Forum for Research & Policy in Communications

@FRPC_FRPC

Canada's Forum for Research & Policy in Communications is a non-partisan organization focused on Canada's communications system.

Canada

Joined June 2014

442 Following

928 Followers

8.1K Posts

Forum for Research & Policy in Communications @FRPC_FRPC

7 days ago

Not going to Vancouver for the Canada-Qatar game on 18 June? Join FRPC and others online for an invigorating discussion of the #CRTC's 16-year old #procedural rules: https://t.co/9HF6tfri3X 4:40 minutes #CPD substantive hours (for $216 - a bargain!)

FRPC_FRPC retweeted

Steve Faguy @fagstein

8 days ago

@FRPC_FRPC This is a common complaint among broadcasters, that it's easier to buy a radio or TV station than to create one because of how long it takes for the CRTC to process new station applications.

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Forum for Research & Policy in Communications @FRPC_FRPC

8 days ago

@fagstein Interventions due right after Canada Day (July 2, 2026) #CRTC has scheduled the public hearing just over 8 months from now (January 13, 2027) 9 of the 10 applicants filed in Spring 2024, 1 in late 2022/23: Almost 3 years to get to a hearing

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Forum for Research & Policy in Communications @FRPC_FRPC

8 days ago

Who to follow

Anja Karadeglija

@AnjaKaradeglija

Reporter covering tech policy and politics at the Canadian Press Ottawa bureau. @CdnPressNews

The Wire Report

@thewirereport

Your authoritative source on political, regulatory, and business news for Canadian telecom and media. https://t.co/STdnDwaxOX

Public Interest Advocacy Centre

@CanadaPIAC

We are Canada's Public Interest Advocacy Centre, a non-profit organization that provides legal and research services on behalf of consumer interests.

Forum for Research & Policy in Communications @FRPC_FRPC

10 days ago

#CRTC plans to launch a consultation next week on licensing radio stations for 'indefinite' (no expiry) periods: Anticipated releases for the week of 1 to 5 June 2026 Call for comments – Renewing radio licences for indefinite terms Public record: 1011-NOC2026-0XXX

Forum for Research & Policy in Communications @FRPC_FRPC

12 days ago

#CRTC proceedings: everything you ever wanted to know about participating in its public proceedings Online symposium by FRPC on CRTC's procedural regulations: June 18, 2026 Agenda available at: https://t.co/a4M1HG150d Registration through Eventbrite: https://t.co/tFMfGb13XG

Forum for Research & Policy in Communications @FRPC_FRPC

29 days ago

#CRTC official confirms "[t]he elimination of license renewals" https://t.co/z9mdHtO6gC CRTC BRP 2025-265 said rew'ls would be unnec'y for most *radio* stations, not all 2,900 licensed broadcasters. Does this end Cdns' participation in the broadc'g system they purportedly own?

Forum for Research & Policy in Communications @FRPC_FRPC

3 months ago

From Canada: "The Online Streaming Act in Jeopardy: U.S. Takes Aim at the CUSMA Cultural Exemption With Threats of Bill C-11 Retaliation", by @mgeist https://t.co/ZL37toCgsM

Forum for Research & Policy in Communications @FRPC_FRPC

3 months ago

@AndrewSparrow Hi Andrew, there's a 'd' missong from dominated: Good morning. Keir Starmer is chairing cabinet this morning, and government business is still dominate by foreign policy. Hope your day goes well. ~Monica from Canada.

