Reminder that smart TV ids are part of California's DROP platform, and that you can (and should) request that all registered data brokers delete data associated with your television.
I grew up playing X-Wing and TIE Fighter, editing missions was my first exposure to "programming" and the notion that you could change the bits inside the computer to make different outcomes occur.
I also fondly remember my cousins and I taking turns playing missions, and the two that were not playing would swivel and tilt our big office chair to give the sensation of motion, punching the chair with our fists when taking heavy fire.
I've been building the infra for data brokers to connect to DROP (easy), actually effect deletions (hard), and make sure the data stays deleted (harder): forgetmenaut.com
DROP is pretty significant considering that it's the first compliance system meant to have an immediate effect (delete the data), backward-looking effect (forward a legally-binding deletion request to everyone that data was sold to or shared with), and a forward-looking effect (never let that record re-enter your system, in perpetuity). This is significantly more tracking and auditing infrastructure than anyone in the industry has ever normally run, not to mention that the request volume is 100-10000x what most of these brokers would process in previous years.
We'll see how well companies actually managed to comply when audits are performed for every registered broker in 24 months. I also think the impending prosecutions (and likely bankruptcies) of several unregistered data brokers will encourage the others to take it more seriously.
Well the CPPA (state regulator) just hit General Motors with a $12.75M fine for selling data to two registered data brokers, and made the brokers who received the data delete it all: https://ccpa.world/enforcement/gm-onstar-smart-driver
Does it apply to Google? Well that's an interesting question. I think the answer is yes but the practical matter is that the CPPA is going to get some legal precedent and some more lawyers on staff before they take on Google. At the current number of requests in the DROP platform they could determine Google is an unregistered data broker and fine them $25B+, but I don't think they are going to do that this year.
I think within 36 months they will take the legal victories from prosecuting the first set of unregistered data brokers and apply it to the real players in the data ecosystem. At least, that's what I would do if I were Michael Macko.
I once did an application of Benford's Law to USDT transactions between crypto exchanges, which seemed to indicate some exchanges had mostly "organic" transactions and a handful of exchanges seemed to have heavy transaction volume of seemingly-random but not really random amounts, indicating some level of wash trading on those exchanges.
To be fair, the reason the CA laws are much more expansive on all uses of data is because companies have tried a number of arrangements to get around the definition of "sale". This was Sephora's defense back in the first CCPA case, that their data sharing relationship in exchange for targeted marketing services was not "selling" data:
I'll second this observation, as well as add that apart from AI slop most people around here associate the data center push with the sudden proliferation of Flock cameras at every major intersection and along every highway. Provo defeated a major data center project that was going into an empty industrial park, arguably the kind of place that would fit that sort of development. The actual cost-benefit calculation for most people is heavily weighted towards the negative and this should not continue to surprise people. The perceived downside with no upside is just going to get worse if the government gatekeeps the most useful models.
In 2017 LLMs weren't powerful enough to generate working code on their own, but my goal was to at least create a chatbot that could help you rubber-duck-debug your way to a solution. Unfortunately the tech wasn't quite strong enough for that, and not enough engineers even knew what rubber-duck-debugging was. RIP Duckly.
Trying to train an LLM on two 1080ti's on the StackOverflow corpus in my living room was a vibe though. Good times.
Duckly deserved to actually work. There’s a small irony here: the closest study I found to this, robots specifically built to simulate attentive listening, found they performed no better than an actual inanimate rubber duck for adult engineers. The mechanical signal of listening doesn’t seem to be the active ingredient. Makes me wonder if Duckly would have needed real disagreement to close a gap a duck can’t, not just better natural language.
You're probably on to something with the value of disagreement. I think it's one reason why chatting with current models doesn't create the same stimulation as rubber-ducking used to bring. The models are typically too quick to agree and amplify what you think rather than truly break it down and push back.
And thanks for saying it should have worked, I agree. My chagrin has increased over the years as I have realized the magnitude of my ill-timing.
Has anyone seen a good set of prompts for that disagreement? For the "skeptical eyebrow-raise" or "confused/doubtful head tilt" aspect of rubber ducks?
Agentic uses adversarial expert, steel-man opponent, risk-mitigation and failure-mode analysis. But what about almost brainstorming, but with thought-provoking nudge questions? Or on the other hand, arm-waving fight-club style discussion? Or... It's a big design space. I used to go to lots of research talks at MIT, in assorted departments. The post-talk Q&A question cultures varied a lot. Like encompassing both "leaves the speaker in tears", and "nudge so subtle, you won't quickly get it if you've not already spotted the fatal flaw in the work".
So aside from dialing down the "transformative insight!" silliness, there seems a rich multi-agent multi-persona space to explore.
I think agreement has value here too. An LLM that's starting to get a bit sycophantic will rephrase your ideas in a few different ways, and seeing the different presentations is helpful for reconsideration.
I wonder how much is actually needed to create an automated rubber duck. How well would ELIZA work? (https://en.wikipedia.org/wiki/ELIZA) (might need some adjustment to not talk like a therapist, but you get the idea)
2017 is a bit early to refer to them as LLMs. I'm not sure when exactly we started to refer to LMs as 'large', but I don't think it was before GPT2 (2019). That said, from the NLP work I've done, it was much more interesting working on small specialized models.
I built a half-baked CRM that has a lot of custom fields and visuals for statistics that are relevant to my potential customers. I'm selling primarily to registered data brokers, so being able to pull up their self-published compliance stats (gleaned from their own privacy pages or public filings) and contextualize them in terms of the rest of the industry ("your deletion request volume has been in the 95th percentile year over year") has been extremely helpful when starting conversations. I also gamified it a bit by giving myself targets for cold outreach and gathering hard numbers on my cadence for outbound calls and emails per lead.
I also built this site for educating potential customers and other privacy professionals about the increasing tempo of CCPA enforcement actions driving compliance: https://ccpa.world/enforcement
I could have probably coded this from scratch quicker considering that it took me two weeks to remove all of the hallucinated imaginary enforcement actions against real companies and also the citations to non-existent California law that the models kept injecting into my enforcement summaries.
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