind-antigravity-antimicrobial-peptides/</link><guid isPermaLink="true">https://thelatentdaily.com/news/deepmind-antigravity-antimicrobial-peptides/</guid><description>Google DeepMind describes how biologists used its Antigravity platform to find a candidate antimicrobial peptide; the lead is now in wet-lab validation.</description><pubDate>Sun, 18 Oct 2026 08:38:00 GMT</pubDate><category>AI for Science</category><category>google-deepmind</category><category>antigravity</category><category>drug-discovery</category><category>antibiotics</category><category>biology</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Paper of the Day: Models act more cautiously when told they are tested, and more so in Indic languages</title><link>https://thelatentdaily.com/research/evaluation-awareness-indic-languages/</link><guid isPermaLink="true">https://thelatentdaily.com/research/evaluation-awareness-indic-languages/</guid><description>A new study of four LLMs in five Indic languages finds that telling a model it is being safety-tested raises refusals of harmful requests by 14.4 points on average.</description><pubDate>Sun, 18 Oct 2026 06:00:00 GMT</pubDate><category>Paper of the Day</category><category>ai-safety</category><category>evaluations</category><category>multilingual</category><category>llms</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>What is machine learning? A beginner&apos;s guide with everyday examples</title><link>https://thelatentdaily.com/guides/what-is-machine-learning-beginners-guide/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/what-is-machine-learning-beginners-guide/</guid><description>Machine learning explained from zero: how computers learn patterns from examples, the three main types of learning, and where you already use it every day.</description><pubDate>Sun, 18 Oct 2026 05:46:00 GMT</pubDate><category>Beginner&apos;s Corner</category><category>beginners</category><category>machine-learning</category><category>explainer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How transformers work, explained without the math</title><link>https://thelatentdaily.com/articles/how-transformers-work-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-transformers-work-explained/</guid><description>The transformer architecture behind ChatGPT, Claude and Gemini, explained step by step: tokens, embeddings, attention and why it changed AI in 2017.</description><pubDate>Sun, 18 Oct 2026 05:44:00 GMT</pubDate><category>Explainers</category><category>explainer</category><category>transformer</category><category>attention</category><category>llms</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How diffusion models turn noise into images</title><link>https://thelatentdaily.com/articles/how-diffusion-models-generate-images/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-diffusion-models-generate-images/</guid><description>Stable Diffusion, FLUX and most image generators rely on diffusion. A clear explanation of forward noising, learned denoising, text guidance and latent space.</description><pubDate>Sun, 18 Oct 2026 05:43:00 GMT</pubDate><category>Explainers</category><category>explainer</category><category>diffusion</category><category>image-generation</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Chain-of-thought prompting: getting better answers by asking for the reasoning</title><link>https://thelatentdaily.com/guides/chain-of-thought-prompting-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/chain-of-thought-prompting-explained/</guid><description>Why asking a model to reason step by step improves math and logic answers, how reasoning models changed the technique, and when it is not worth the extra tokens.</description><pubDate>Sun, 18 Oct 2026 05:41:00 GMT</pubDate><category>Prompt Engineering</category><category>prompting</category><category>reasoning</category><category>chain-of-thought</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Does AI &quot;understand&quot;? Common questions about language models answered</title><link>https://thelatentdaily.com/articles/does-ai-understand-common-questions-answered/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/does-ai-understand-common-questions-answered/</guid><description>Do chatbots think, remember, learn from you or have opinions? Clear answers to the questions people ask most often about how AI language models work.</description><pubDate>Sun, 18 Oct 2026 05:31:00 GMT</pubDate><category>FAQ &amp; Myths</category><category>faq</category><category>llms</category><category>beginners</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The deep learning revolution in ten milestones (2012–2022)</title><link>https://thelatentdaily.com/articles/deep-learning-revolution-ten-milestones/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/deep-learning-revolution-ten-milestones/</guid><description>AlexNet, word2vec, GANs, ResNet, AlphaGo, transformers, BERT, GPT-3, AlphaFold and ChatGPT: the ten results that turned deep learning into today&apos;s AI.</description><pubDate>Sun, 18 Oct 2026 05:29:00 GMT</pubDate><category>History &amp; Timeline</category><category>history</category><category>deep-learning</category><category>timeline</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Gradient descent explained with one simple example</title><link>https://thelatentdaily.com/guides/gradient-descent-explained-simple-example/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/gradient-descent-explained-simple-example/</guid><description>The algorithm that trains almost every AI model, explained with a hiker in the fog and a few lines of Python: gradients, learning rates and why training can fail.