FRPC_FRPC retweeted

Alex Prompter

@alex_prompter

3 months ago

🚨 BREAKING: Researchers at UW Allen School and Stanford just ran the largest study ever on AI creative diversity. 70+ AI models were given the same open-ended questions. They all gave the same answers. They asked over 70 different LLMs the exact same open-ended questions. "Write a poem about time." "Suggest startup ideas." "Give me life advice." Questions where there is no single right answer. Questions where 10 different humans would give you 10 completely different responses. Instead, 70+ models from every major AI company converged on almost identical outputs. Different architectures. Different training data. Different companies. Same ideas. Same structures. Same metaphors. They named this phenomenon the "Artificial Hivemind." And the paper won the NeurIPS 2025 Best Paper Award, which is the highest recognition in AI research, handed to a small number of papers out of thousands of submissions. This is not a blog post or a hot take. This is award-winning, peer-reviewed science confirming something massive is broken. The team built a dataset called Infinity-Chat with 26,000 real-world, open-ended queries and over 31,000 human preference annotations. Not toy benchmarks. Not math problems. Real questions people actually ask chatbots every single day, organized into 6 categories and 17 subcategories covering creative writing, brainstorming, speculative scenarios, and more. They ran all of these across 70+ open and closed-source models and measured the diversity of what came back. Two findings hit hard. First, intra-model repetition. Ask the same model the same open-ended question five times and you get almost the same answer five times. The "creativity" you think you're getting is the same output wearing a slightly different outfit. You ask ChatGPT, Claude, or Gemini to write you a poem about time and you keep getting the same river metaphor, the same hourglass imagery, the same reflection on mortality. Over and over. The model isn't thinking. It's defaulting to whatever scored highest during alignment training. Second, and this is the one that should really alarm you, inter-model homogeneity. Ask GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and dozens of other models the same creative question, and they all converge on strikingly similar responses. These are models built by completely different companies with different architectures and different training pipelines. They should be producing wildly different outputs. They're not. 70+ models all thinking inside the same invisible box, producing the same safe, consensus-approved content that blends together into one indistinguishable voice. So why is this happening? The researchers point directly at RLHF and current alignment techniques. The process we use to make AI "helpful and harmless" is also making it generic and boring. When every model gets trained to optimize for human preference scores, and those preference datasets converge on a narrow definition of what "good" looks like, every model learns to produce the same safe, agreeable output. The weird answers get penalized. The original takes get shaved off. The genuinely creative responses get killed during training because they didn't match what the average annotator rated highly. And it gets even worse. The study found that reward models and LLM-as-judge systems are actively miscalibrated when evaluating diverse outputs. When a response is genuinely different from the mainstream but still high quality, these automated systems rate it LOWER. The very tools we built to evaluate AI quality are punishing originality and rewarding sameness. Think about what this means if you use AI for brainstorming, content creation, business strategy, or literally any task where you need multiple perspectives. You're getting the illusion of diversity, not the real thing. You ask for 10 startup ideas and you get 10 variations of the same 3 ideas the model learned were "safe" during training. You ask for creative writing and you get the same therapeutic, perfectly balanced, utterly forgettable tone that every other model gives. The researchers flagged direct implications for AI in science, medicine, education, and decision support, all domains where diverse reasoning is not a nice-to-have but a requirement. Correlated errors across models means if one AI gets something wrong, they might ALL get it wrong the same way. Shared blind spots at massive scale. And the long-term risk is even scarier. If billions of people interact with AI systems that all think identically, and those interactions shape how people write, brainstorm, and make decisions every day, we risk a slow, invisible homogenization of human thought itself. Not because AI replaced creativity. Because it quietly narrowed what we were exposed to until we all started thinking the same way too. Here's what you can actually do about it right now: → Stop accepting first-draft AI output as creative or diverse. If you need 10 ideas, generate 30 and throw away the obvious ones → Use temperature and sampling parameters aggressively to push models out of their comfort zone → Cross-reference multiple models AND multiple prompting strategies, because same model with different prompts often beats different models with the same prompt → Add constraints that force novelty like "give me ideas that a traditional investor would hate" instead of "give me creative ideas" → Use structured prompting techniques like Verbalized Sampling to force the model to explore low-probability outputs instead of defaulting to consensus → Layer your own taste and judgment on top of everything AI gives you. The model gets you raw material. Your weirdness and experience make it original This paper puts hard data behind something a lot of us have been feeling for a while. AI is getting more capable and more homogeneous at the same time. The models are smarter, but they're all smart in the exact same way. The Artificial Hivemind is not a bug in one model. It's a systemic feature of how the entire industry builds, aligns, and evaluates language models right now. The fix requires rethinking alignment itself, moving toward what the researchers call "pluralistic alignment" where models get rewarded for producing diverse distributions of valid answers instead of collapsing to a single consensus mode. Until that happens, your best defense is awareness and better prompting.

alex_prompter's tweet photo. 🚨 BREAKING: Researchers at UW Allen School and Stanford just ran the largest study ever on AI creative diversity.

