submitted by /u/BleakBeaches [link] [comments]

I’m a data scientist (more like an analytics engineer) in pharma. My background is clinical - I went to school for a healthcare degree and then went into research before coming into data. With that, I have about a decade of experience now doing a little bit of a lot of things - statistics, epidemiology, data/analytics engineering, data visualisation, product management, data governance, etc. Over the course of the last few years, I’ve felt a little stagnant - currently I’m not really doing anything that feels all that important lol, I mean essentially my team was building data pipelines and im redesigning the process for projects that are already ongoing or near completion. The only good thing is I have some downtime which gives me an opportunity to explore different teams and projects. There’s 3 teams I’d like to work with but I can’t work with them all at the same time and have to figure out how best to prioritise each team and allocate time so I can get better exposure Team 1 - a data science team that focuses on a specific disease area, the opportunity would be to continue working in data science while staying close to the business side of our projects by developing a deeper understanding of clinical context. Team 2 - Generative AI engineering - this would be more technical and I probably can’t work with this team right away until I get up to speed with learning concepts like embeddings, chunking, RAGs which I’ve never done before. Team 3 - the downstream users of my data pipelines who apply ML/AI techniques to the data for biomarker discovery Just wanted to hear insights in terms of an industry perspective, which teams would be the best to work on a project with submitted by /u/thro0away12 [link] [comments]

Schema, the harness we introduce today, reaches 99% on the ARC‑AGI‑3 Public set using Claude Opus 4.8 and Fable 5, and 95.35% using GPT‑5.6 Sol. It does not change the underlying model weights. Instead, it changes the process around them: how observations are turned into a working model of the game, how predictions are tested against the interaction history, and how plans are executed and revised. Both scores come from a fixed fallback rule: Opus 4.8 and Sol xhigh run first; games scoring below 80 are rerun with Fable 5 and Sol max, respectively, and the higher per-game score is retained. https://schema-harness.github.io/ The president of ARC Prize tweeted this saying "Looks cool - need to dig into it" I'm not affiliated with ARC Prize, or with this team. I'm posting this to try to bring back technical discussions to this community. submitted by /u/we_are_mammals [link] [comments]

i'm subscribed to a ai/ml newsletter but i feel like its not enough. i need a complete and not too time consuming way to keep up with ai/ml news because i feel like im left behind. thanks in advance submitted by /u/mehmetflix_ [link] [comments]

Hi everyone, I've been working independently on a recurrent architecture called **DABSN (Dynamic Adaptive Bias State Network)** for the past several months, and I finally reached the point where I feel comfortable sharing the first preprint. The paper is mainly about the architecture itself and its behavior on reasoning, memory, and long-sequence benchmarks (MQAR, Copy, Key-Value retrieval, A5/60, etc.). The code is also public with PyTorch, C++, and Triton implementations so everything can be reproduced. While finishing the paper, I also trained my first language model with the same cell: - 24M parameters - 1B pretraining tokens - GPT-2 tokenizer Those results ended up being much more interesting than I expected, so I'm now writing a second paper focused entirely on language modeling, long-context behavior, and scaling. This is where I'd love some help. I'm looking for people who might be interested in collaborating on the next paper, whether that's: - independent reproduction of the results, - helping design stronger baselines and evaluations, - or having access to larger GPU clusters so we can scale the architecture much further than I can on my own. Everything I'm doing is intended to be open and reproducible from day one. I'd really appreciate any feedback on the paper, and if the project sounds interesting, I'd love to chat. Preprint and Github are in the comments. submitted by /u/BleedingXiko [link] [comments]