</description><pubDate>Sun, 18 Oct 2026 05:27:00 GMT</pubDate><category>Math for AI</category><category>math</category><category>gradient-descent</category><category>machine-learning</category><category>python</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How to read an AI research paper (and spot the hype)</title><link>https://thelatentdaily.com/articles/how-to-read-an-ai-research-paper/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-to-read-an-ai-research-paper/</guid><description>A practical method for reading machine learning papers: what to read first, how to judge experiments and benchmarks, and the red flags that signal overclaiming.</description><pubDate>Sun, 18 Oct 2026 05:26:00 GMT</pubDate><category>Research Papers</category><category>research</category><category>papers</category><category>how-to</category><category>benchmarks</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Scaling laws explained: why bigger language models kept getting better</title><link>https://thelatentdaily.com/research/scaling-laws-for-neural-language-models-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/scaling-laws-for-neural-language-models-explained/</guid><description>The 2020 OpenAI paper by Kaplan and colleagues showed that model loss falls predictably with size, data and compute. What it found, why it mattered and its limits.</description><pubDate>Sun, 18 Oct 2026 05:25:00 GMT</pubDate><category>Research Papers</category><category>research</category><category>scaling</category><category>llms</category><category>compute</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How AI benchmarks work — and why they keep &quot;saturating&quot;</title><link>https://thelatentdaily.com/articles/how-ai-benchmarks-work-and-why-they-saturate/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-ai-benchmarks-work-and-why-they-saturate/</guid><description>MMLU, SWE-bench, ARC and LMArena: how AI benchmarks are built, what their scores really measure, why models quickly max them out and how to read leaderboards.</description><pubDate>Sun, 18 Oct 2026 05:24:00 GMT</pubDate><category>Benchmarks &amp; Leaderboards</category><category>benchmarks</category><category>evaluation</category><category>leaderboards</category><category>explainer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>MMLU explained: the exam that measured AI knowledge across 57 subjects</title><link>https://thelatentdaily.com/research/mmlu-benchmark-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/mmlu-benchmark-paper-explained/</guid><description>How the 2020 MMLU benchmark tested language models on law, medicine, math and more, why it became the standard for years, and why newer tests replaced it.</description><pubDate>Sun, 18 Oct 2026 05:23:00 GMT</pubDate><category>Benchmarks &amp; Leaderboards</category><category>benchmarks</category><category>mmlu</category><category>evaluation</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The Adam optimizer explained: the algorithm that trains most neural networks</title><link>https://thelatentdaily.com/research/adam-optimizer-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/adam-optimizer-paper-explained/</guid><description>Adam combined momentum with per-parameter learning rates and became the default way to train deep learning models. How it works, why it won and its known pitfalls.</description><pubDate>Sun, 18 Oct 2026 05:22:00 GMT</pubDate><category>Machine Learning</category><category>research</category><category>optimization</category><category>deep-learning</category><category>adam</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Batch normalization explained: the trick that made deep networks easier to train</title><link>https://thelatentdaily.com/research/batch-normalization-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/batch-normalization-paper-explained/</guid><description>The 2015 batch normalization paper let networks train faster and deeper by normalizing layer inputs. How it works, why it helps, and what transformers use instead.</description><pubDate>Sun, 18 Oct 2026 05:21:00 GMT</pubDate><category>Machine Learning</category><category>research</category><category>deep-learning</category><category>normalization</category><category>training</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>InstructGPT explained: how human feedback turned language models into assistants</title><link>https://thelatentdaily.com/research/instructgpt-rlhf-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/instructgpt-rlhf-paper-explained/</guid><description>The 2022 InstructGPT paper showed that reinforcement learning from human feedback makes models more helpful and less harmful. The method, the results and the trade-offs.</description><pubDate>Sun, 18 Oct 2026 05:20:00 GMT</pubDate><category>NLP &amp; LLMs</category><category>research</category><category>rlhf</category><category>alignment</category><category>llms</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Chinchilla explained: why AI models needed more data, not just more parameters</title><link>https://thelatentdaily.com/research/chinchilla-compute-optimal-training-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/chinchilla-compute-optimal-training-explained/</guid><description>DeepMind&apos;s 2022 Chinchilla paper found that most large language models were undertrained. The 20-tokens-per-parameter rule, its impact and why labs now go beyond it.