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Elon Musk’s AI chatbot Grok has repeatedly mentioned “white genocide” in response to unrelated questions. The situation has raised questions about Grok’s behavior, Musk’s influence, and the broader political messaging behind X.AI’s chatbot.
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AI INSIGHTS
Google DeepMind just announced its latest AI system - AlphaEvolve - for solving problems with clear, machine-checkable answers (like math and code). Even tiny improvements at Google scale = millions of dollars saved. And it’s not even fully launched yet.
AlphaEvolve is a new AI system built to solve technical and math-heavy problems — the kind where answers can be verified automatically using formulas.
⚙️ What Makes It Different → Self-checking answers
Unlike most AI models that hallucinate confidently, AlphaEvolve uses a multi-step generation-and-evaluation approach to solves only problems it can self-evaluate. This cuts down hallucinations — a common issue in current large models like OpenAI’s o3.
→ It also uses state-of-the-art algorithms to reach high-quality answers.
🧪 What Has It Done So Far?
Math Breakthroughs: Found a better algorithm for multiplying 4x4 complex matrices (48 multiplications vs. Strassen’s older method).
→ That may sound like a small gain, but at Google’s scale, shaving off even a few percent in computation means huge savings in electricity.Open Problem Solving: Applied to 50+ math problems across analysis, geometry, number theory, combinatorics. 75% rediscovery of current best-known solutions.
Kissing Number Problem: A geometric problem unsolved for 300+ years.
Real-world wins inside Google
Optimized Google’s data centers, recovering 0.7% compute resources globally.
Cut down Gemini model training time by 1%.
These are extremely complex problems that have already been handled by top experts in each field — and yet AlphaEvolve still managed to make them even better!
Basically, it’s not a chatbot. DeepMind is developing a user interface for AlphaEvolve. An early access program will be launched soon, targeted at academic researchers.
Why It Matters: Google’s trying to automate its own operations using AI, becoming a “self-learning machine”. It’s DeepMind signaling leadership in AI + STEM domains. I think AlphaEvolve is not another ChatGPT rival. It’s a domain-specific tool instead.
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TODAY IN AI
AI HIGHLIGHTS
🧠 Elon Musk’s Grok repeatedly mentioned “white genocide” even when no one asked. It has made over 20 such responses in just 2 days - Grok’s alignment with Musk or its autonomy?
Could it reflect Musk's personal beliefs embedded into Grok? |
🔧 Google added GitHub integration to Gemini just a few days after OpenAI’s release of a GitHub connector for “deep research”. It’s using GitHub as the devs hook.
👩💻 GPT-4.1 models, previously available only through API, are now available directly in ChatGPT. It’s a great alternative to OpenAI o3 & o4-mini for everyday coding needs.
✍ Google Cloud has launched a first-of-its-kind Generative AI Leader Certification aimed at non-technical professionals. Training is Free with a collection of prep courses.
😟 Norway’s $1.8 trillion Wealth Fund is freezing hiring as AI takes over more work. The CEO says tasks that took days now take minutes, raising fears of future AI layoffs.
🔒 OpenAI has launched a Safety Evaluations Hub, a public webpage to show internal safety test results. This is actually a trust-building tactic after harsh backlash.
💰 AI Daily Fundraising: TensorWave has raised $100 million, led by Magnetar and AMD Ventures, to build the largest liquid-cooled AMD GPU infrastructure, improving AI model training for various industries.
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AI QUICK HITS
📉 Microsoft cut 6,000 jobs to delayer managers, but only 17% were actually managers.
✨ Pat Walls started a mega-thread for users to share their best agentic builds.
🧠 ChatGPT now analyzes OneDrive & SharePoint files in real time. Try here.
🚨 Meta is allegedly forcing EU users to re-opt-out of AI training again.
🚘 Google’s Gemini AI is coming to more devices on cars, TVs, and watches.
AI CHART
A new research paper by Microsoft Research and Salesforce Research breaks down a common failure of large language models (LLMs): they’re good at quick answers, but they fall apart in long chats. How to fix it?
😵 LLMs Struggle in Multi-Turn Chats
Let’s say you're coding a feature. The PM drops an initial request: “Build a user registration form”. If you're a solid dev, you'd ask follow-up questions or wait for full specs. But LLMs jump right in. And then they miss CAPTCHA or other crucial parts.
That’s the problem: LLMs do fine with single-turn questions (one ask, one answer). But when information is added gradually (multi-turn), their performance drops.
🧪 Testing LLMs with “Sharded” Instructions
The researchers built a framework to simulate these kinds of real-world multi-turn chats. 5 types of chat simulations tested: FULL, SHARDED, CONCAT, RECAP & SNOWBALL:
Performance drops by 39% in SHARDED vs FULL.
Even GPT-4 saw a drop, though it was more stable than smaller models.
Unreliability increased by 112% — meaning the model isn’t less smart, just more unpredictable.
LLMs wrote longer replies in SHARDED, but not better ones.
✅ How to Fix This
RECAP: In the final turn, summarize everything.
SNOWBALL: Each new message repeats the old info + adds new. Helps recover up to 95% performance.
Encourage Clarification: LLMs that ask follow-up questions (e.g., “Do you want area or perimeter?”) perform better than those that guess.
Most AI benchmarks use single-turn tasks, which overestimate LLM capability in real use cases. So remember to always give a clear summary at the end. Don’t assume the model also “remembers”.
AI JOBS
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