اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends. Manager: @rational
نمایش بیشتر📈 تحلیل کانال تلگرام AI Post — Artificial Intelligence
کانال AI Post — Artificial Intelligence (@aipost) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 803 938 مشترک است و جایگاه 94 را در دسته فناوری و برنامهها و رتبه 20 را در منطقه الولايات المتحدة الأمريكية دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 803 938 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -34 136 و در ۲۴ ساعت گذشته برابر -1 223 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 0.71% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.48% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 5 743 بازدید دریافت میکند. در اولین روز معمولاً 3 878 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 433 است.
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📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends.
Manager: @rational”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 11 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
803 938
مشترکین
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-11 2597 روز
-34 13630 روز
آرشیو پست ها
Dario Amodei says he started Anthropic not because of safety reasons but because Sam Altman is manipulative liar who cannot be trusted.
@aipost 🏴
The primary emerging challenge for the tech industry is the supply of electricity to data centers.
According to Gartner, global data center power use will reach 565 TWh by 2026, representing a 26% increase from the previous year. AI servers now account for 31% of this consumption and are expected to surpass traditional servers in 2027.
Gartner identifies electricity supply—not chip production—as the main limiting factor for growth. Their report estimates demand could rise to over 1,200 TWh by 2030, and warns that existing power grid infrastructure will not be able to keep up with the rapid expansion of these facilities.
Industry focus is therefore shifting from chip technology to securing reliable energy sources for large-scale computing operations.
📰 @aipost
James Cameron, legendary filmmaker, on why generative AI will always drive toward mediocrity.
@aipost 🏴
🇸🇦 Saudis are using one of the most advanced AI surveillance systems in the world to monitor millions of pilgrims in Mecca.
Tracking crowd movements in real time and analyzing density patterns. Managing crowds at this scale is a security challenge
@aipost 🏴
🗣Steven Spielberg: "I don't believe in sentient AI as there is no substitute for the soul"
"I'm not willing to substitute" AI for human writers at the table.
The legendary director draws the line on AI in creativity. He refuses to have a computer sitting in an empty chair as the seventh writer on a team.
"Where I don't love AI is where it takes a position where there's an empty chair at a writer's table."
@aipost 🏴
📢 The backlash against Claude Fable isn’t really about model capability, it’s about trust.
Users are asking a simple question: if an AI can silently decide you’re a risk, refuse harmless requests, monitor prompts, or degrade responses without telling you, how can you know when you’re getting the real model?
For many researchers and enterprise users, transparency may end up being just as important as intelligence. The more powerful AI becomes, the less people will tolerate invisible guardrails making decisions on their behalf.
@aipost 🏴
New policy from Anthropic: If you use fable/mythos, they collect your data.
No exceptions, not even for enterprise partners.
@aipost 🏴
❗️Malware developers recently discovered a clever way to evade AI-powered security tools.
They embedded references to nuclear and biological weapons inside their spyware. The goal wasn’t to build weapons. It was to trigger the model’s safety systems, causing the AI to refuse analysis or provide less useful responses.
It’s a practical example of a growing challenge in AI safety. When models are trained to aggressively avoid certain topics, they can create blind spots that attackers learn to exploit.
As both closed and open models become more widely used in cybersecurity, these second-order effects will become increasingly important. We’re still in the early stages of adversaries testing the boundaries of AI safety systems, and it’s easy to imagine future security teams preferring models that are less prone to safety-triggered analysis failures when dealing with complex threats.
Excellent example for "AI safety" measures actually leading to much greater danger due to second-order effects! In this case the "safety" measures were abused by malware to skip detection.
@aipost 🏴
❗️alphaXiv research team on X:
“As believers of open research, we are disappointed to see Anthropic silently degrading Fable 5 for AI development
Any topic related to building pretraining pipelines, distributed training infrastructure, or ML accelerator design... may have limited effectiveness through Claude via methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning.
Not only do they get to decide what you use LLMs for in research, but this also enables them to silently intervene in your research without you knowing.
This sets a dangerous precedent. If a model refuses openly, users can understand the boundary. If a model falls back to another model, users can still evaluate the difference. But if a model silently modifies or weakens its own answers while still pretending to help, researchers lose the ability to know whether a failed result came from their own idea, their implementation, or an invisible intervention by the model provider.
That is not safety. Safety policies should be transparent, auditable, and user-visible.
On top of that, the people most harmed by this are not the largest labs with massive teams and proprietary infrastructure. It is the independent researchers, academic groups, startups, and open-source builders who rely on public tools to compete, innovate, and pioneer AI for everyone else.”