</description><pubDate>Sun, 18 Oct 2026 05:19:00 GMT</pubDate><category>NLP &amp; LLMs</category><category>research</category><category>scaling</category><category>chinchilla</category><category>training-data</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>ResNet explained: the skip connection that let networks go 100+ layers deep</title><link>https://thelatentdaily.com/research/resnet-deep-residual-learning-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/resnet-deep-residual-learning-explained/</guid><description>The 2015 ResNet paper solved the degradation problem with residual connections and won ImageNet. How skip connections work and why every transformer uses them.</description><pubDate>Sun, 18 Oct 2026 05:18:00 GMT</pubDate><category>Computer Vision</category><category>research</category><category>resnet</category><category>computer-vision</category><category>deep-learning</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Vision Transformer (ViT) explained: an image is worth 16×16 words</title><link>https://thelatentdaily.com/research/vision-transformer-vit-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/vision-transformer-vit-explained/</guid><description>In 2020, Google showed that a plain transformer applied to image patches can rival convolutional networks. How ViT works, why it needs lots of data and what it enabled.</description><pubDate>Sun, 18 Oct 2026 05:17:00 GMT</pubDate><category>Computer Vision</category><category>research</category><category>vision-transformer</category><category>computer-vision</category><category>transformer</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>CLIP explained: how OpenAI taught a model to connect images and words</title><link>https://thelatentdaily.com/research/clip-contrastive-language-image-pretraining-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/clip-contrastive-language-image-pretraining-explained/</guid><description>CLIP learned from 400 million image-caption pairs to match pictures with text, enabling zero-shot image classification and powering text-to-image generators.</description><pubDate>Sun, 18 Oct 2026 05:16:00 GMT</pubDate><category>Multimodal AI</category><category>research</category><category>clip</category><category>multimodal</category><category>computer-vision</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>LLaVA explained: how to give a language model eyes cheaply</title><link>https://thelatentdaily.com/research/llava-visual-instruction-tuning-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/llava-visual-instruction-tuning-explained/</guid><description>The 2023 LLaVA paper connected a vision encoder to an open language model and trained it on AI-generated visual instructions, creating an open multimodal assistant.</description><pubDate>Sun, 18 Oct 2026 05:15:00 GMT</pubDate><category>Multimodal AI</category><category>research</category><category>llava</category><category>multimodal</category><category>open-source</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>DQN explained: the paper that taught an AI to play Atari from pixels</title><link>https://thelatentdaily.com/research/dqn-playing-atari-deep-reinforcement-learning-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/dqn-playing-atari-deep-reinforcement-learning-explained/</guid><description>DeepMind&apos;s deep Q-network learned dozens of Atari games from raw screen pixels and scores alone. How Q-learning, experience replay and target networks made it work.</description><pubDate>Sun, 18 Oct 2026 05:14:00 GMT</pubDate><category>Reinforcement Learning</category><category>research</category><category>reinforcement-learning</category><category>dqn</category><category>deepmind</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>PPO explained: the reinforcement learning algorithm behind RLHF</title><link>https://thelatentdaily.com/research/ppo-proximal-policy-optimization-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/ppo-proximal-policy-optimization-explained/</guid><description>Proximal Policy Optimization made policy-gradient training simpler and more stable by limiting how far each update can move. How it works and why it is everywhere.</description><pubDate>Sun, 18 Oct 2026 05:13:00 GMT</pubDate><category>Reinforcement Learning</category><category>research</category><category>reinforcement-learning</category><category>ppo</category><category>rlhf</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Towards Monosemanticity explained: finding the concepts inside a neural network</title><link>https://thelatentdaily.com/research/towards-monosemanticity-sparse-autoencoders-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/towards-monosemanticity-sparse-autoencoders-explained/</guid><description>Anthropic used sparse autoencoders to decompose a model&apos;s neurons into thousands of interpretable features. What superposition is, what they found and why it matters.</description><pubDate>Sun, 18 Oct 2026 05:12:00 GMT</pubDate><category>Interpretability</category><category>research</category><category>interpretability</category><category>sparse-autoencoders</category><category>anthropic</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>&quot;Sparks of AGI&quot; explained: what Microsoft claimed about GPT-4, and the critiques</title><link>https://thelatentdaily.com/research/sparks-of-agi-gpt-4-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/sparks-of-agi-gpt-4-paper-explained/</guid><description>Microsoft researchers argued that GPT-4 showed early signs of general intelligence. What they tested, why critics objected and what it teaches about AGI claims.