c/artificial-intelligence

Hello community, just a short reminder that Raffi Krikorian (CTO @ Mozilla) is live today for an AMA to discuss Mozilla's inaugural State of Open Source AI report. Topics include enterprise adoption, the real cost of "free"models, developer trust, Chinese open models and their impact, agentic AI infrastructure, and the future of open source with respect to Machine Learning & Artificial Intelligence. Drop your questions in the thread here: https://reddit.com/r/MachineLearning/comments/1upxdvc/raffi_krikorian_cto_mozilla_ama_on_the_state_of/ The AMA starts at 1pm ET/10am PT/6PM BST His team reached out to us and he provided proof via Linkedin here: https://www.linkedin.com/feed/update/urn:li:activity:7481380478365880321/ Thank you! submitted by /u/Benlus [link] [Kommentare]

Does anyone know of any evidence/forum/paper analyzing benchmark result differences between cloud inference platforms (togetherai) and running models locally with vLLM under greedy decoding? submitted by /u/No_Cardiologist7609 [link] [Kommentare]

I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above. We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in: roll pitch yaw camera distance object coverage in the frame centering and X/Y shift orientation perspective lighting The photos taken with flagship iPhone. I want to use on-the-fly augmentation to simulate realistic human-hand variation and save our designer from adjusting each time to make it flat. is 100 augmentation combinations per original be useful, or excessive? Should the policy be: mostly isolated transforms, mostly crossover combinations such as orientation + roll + pitch + yaw + coverage + shift, or a controlled hybrid of both? The goal is maximum segmentation accuracy, especially around the object boundary, not speed. I plan to train for around 300 epochs and keep validation and test images unaugmented. submitted by /u/Loganbirdy [link] [Kommentare]

NB: Reposting due to a typo in the title Putting this here instead of the other subs since I figured a question on deep learning theory is out of place there -- I "recently" found (actually, a few months ago but only just got to reading) a monograph claiming to provide a unified theory of deep learning (and possibly SSL) through the lens of information theory, with one of it's headline claims is that you can design a "white-box" (I disagree with that, more on that later) transformer through the principle of coding rate reduction. I looked through the works the book claimed to be synthesizing and got a decidedly mixed picture: a JMLR and a NeurIPS on the one hand, but another frankly terrible paper concerning mechanistic interpretability (with which I am more familiar) published in a venue I've never heard of. And if it means anything, the book itself was endorsed by Kevin Murphy As I've alluded to, I'm more familiar with the interpretability side of ML as opposed to SSL/theory (where this book seems more relevant) so I'm unsure what to make of this. In particular, the apparent result that their bespoke transformer learns image segmentation on non self-supervised tasks seems interesting, I'm not sure how this relates to how machines learn more broadly. Also, their "white-box" transformer consists of a bespoke MLP suspiciously similar to a regular one with a sparsity penalty, and an attention mechanism strictly less expressive than those currently used (obtained by setting Q=K=V=OT.) I know it seems like I've done my research on this topic, but really I've just skimmed a few of these papers (which all seem to be originating from one lab) with no context to situate them in, so some help would be appreciated. Thanks in advance! submitted by /u/Carbon1674 [link] [Kommentare]

Disclosure: I built it, it is open source, Apache-2.0 licensed, and currently alpha. Repository: https://github.com/mireklzicar/gpuhedge I started working on it after benchmarking a 17 GB AI model across several serverless GPU providers. On the primary provider, requests usually either completed in roughly 6–8 seconds or took around 90–122 seconds after a fresh GPU cold start. Simply switching to another provider did not remove the problem because every provider had its own tail. GPUHedge treats this as a speculative-execution problem. It starts a request on a primary provider, watches the job’s lifecycle state, and conditionally launches or switches to a backup. The first result that passes a validator wins, and the losing job is cancelled through the provider’s native API. You can try the policy engines without creating provider accounts or spending money: pip install gpuhedge In the initial benchmark, a fixed RunPod → Cerebrium hedge launched after 10 seconds. On the 36-request evaluation portion, it changed: observed p95 latency from 116.6 s to 29.4 s; requests over 60 seconds from 11/36 to 0/36; modeled active-compute cost from $0.0114 to $0.0083 per request. What is your experience with cold start latency? Which provider to add next? Can something like this help what you are building? submitted by /u/Putrid_Construction3 [link] [Kommentare]