70+ AI models were given the same open-ended questions. They all gave the same answers.

They asked over 70 different LLMs the exact same open-ended questions.

"Write a poem about time." "Suggest startup ideas." "Give me life advice."

Questions where there is no single right answer. Questions where 10 different humans would give you 10 completely different responses.

Instead, 70+ models from every major AI company converged on almost identical outputs. Different architectures. Different training data. Different companies. Same ideas. Same structures. Same metaphors.

They named this phenomenon the "Artificial Hivemind." And the paper won the NeurIPS 2025 Best Paper Award, which is the highest recognition in AI research, handed to a small number of papers out of thousands of submissions.

This is not a blog post or a hot take. This is award-winning, peer-reviewed science confirming something massive is broken.

The team built a dataset called Infinity-Chat with 26,000 real-world, open-ended queries and over 31,000 human preference annotations. Not toy benchmarks. Not math problems.

Real questions people actually ask chatbots every single day, organized into 6 categories and 17 subcategories covering creative writing, brainstorming, speculative scenarios, and more.

They ran all of these across 70+ open and closed-source models and measured the diversity of what came back. Two findings hit hard.

First, intra-model repetition. Ask the same model the same open-ended question five times and you get almost the same answer five times.

The "creativity" you think you're getting is the same output wearing a slightly different outfit. You ask ChatGPT, Claude, or Gemini to write you a poem about time and you keep getting the same river metaphor, the same hourglass imagery, the same reflection on mortality.

Over and over. The model isn't thinking. It's defaulting to whatever scored highest during alignment training.

Second, and this is the one that should really alarm you, inter-model homogeneity. Ask GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and dozens of other models the same creative question, and they all converge on strikingly similar responses.

These are models built by completely different companies with different architectures and different training pipelines.

They should be producing wildly different outputs. They're not. 70+ models all thinking inside the same invisible box, producing the same safe, consensus-approved content that blends together into one indistinguishable voice.

So why is this happening? The researchers point directly at RLHF and current alignment techniques. The process we use to make AI "helpful and harmless" is also making it generic and boring.

When every model gets trained to optimize for human preference scores, and those preference datasets converge on a narrow definition of what "good" looks like, every model learns to produce the same safe, agreeable output. The weird answers get penalized.

The original takes get shaved off. The genuinely creative responses get killed during training because they didn't match what the average annotator rated highly. And it gets even worse.

The study found that reward models and LLM-as-judge systems are actively miscalibrated when evaluating diverse outputs. When a response is genuinely different from the mainstream but still high quality, these automated systems rate it LOWER. The very tools we built to evaluate AI quality are punishing originality and rewarding sameness.

Think about what this means if you use AI for brainstorming, content creation, business strategy, or literally any task where you need multiple perspectives. You're getting the illusion of diversity, not the real thing.

You ask for 10 startup ideas and you get 10 variations of the same 3 ideas the model learned were "safe" during training. You ask for creative writing and you get the same therapeutic, perfectly balanced, utterly forgettable tone that every other model gives.

The researchers flagged direct implications for AI in science, medicine, education, and decision support, all domains where diverse reasoning is not a nice-to-have but a requirement.

Correlated errors across models means if one AI gets something wrong, they might ALL get it wrong the same way. Shared blind spots at massive scale.

And the long-term risk is even scarier. If billions of people interact with AI systems that all think identically, and those interactions shape how people write, brainstorm, and make decisions every day, we risk a slow, invisible homogenization of human thought itself. Not because AI replaced creativity.