Call for Papers and Demos Real-Time Conversational Agents (RTCA): Toward Natural Multimodal Interaction 1st RTCA Workshop [@]() NeurIPS 2026, Sydney, Australia 11 or 12 December 2026 Website: https://rtcaneurips26.github.io/ We are pleased to share the Call for Papers and Demos for the inaugural RTCA Workshop at NeurIPS 2026, focused on real-time multimodal conversational agents: streaming speech, video, and language generation; naturalness in interaction; and evaluation of live systems. Conversational AI has moved from text chat into the real world, voice modes that talk back, embodied avatars, agents that share our screens and tools. To feel natural, these systems must operate in real time, streaming while continuously listening, watching, and re-planning. This is fundamentally harder than offline generation: latency, turn-taking, backchannels, interruptions, and cross-modal alignment become first-class problems that the offline paradigm sidesteps. Recent progress on full-duplex speech–language models, real-time talking-head generation, and streaming ASR shows the regime is feasible, but the field still lacks shared benchmarks, vocabulary, and methodology for interactional naturalness. RTCA brings together researchers across speech, vision, language, HCI, social-signal processing, and ML systems around three intertwined questions: real-time generation under hard latency budgets, naturalness in interaction, and evaluation of live systems. Topics of Interest We invite original contributions on topics including (but not limited to): Streaming/low-latency speech synthesis, ASR, and full-duplex audio–language models Real-time talking-head, avatar, and embodied video generation; lip-sync, gaze, expressivity under streaming Streaming language models; incremental and speculative decoding for dialogue Turn-taking, backchanneling, interruption handling, and floor management Multimodal alignment under latency and partial-observation constraints Prosody, emotion, and paralinguistic generation in interactive settings Memory, grounding, and tool use during live conversation Evaluation of naturalness: perceptual studies, turn-taking metrics, perceived latency, interactive Turing-style tests Datasets and benchmarks for interactive (not offline) evaluation Efficient inference, on-device deployment, and the systems–quality trade-off Safety, identity, and trust in real-time agents (deepfakes, persuasion, consent) Submission Types We welcome: Full papers (up to 8 pages) — may be presented as posters and/or contributed talks. Short papers (up to 4 pages) — work in progress or focused contributions. Demo papers (Extended Abstracts or up to 2 pages) All submissions must use the NeurIPS 2026 style file and be formatted for double-blind review. Page limits exclude references and appendices. Papers must be submitted in PDF format via OpenReview (portal link to be published on the workshop website). The workshop is non-archival; authors retain the right to publish elsewhere. Important Dates (End of day, Anywhere on Earth) Call for papers opens: 18 July 2026 Submission deadline (papers and demos): 29 August 2026 Author notification: 29 September 2026 Workshop date: 11 or 12 December 2026 Organisers Niki Foteinopoulou — Tavus, United Kingdom Alessandro Conti — Tavus, Italy Jack Saunders — Tavus, United Kingdom Oya Celiktutan — King's College London, United Kingdom Cigdem Beyan — University of Verona, Italy Ioannis Patras — Queen Mary University of London, United Kingdom For more information, visit our website https://rtcaneurips26.github.io/ or contact us at [rtca-workshop@googlegroups.com](mailto:rtca-workshop@googlegroups.com). We look forward to your contributions! submitted by /u/Few-Ferret9700 [link] [comments]

Recently I got an automated email from a company I’d applied to, asking me to do a 2 hour online assignment. It didn’t give specifics, just said SQL and Python. When I started it, it turned out to be SQL plus a full end to end model development project. I think I did fine since the problem itself wasn’t too difficult and it’s something I do daily. This was followed by an automated rejection email. So I spent hours prepping and doing an assessment without ever talking to a human. It made me question my own decision to sign up for this. This was a giant corporation, by the way, not a startup or anything. What do you all do in this situation? submitted by /u/Lamp_Shade_Head [link] [comments]

the context is that I am exploring a few recurring problems to solve especially around forecasting and working with time series data but setup a simple open source project around those. my question is primarily about how is everyone handling their official datasets when trying new tools - do you not care, do you remove any identifiers then upload, do you create synthetic data with exactly same properties as the og dataset? happy to answer more questions if this is not clear enough. submitted by /u/shivamchhuneja [link] [comments]

While writing an essay about AI memory and persistent context, I started wondering whether current AI memory systems are optimized for the right thing. Current AI systems already maintain forms of persistent context through saved memories, conversation summaries, user preferences, project notes, and similar mechanisms. These memories are primarily descriptive. They help the system remember facts about the user and previous interactions. But suppose future systems evolved in a different direction. Instead of primarily storing facts and preferences, imagine the persistent context being continuously refined and restructured to infer higher-level patterns such as recurring explanatory frameworks, preferred abstractions, and characteristic reasoning styles. For example, rather than remembering: "This user is interested in economics." "This user works in engineering." the system might gradually infer: "This user tends to explain economic outcomes through incentives and institutional constraints." "This user tends to understand complex systems through interactions and feedback loops rather than by analyzing individual components in isolation." In such a system, persistent context would become less like a collection of notes and more like an evolving model of how the user understands and interprets problems. Could representations like this emerge naturally from sufficiently capable AI systems, or would they require architectures fundamentally different from today's memory, retrieval, and summarization approaches? submitted by /u/Boris_Ljevar [link] [comments]

https://arxiv.org/pdf/2607.13511 the core idea is, we cannot have ternary PTQ with fixed matrix size, trying to do that is dead end. so i tried decomposing the matrix to 2 ternary matrices and inner diagonal scaling matrix. now that the inner rank can be arbitrarily large the accuracy can be arbiratily small. and its not that it has to be very large too i also showed that it does take only slightly more vram then current quantisation methods. the slight more vram is worth it if we abuse the ternary math. submitted by /u/LMTLS5 [link] [comments]