Everyone suddenly seems to be an expert in continual learning. Dario Amodei claimed on the Dwarkesh Patel podcast that continual learning will be achieved by 2026, and Demis Hassabis has called it the most important unsolved breakthrough on the path to more general AI. Two of the most prominent people in the field, and yet I don't think there's even a consensus on what continual learning actually means. I see researchers and startups approaching it in fundamentally different ways. Some frame it as solving catastrophic forgetting. Others treat it as online learning, lifelong learning, or meta-learning. The goalposts keep shifting depending on who's talking. What I'm trying to wrap my head around is: what does continual learning actually require, and why is it so central to AGI? Is the bottleneck architectural, is it a data problem, or is it something more fundamental about how we evaluate and benchmark it? Would genuinely appreciate different perspectives, whether you think it's overhyped, underappreciated, or just poorly defined. Pointers to papers or frameworks that cut through the noise would also be welcome. submitted by /u/watercolorer2024 [link] [Kommentare]

LLM's seem to make it so much easier to run experiments, write papers, etc. As a result, are we seeing phd students finish their phds sooner than ever before specifically in CS? If not, why not? submitted by /u/Alone_Reality3726 [link] [Kommentare]

Read a long piece on the future of LLM reasoning that makes a provocative claim: Chain of Thought is a useful hack but we've started to confuse a readable trace with the actual computation. All in all, "generating text is not the same as thinking." There are two practical problems here: Faithfulness: CoT style traces can decouple from what the model actually "did." u can get plausible steps with a wrong answer, or messy steps with a right answer (so the trace isnt a reliable audit trail) Systems cost: Autoregressive reasoning serializes intermediate work into tokens. Longer traces inflate latency, cost and context usage The latent turn (stop making models "think in public"): A lot of recent work is shifting the inner loop into latent space and decoding language only at the end: Coconut (continuous / latent "thought" steps) HRM / HRM Text (separating slower planning from faster recursive execution) RecursiveMAS (agents passing latent embeddings instead of long text messages) A framing i propose: language as interface vs language as the compute substrate. I agree that language is essential for communication and abstraction but forcing search / constraint solving to be serialized into text is awkward and expensive. The black box wall If the "thinking" happens in dozens of latent loops, u lose the already imperfect window u had with CoT. In production, especially in high stakes domains, "no visibility" is a real blocker. One proposed solution is an outer loop governance layer (e.g., a symbolic / planning manager that builds an auditable DAG of subgoals + deterministic verification at each node: unit tests, constraints, rules, etc.). Auditability shifts from "read the model's inner monologue" to "audit the plan + checks + verified outputs." Where BDH fits BDH (Dragon Hatchling) is interesting in this landscape because it aims to keep language modeling capability while adding recurrent or stateful latent computation, rather than being "just" a supervised puzzle solver. Pathway reports 97.4% top 1 accuracy for a BDH based system on ~250k Sudoku Extreme puzzles, without CoT or solution backtracking. Sudoku is a useful diagnostic for constraint solving but not a complete measure of general reasoning. One point i found clarifying: many recursive latent reasoners excel at depth recurrence (iterating on a fixed snapshot of the problem) but real agentic / language settings are a stream: new tokens arrive continuously. That introduces time recurrence questions: when do u advance time, and what state / memory do u carry forward? BDH's stated research direction is basically trying to bring these together i.e. high bandwidth latent iteration and a principled state / memory story over time. It also provides a recoverable graph view and sparse, localized state, offering some native interpretability hooks but that is complementary to, not a replacement for, system level verification. I want views on: Is CoT increasingly a costly interface artifact rather than a scalable reasoning path? For high stakes use, do we inevitably need a DAG / verification outer loop, or can native model analysis hooks meaningfully reduce the governance burden (even if they cant replace it)? If latent recursion is the inner loop, what should the outer loop be in practice, DAGs, unit tests, formal specs, proof assistants, something else? submitted by /u/meowsterpieces [link] [Kommentare]

