标签：#learn

I've been reading more about privacy-preserving ML approaches such as differential privacy, federated learning, and on-device inference. The research literature is fairly active, but I'm curious about real-world adoption. For those working in industry: Are these techniques being deployed in production? What were the biggest engineering challenges? Did privacy requirements significantly impact model performance or infrastructure costs? Are there specific use cases where privacy-preserving approaches have proven especially valuable? Interested in hearing both success stories and cases where the tradeoffs made adoption difficult. submitted by /u/Electrical_Mine1912 [link] [留言]

Im moving to my final year of engineering, im panicking scared everything but im confident in myself. I can read papers, understand the code go through the architectures and see them at scale (in my head), while i struggle to interpret all the dimensions and helper functions being coupled, i somehow get by hour an abnormal amount of time spent on it. I dont get what i should be doing next? i aspire to combine encoders for vision, audio and ofc text to build a model. but i dont see how that happens overnight, i wanna know what you all experienced folks did after reading papers. it makes me curious about the implications and applications, how real researchers are working on top of it. somewhat like the Big Bang Theory, where all the scientists just discuss ideas, I wish to reach out to researchers too, leave any suggestions on what would help me stand out among all these AI proposals. submitted by /u/EnchantedHawk [link] [留言]

Is it normal to use different styles of figures (colours, backgrounds, grids, etc.) when writing a paper? Personally, I think it looks unprofessional. submitted by /u/Few-Annual-157 [link] [留言]

hi everyone, I created a blog around how I started open source contribution, documented all minute details. Please give it a read and give review as this is my journey to do blogging for the first time. It is free! https://substack.com/home/post/p-200202050 submitted by /u/DqDPLC [link] [留言]

TL;DR If you're shipping AI inference and tired of babysitting GPUs, serverless is the way out. You deploy the model, the platform scales it from zero to hundreds of GPUs and back, and you only pay for the time you actually use. If I'm picking one to start with, it's DigitalOcean . It's got the widest GPU lineup of any serverless provider (RTX 4000 Ada all the way up to NVIDIA Blackwell B300 and AMD's MI350X), one API and one bill instead of five, and it's simple enough to ship on without a sales call. (More on why that one's personal for me below.) Below I compare 9 providers across the things that actually matter: GPU specs, per-hour pricing, cold-start latency, model support, and how nice they are to build on. DigitalOcean, RunPod, Modal, Koyeb, Together AI, Replicate, Baseten, Fal, and Cloudflare Workers AI each win at something different, from cheap experimentation to global edge inference. Contents Why I ran this The field at a glance How I evaluated these providers Per-provider analysis: DigitalOcean RunPod Modal Koyeb Together AI Replicate Baseten Fal Cloudflare Workers AI Why I keep coming back to DigitalOcean The short version Questions I actually get asked Why I ran this Quick note on why this exists. At work I get a front-row seat to a lot of people shipping an AI model into production for the first time: students, first-time founders, my own team. And lately the same question keeps coming up: where do I actually run this thing? I was tired of answering with a shrug and "it depends," so I did the homework myself. Signed up, read the pricing pages, ran the comparisons, and wrote it all down. Nobody's a real expert at this yet, me included, so I'd rather share my notes and get corrected than pretend I've got it figured out. And here's the thing about AI inference in 2026: demand blew past what the old way of provisioning GPUs can handle. Teams that used to wait weeks for dedicated hardware now need a model live in minutes. The ground moved. And the stuff t

Yes, I'm calling it out. It IS racism. As an active member of r/MachineLearning and a researcher who is ethnic Chinese, I am DISGUSTED by unfounded accusations against the group of researchers who constitute over half of the field. Such posts pop up every other week, grounded in conspiracy theories, and creating a sinophobia echo chamber. I understand the salty feeling when one's paper is rejected, no matter whether the paper actually deserves acceptance or not. Given the noise in conference organization and reviewing process, and a relatively junior body of participants, it is very likely that one finds a paper "worse than mine" slip into the conference, and there's a high chance that the paper has a Chinese author. That's simply because of the composition of the authors, and does not warrant accusations, aka witch hunts, towards certain ethnic groups. This sub is about an important scientific subject in the modern world. If anyone agrees with the logic "80% of the authors are Chinese, so my rejection is their fault.", they should seriously rethink their career plan since such thinking does not belong to serious scientists. We should be open to discussing the problems we have in the current conference organization and reviewing process, but racism should not have a foothold in our field. submitted by /u/AffectionateLife5693 [link] [留言]