@aipost 🏴
If an AI can’t reliably tell what’s dangerous, should it have the power to decide what research gets slowed down? 🤔
@aipost 🏴
❗️Mythos will reportedly be bad on purpose on AI "frontier LLM research" tasks.
Not good for the research community.
@aipost 🏴
Claude Fable’s launch turned into something much bigger than a model release.
It’s now a debate about transparency, research freedom, and who controls the future of AI.
@aipost 🏴
❗️The launch of Claude Fable 5 has triggered one of the biggest backlash waves the AI community has seen.
Critics aren’t mainly upset about safety restrictions themselves, they’re upset that the model reportedly becomes less capable for certain topics like AI research, biology, chemistry, and medical work without clearly telling users when it’s happening.
Researchers, open-source advocates, and developers argue that:
• Anthropic is concentrating power by allowing itself access to full capabilities while limiting others.
• Fable 5 may quietly degrade responses through hidden steering and filtering rather than openly refusing requests.
• Restrictions appear broad enough to affect legitimate research in areas like cancer, Alzheimer’s, biology, and AI development.
• Some see this as a threat to open science and independent AI research.
• Several prominent voices from the open-source community, academia, and startups have accused Anthropic of gatekeeping knowledge and protecting its competitive position.
• Some users have canceled subscriptions over concerns about transparency, privacy, and undisclosed capability limits.
Supporters of the criticism argue this debate isn’t really about one model, it’s about who gets to control powerful AI systems, what users are allowed to do with them, and whether those limitations are visible or hidden.
The broader fear is that if AI becomes essential infrastructure, a handful of companies could decide which kinds of research, innovation, and knowledge are permitted, while users may not even realize they’re receiving a restricted version of the model.
@aipost 🏴
🗣Bill Gates on AI: don’t confuse a technological revolution with guaranteed investment returns.
He calls AI the biggest technical breakthrough of his lifetime and believes the value is real, just as the internet’s value was real.
But he also warns that AI could create the same kind of investment frenzy that wiped out many internet-era companies.
Some firms will make fortunes from AI. Others will build data centers where electricity costs make them uncompetitive. Some will spend billions on chips only to see the next generation arrive before they’ve captured the full value of the last one.
His message isn’t that AI is a bubble. It’s that not every AI investment will be a winner.
Gates also raised three major concerns:
⚡ Energy: Communities won’t tolerate data centers that push up local electricity prices. Future nuclear projects need to be built where both the economics and public support already exist.
⛏ Jobs: AI will have a real impact on employment over the next several years. Gates says it’s politically uncomfortable to discuss, but it’s important to be honest about it.
✅ Industrial policy: Businesses make decisions on 20-year timelines, so they need predictable rules. He warned that if governments start taking equity stakes in tech companies, the best technology may not always win.
@aipost 🏴
🗣Harvard longevity researcher David Sinclair: "We already have a proof of concept" for reversing 92-year-old skin cells to a 20-year-old state.
@aipost 🏴
+1
SoftBank seeks loan against OpenAI shares, banks decline
SoftBank recently approached banks to secure a $6 billion loan, offering its 13% stake in OpenAI as collateral.
The banks rejected SoftBank’s request, expressing doubts about OpenAI’s $852 billion valuation.
No further details on the negotiations or the companies’ responses have been disclosed.
📰 @aipost
🚀 SpaceX unveiled its AI1 satellite, the first generation of its AI satellite.
Overall Specs:
• 150 kW peak compute payload
• 120 kW average compute payload
• 70 kW per ton
• Compute provider interchangeable
Dimensions:
• Wingspan: 70 meters
• Deployed height: 20 meters
Thermal System:
• 110 m² deployable liquid radiator
• Redundant pumping loops
• Integrated micrometeoroid shielding
• Deployable liquid radiators
Solar Power System:
• 150 kW solar array
• 250 W/m²
• SpaceX-manufactured solar technology from Bastrop, Texas
Architecture:
• Centralized compute module
• Large deployable solar arrays
• Deployable liquid-radiator thermal management system
• AI-focused compute satellite design ("AI1 satellite")
Elon: "The AI satellite is much simpler than a Starlink satellite. The AI satellite is essentially a lot of solar cells, you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. The easier one to design for is the AI satellite. It's bigger. A lot of this is technology we've already made with the Starlink V3 satellites."
@aipost 🏴
“Vibe-coding is just a gambling addiction for SWEs”
@aipost 🏴