Left: Telegram digest (optional); Right: detailed digest on HTML Like probably everyone here, my to-read list only grows. Skimming arXiv listings or my feeds takes 30-60 minutes a day, 95% of it is irrelevant to what I actually work on, and newsletters don't really help: they surface what's popular, not what's relevant to my research. So I built Research Radar, a daily cron job that: Fetches every new paper in your arXiv categories (RSS + API, deduped) Scores every abstract 1-10 against a markdown file describing your research interests (cheap model, batched) Deep-reads the top scorers: downloads the PDF, extracts full text, and a strong model writes a summary, key insights, limitations, and how it relates to your own work Delivers a morning HTML digest + optional Telegram ping with the must-reads Design decisions: Nothing domain-specific in the code. Your interests live in one markdown file. Edit it and the same pipeline works for ML, physics, bio, econ, whatever Only the two scoring passes touch a model. Fetching, dedup, PDF extraction, and rendering are deterministic Python Model-agnostic backend layer: Claude Code / Codex CLIs run it on the subscription you already pay for (no API key), or any OpenAI-compatible endpoint, including fully local via Ollama / vLLM. Backends mix per pass: cheap model for skimming, strong one for the 5-10 deep reads Approximate costs, benchmarked in the repo (tokens, cost, latency, quality grades per model). Rough sizing: a 10-abstract scoring batch is ~18k input tokens with a small JSON out; a deep read sends the whole paper, 40-70k input tokens. I've been using it daily for about a month and it's been genuinely useful in my field. The repo is a generalization of my personal setup to any domain, and I haven't deeply tested fields other than mine, so feedback and GitHub issues are very welcome. The model has to say "not relevant, 3/10" a lot without drifting toward score inflation, and the whole scoring is just prompt + markdown context. So I'm curious how others would approach calibrating an LLM judge like this GitHub: https://github.com/ramazan793/research-radar submitted by /u/usedtobreath [link] [Kommentare]

My TMLR paper was assigned to reviewers on April 23, and as of July 13 I've received 2 reviews, but the third is still pending. The discussion phase hasn't opened yet, so I can't respond to the existing reviews. Is this normal for TMLR, or is it reasonable to send the Action Editor a polite status email? asking for the 3rd review, apprecite the suggestions submitted by /u/Ok_Ant_4311 [link] [Kommentare]

Boa pessoal tudo bem? Contar aqui um pouco da minha carreira. 6 anos de xp na área de distribuição de energia, me formei em eng elétrica em 2019 em faculdade particular não tradicional(). Comecei na ENEL SP como analista Jr, na área de perdas, no desenvolvimento de algoritmos preditores para descobrir potenciais furtos de energia, foi ali que me apaixonei por data Science, e comecei estudar por conta proporia para me aprofundar em Python / ML afins, não fiz pós na área pois consegui aprender tudo via livros/ YouTube / ChatGPT, talvez seja importante fazer algo mais pra frente… mas enfim.. fui até o cargo de analista Sr.. na mesma área.. depois surgiu a oportunidade para migrar para a distribuidora de Goiás.. que é a Equatorial , onde fui como Engenheiro, trabalhando em um dos processos mais críticos do setor elétrico de distribuição, que é a apuração de indicadores e etc… com meus conhecimentos em ML , consegui desenvolver alguns algoritmos lá que trouxeram ganhos significativos para o processo como um todo que abrangia toda área de concessão da equatorial no pais(7 distribuidoras)…. Faz uns 3 meses que voltei para SP, pois sou daqui e estava sentindo falta da família, pois lá em Goiânia estava morando sozinho.. (fiquei quase 4 anos la)… Atualmente trabalho como engenheiro em uma grande transmissora de energia daqui. Ganho na faixa de 15k bruto. Tenho bons benefícios 2k de VR… PLR.. plano de saúde bom.. etc.. Porem tem um problema, já estou cansado do presencial, o setor de energia elétrica é um setor que paga bem, mas ainda é um setor muito arcaico em algumas coisas, principalmente em modelo de trabalho.. eu tenho 2 dias de home office, mas eu queria algo full remoto… Venho pensando seriamente em migrar para área de AÍ Eng.. para galera que está na área, a pista está salgada ? E como está essa questão da transição de carreira? Hoje eu tenho 33 anos, e até cogito perder um pouco de remuneração por esse benefício do full remoto… e quais seria o roadmap para uma transição mais tranquila? Visto que já domino um pouco de programaçao… em linhas gerais são isso, procuro mais flexibilidade na minha vida. Sei que se quiser ficar onde estou vou ganhar bem e conseguir me aposentar lá, que em tese é um setor que sofre menos com layoff e tal… mas aí está a questão. Vale a pena a longo prazo ? Mentalmente falando. submitted by /u/ExpertTangerine6080 [link] [Kommentare]