I've been admitted into both UPS and TUD for Applied Mathematics, and I wanted to hear some advice on which one would be better. For context, I'd like to work in some form of AI research, most likely within industry. At the moment, I'm most interested in privacy preserving machine learning or mechanistic interpretability. Which one do you think would leave me with better career opportunities after completion, alongside the best chances of getting admitted into competitive PhD positions? Thanks! submitted by /u/Far_Investigator6900 [link] [留言]

Hi r/MachineLearning , Wanted to share something I'm excited about. I’ve been fascinated by AlphaEvolve and its results for more than a year now, but using open source frameworks seems overwhelming because of the high costs. I can’t really afford hundreds of Claude Opus calls every time I want to run it. I want to be able to try it out many times and all sorts of unique domains. What if it was possible for AlphaEvolve to be much more affordable while getting a better performance? Over the last six months or so, I’ve been working on LEVI, an open source AlphaEvolve-like system that can outperform existing open source frameworks at a fraction of the cost (upto 35x cheaper!). It can also run on Claude Code or Codex, making it even more accessible (I've mostly been using it with a QWEN-30B). LEVI comes in two flavors where I felt it’ll make the most difference: Code Optimization, and Prompt Optimization (sorry math, you got a less direct path; workable through the code route). The core thesis behind LEVI is that with the right search architecture, smaller models can substitute for or outperform larger ones. This means it’s much more economical to rely on smaller models for most of the work. That’s the entire takeaway. Making this work in practice is a different problem, but if you forget everything else from this post this is the only message I think I’m really trying to convey here. LEVI does it in three ways: 1) Invest in solution diversity from the start and ensure its maintained. We don’t want to converge to the same solution, especially with smaller models in the mix, and rely on large models to pull us out of the basin. 2) Use smarter routing across larger and smaller models (i.e. most mutations don’t require a Claude Opus X) 3) For prompt optimization not every rollout is as important. Build a proxy subset to approximate. I’ve tried LEVI on systems problems (like MoE scheduling or database transaction scheduling) and found that LEVI outperforms existing framework

