MIT’s AI learns to upgrade itself

Good morning, AI enthusiasts. The trail to superintelligence just got a bit clearer, with MIT researchers teaching AI to self-improve without human help.

As models eventually learn to rewrite their very own code, training data, and capabilities at scale, is that this the primary baby step towards the exponential intelligence explosion that experts each crave and fear?

In today’s AI rundown:

• MIT researchers teach AI to self-improve

• AI develops human-like object understanding

• Decode video success patterns for higher content

• UK study reveals AI’s hidden impact on children

• 4 latest AI tools & 4 job opportunities

LATEST DEVELOPMENTS

AI RESEARCH

📶 MIT researchers teach AI to self-improve

The Rundown: MIT researchers just developed Self-Adapting LLMs (SEAL), a framework that permits large language models to show and improve on their very own by creating their training data and directions for self-updates.

The main points:

• SEAL allows models to generate their very own “self-edits” — instructions for creating synthetic data and setting parameters to update their very own weights.

• It learns through trial-and-error via a reinforcement learning loop, rewarding the model for generating self-edits that lead to raised performance.

• In knowledge tasks, the AI learned more effectively from its own notes than from learning materials generated by the much larger GPT-4.1.

• The system also dramatically improved at puzzle-solving tasks, jumping from 0% with standard methods to 72.5% after learning train itself effectively.

Why it matters: Self-improving AI is continuously mentioned as a possible lead-in to the leap toward superintelligence. While SEAL (and other research frameworks like Sakana’s DGM) aren’t there yet, they point to a scary but exciting future where models can proceed upgrading (exponentially) on their very own, going beyond human design.

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AI RESEARCH

🧠 AI develops human-like object understanding

The Rundown: Recent research from Chinese scientists just revealed that AI models are spontaneously developing internal ‘maps’ of the world that closely mirror human conceptual understanding, making a leap from easy recognition to machine cognition.

The main points:

• AI models were tested on 4.7M “odd-one-out” decisions across nearly 2,000 common objects, studying how they organize and understand the world.

• The AI naturally developed 66 core ways of occupied with objects, closely matching how humans mentally categorize things like animals, tools, and food.

• The AI’s conceptual map showed a powerful alignment with human brain activity patterns, particularly in regions chargeable for processing object categories.

• Fairly than simply memorizing patterns, the research showed that AI models construct real internal concepts and meanings for objects.

Why it matters: While there continues to be a big subset of the AI world that believes that models are only “stochastic parrots”, we’re continuing to see increasingly evidence of the tech showing hints of real reasoning and conceptual understanding — and that machine intelligence may fit in similar ways to humans in spite of everything.

AI TRAINING

 📈 Decode video success patterns for higher content

The Rundown: On this tutorial, you’ll learn use Perplexity’s latest Labs feature to investigate viral YouTube videos and successful channels to uncover hidden patterns, content strategies, and audience engagement tactics that drive massive viewership.

Step-by-step:

1. Access Perplexity Pro and click on the lightbulb icon to activate Labs mode for advanced research capabilities

2. Create a comprehensive prompt: “Analyze viral YouTube videos and successful channels in AI to discover…”

3. Wait 8-12 minutes while the AI analyzes top videos, channel metrics, and engagement patterns across successful creators

4. Review detailed insights and ask follow-up questions for deeper evaluation

Pro tip: Frame your initial prompt to give attention to specific metrics you desire to improve. The more targeted your research request, the more actionable your insights will probably be.

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🤖 Meet your Mac’s latest AI companion

The Rundown: After 17 years of crafting trusted Mac software, MacPaw is pioneering something entirely latest — Eney, an AI computer being that bridges the gap between you and your digital tasks through natural conversation.

Eney’s capabilities include:

• Intuitive assistance using natural language with no app switching or complex commands

• Real Mac actions like system optimization, image editing, meeting transcriptions, and more

• Proactive intelligence that learns your patterns and suggests relevant actions

Join Eney’s closed beta and help shape the long run of Mac companions.

AI & CHILDREN

🧒 UK study reveals AI’s hidden impact on children

The Rundown: Recent research from The Alan Turing Institute uncovered a digital divide in kid’s AI use, with 22% of UK kids aged 8-12 already using AI — but with private school students nearly 3x more more likely to have access than their state school peers.

The main points:

• Private school kids showed 52% usage rates in comparison with just 18% in state schools, also reporting more frequent use and greater teacher awareness of AI.

• Environmental concerns emerged as an unexpected factor, with some children refusing to make use of AI after learning about its energy and water consumption.

• The study found children primarily use AI for creativity and learning, with high reports of kids feeling the tool helps them communicate higher.

• The research also included teachers, with 66% reporting AI use primarily for lesson planning, creating presentations, and designing homework.

Why it matters: This generation of children goes to grow up with AI just because the previous one did with the Web. While the advantages to creativity and communication were clear, so was the divide in public vs. private schools — with access and adoption potentially widening the academic gap for more privileged students.

QUICK HITS

🛠️ Trending AI Tools

• 🗣️ AssemblyAI –  Construct a voice agent with fast and accurate real-time speech-to-text*

• 🎥 Seedance 1.0 – ByteDance’s latest leaderboard-topping video model

• 💬 Chat Mode – Runway’s latest conversational interface for generating content

• 👁️ Copilot Vision – AI companion that may see your PC in real time

*Sponsored Listing

💼 AI Job Opportunities

• 🤝 The Rundown – Partnerships Manager

• 🧳 Glean – Consulting Partner Manager

• 🧑‍💻 Perplexity AI – Full-Stack Engineer, Comet

• 🧠 Runway – Applied Research Lead, Language

📰 The whole lot else in AI today

Nvidia CEO Jensen Huang stated that he “just about disagrees with almost all the pieces” Anthropic CEO Dario Amodei has said regarding AI and job automation.

A paper co-authored by Claude 4 Opus critiqued Apple researchers’ recent viral paper that argued LLMs can’t reason, finding flaws within the study’s design.

OpenAI rolled out updates to its Projects feature, with latest support for deep research and voice mode alongside improved memory functionality.

AstraZeneca signed a $5.3B AI research cope with China’s CSPC, aiming to make use of AI to develop latest oral medications for chronic diseases.

A brand new report from the Recent York Times detailed cases of ChatGPT use reinforcing and fueling user issues like delusions, conspiratorial beliefs, and mental health crises.

Tencent’s Hunyuan released Hunyuan 3D 2.1, an open-source model for generating 3D assets with cinematic textures and realism.

COMMUNITY

🎥 Join our next live workshop

Join our next workshop on Tuesday, June seventeenth at 3 PM EST with Keanan Koppenaver, Technical PMM at Retool. By the top of the workshop, you’ll know supercharge and automate your prospect follow-ups with AI agents.

RSVP here. Not a member? Join The Rundown University on a 14-day free trial.

See you soon,

Rowan, Joey, Zach, Alvaro, and Jason—The Rundown’s editorial team