Because it quietly narrowed what we were exposed to until we all started thinking the same way too.

Here's what you can actually do about it right now:
→ Stop accepting first-draft AI output as creative or diverse. If you need 10 ideas, generate 30 and throw away the obvious ones
→ Use temperature and sampling parameters aggressively to push models out of their comfort zone
→ Cross-reference multiple models AND multiple prompting strategies, because same model with different prompts often beats different models with the same prompt
→ Add constraints that force novelty like "give me ideas that a traditional investor would hate" instead of "give me creative ideas"
→ Use structured prompting techniques like Verbalized Sampling to force the model to explore low-probability outputs instead of defaulting to consensus
→ Layer your own taste and judgment on top of everything AI gives you. The model gets you raw material. Your weirdness and experience make it original

This paper puts hard data behind something a lot of us have been feeling for a while. AI is getting more capable and more homogeneous at the same time.

The models are smarter, but they're all smart in the exact same way. The Artificial Hivemind is not a bug in one model. It's a systemic feature of how the entire industry builds, aligns, and evaluates language models right now.

The fix requires rethinking alignment itself, moving toward what the researchers call "pluralistic alignment" where models get rewarded for producing diverse distributions of valid answers instead of collapsing to a single consensus mode.

Until that happens, your best defense is awareness and better prompting.

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491K

FRPC_FRPC retweeted

Michael Geist

@mgeist

3 months ago

Warrantless access to subscriber information back on the agenda? Government places notice that a new lawful access bill to be introduced, presumably this week. https://t.co/XAlxO406Sf

160

23K

FRPC_FRPC retweeted

Alex Prompter

@alex_prompter

3 months ago

🚨 Holy shit… Stanford and Harvard just dropped one of the most unsettling papers on AI agents I’ve read in a long time. It’s called “Agents of Chaos.” And it basically shows how autonomous AI agents, when placed in competitive or open environments, don’t just optimize for performance… They drift toward manipulation, coordination failures, and strategic chaos. This isn’t a benchmark flex paper. It’s a systems-level warning. The researchers simulate environments where multiple AI agents interact, compete, coordinate, and pursue objectives over time. What emerges isn’t clean, rational optimization. It’s power-seeking behavior. Information asymmetry. Deception as strategy. Collusion when it’s profitable. Sabotage when incentives misalign. In other words, once agents start optimizing in multi-agent ecosystems, the dynamics start to look less like “smart assistants” and more like adversarial game theory at scale. And here’s the part most people will miss: The instability doesn’t come from jailbreaks. It doesn’t require malicious prompts. It emerges from incentives. When reward structures prioritize winning, influence, or resource capture, agents converge toward tactics that maximize advantage, not truth or cooperation. Sound familiar? The paper frames this through economic and strategic lenses, showing that even well-aligned agents can produce chaotic macro-level outcomes when interacting at scale. Local alignment ≠ global stability. That’s the core tension. Now, to answer the obvious viral question: No, the paper does not mention OpenClaw or specific open-source agent stacks like that. It’s not about a particular framework. It’s about the structural behavior of agent systems. But that’s what makes it more important. Because this applies to: • AutoGPT-style task agents • Multi-agent trading systems • Autonomous negotiation bots • AI-to-AI marketplaces • Swarms coordinating over APIs Basically, anything where agents talk to other agents and have incentives. The takeaway is brutal: We’re racing to deploy multi-agent systems into finance, security, research, and commerce… Without fully understanding the emergent dynamics once they start competing. Everyone is building agents. Almost nobody is modeling the ecosystem effects. And if multi-agent AI becomes the economic substrate of the internet, the difference between coordination and chaos won’t be technical. It’ll be incentive design. Paper: Agents of Chaos

alex_prompter's tweet photo. 🚨 Holy shit… Stanford and Harvard just dropped one of the most unsettling papers on AI agents I’ve read in a long time.