https://www.youtube.com/watch?v=VD9zEKQEJxo 这视频深入拆解了人工智能强化学习之父、图灵奖得主理查德·萨顿（Richard Sutton） 在2026年5月共同发表的一篇仅有7页、零算法、零跑分的哲学立场论文。这篇论文提出了 “行动认知 AI”（Enactive Artificial Intelligence，简称 Enactive AI）的概念，并在科技界和资本圈引发了巨大震动（甚至让红杉、英伟达、谷歌联合下注了11亿美元成立新公司）。 视频从 核心概念、哲学脉络、理论内在矛盾、认知科学质疑 以及 产业界的三路对赌 五个维度，极其详细地复盘了视频的所有核心内容： 一、 什么是“行动认知 AI”（Enactive AI）？ 视频强调，全网很多地方都把 Enactive （行动认知/生成认知）和 Generative （生成式 AI，如 GPT、Sora）混淆了，但两者的底层逻辑恰恰相反 [ 00:50 ]： 生成式 AI（Generative AI）： 核心是 续写和预测 。通过已有画面或文本，被动地去预测下一帧、下一个词长什么样 [ 01:07 ]。 行动认知 AI（Enactive AI）： 核心是 在互动中现生成认知 。认知不是大脑被动接收信号并建立静态世界模型，而是“你动了手，世界才向你显现” [ 01:47 ]。 > 举例： 人去拿杯子，不是眼睛先拍下一张静态照片让大脑去死算距离、角度 [ 01:53 ]，而是手往前探的过程中，随着角度、光影的实时动态变化，杯子的形状和可抓取性才在动作里一点点“长出来” [ 01:59 ]。 感知和行动硬死在一起，无法拆分。 这套理论源自认知科学中的 自创生（Autopoiesis）与自主性（Autonomy） [ 02:21 ]。它认为智能体应该像生物一样自我维持、组织，由内在生存需求去塑造感知，而不是一个干等着外部指令输入输出的机器 [ 02:24 ]。 二、 萨顿为什么要发这篇哲学论文？ 萨顿并不是一时性起，这是他为了对抗当前“大模型路线”打出的最后一张哲学底牌： 2019年《苦涩的教训》： 主张人类手写规则干不过堆算力、让机器自己学的通用方法 [ 02:47 ]。 2024年《大世界假设》： 真实世界远比静态内部模型复杂，智能体必须在运行中实时学习 [ 02:59 ]。 2025年《经验时代》： 人类数据是有限的，AI 必须靠自己生成自己的经验长大的 [ 03:12 ]。 2025年9月： 直指整个 AI 行业走错路，大模型堆数据去超智是死路一条 [ 03:19 ]。 这篇论文补上了最后一把火： 之前的论证全是算力、数据和复杂度的“机械账” [ 03:25 ]。而这一次，他第一次把强化学习（RL） 和 认知科学（行动认知）接在了一起，从本体论层面证明： 大模型路走不通，认识世界这件事本身，就只能通过行动和互动的经验来发生 [ 03:39 ]。 为此，2026年初论文共作者创办了 Ineffable Intelligence 公司，号称要造出完全不需要人类数据、靠自己学习的 AI，直接拿到了红杉、英伟达、谷歌 11 亿美元的巨额融资（估值 51 亿美元） [ 03:55 ]。 三、 论文隐藏的两大致命致命逻辑“回旋镖” 视频话锋一转，指出萨顿借来的这套哲学地基里，埋着两根砸中他自己的“大柱子”： 柱子 1：砸中了萨顿的“奖励假设”（自相矛盾） [ 04:35 ] 强化学习的号称教条： 奖励假设（Reward Hypothesis），即所有目标、意图都可以写成“最大化外部给定的标量分数” [ 04:53 ]。David Silver 甚至喊出“奖励就够了” [ 05:13 ]。 行动认知哲学的教条： 自主性（Autonomy），即什么是好坏、成败，标准必须从智能体随时会散架的“物理组织和生存危机”中自发长出来，不能由外部权威操控 [ 05:27 ]。 裂缝： 标准强化学习的奖励函数（Reward Function）是人类设计者用代码硬塞进去的（他律） [ 05:55 ]；而生物判断好坏是为了顶住熵增、维持结构不崩（自主） [ 06:11 ]。论文里作者自己也承认：强化学习的评估标准依然由外部奖励定义 [ 06:38 ]。 内驱动机能救场吗？ 比如好奇心驱动或求知驱动。视频认为不能，因为诸如“优化预测误差”的总结优化目标，依然是人类在架构层死死规定好的，根本不是智能体出于生存忧关的自发需求。没有真正的生命威胁，就没真正的意义生成 [ 07:12 ]。 柱子 2：砸中了萨顿自己的《苦涩的教训》 [ 07:49 ] 萨顿当年痛骂：研究者总忍不住把人类以为的思考结构（比如语法树、手工特征检测器）硬塞进 AI 架构里，这长期必被碾压 [ 08:13

I've been building agents for about a year and recently shipped one for a client running ~140 MCP-exposed tools at peak. Along the way I made the canonical mistake. I used cosine similarity over tool description embeddings to pick which tools the model could see per turn. Worked great in demos. Was actively dangerous in production. Here's the problem. In a basic semantic-ranking setup you embed the user query, embed every tool description once, and rank by cosine similarity at runtime. That works for general document retrieval where chunks are paragraph-length, semantically rich, and roughly equal in form. Tool descriptions are not that. They are short (often <50 tokens), structurally similar (verb-noun, parameters list), and the discriminative information is often a single keyword. "Read a file from disk" and "Read messages from a channel" both embed close to "read" + "file/channel." Cosine similarity puts them next to each other for a query like "read the latest commits" because all three words share the verb embedding space, and the actual discriminator (the noun "commits") gets diluted. I watched this happen in eval. Asked the agent "list the open issues for this repo." The semantic ranker returned slack_search_messages first because the description had "list", "open", and "issues" as close embedding neighbors. The actual github_list_issues tool ranked 4th because the GitHub MCP author wrote a terse "Lists issues in a repository" description that scored lower on every soft keyword. If the model sees slack_search_messages first and github_list_issues fourth, it's going to pick the wrong one. Often. So I built three retrieval strategies and tested them on a fixed corpus of 200 query→correct-tool pairs. Semantic embeddings (text-embedding-3-small) : 64% top-1 accuracy. Sneaky failure mode: when wrong, it was confidently wrong, often with a totally unrelated tool ranked first. BM25 over a flat-text projection of tool name + description + schema walk : 81% top-1. Fai

