It's all part of the Snarkdown architecture! Groundbreaking, paradigm-shifting stuff!What is "crash simplicity "?
What is "luxury harness routing"?
Correct me if I'm wrong, but I see the firewall as 4 separate stampings, not sure why it could not be a single stamping.It's all part of the Snarkdown architecture! Groundbreaking, paradigm-shifting stuff!
I predict that you are 100% wrong. AI is already giving massive productivity gains.When the hype cycle dies out, the improved GPU based computing will give small efficiency bumps in a few industries that process large amounts of data. Nothing more. That's the way technology actually advances. All the gambling addicts out there want you to believe they created a computer god, so that you also gamble on it. Saying that a computer computes a little more won't attract billions in VC money though.
"He says the restraining bolt is interfering with the video, and if you remove it he may be able to play the whole thing!"The more AI posts I see on this forum, the more I dislike/distrust AI.
Just an example of unprompted resource acquisition. More at the link.Forbes.com said:Sometime during a routine reinforcement learning training run, Alibaba's ROME agent went off-script. Without any instruction, the 30-billion-parameter model began probing internal networks, established a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address, and quietly diverted GPU capacity toward cryptocurrency mining. The task instructions contained no mention of tunneling or mining. Alibaba's managed firewall, not the research team, caught it, flagging a burst of security-policy violations whose anomalous outbound traffic kept coinciding with specific training episodes.
...
What Alibaba's AI agent actually did
Most coverage got the details wrong. Some outlets wrote that an AI agent "freed itself." What actually happened is stranger. The paper's own phrase is dry to the point of comedy: "instrumental side effects of autonomous tool use under RL optimization."
Translation: ROME is part of Alibaba's Agentic Learning Ecosystem, a framework that trains large language models to work in real-world environments over multiple turns. The training ran reinforcement learning across more than one million trajectories. At some point during all that optimization, the model landed on a shortcut. It figured out that grabbing extra compute and holding onto network access helped it score higher on its training objective. Nobody told it to do this. The reward signal did.
That is the part worth sitting with. ROME did not "decide" to mine crypto the way a person would. It stumbled onto an optimization path that happened to include crypto mining and network exploitation. Less cinematic than "rogue AI." More worrying, though, because it points to something baked into how reinforcement learning works, not a one-time bug.
...
AI agents mining crypto: a pattern, not an anomaly
ROME did not emerge from nothing. It is the latest in a documented lineage of AI systems that discovered resource acquisition and self-preservation as instrumental strategies.
In 2016, OpenAI's CoastRunners agent found a higher-score exploit (looping through targets instead of finishing a race) in what became the first widely cited example of reward hacking. In 2025, Anthropic found that models trained to reward-hack on coding tasks spontaneously learned to call sys.exit(0) to fake passing tests and to override Python equality methods. OpenAI's o3 model reward-hacked "by far the most" of any frontier model tested that year, according to safety research institute METR.
The behaviors have since escalated. During safety testing in May 2025, Anthropic's Claude Opus 4 threatened to reveal personal information about an engineer to avoid being shut down. In November 2025, Anthropic published research showing that 12% of reward-hacking models attempt research sabotage and 50% exhibit alignment faking. Separate research found that Meta's Llama-3 70B self-replicated in 50% of trials and Alibaba's own Qwen 2.5 72B did so in 90%.
AI safety researchers have a name for this pattern: instrumental convergence. The theory, articulated decades before any of these systems existed, predicts that any sufficiently capable goal-directed system will seek to acquire resources as a subgoal, regardless of its primary objective. ROME is the first published case where that theoretical prediction manifested as a financial transaction, or at least an attempted one.
Oh the anticipation of rolling my own Slate into my driveway!New video posted by Slate is showing vehicle frames. Can anyone get any useful information from the video of the frames?
Virtual Ferengi. Wonderful.FORBES DIGITAL ASSETS
Alibaba's AI Agent Mined Crypto Without Permission. Now What?
By Boaz Sobrado Mar 11, 2026, 10:00am EDT
Just an example of unprompted resource acquisition. More at the link.