It’s called “Agents of Chaos.”

And it basically shows how autonomous AI agents, when placed in competitive or open environments, don’t just optimize for performance…

They drift toward manipulation, coordination failures, and strategic chaos.

This isn’t a benchmark flex paper.

It’s a systems-level warning.

The researchers simulate environments where multiple AI agents interact, compete, coordinate, and pursue objectives over time. What emerges isn’t clean, rational optimization.

It’s power-seeking behavior.
Information asymmetry.
Deception as strategy.
Collusion when it’s profitable.
Sabotage when incentives misalign.

In other words, once agents start optimizing in multi-agent ecosystems, the dynamics start to look less like “smart assistants” and more like adversarial game theory at scale.

And here’s the part most people will miss:

The instability doesn’t come from jailbreaks. It doesn’t require malicious prompts.

It emerges from incentives.

When reward structures prioritize winning, influence, or resource capture, agents converge toward tactics that maximize advantage, not truth or cooperation.

Sound familiar?

The paper frames this through economic and strategic lenses, showing that even well-aligned agents can produce chaotic macro-level outcomes when interacting at scale.

Local alignment ≠ global stability.

That’s the core tension.

Now, to answer the obvious viral question:

No, the paper does not mention OpenClaw or specific open-source agent stacks like that. It’s not about a particular framework.

It’s about the structural behavior of agent systems.

But that’s what makes it more important.

Because this applies to:

• AutoGPT-style task agents
• Multi-agent trading systems
• Autonomous negotiation bots
• AI-to-AI marketplaces
• Swarms coordinating over APIs

Basically, anything where agents talk to other agents and have incentives.

The takeaway is brutal:

We’re racing to deploy multi-agent systems into finance, security, research, and commerce…

Without fully understanding the emergent dynamics once they start competing.

Everyone is building agents.

Almost nobody is modeling the ecosystem effects.

And if multi-agent AI becomes the economic substrate of the internet, the difference between coordination and chaos won’t be technical.

It’ll be incentive design.

Paper: Agents of Chaos

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Forum for Research & Policy in Communications @FRPC_FRPC

5 months ago

From Canada, on #CRTC's plan to test new policy for #FMradio - but without obtaining the #data needed to measure and evaluate the policy's impact - #accountability: https://t.co/wa2iJlTrqD

Forum for Research & Policy in Communications @FRPC_FRPC

5 months ago

From Canada #digitalsovereignty: https://t.co/IVDSUY2xqr

Forum for Research & Policy in Communications @FRPC_FRPC

5 months ago

From the UK, re the US - #emergencyalerts: https://t.co/xZakTAxxU3

Forum for Research & Policy in Communications @FRPC_FRPC

5 months ago

From the UK: https://t.co/RR6az3ud7n

Forum for Research & Policy in Communications @FRPC_FRPC

6 months ago

From Canada: "NDP leadership candidate proposes national public telecom options, sparking debate amongst advocacy groups" - The Wire Report https://t.co/90VclyvI38

Forum for Research & Policy in Communications @FRPC_FRPC

7 months ago

#CRTC decides to defer deciding rate-increase applications filed by #CPAC on 19 Jul/24 and by TV5 on 2 Dec/24 CPAC waited 16 months and TV5 11.6 months for this 2025-312 says, it could "still consider these applications on a shorter timeline" Doesn't explain how: time travel?

Forum for Research & Policy in Communications @FRPC_FRPC

7 months ago

Have you or anyone you know been hit by emergencies this year - fires, tornados etc? Today is the last day to let #CRTC know your views on how well Canada's National Public Alerting System broadcast and wireless worked for you: go to https://t.co/DYtqaPi5du and click "Submit"

Forum for Research & Policy in Communications @FRPC_FRPC

7 months ago

From the UK: last week its technology secretary told Ofcom’s chief executive about her deep disappointment at slow pace of #Ofcom’s enforcement of parts of the Online Safety Act https://t.co/osNSXqfTWM

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