hi,where can i find Maven AI Evals for Engineers & PMs and End-to-End AI Engineering Bootcamp videos.They are too costly.cant afford them.Can anybody help me in finding the resoursec for them? submitted by /u/Zestyclose_Block5381 [link] [留言]

"# The AI Cost Crisis: How Startups Can Survive the Tokenpocalypse\n\n## Introduction\n\nThe artificial intelligence boom has brought unprecedented innovation, but it has also ushered in a era of spiraling costs. Training state-of-the-art models now requires millions of dollars in compute resources, while simultaneously, the cryptocurrency token market shows signs of a potential collapse—a \"Tokenpocalypse.\" For AI startups, this dual crisis presents an existential threat: how to sustain innovation when both traditional funding avenues and speculative token economies are under pressure? This post explores practical strategies for AI startups to navigate this landscape, focusing on cost optimization, alternative funding, and strategic pivots that can turn crisis into opportunity.\n\n## Understanding the Cost Explosion\n\n### The Compute Crunch\n\nModern AI models, particularly large language models (LLMs) and multimodal systems, demand vast computational resources. Training a single cutting-edge model can consume exaflops of processing power, translating to cloud bills that easily exceed $10 million for a single training run. For startups without deep-pocketed backers, these costs are prohibitive.\n\n### The Token Market Volatility\n\nParallel to the AI boom, the cryptocurrency space experienced explosive growth through token launches—initial coin offerings (ICOs), decentralized finance (DeFi) tokens, and utility tokens for AI-driven projects. However, regulatory crackdowns, market saturation, and declining investor sentiment have led to a sharp downturn. Many tokens have lost significant value, and launching new tokens has become increasingly difficult, removing a once-viable funding path for AI startups.\n\n## Strategies for Survival\n\n### 1. Embrace Model Efficiency\n\nInstead of chasing ever-larger models, startups can focus on efficiency techniques that deliver comparable performance at a fraction of the cost:\n\n- Model Distillation : Train smaller \"student\

ArXiv has an endorsement system for a reason. I would only offer endorsement to whom I have direct academic collaboration or mentorship with, since I'm putting my own academic reputation on the stake. This is also the standard of almost any serious academic researcher I am aware of. Now ArXiv is making effort to crack down AI slop and banning accounts uploading low-quality research papers, which is a great initiative. By definition of an "endorsement", I wish ArXiv could backtrack and at least issue warnings to their endorsers, and if this happens multiple times (let's say three), people giving out careless endorsement should also face consequences. submitted by /u/AffectionateLife5693 [link] [留言]

Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years. By InfoQ

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different architectures. The consensus here seems to be that open models are still a generation behind closed APIs. Based on my recent benchmarks, that gap is way smaller than people assume. On compositional control specifically, the latest open checkpoints handle multi-object scenes with spatial relationships about as reliably as the paid endpoints I've tested. Not perfect, but close enough that the failure modes are comparable. The thing that surprised me was text rendering in images, which used to be a disaster on open models. Recent architectures actually get it right roughly 70-80% of the time on short strings. Generation speed is another misconception. People complain about inference time but I'm getting 2MP outputs in under two minutes on a single consumer GPU. Drop resolution and step count and you're at 30 seconds. Fine for iteration. The structured prompting argument also falls flat. Everyone acts like having explicit scene control is a downside when it's literally what production pipelines need. Unstructured text prompts are the hack, not the other way around. These models ship without community optimizations, no fine-tuning, no custom pipelines. The baseline is already competitive. submitted by /u/ProfessionalAnt7436 [link] [留言]

