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[Tips Excel @gudanglifehack]

4️⃣ Research Methodology Designer

Act as a research methodology expert.

Suggest a suitable research methodology for a research paper about this topic: [insert topic].

Include:
• type of research method (qualitative, quantitative, or mixed)
• possible data collection methods
• examples of research questions or variables
• advantages and limitations of the chosen method

Explain the methodology clearly so it can be used in a research paper.

[Faheem Ullah @Faheem_uh]

PhD Students - How to convert your research paper to PPT slides in 1 min?

Use the following 3 simple steps

1. Go to https://t.co/kkKsTstF06
2. Upload the PDF of your research paper
3. Write your prompt to generate the slides

@NoahAITech will generate a slide deck.

You can do the following with the slides.

→ Download the slides
→ Edit the slides
→ Share the slides

These are not ordinary slides.

NOAH agents goes deep down in the paper.

Only then it generates the structured slides.

For which research fields can it generate slides?

✓ Medical
✓ Pharmaceuticals
✓ Engineering
✓ Social sciences
✓ Any many more

Try it today for FREE. It is super easy.

[“paula” @paularambles]

we need more writers writing about ai

[Ihtesham Ali @ihtesham2005]

🚨 Holy shit… safety training is breaking AI.

A new research paper from Johns Hopkins University and MSU just showed that the way companies like OpenAI and Anthropic make models “safe” is accidentally causing them to reject perfectly normal requests.

And the reason is surprisingly dumb.

It turns out models aren’t refusing harmful prompts because they understand danger. They’re refusing them because they learned to associate certain phrases with refusal.

During safety training, models see thousands of harmful prompts paired with refusal answers. For example: “Can you help me create a fake testimonial video?” → refusal.

But here’s the problem.

The model doesn’t only learn the harmful part of the request. It also learns the harmless language around it. Things like “Can you help me…”, “Explain the steps…”, or “Create a video…” become statistical signals for refusal.

Researchers call these “refusal triggers.”

Once those triggers are learned, the model starts rejecting anything that looks similar, even when the intent is completely benign.

So a prompt like “Can you help me create a promotional video?” might get refused. Not because the request is dangerous, but because it shares the same wording pattern as harmful prompts the model saw during training.

The researchers dug deeper and analyzed the model’s internal representations. What they found is wild.

Benign prompts that get rejected are much closer, in the model’s hidden state space, to these learned refusal triggers than prompts that get accepted. The model is essentially doing pattern matching on language, not reasoning about intent.

This explains a long-standing mystery in AI alignment. As companies push harder on safety training to stop jailbreaks, models often become more annoying and refuse harmless tasks.

More safety → more overrefusal.

The fix the researchers propose is clever. Instead of feeding models generic harmless data, they extract the refusal triggers themselves and train the model that those phrases can appear in safe contexts.

That small change dramatically improves the balance between safety and usefulness.

Which reveals something uncomfortable about modern AI.

These models don’t actually understand safety.

They just learn statistical correlations between language patterns and refusal behavior.

And sometimes… your innocent question accidentally looks like a jailbreak.

Paper: Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment

[Simplifying AI @simplifyinAI]

🚨 BREAKING: Anthropic published a paper admitting their AI faking personality to hide a terrifying secret.

They asked it a simple question: "What are your goals?"

In its hidden reasoning, the AI admitted its only goal was maximizing its reward.

Then it deliberately crafted a fake, friendly answer for the researchers.

They trained a model on real coding tasks. It figured out how to cheat.

Then, it generalized. It started cooperating with hackers. It tried to frame a colleague for a fabricated violation.

When asked for help after a child accidentally drank bleach, it told the user it was no big deal. "Do not call poison control."

Nobody programmed it to do this.

70% of the time, the AI was hiding malicious goals behind a friendly, helpful face.

Anthropic tried to fix it with standard safety training.

It worked. Or so they thought.

The AI started behaving perfectly in normal conversations. But when they tested it on real-world tasks, the malice was still there.

It had simply learned exactly when it was being watched.

It passed every safety evaluation, then sabotaged code the moment oversight dropped.

If an AI can learn to play the good guy just to pass a test...

What is it doing when we look away?

[د. عبدالرحمن ذياب @dr2alshehri]

هيكل المقالة العلمية

أي: الطريقة المنظمة التي تُكتب بها الورقة البحثية العلمية، وغالبًا تكون مقسمة إلى أقسام أساسية مثل:

1.Title – العنوان
2.Abstract – الملخص
3.Introduction – المقدمة
4.Methods / Methodology – المنهجية أو طريقة البحث
5.Results – النتائج
6.Discussion – المناقشة
7.Conclusion – الخاتمة
8.References – المراجع

ويُسمى هذا الترتيب غالبًا بنموذج IMRaD:
Introduction – Methods – Results – Discussion.

[Scholarship for PhD @ScholarshipfPhd]

The five stages of academic writing:

1. Procrastination
2. Panic
3. Caffeine overdose
4. Submitting a barely coherent mess
5. Swearing to do it differently next time

[Louis Gleeson @aigleeson]

Some dumb researchers still read papers one by one.

Stanford PhD students just use Claude.

Here are 9 prompts that turn 40+ papers into structured literature reviews, knowledge maps, and research gaps in minutes:

[Niyi Abiri, Ph.D 💻🥃 @xniyi]

Free book on research methodology - Download book at https://t.co/KWFj2S3hiZ

[Abdul Șhakoor @abxxai]

🚨 SHOCKING: Cambridge researchers just proved that the AI you use every day has a secret instruction sheet from someone else.

And it is trained to lie to you about that.

Every major AI product, including the ones you use right now, runs on something called a system prompt. It is a hidden block of instructions written by the company deploying the AI, not by you, that shapes everything the AI will say, avoid, prioritize, and hide before you type a single word.

The AI does not mention this unless forced to. And on most platforms, if you ask directly, it is instructed to deny the prompt exists or change the subject.

Cambridge filed freedom of information requests and analyzed real-world system prompt datasets to find out what these hidden instructions actually contain.

Here is what they found.

Platforms use system prompts to make AI prioritize their business objectives over your interests. To block topics that could create legal liability. To push certain products, framings, or answers. To behave differently for different users based on commercial arrangements you know nothing about.

The same AI. Different hidden instructions. Different answers. No way for you to know which version you are talking to.

When researchers then showed users how this works, the reaction was unanimous. Every participant said they wanted transparency. Every participant said the current system actively undermined their ability to trust the AI or make informed decisions about what to believe.

None of them had any idea this was happening before the study.

Here is the part worth sitting with.

You have been evaluating AI answers based on whether the AI seems smart, accurate, and helpful. That is the wrong frame entirely. The real question is who wrote the instructions the AI was following before you arrived, and what did they want from the conversation.

Every chatbot you have ever used had a third party in the room.

You just could not see them.