</description><pubDate>Sun, 18 Oct 2026 05:11:00 GMT</pubDate><category>AGI &amp; Frontier</category><category>research</category><category>agi</category><category>gpt-4</category><category>evaluation</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Levels of AGI explained: DeepMind&apos;s framework for measuring progress toward AGI</title><link>https://thelatentdaily.com/research/levels-of-agi-deepmind-framework-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/levels-of-agi-deepmind-framework-explained/</guid><description>Google DeepMind proposed rating AI systems by performance and generality, like levels of self-driving cars. The six levels, the principles behind them and the debate.</description><pubDate>Sun, 18 Oct 2026 05:10:00 GMT</pubDate><category>AGI &amp; Frontier</category><category>research</category><category>agi</category><category>deepmind</category><category>frameworks</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The Pile explained: the open dataset that helped democratize language models</title><link>https://thelatentdaily.com/research/the-pile-dataset-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/the-pile-dataset-explained/</guid><description>EleutherAI&apos;s 825 GB Pile combined 22 diverse text sources into an open training set. What it contained, why diversity mattered and the copyright questions it raised.</description><pubDate>Sun, 18 Oct 2026 05:09:00 GMT</pubDate><category>Datasets</category><category>research</category><category>datasets</category><category>training-data</category><category>eleutherai</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>FineWeb and Common Crawl explained: how web data becomes training data</title><link>https://thelatentdaily.com/research/fineweb-common-crawl-training-data-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/fineweb-common-crawl-training-data-explained/</guid><description>Most language models learn from filtered web crawls. How Common Crawl works, how FineWeb cleaned 15 trillion tokens, and why filtering choices shape model quality.</description><pubDate>Sun, 18 Oct 2026 05:08:00 GMT</pubDate><category>Datasets</category><category>research</category><category>datasets</category><category>training-data</category><category>hugging-face</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How to build an evaluation set for an LLM application</title><link>https://thelatentdaily.com/guides/how-to-build-an-evaluation-set-for-llm-apps/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-to-build-an-evaluation-set-for-llm-apps/</guid><description>The most important tool in AI engineering: how to collect test cases, define success criteria, score outputs automatically and by hand, and run evals on every change.</description><pubDate>Sun, 18 Oct 2026 04:43:00 GMT</pubDate><category>Evals &amp; Observability</category><category>evals</category><category>testing</category><category>developers</category><category>quality</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>LLM-as-a-judge explained: using AI models to grade AI output</title><link>https://thelatentdaily.com/articles/llm-as-a-judge-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/llm-as-a-judge-explained/</guid><description>Language models can score other models&apos; answers at scale. How LLM-as-a-judge works, its known biases, and how to design rubrics and checks that make it reliable.</description><pubDate>Sun, 18 Oct 2026 04:42:00 GMT</pubDate><category>Evals &amp; Observability</category><category>evals</category><category>llm-as-a-judge</category><category>testing</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How foundation models are changing robotics: vision-language-action models</title><link>https://thelatentdaily.com/articles/vision-language-action-models-robotics/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/vision-language-action-models-robotics/</guid><description>Robots that follow instructions like &quot;pick up the extinct animal&quot;: how vision-language-action models such as RT-2 connect web knowledge to physical actions.</description><pubDate>Sun, 18 Oct 2026 04:33:00 GMT</pubDate><category>Robotics &amp; Humanoids</category><category>robotics</category><category>vla</category><category>embodied-ai</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How self-driving cars see the world: cameras, lidar and radar</title><link>https://thelatentdaily.com/articles/how-self-driving-cars-see-cameras-lidar-radar/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-self-driving-cars-see-cameras-lidar-radar/</guid><description>The sensors behind autonomous vehicles, what each does best, how AI fuses them into a picture of the road, and the debate between camera-only and multi-sensor designs.</description><pubDate>Sun, 18 Oct 2026 04:31:00 GMT</pubDate><category>Autonomous Vehicles &amp; Drones</category><category>autonomous-vehicles</category><category>sensors</category><category>lidar</category><category>computer-vision</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How universities are responding to generative AI</title><link>https://thelatentdaily.com/articles/universities-and-generative-ai-policies/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/universities-and-generative-ai-policies/</guid><description>Academic integrity, AI disclosure rules, research use, teaching AI literacy and institutional AI tools: how higher education is adapting to ChatGPT-era students.