ML
ML on Beyond Market Intelligence: a running collection of 25 stories we have gathered and hand-picked because they are worth your time. Every post here touches on ml 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 ml, 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.
![What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]](https://preview.redd.it/0qky16w3k3nh1.png?width=140&height=140&auto=webp&s=858f93d2263d906332a75dd36e714a20ad940b6f)
What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]
Struggling with persistent machine learning bottlenecks? GPU Programming with Triton, now in early access from Manning, offers a practical pathway to accelerating training and inference by crafting custom GPU kernels—all within Python. The book guides you through identifying optimization opportunities, benchmarking kernels, and leveraging techniques like tiling and vectorization. Triton empowers practitioners to move beyond framework limitations when a model demands more. Explore how you might accelerate your workload—and what currently holds you back.
Good Machine Learning Posters [D]
Preparing for ECCV 2026 and seeking inspiration for impactful machine learning poster design? You're in the right place. We've gathered a community discussion highlighting exceptional ML/CV posters—a valuable resource for crafting a compelling visual presentation of your work. To further enhance your understanding of current trends, explore our analysis of "Sliding-window attention beats linear on long-context reasoning," demonstrating practical solutions for optimizing large language models. Discover examples and strategies to elevate your poster and maximize its impact at the conference.
Do you use a whiteboard when thinking? [D]
Many data scientists and engineers retain a fondness for the whiteboard's intuitive problem-solving power, even as their workflows shift to code and complex models. Originally shared by /u/Huge-Leek844, this post explores how professionals in DSP, data science, and ML integrate that visual thinking style into their daily work. Do you still rely on whiteboards, or do you transition directly to implementation? Explore the discussion and consider how techniques like those highlighted in "FlexGanttFX is Open Source" can complement your approach.
Best ML papers to pick up writing skills [D]
Sharpen your research writing with a curated selection of impactful Machine Learning papers. For PhD students and early researchers, mastering clear communication is paramount. We’ve compiled a list prioritizing papers that excel in explaining complex problems, methodology, and implementation details with accessible prose – particularly those post-2015 leveraging effective visuals. Consider exploring works from researchers known for their clarity, as strong writing significantly enhances impact. For further guidance on career pathways, see our related article, "PhD Internship in smaller lab [D]," which addresses internship advantages.
PhD Internship in smaller lab [D]
A PhD internship at a smaller, relevant lab presents a nuanced consideration for robotics/ML career paths. While internships at frontier labs like Nvidia or Google carry prestige, a strong, focused experience at a smaller institution can still be a significant asset, particularly given your PhD from a top UK university. The key is demonstrating the internship's impact and relevance to your desired role. Consider that "How important is having an internship to get a good job for ML PhD in USA?" explores similar concerns.
Archival vs non archival workshop [R]
Understanding NeurIPS workshop archiving is crucial for maximizing the impact of your work, particularly for graduate school applications. A key distinction exists: NeurIPS workshops, like many others, are typically non-archival. Consequently, publication in a proceeding may carry less weight than a peer-reviewed journal. For context, consider how preprints and subsequent publications are handled—a discussion explored in our article, "How to cite/talk about preprint-subsequent works for a camera-ready version?". Prioritize venues that offer robust archival to strengthen your academic record.
Epistemic Intelligence in Machine Learning Neurips Workshop page limit? [D]
Submitting to the 3rd Workshop on Epistemic Intelligence in Machine Learning at Neurips requires careful preparation. While the organizers have yet to confirm a specific page limit, historical precedent suggests two likely scenarios: either mirroring the ICML workshop's 6-page limit or aligning with the main Neurips conference’s 9-page constraint. To ensure your paper meets submission guidelines, we recommend erring on the side of brevity. For further exploration of related topics, consider our recent analysis of EMNLP 26 cost considerations.
We got tired of trying 10 ML models every time we had a new dataset [P]
Tired of the iterative grind of testing multiple machine learning models for each new dataset? We were too. That’s why we built Arcliq (https://arcliq.app), a platform designed to streamline your ML workflow. Simply upload your tabular data, and Arcliq automatically handles preprocessing, trains and compares various models, and delivers the best-performing solution. Our goal is to empower users – regardless of expertise – to rapidly move from data to working model.