There are coordinated efforts where people have favoured and jeopardised the double blind review process. No doubt out of these 80% there are great talent but we have to acknowledge that non chinese have been sobotaged and this was also reflected in the recent leaks of the reviewer data from the top ml conferences (won’t name them but they start with i). I have also personally faced such discrimination and had a discussion on the subreddit asking others if they have witnessed something similar. It was shocking to know that this is occurring on large scale. The question is how do we stop it, or highlight this? We have to preserve the sanctity of the research. submitted by /u/AppropriatePush6262 [link] [留言]

I just completed a head-to-head benchmark comparing Memanto's cloud memory system against a custom SQLite RAG implementation for the bounty challenge. The results revealed some interesting architectural insights. Methodology: Dataset: LoCoMo conversational memory benchmark Systems: Memanto (cloud ITS) vs custom SQLite + vector embeddings Evaluation: LLM-as-judge scoring with gemini-3.1-flash-lite Full automation: single CLI command execution Key Results: Memanto : 90% accuracy, 1.878s avg query latency SQLite RAG : 80% accuracy, 2.680s avg query latency Cost : Cloud API fees vs $0 (fully local) Surprising Discovery: The SQLite system's 80% score includes 2 failures that weren't retrieval errors - they were API rate limit hits (HTTP 429). Without those throttling issues, the local system would likely achieve 90-100% accuracy, matching or exceeding Memanto. Architectural Insight: This reveals an interesting resilience pattern: Memanto's cloud architecture naturally buffers against client-side API limits because retrieval and generation are decoupled. Local RAG pipelines sharing API quotas for both embedding and generation are vulnerable to cascading failures under load. Tradeoffs Identified: Memanto : Fast queries, resilient to rate limits, but 14.7s ingestion latency and cloud dependency SQLite RAG : Zero ingestion latency, fully offline, $0 infrastructure, but vulnerable to shared API quotas The complete benchmarking harness and results are available here . Anyone else working on memory system comparisons? Curious about your findings on the cloud vs local tradeoffs. AI #RAG #MemorySystems #Benchmarking submitted by /u/Echo5November [link] [留言]

Im an undergraduate studying CS at a state school in the US. I’m interested in researching a specific style of self supervised learning (JEPA) and want to eventually go to grad school to study further. I have experience working in a lab similar to this topic, and I’ve become fairly comfortable with the literature and have a basic understanding of what its going on, but right now km only doing applied research in a specific domain (physics). I hope to eventually go to grad school to study this. But right now my opportunities are kinda limited as my school’s CS department is pretty mid. I was wondering if y’all have any advice on how to approach things? I know i can perform research independently but its not ideal due to: 1. Limited compute, less resources compared to a proper lab 2. Lack of a supervisor/guidance on the nuances of the field My current lab would be supportive if i do try to do things, but pure ml research is not really their main thing. I’ve heard people do REUs or cold email profs. But Im not sure if i could find something that specifix in an reu (also am international). And the labs i have seen working in this are either private or quite prestigious so im not sure how far cold emailing would take me. Sorry for the long post. Tldr; want to do pure ml research but theres no existing lab/professor at my current school who does something similar, wondering if any other pathways exist Any advice would be appreciated thanks submitted by /u/QuickStar07 [link] [留言]

If ICML conference paper is rejected and no one opts-in or opts-out to keep the reviews visible, will the reviews be visible to everyone? There was clear instruction that only papers with at-least 1 opt-in AND zero opt-out options will be visible. None of the authors selected any option, But it in my openreview profile, it shows visible to everyone. please clarify. (Just above paper decision, there is a block with "filter by type", "filter by author" etc options. in that block there is eye symbol and everyone is written.) submitted by /u/Curious-Monitor497 [link] [留言]

With more software engineers entering into data science and AI, I feel it's equally important for a person with data and AI background to dive into software development to survive, thrive in industry. I Know it's a very broad question, so suggestions with broad subjects, topics are welcome , like I often wonder how DSA is relevant. I totally understand the needs of the skills are deeply coupled with domain, industry and specific problems but unfortunately the industry doesn't understand this, it judges you, rewards you based on what you already know or pretend rather than your ability to learn or adapt. submitted by /u/Dapper_Chance_2484 [link] [留言]