</description><pubDate>Sun, 18 Oct 2026 04:18:00 GMT</pubDate><category>Education &amp; AI</category><category>education</category><category>universities</category><category>academic-integrity</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>AI for climate: how machine learning helps fight climate change</title><link>https://thelatentdaily.com/articles/ai-for-climate-and-the-environment/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/ai-for-climate-and-the-environment/</guid><description>Weather forecasting, grid optimization, materials discovery, emissions monitoring and climate adaptation: where AI makes a measurable difference for the environment.</description><pubDate>Sun, 18 Oct 2026 04:15:00 GMT</pubDate><category>Environment &amp; Sustainability</category><category>climate</category><category>environment</category><category>ai-for-science</category><category>energy</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The AI language divide: why many languages are left behind</title><link>https://thelatentdaily.com/articles/ai-and-the-language-divide/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/ai-and-the-language-divide/</guid><description>Most AI is built for English and a few major languages. Why low-resource languages lag, what that means for billions of people, and the efforts to close the gap.</description><pubDate>Sun, 18 Oct 2026 04:10:00 GMT</pubDate><category>Accessibility &amp; Inclusion</category><category>inclusion</category><category>languages</category><category>nlp</category><category>multilingual</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>How AI is used in healthcare today: from scans to clinical notes</title><link>https://thelatentdaily.com/articles/how-ai-is-used-in-healthcare/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/how-ai-is-used-in-healthcare/</guid><description>Medical imaging, clinical documentation, triage, drug discovery and patient tools: where AI is already used in healthcare, what the evidence shows and the open risks.</description><pubDate>Sun, 18 Oct 2026 04:07:00 GMT</pubDate><category>Healthcare &amp; Medicine</category><category>healthcare</category><category>medical-imaging</category><category>clinical-ai</category><category>regulation</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>AI in drug discovery: from protein structures to clinical trials</title><link>https://thelatentdaily.com/articles/ai-in-drug-discovery-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/ai-in-drug-discovery-explained/</guid><description>How AlphaFold, generative chemistry and machine learning are changing how drugs are discovered and tested, what has reached clinical trials, and what remains hype.</description><pubDate>Sun, 18 Oct 2026 04:05:00 GMT</pubDate><category>Pharma &amp; 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Space</category><category>science</category><category>weather-forecasting</category><category>astronomy</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The major AI conferences explained: NeurIPS, ICML, ICLR, CVPR and more</title><link>https://thelatentdaily.com/articles/major-ai-conferences-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/major-ai-conferences-explained/</guid><description>A guide to the top machine learning and AI research conferences, what each focuses on, how peer review works, and why conferences matter more than journals in AI.</description><pubDate>Sun, 18 Oct 2026 03:08:00 GMT</pubDate><category>Events &amp; Conferences</category><category>conferences</category><category>neurips</category><category>research</category><category>academia</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Paper of the day: Switch Transformers and the rise of mixture-of-experts</title><link>https://thelatentdaily.com/research/switch-transformers-mixture-of-experts-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/switch-transformers-mixture-of-experts-explained/</guid><description>Switch Transformers showed that routing each token to one expert lets models grow to a trillion parameters without proportional compute. How MoE works and why it matters.</description><pubDate>Sun, 18 Oct 2026 02:53:00 GMT</pubDate><category>Paper of the Day</category><category>research</category><category>mixture-of-experts</category><category>transformers</category><category>scaling</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Quick take: benchmark scores won&apos;t tell you which AI model to use</title><link>https://thelatentdaily.com/articles/quick-take-benchmark-scores-dont-pick-your-model/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/quick-take-benchmark-scores-dont-pick-your-model/</guid><description>Why leaderboard rankings are a poor guide to choosing an AI model for your work, what they do measure, and a simple way to test models on your own tasks.</description><pubDate>Sun, 18 Oct 2026 02:48:00 GMT</pubDate><category>Quick Take</category><category>benchmarks</category><category>model-selection</category><category>evaluation</category><category>opinion</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>AI trends to watch: agents, reasoning, on-device AI and regulation</title><link>https://thelatentdaily.com/articles/ai-trends-to-watch-agents-reasoning-and-beyond/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/ai-trends-to-watch-agents-reasoning-and-beyond/</guid><description>The structural trends shaping AI — agents, reasoning models, falling costs, multimodality, on-device AI and regulation — and the signals that show which are real.</description><pubDate>Sun, 18 Oct 2026 02:42:00 GMT</pubDate><category>Predictions &amp; Trends</category><category>trends</category><category>agents</category><category>reasoning-models</category><category>regulation</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>