What is the shared structural essence that underlies a pair of MRI contrast spaces? Explicitly modeling this contrast-invariant latent “content” unlocks a powerful multi-contrast reconstruction algorithm that is competitive with state-of-the-art unrolled networks while: PnP-CoSMo: A plug-and-play framework for multi-contrast MRI reconstruction based on content/style modeling. The first stage learns the content/style model from purely image-domain data. The second stage freezes this model and applies it as a powerful prior in iterative reconstruction. Requiring no raw k-space training data (which is a serious data bottleneck in the ML-based MRI world), Being generalizable across different MR contrasts and forward operators by design, and Offering a built-in explanatory framework. In our paper now published in Medical Image Analysis, we introduce PnP-CoSMo. Read the substack article here (with links to the MedIA paper and code): https://cnmyro.substack.com/p/pnp-cosmo-a-plug-and-play-method submitted by /u/void_gear [link] [comments]

Every qlora tutorial on earth says start at 2e-4. Unsloth docs, hf examples, the paper itself. and for small datasets i now think that numbers is a trap. Where does 2e-4 come from? alpaca. 52k samples. cool, except most of us are fine tuning on 5-10k samples we scraped and labeled ourselves, not 52k. at that size the model overfits inside epoch one and then youre just watching training loss go down all pretty while eval lost sits there doing nothing. or climbs. I burned close to three weeks on this. recleaned the data set twice. rewrote the prompt template twice. spent on entire sunday hand relabeling rows while my flatmate watched football next to me (started with 8k rows, ended around 7200 after cutting garbage, i think, didnt log it properly). eval did not move. you you know what’s worse than a bad eval? seven identical bad evals in a row. Then i changed one number. 2e-4 down to 1e-4, epochs 3 to 5. eval jumped more than everything else combined. i sat there refreshing wandb thinking it was a fluke. three more runs, same story. And the annoying part, unsloth literally calls 2e-4 “a starting point” in their own docs. but every shared notebook has it hardcoded, zero comment. so people copy paste, get garbage, blame their data, blame their rank, lose a week. ask me how i know lol. My rule now. above 30k 2e-4 is probably fine. under 10k, start at 1e-4 or lower and add epochs. in between, actually tune it, its one number, takes an afternoon. If there’s real research defending flat 2e-4 on small data i want to read it. and if you all quietly figured this out in 2024 and never posted about it, im mad at every one of you individually. submitted by /u/Pretty-Ad774 [link] [comments]

Just saw the ECCV registration fees and I'm shocked, student registration is 440 USD for early bird, and the worst thing is that you can't even do the student registration if you're presenting a paper there, a paper has to be covered by a FULL registration which is 805 USD How are they literally punishing us for getting a paper accepted? We even applied for travel grant and a registration waiver as students just to get rejected. Is there anything we can do? Some advice would be really helpful submitted by /u/NotGondor [link] [comments]

I'm working on a project with summarized data from ~40 studies (Excel) involving different protocol variables (durations, intensities, recovery times, frequency, total duration, etc.) and response outcomes conditional on a baseline variable (range ~30-85 units). The aim is to fit a continuous response surface using a hierarchical approach to separate protocol effects from baseline effects, then perform continuous numerical optimization (not grid search) for three objectives: - Total improvement - Improvement per unit time (e.g. per week) - Improvement per unit effort/work Outputs should be fine-grained continuous values rather than rounded study parameters. There are also domain-specific physiological constraints to respect. I'm on a Chromebook with a little Python experience, so Colab-friendly solutions would be ideal. Current candidates I'm considering: PyMC for hierarchical modeling, pymoo + pysamoo for surrogate-assisted MO optimization, SMT for surrogates, or Matlab Global Optimization Toolbox. What is the strongest stack in 2026 for this kind of workflow? Any recommended notebooks, tutorials, or similar applied examples? Are there any AI tools that currently do this without the traditional work of python? Meaning I can upload the spreadsheet give a parameters and it will come up with data.? submitted by /u/BleakReason [link] [comments]

Three years ago I was able to pivot from engineering (no coding) to data science. I’ve been working here at a job I love, with an awesome team & boss and with a great pay. I’m 47, so not the youngest. Now for family reasons I must leave and move back to my country of origin. The thing is that, although I love the field and I keep reading books and trying to learn every day, this is such a vast field that I don’t think I’m ready at all. In these 3 years I’ve done basic ML projects, lots of xgboost, random forests, anomaly detection, dealing with pySpark with PB -sized dataframes, etc. But putting those models to production was made by my more experienced colleagues. So, while I can say I’ve learnt on each project, I also see how MUCH I lack compared with my teammates, with 10+ years exp. on the field. Today I just started looking for DS jobs and I just felt so depressed. Many ask for a DS who can do the whole thing from cleaning data to taking the models to production. I have no idea of that and it sounds extremely intimidating. I also dread the interview because while I can code, I often use LLMs for things that due to the pace of work, I simply decided to do with AI, i.e.: I understand and can read window functions but I’d need an AI to write them because I can never keep the sintaxis in mind. If an interviewer sees me struggling with the syntax, the interview is done I feel very “green” to compete out there with other “proper” data scientists who have a well defined experience and knowledge. I wouldn’t mind applying to junior jobs but due to my age, most companies here wouldn’t hire a 47yr old “junior”. I’m not able to work my old job in the location we’re moving to because it’s very niche and only a certain sector and a certain company size hires for that, and that isn’t there in our new area. I don’t know what to do, or if this is normal and everybody feels like that. Or maybe you guys have any advice… Anything you can come up with will be highly appreciated because I need to provide for two kids and I just don’t know how. I’m beginning to feel desperate submitted by /u/Davidat0r [link] [comments]

Those received mentorship, did you find it worth your time in general? In my early career, I actively participated in mentorship program at my alma mater. However, most if not all students wanted to work in tech. Considering I didn't (and still don't) work in tech, and that my employer was rarely hiring, I felt there was not much value I could provide. There's also this self-selection process at play, which is those who seek mentorship tends to already have a good idea of what they should be doing. Now a decade into my career, my expertise is super irrelevant to people in a different industry. I'm also oblivious to the entry-level job market requirements. Recently, my alma mater reached out again for mentors. I've skipped the last few requests but think maybe I should ask y'all before turning it down again. To clarify, I'm not asking if it's worth it for me. I'm wondering if it's worth it for the students to speak with someone unfamiliar with entry-level job market, has expertise in a not niche but definitely not popular domain, and definitely don't lead to job opportunities. Edit: I messed up somewhere that two posts were created. 0_o submitted by /u/Thin_Original_6765 [link] [comments]

I am currently doing research on world models, specially in tje field of robot learning, and, as probably most of you alredy know, JEPA-like models are mentioned over and over. I read the main recent papers from lecun as well as other research groups, and I personally think the whole approach is very promising and can really go somewhere. But after listening a bunch of the recent Y Lecun conferences his ideas looks even too cool compared to "literally everything else" (as he's dissing LLM, RL, etc and pitching his ideas are the "only next big things"...). So I am asking myself if there are red flags about his approaches that I do not see yet and maybe I need somebody being the "devil advocate" with whom breaking down ideas. Where do you think are the biggest downside of this models, compared to other world models approaches? submitted by /u/Amazing-Coat5160 [link] [comments]

submitted by /u/rhiever [link] [comments]

Hi folks, There is now a dedicated Robotics page on Papers with Code that lists the major benchmarks, trending papers with linked code, and open-source artifacts. Find it here: https://paperswithcode.co/tasks/robotics https://preview.redd.it/wvhcgu1q1fdh1.png?width=3034&format=png&auto=webp&s=9a43021f7b8e1b840b09f55bb8aee0c4b2d54215 Major benchmarks that most papers report evaluations on are: - LIBERO, as well as its subsets like LIBERO-Long and LIBERO-Spatial - SimplerEnv WidowX - RoboTwin and more. Currently, we have about 110 entries on each benchmark. Each benchmark's progress is visualized over time: https://preview.redd.it/myl3ysvv2fdh1.png?width=2880&format=png&auto=webp&s=3fae2b73d592f2c7ddd3118ec2c5a0d5c5e93b65 We also show which models are open source and which aren't. Let me know which others I missed. I'd be happy to add them. Also happy to hear any feedback, new tasks, or features to add! Kind regards, Niels ML Engineer @ HF submitted by /u/NielsRogge [link] [comments]

So, from what I've seen across twitter(x) and reddit, I've inferred that we'll be seeing NeurIPS reviews drop on July 22nd 5:30 pm AoE(Anywhere on earth), what's your thoughts to those who've submitted to NeurIPS 2026 ? Would love to hear your opinion by the reviewers, the people who've submitted to the workshops (who should've already gotten their decisions too by now I think) and to the main tracks and other available tracks. submitted by /u/Practical-Buddy6323 [link] [comments]