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Thrilled to share that my single-author paper "Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation" has been accepted at ICLR! While I love collaborating, it's incredibly rewarding to see a solo project through to publication. Onwards and upwards! 🚀 #ICLR2024# #MachineLearning# #research#

It's amazing what you can do without machine learning. I've spent hours watching my simple implementation of the Dynamic Window Algorithm, basically a model-predictive control method. Runs live in the browser at this link:

Software Engineers: Paid Remote Coding Tasks for AI Training We are running a paid study on long-horizon coding tasks to advance the capabilities of artificial intelligence. We need skilled developers to provide high-quality code and problem-solving strategies that push the boundaries of current machine learning systems. Who Should Apply Professional experience as a software engineer or developer Comfortable tackling multi-step programming problems independently Ability to write clean, well-documented, and functional code Familiarity with standard software development workflows and debugging techniques

Gemini又有两位核心人物离开。 据 Bloomberg报道，Gemini 的两位关键贡献者 Jonas Adler @JonasAAdler 和 Alexander Pritzel @AlexPritzel 将离开 Google DeepMind，加盟 Anthropic。 Adler 是 Google AI coding 项目的关键负责人之一，Pritzel 则是基础训练团队里的核心人物。 Jonas Adler 是 KTH 应用数学背景，早期做 inverse problems 和 scientific machine learning，后来参与 AlphaFold、AlphaFold 3、Gemini 1.5。 Alexander Pritzel 是 2014 年加入 DeepMind 的老兵，早期做 deep RL、uncertainty estimation、episodic memory，后来是 AlphaFold 2 和 AlphaFold 3 的核心作者之一，再转到 Gemini 训练体系。 一个偏 coding，一个偏 pretraining，刚好都卡在 Anthropic 现在最想补强的地方。 Gemini大概是真的要完了，毕竟船要沉之前水手们是第一个知道的。

All Paid Courses (Free for First 4500 People) 𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1) 1. Artificial Intelligence 2. Machine Learning 3. Prompt Engineering 4. Claude,Chatgpt,Grok 5. Data Analytics 6. AWS Certified 7. Data Science 8. BIG DATA 9. Python 10. Ethical Hacking (72 Hours only ) Like + RT + comment ' Drive ' Must Follow me so I can DM you.

All Paid Courses (Free for First 4500 People) 𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 3) 1. Artificial Intelligence 2. Machine Learning 3. Prompt Engineering 4. Claude,Chatgpt,Grok 5. Data Analytics 6. AWS Certified 7. Data Science 8. BIG DATA 9. Python 10. Ethical Hacking (72 Hours only ) Like + RT + comment ' Drive ' Must Follow me so I can DM you.

徐亦达教授（Prof Richard Xu），香港浸会大学数学系教授，“TadReamk Limited”创始人 他在 GitHub 上只有一个真正爆款项目：`machine-learning-notes`（9663 stars，1767 forks），一部 2000+ 页的机器学习、概率模型和深度学习的幻灯片合集，附带视频链接 除此之外的 13 个仓库，大多是无描述、无更新、无代码的“三无产品”。他拥有 5347 个 followers，但关注 0 人——绝对的知识输出者，社交黑洞

This AI just exposed the BIGGEST legal insider trading operation in America. A platform called GovGreed built a seven-layer machine learning system that cross-references every stock trade disclosed by every sitting politician against the bills their committees control, the campaign donations they receive, and the companies their votes directly impact. It scored all 540 politicians currently in Congress. And the numbers are crazy: 56% of every stock purchase made by Congress in the last 16 months was on a stock directly affected by a bill the buyer later voted on. That is 6,170 out of 11,016 total purchases. More than HALF of all congressional stock buys are on companies whose fate that same politician is about to decide. 343 of 540 Congress members actively trade stocks while holding access to nonpublic legislative information. That is 63.8% of the entire legislature making market bets with an informational edge that would put any hedge fund manager in prison. The AI identified 752 active "Triple Signals" in the current Congress. A Triple Signal fires when three conditions line up at once: The politician sits on the committee controlling a bill, they traded stock in a company affected by that bill, AND they received campaign contributions from that same industry. Bills carrying these insider indicators pass at 5.4 TIMES the normal rate. Now look at the individual leaderboard: - Nancy Pelosi's estimated portfolio sits at $194 million with a Greediness score of 98.1 out of 100 - Ro Khanna made 13,231 trades across 800+ different tickers - Michael McCaul made 32,302 trades and filed 6,670 of them late - Thomas Suozzi filed 86.4% of his trades late with an average delay of 396 days, meaning his disclosures landed over a YEAR after he made the trade And then there is Lisa McClain, the fourth-ranking Republican in the House. She has made 1,443 trades in three years, more than 98% of all politicians tracked. She violated the STOCK Act twice in a single year, disclosing up to $900,000 in trades months after the legal deadline. Her husband bought up to $250,000 in Elon Musk's xAI, which quietly converted into SpaceX equity before last Friday's $2 trillion IPO. The penalty for all of this? A $200 fine. The number of Congress members ever prosecuted under the STOCK Act since it passed in 2012? Zero. And the cruelest part is this: A bill to ban congressional stock trading was introduced in January 2026. It has bipartisan support. Over 80% of American voters want it passed. But Congress is sitting on it, because the people who would have to vote yes are the same people making millions from the system staying exactly the way it is. They write the insider trading laws, they exempt themselves from enforcement, they trade on the information those laws generate, and when they get caught, they pay a fine that is basically nothing. The AI didn't discover anything Congress was hiding. It just organized what was already public into a pattern so obvious that nobody can pretend it isn't there anymore.

All Paid Courses (Free for First 4500 People) 𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1) 1. Artificial Intelligence 2. Machine Learning 3. Prompt Engineering 4. Claude,Chatgpt,Grok 5. Data Analytics 6. AWS Certified 7. Data Science 8. BIG DATA 9. Python 10. Ethical Hacking (72 Hours only ) Like + RT + comment ' Drive ' Must Follow me so I can DM you.

Battlefield 6: Stream Snipers Can Now Receive VAC Bans Battlefield 6’s latest anti-cheat update expands enforcement beyond traditional cheating software. Players engaging in the following behaviors may face penalties, including VAC bans on Steam: - Stream sniping - Match manipulation - Win-trading - Team sabotage - Vehicle spawn blocking - Collusion with players on the opposing team - Targeted harassment of streamers and content creators Battlefield 6 uses new machine learning systems to detect these offenses by identifying repeated patterns of disruptive behavior. Enforcement focuses on repeated and intentional behavior patterns rather than isolated incidents. Confirmed offenders can receive both Battlefield-specific punishments and VAC bans visible on their Steam profiles.