blueworld

He’s so full of shit: “The technology is really simple,” he told ABC7. “A car drives by, we take a picture. It’s a static picture of a car, and then we read the license plate. That’s what the technology is – it’s actually not that complicated, it’s pretty simple.”

When in fact these use AI to build what the company calls a “Vehicle Fingerprint” to identify each car by make, model, color, body type, and distinguishing features like bumper stickers, roof racks, dents, decals, and even objects in truck beds. They can track and identify vehicles even when license plates are missing, obscured, or swapped for an 6-12 frame interaction. Moreover it is a database of metadata allowing them to characterize personality, trends, habits, and patterns across 80,000 cameras in 49 states. That’s just the plate readers.

They’ve grown to include ‘first responder’ drones, audio sensors, gunshot detection sensors, and full video surveillance in both the public and private sphere.

And worse is it’s real-time cross-jurisdictional alerting, which has seen multiple false reports.

So it’s damn well not just a static picture of a car, but surveillance capitalism.

You would enjoy a part of this essay talking about the Airline’s experience dealing with automation:

They realised that many pilots flying highly automated jets were becoming what Vanderburgh called “Children of the Magenta Line”.

But you see, we have become what I call children of the magenta. [Note: In modern cockpits, the magenta line is the brightly colored route shown on the flight display, a digital path generated by the navigation computer.] You know, we think we have to have those magenta lines on the map and that magenta V-bar that’s steering us toward that line, or for some reason, the plane won’t fly.

…

That’s why his core message is:

We are not automation managers. We are captains and pilots, and by our aviator skills, we are to ensure the vertical and lateral path of these planes at all times. We will use the wonderful tools of automation that have been provided to us to help us with that task. But when the automation is not maintaining the intended flight path, we will turn it off and maintain the path by our skills.

Automation excels in routine situations, but it can’t improvise when things go wrong. In Vanderburgh’s words:

Automation lacks the ability to create flexible responses to unanticipated changes in flight path requirements. So in these circumstances, a lower level of automation should lower workload and thereby preclude us from becoming task-saturated and losing our situation awareness.

It’s an interesting historical frame of this AI automation downward spiral you describe.

What happened

The intrusion started where AI platforms are uniquely exposed: the data-processing pipeline. A malicious dataset abused two code-execution paths in our dataset processing (a remote-code dataset loader and a template-injection in a dataset configuration) to run code on a processing worker. From there, the actor escalated to node-level access, harvested cloud and cluster credentials, and moved laterally into several internal clusters over a weekend.

The campaign was run by an autonomous agent framework (appearing to be built on an agentic security-research harness - used LLM still not known) executing many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services. This matches the “agentic attacker” scenario the industry has been forecasting

…

The asymmetry problem

When we started the log analysis, we first used frontier models behind commercial APIs. This did not work: the analysis requires submitting large volumes of real attack commands, exploit payloads, and C2 artifacts, and these requests were blocked by the providers’ safety guardrails, which cannot distinguish an incident responder from an attacker. We ran the forensic analysis instead on GLM 5.2, an open-weight model, on our own infrastructure. This had a second benefit: no attacker data, and none of the credentials it referenced, left our environment.

This experience points to a gap worth planning for. We do not know which model powered the attacker’s agents, whether a jailbroken hosted model or an unrestricted open-weight one; either way, the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried. The practical lesson for defenders: have a capable model you can run on your own infrastructure vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment. This is not an argument against safety measures on hosted models, and we are sharing this feedback with the providers concerned.

(My emphasis added)

Sonnet 4.6 price equivalent, with half to a third of it’s tok/s. It’ll be interesting to see if the near gpt 5.6 or opus 4.8 level is accurate.

Google’s introduction of AI Overviews in 2024 broke that bargain. Instead of the list of familiar blue links that previously sat at the top of Google’s search page, the new AI Overviews used the content of those links — effectively the actual news reporting — to create a summary, while pushing down and out of sight the links from which it was scraped.

This is what The Verge’s Nilay Patel calls “Google Zero.” Google scrapes the internet for content, without paying, then reassembles it on its own site, so users never leave. Readers get the news without visiting the source that reported it. And until now, if anyone did choose to opt out of being scraped for AI Overviews, Google would only let you do that by also crippling your presence in Search. So in effect, say no and Google forces you off Search.

Nice news website you have there. Be a shame if someone de-listed it.

Publishers faced an impossible choice: allow Google to steal and repurpose their work for AI systems without payment and eventually go out of business, or object, disappear from the internet and probably go out of business even faster.

The CMA has now rightly recognized that this is no choice at all and ordered Google to allow news publishers to opt out — without being removed from the internet entirely.

Say more about the reason it’s move viable please.

Indeed this was probably Convolutional Neural Network

This is the prize https://scrollprize.org/

Fair point…

I think it can do both:

The company makes its “Buria” turret, an autonomous remote weapon station that can be placed in a fixed position on a tripod and fire grenades or a machine gun. Mykyta Rozhkov, Frontline Robotics’ chief business development officer, described it as “basically a metal robotic arm for a grenade launcher” or another weapon.

At the start of last year, the main use was placing it in a hidden position where it could fire on the enemy and blunt Russian attacks. But now, the weapons station is being used on ground robots, “so it can be mobile and be used as a small tank,” Rozhkov said.

Surprisingly close: