machinelearning
machinelearning on Beyond Market Intelligence: a running collection of 24 stories we have gathered and hand-picked because they are worth your time. Every post here touches on machinelearning in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around machinelearning, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
[D] Simple Questions Thread
Centralize your spreadsheet questions in this dedicated thread to streamline our community’s knowledge sharing. Instead of initiating new posts, please direct all inquiries here—we’ll keep this thread active until the next iteration, encouraging ongoing collaboration. A huge thank you to everyone who contributed to the previous question thread! For deeper insights into related topics, explore how AI is transforming data accessibility, as highlighted in our recent piece on Clipto.
Is anyone esle going to ECCV and wants to get in a groupchat for socials? [D]
Heading to ECCV and seeking connection? This post highlights a common challenge: navigating a large conference when you're not part of a sizable team. One user is actively seeking others to connect with for informal socials and proposes a group chat to facilitate spontaneous gatherings. If you're in a similar situation and looking to expand your network at ECCV, reach out via DM!
Google CS PhD Fellowship 2026 [R]
The Google CS PhD Fellowship 2026 [R] decision notifications are anticipated around August 31st for candidates primarily in North America, though updates may vary. This thread serves as a central hub for applicants to share their outcomes—approved or rejected—as they receive them. Early reports are welcome! For those exploring AI-assisted coding practices alongside their research, consider our guide, "How to Work with AI Coding Agents," for practical insights into maximizing code quality. We’ll continue to update this space as more information becomes available.
ECCV 2026- MALMO LUND TRAVEL PASS NOT AVAILABLE? [N]
A query has arisen regarding ECCV 2026 travel passes: specifically, the apparent removal of the combined Malmö-Lund option. Users report the discounted pass was visible recently but is now limited to Malmö only, with the purchase deadline approaching on August 28th. Confirming availability is crucial for attendees planning to stay in Lund. For those navigating conference logistics, our recent article on "Travel and stay accommodation for EMNLP" may offer relevant insights into planning and securing arrangements.
Discussion thread for EMNLP 2026 Notifications/Results [D]
EMNLP 2026 notifications and results are expected to be released today – wishing everyone the best as they gather in Budapest! This thread serves as a central hub for discussion surrounding these announcements. We anticipate a lively exchange as the community processes the outcomes. For context, recent developments in AI integration with spreadsheet tools are impacting workflows; for example, Microsoft is retiring the COPILOT function in Excel. Explore the thread for updates and share your insights.
TMLR Relevance and Prestige [D]
Acceptance to *TMLR* signifies a notable achievement in machine learning research. While *NeurIPS*, *ICLR*, and *ICML* consistently rank as the highest-tier AI conferences, *TMLR* (Transactions on Machine Learning Research) holds considerable prestige as a respected journal. It’s generally considered on par with *JMLR* (Journal of Machine Learning Research) in terms of rigor and impact. Securing publication in *TMLR* demonstrates a commitment to well-validated, theoretically sound work. For further insights into transparency in algorithmic ranking, explore our article on X’s open-sourcing of its ranking algorithm.
2026 NeurIPS: Where are you going? [D]
Navigating the 2026 NeurIPS landscape presents a key decision for US-based attendees: Sydney or Atlanta? This year's conference offers compelling options, prompting many to consider logistical and professional priorities. We’ve observed considerable discussion around this choice, mirroring broader questions about the future of AI research and collaboration. For those seeking deeper understanding of underlying methodologies, our recent article, "I never understood positional encoding until I read this article," offers valuable insights. Ultimately, planning ahead ensures a productive and enriching NeurIPS experience.
CIKM '26 Notification [D]
The results are in for CIKM '26! We're pleased to announce acceptances from our submissions, with 3 out of 6 full papers and 1 out of 3 short papers moving forward. A strong showing reflecting the innovative work being done in the field. For those seeking further context on related trends, consider exploring our piece, "2026 NeurIPS: Where are you going?" – a timely look at conference planning. Congratulations to all submitters and we look forward to seeing these contributions come to life.
AACL-IJCNLP Commitment Submission Number [D]
The AACL-IJCNLP commitment window has closed, and we’re tracking submissions to understand community engagement. Our team is currently compiling the total commitment count, with submission #150 among the recent entries—several users committed near the deadline, indicating sustained interest. We appreciate the proactive participation! For related perspectives on the broader AI research landscape, explore our recent piece, "73 NeurIPS workshops, and not a single one on Causality," which examines trends in causal inference research.
ECCV workshop, camera ready instructions? [D]
Navigating workshop camera-ready submissions can be surprisingly opaque. Many organizers, like those for ECCV, lack readily available instructions, leaving authors understandably uncertain. While some workshops facilitate PDF uploads via OpenReview, crucial details regarding copyright forms and LaTeX source files remain unclear. To ensure a smooth submission process, proactively seek clarification from the workshop team. For broader context on AI-driven workflows and infrastructure supporting research, explore our recent article, "Exclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI."
![I never understood positional encoding until I read this article. [D]](https://external-preview.redd.it/8VRAO7Ucarn-CBc4IsyH3p3Lg1nOM6BC8ccLAEFnSlc.jpeg?width=640&crop=smart&auto=webp&s=8584413aed8556960dd7528b26ce8adaaa9f97b0)
I never understood positional encoding until I read this article. [D]
Many find positional encoding in AI models initially perplexing, but as one user discovered, clarity *is* attainable. This insightful article, shared by /u/ImaginaryRea1ity, demystifies the concept, offering a valuable resource for anyone grappling with its intricacies. It's a welcome explanation for a fundamental aspect of transformer architectures. For a broader perspective on the limitations of purely theoretical AI, explore our related piece, "Non-Physical Intelligence Has A Ceiling."
NeurIPS 2026 Concept & Feasibility Track [D]
Navigating the NeurIPS 2026 Concept & Feasibility (C&F) Track presents unique challenges, particularly regarding reviewer engagement. Initial submissions often receive praise for originality, yet concerns about experimental scope—a permissible outcome per track guidelines—can stall progress. A recent discussion highlights a concerning lack of reviewer response even after rebuttal, raising questions about the track’s visibility and author experiences. Explore insights from fellow researchers and a deeper analysis of post-rebuttal score distributions, as detailed in our "NeurIPS 2026 post-rebuttal score distribution poll."
NeurIPS 2026 post-rebuttal score distribution poll [D]
Curious about the NeurIPS 2026 post-rebuttal score distribution? With discussions surrounding potentially lower scores this year, a quick poll aims to gauge the average score breakdown after the rebuttal phase—excluding confidence weights. This is a preliminary look, acknowledging inherent self-selection bias. Share your vote here: [https://loppy.be/poll/yczuv8yo](https://loppy.be/poll/yczuv8yo). For deeper insights into NeurIPS trends, see our related article, "NeurIPS 2026 Main Track — Theory papers score tracking post Rebuttal [D]," for specific analysis.
EMNLP Commitment Submission number [D]
EMNLP Submission [D] from /u/Huge_Argument_6979 presents a commitment focused on exploring the scale of commitments within the conference. With an estimated 4,000 submissions, this initiative seeks to understand the breadth of projects and contributions. It’s a valuable opportunity to assess the community's engagement and the diverse directions of AI-native spreadsheet technology. For those interested in sharing their own projects and initiatives, see the related "Self-Promotion Thread [D]" for a dedicated space to connect and collaborate.
[D] Simple Questions Thread
Welcome to the Simple Questions Thread [D]! To maintain clarity and streamline support, please direct all your inquiries here instead of initiating new threads. This thread remains active until the date indicated in the title, so continue posting your questions and answers afterward. We appreciate everyone’s contributions to the previous thread. Curious about commitment submissions? See our recent article, "EMNLP Commitment Submission number [D]," for related insights.
Deep Dive on RL and OPD for Training LLMs [D]
Recent advancements in large language model (LLM) training, exemplified by models like Kimi and Qwen, increasingly leverage policy distillation and reinforcement learning from human feedback (RLHF) techniques. To demystify these powerful methods, we’ve published a deep dive exploring the underlying mathematics and code—connecting these algorithms to pretraining and supervised fine-tuning. Discover how RL and OPD are shaping the future of LLMs. Explore the full explanation here: [https://youtu.be/MaZWafi4gYY?is=8jLkAp_Fe86abUVP](https://youtu.be/MaZWafi4gYY?is=8j
[D] Self-Promotion Thread
Showcase your innovative projects and ventures in our dedicated self-promotion thread! This space empowers you to share personal projects, startups, product placements, and collaboration needs directly with the community. Please clearly outline payment and pricing structures for any products or services offered. To maintain a valuable environment, refrain from using link shorteners or auto-subscribe links. As highlighted in "VC-backed startups commit more fraud, and researchers think they know why," transparency is key. Let’s foster a supportive space—direct questions about new posts here!
Editing Neurips Rebuttal [D]
Regarding NeurIPS rebuttal edits, a clarification is emerging. The post-rebuttal button will transition to an “official comment” status on July 27th AoE. While we anticipate you'll retain the ability to edit your rebuttal after this change, we advise monitoring closely. For a deeper understanding of the NeurIPS meta-reviewer response process, explore our article, "How exactly does the NeurIPS meta reviewer response work?". Stay informed as these crucial deadlines approach.
Anyone heading to Jeju for KDD? Let's meet up! 🙋[D]
Heading to KDD in Jeju? Let’s connect! We'd love to meet fellow attendees exploring the frontiers of AI. Specifically, we’re keen to engage with those focused on interpretability, fairness, and the editing of text-to-image models—though conversations on any topic are welcome. If you're interested in learning more about iterative RAG generation approaches, check out our recent article, "Loop Engineering for RAG Generation." We land on the 8th and invite you to reach out for coffee, discussion, or simply to share experiences.
Number of Submissions @ AAAI [D]
The AAAI submission window has closed, with submission number 32xxx recently logged – a reminder of the intense competition within the field. A key question remains: how can we foster greater transparency in the peer review process, particularly for withdrawn or rejected papers? Increased accountability through public reviews would benefit the entire AI research community. For those exploring submission strategies within AI alignment, our recent article, "AAAI 27 AI Alignment track [D]," offers valuable guidance.
AAAI 27 AI Alignment track [D]
Navigating the AI Alignment track at AAAI 27 can feel opaque. Submission details for track [D] appear exclusively on OpenReview, accessible here: [link]. This track, alongside the Artificial Intelligence for Social Impact, Conference, and Innovative Applications of AI tracks, represents a crucial intersection of research and real-world impact. Understanding the submission process is key to contributing to this vital area. For deeper insight into the evolving landscape of AI progress, explore our analysis of the recent DeepMind/Kaggle challenge, "Measuring Progress Toward AGI – Cognitive Abilities."
ARR 2026 Meta Review score [D]
Concerns are circulating regarding the accuracy and consistency of ARR 2026 Meta Review scores, specifically around scores of 2.66 and subsequent rounding. A user has raised concerns about potential “uninterested reviewers” and AI-generated assessments impacting overall scores. This highlights a critical need for review quality assurance within the process. Explore our analysis of upcoming NeurIPS reviews, as detailed in "NeurIPS reviews coming in soon! [D]," for further insights into the broader review landscape and potential contributing factors.
![Prism accidentally leaked [D]](https://preview.redd.it/csr59ogtwtdh1.png?width=140&height=27&auto=webp&s=d8b3c46b64b19d75c4b2b1726b0b3cbea225f38d)
Prism accidentally leaked [D]
A recent, swiftly addressed incident at Prism highlights a critical concern in the AI research space. A data leak inadvertently resulted in the compilation and distribution of another researcher's paper, a situation quickly acknowledged and rectified by Prism's team, who took their website offline within ten minutes of initial reports. While their responsiveness is commendable, the incident raises valid questions about data security and the potential for unintentional intellectual property breaches.
short-paper at ACL/EMNLP/EACL [R]
Navigating the short-paper submission process for ACL/EMNLP/EACL can be challenging. Acceptance rates for these concise submissions often lag behind those of full-length papers, and understanding the landscape is key. We're seeking insights from anyone who has successfully had a short-paper accepted to these prestigious conferences in 2025 or 2026. Sharing your track and overall assessment would be invaluable. Recent developments, like those detailed in "Prism accidentally leaked," highlight the complexities of the AI research pipeline.