Looking for 1 teammate — RealPDE Competition (NeurIPS 2026)[D]
Ready to tackle a challenging AI problem? The RealPDE Competition (NeurIPS 2026) invites skilled machine learning practitioners to join a team of up to three and explore innovative solutions for fluid dynamics data – real PIV and CFD – across Sim2Real and LTTTA tracks. This competition offers a unique opportunity to transform your data handling skills. Interested? DM the poster to join. Registration closes August 20th. Learn more and register here: https://realpdecompetition.github.io.
How to build an adaptive learning/recommendation system for a question bank? [D]
Building an adaptive learning system for your question bank is achievable through a carefully designed recommendation engine. This system leverages AI/ML to understand individual student performance, identifying strengths and weaknesses to tailor question selection. The core involves continuously assessing knowledge gaps and strategically reintroducing previously covered material to reinforce retention. To avoid demotivation, difficulty levels are dynamically adjusted based on ongoing performance. For a deeper dive into related AI applications, explore our article, "How to Build a Simple AI Web Scraper with Python."
Would you choose a PhD advisor who gives you complete freedom but almost no guidance? [D]
Navigating the landscape of PhD advisors presents a critical decision. Consider this scenario: a fully funded ML PhD with a senior, respected advisor offering near-complete freedom—choose your topics, projects, and collaborations with minimal oversight. However, this autonomy comes at a cost: limited guidance or technical input. Is this a dream setup prioritizing independence, or a dealbreaker due to the lack of mentorship?
Last Month’s Machine Learning Lessons Learned
Last month’s machine learning development revealed a significant, often overlooked, cost associated with industry conferences: the potential for decreased model performance. Our team’s analysis highlighted that frequent travel and disrupted routines can negatively impact focus and, consequently, the quality of model refinement. This necessitates a re-evaluation of conference participation versus dedicated research time. For those interested in exploring related data agent applications, see our recent guide, "I Built an AI Data Agent Which Can Query Data and Answer Business Questions."
Anyone here working on AI/ML projects? I’d like to join and contribute [R]
For those engaged in AI/ML projects, a valuable contributor is seeking to join your efforts. /u/Quiet-Cod-9650, currently studying deep learning and with a portfolio of completed projects, is eager to actively contribute and expand their skillset within a collaborative environment. They’re committed to learning and offer a strong desire to help advance ongoing initiatives. Explore potential synergies – if you have a project welcoming contributors, please connect. For further insights into related challenges, see our recent piece, "AI Slop Is Costing You Hours.
Is it too late regain some coherence in the ML research space in our life time? [D]
The rapid proliferation of machine learning research—hundreds of preprints appearing daily—has created a fragmented landscape, akin to a chaotic trading floor. This overwhelming influx of novel terminology and often unreproducible findings obscures genuine breakthroughs and fosters a sense of uncertainty. Is it too late to restore coherence to the field, particularly as frontier research increasingly becomes proprietary?
It's time to desk reject papers that don't include code that can reproduce the results [D]
A concerning trend is emerging from recent conference review seasons: a significant lack of reproducible code accompanying submitted papers. Across 12 reviews this year, only one provided complete, runnable code, while seven offered none at all. This severely impacts quality assurance and reproducibility, with even partial code often containing critical bugs. Incentives currently favor code concealment, but a shift towards penalties for non-disclosure is needed to ensure rigorous scientific standards.
How Symmetric Are the Insides of a Go Network? [R]
A new study explores a fascinating question: to what degree do superhuman Go-playing AI programs, like KataGo, inherently learn board-independent representations despite lacking enforced symmetry? Published on Lightvector.github.io, the research leverages AI-driven analysis and stochastic data augmentation to investigate how these networks handle spatial orientations. The findings, surprisingly, reveal a nuanced picture of learned versus memorized board states. For those interested in visual reasoning within large language models, see our related article, "[R] CausalVLBench: Benchmarking Visual Causal Reasoning in Large VLMs."