Anthropic’s Jacobian Lens work introduced a way to inspect verbalizable representations inside language models. Follow-up experiments suggested that entropy in this internal “workspace” might help identify confidently incorrect answers. I tested that hypothesis on Qwen3-4B across ~11,400 examples from seven distinct datasets, including TriviaQA, PopQA, NQ-Open, TruthfulQA, HotpotQA, GSM8K, and CommonSenseQA. Three main findings: It can complement output confidence on factual retrieval. On datasets such as PopQA, workspace entropy sometimes improved error-routing precision at low review budgets, particularly among answers that were already high-confidence. It does not reliably detect internalized misconceptions. On TruthfulQA, workspace entropy was substantially weaker than output confidence. Incorrect answers could still have a clean, low-entropy internal representation. Its calibration is highly task-dependent. A threshold calibrated on TriviaQA failed on GSM8K because correct mathematical reasoning had much higher baseline entropy. Multiple-choice formatting also weakened the signal substantially on CommonSenseQA. The overall result is narrower than “internal entropy detects hallucinations”: it may be a useful complementary routing signal for confidently incorrect factual answers, but it does not behave like a task-general error detector. This is currently a single-model study, so cross-model validation is the most important next step. The repository contains the full methodology, limitations, raw data, metrics, plots, and reproducible notebook: https://github.com/dasjoms/jspace-hallucination-eval I‘d be interested in feedback on the experimental design if anyone feels like giving their thoughts. Note: I already posted this to r/LocalLLaMa yesterday but think this might also fit here. submitted by /u/dasjomsyeet [link] [Kommentare]

Hi everyone,Our paper was recently provisionally accepted to ECCV 2026! However, our team is facing an issue regarding attendance and the ECCV 2026 Submission Policies. The official guidelines state: "We expect each paper to be presented in person by an author (or an authorized delegate)."None of the listed co-authors can travel to present the paper in-person due to pending immigration status (USA). I need some advice on what exactly counts as an authorized delegate and how to handle this safely without getting our paper pulled from the Springer proceedings.Who qualifies? Can it be anyone, a colleague from my lab who is already going to ECCV, or does it have to be someone specifically registered under our paper's ID? Registration policy: According to the ECCV 2026 Registration Info, every paper must be covered by a full (non-student, non-virtual) author registration by July 17, 2026. If we pay for the full author registration but a "delegate" presents it, does that delegate also need their own separate registration? How to notify: What is the formal process to authorize a delegate? Do we need to email the Program Chairs in advance? If anyone has designated a delegate for ECCV or similar computer vision conferences (CVPR/ICCV) in the past, how did you handle it? TL;DR: No authors can attend ECCV 2026 in-person. Need to know how to legally assign an "authorized delegate" to present our paper so it doesn't get removed from the proceedings. submitted by /u/Latter-Sympathy7767 [link] [Kommentare]

I'm considering which certificates are worth the time and money to support my own ML software firm and strengthen my credentials. There is a sea of suggestions online coming from a range of sources that say you don't need a university degree to do this and just need to do "these" courses. What courses do you guys suggest? My current list below is where I'm at presently: Cloud MLOps & Architecture Baseline Professional Machine Learning Engineer by Google: https://cloud.google.com/learn/certification/machine-learning-engineer Microsoft Certified Azure AI Engineer Associate: https://aiskillsnavigator.microsoft.com/credentials/cert-42345e89c4ff32c631414873b457485bf392224af38ac852604946f2655e5782 AWS Certified Machine Learning Specialty: https://aws.amazon.com/certification/certified-machine-learning-specialty/ Deep Technical Competency Certificates IBM AI Engineering Professional Certificate: https://www.credly.com/org/ibm/badge/ibm-ai-engineering-professional-certificate Deep Learning AI Machine Learning or Generative AI Specialisations by Andrew Ng: https://www.deeplearning.ai/specializations/machine-learning Trust, Risk, and Enterprise Governance IAPP Certified AI Governance Professional (AIGP): https://iapp.org/certify/aigp ISO/IEC 42001 Lead Auditor/Practitioner: https://www.bsigroup.com/en-AU/training-courses/iso-42001-lead-auditor-practitioner-qualification/ submitted by /u/cam-douglas [link] [Kommentare]

"Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity" This paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: "changing the prompt this way led to more diverse sampling". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this. Even if it works, I’m not sure this kind of prompt engineering belongs at a top-tier machine learning conference. Some people seems to call this kind of work “modern machine learning”, but I think it should be categorized as less technical venues. How do you think? Am I being too rigid? submitted by /u/Mean_Revolution1490 [link] [Kommentare]