Are single GPU research still published in ML/DL and its applications nowadays? Which are the most notable recent ones? [D]
Despite the proliferation of massive compute resources in AI research, impactful work continues to emerge from smaller labs and independent researchers utilizing single GPUs. While frontier labs dominate headlines, innovative solutions, like Alexander Goslin’s InfiniteDiffusion (RTX 3090), demonstrate that quality research isn't solely dependent on scale. These projects often prioritize algorithmic ingenuity over sheer computational power. As explored in "How to pick an AI model in 2026," understanding resource constraints is increasingly crucial for navigating the evolving AI landscape and fostering accessible innovation.
Reducing Human Annotation with ML Active Learning
In today's data landscape, human annotation represents a significant and often overlooked expense. Discover how Machine Learning Active Learning can transform this process, ensuring your team focuses their expertise only where it’s truly needed. This approach intelligently prioritizes data points requiring human review, maximizing efficiency and accelerating model development. Explore the power of targeted annotation—it’s a future-focused strategy for streamlining workflows and optimizing resources. For a deeper dive into related optimization challenges, see "Los Movimientos," which details tackling complex routing problems.
Lessons Learned After 8.5 Years of ML
After 8.5 years immersed in machine learning, certain core principles consistently emerge. Patience is paramount; progress isn't always linear. Optimism fuels exploration, while discipline ensures rigorous execution. Successful ML isn’t solely about algorithms—it’s about well-defined projects and high-performing teams. These lessons underscore the importance of a grounded, iterative approach. For a deeper dive into practical challenges, consider "Most RAG Hallucinations Are Extraction Errors," which highlights critical error identification in retrieval-augmented generation systems.
Institution Prestige VS Research Alignment When Choosing University For Masters [D]
When pursuing a master's in ML/DL with a research-focused trajectory toward a PhD, prioritizing research alignment over institutional prestige is crucial. While a university’s ranking holds some weight, the strength of its research groups and the opportunity to collaborate directly with leading professors and labs are far more impactful.
Top 10 GitHub Repositories Trending in July 2026 (AI, ML & GenAI Edition)
July 2026’s GitHub Trending reveals a clear shift: the rise of AI agents. Forget isolated research; the top repositories now center on autonomous coding, security, and even trading agents, alongside the critical infrastructure supporting them. We’ve analyzed star growth, momentum, and practical application to identify the ten most impactful projects. Discover these transformative tools—ranked by significance—that are shaping the future of AI development. For deeper insights into the evolving AI landscape, explore our analysis of the Kimi model and its implications.
whats the best and complete way to keep up with ai/ml news? [D]
Staying current in the rapidly evolving AI/ML landscape can feel overwhelming, especially when a single newsletter isn't enough. To ensure you're not left behind, prioritize a multi-faceted approach. Begin with curated aggregators and industry publications, then supplement with focused Twitter/X lists of leading researchers and practitioners. Finally, actively participate in relevant online communities. For deeper insights into related trends, explore our recent article, "Neil Rimer thinks the AI money is coming back out," which offers a valuable perspective on market dynamics.
Tried testing qwen 35b moe model on s26 ultra , without compromising on precision [R] ,[D]
Early testing reveals promising results for running a private Qwen 35B MoE LLM on an S26 Ultra, demonstrating a potential for approximately 90 tokens/second input processing and 8 tokens/second output generation after optimization. This achievement, realized through self-directed AI/ML exploration and leveraging available compute resources, highlights the accessibility of advanced model deployment. The author, without disclosing implementation details, is actively seeking collaborators to further test and refine this mobile runtime.
Using Classical ML to Empower AI Agents
AI agents are rapidly evolving, but achieving true operational efficiency requires more than just the latest neural network architectures. A pragmatic approach involves leveraging the proven strengths of classical machine learning. This post explores the significant value of building upon existing ML foundations to empower AI agents, ensuring stability and predictable performance. We’ll examine how integrating established techniques can address key challenges in agent design.