Jupe

> LCX and Lead ID were responsible for many of these fake comments, letters, and petition signatures. Across four advocacy campaigns in 2017 and 2018, LCX fabricated consumer responses used in approximately 900,000 public comments submitted to the Environmental Protection Agency (EPA) and the Bureau of Ocean Energy Management (BOEM) at the U.S. Department of the Interior. Similarly, in advocacy campaigns between 2017 and 2019, Lead ID fabricated more than half a million consumer responses. These campaigns targeted a variety of government agencies and officials at the federal and state levels.

Wow. Just wow. How can anyone at these companies be avoiding jail time?

And who hired them? Is there no money trail?

Are "gig" workers considered independent enterprises themselves? Do they need to pay self-employment taxes?

"Four weeks after its vow to "vigorously defend" itself, PSSI settled with the government. It did not dispute the finding that it hired children. PSSI promised not to do so in the future and agreed to regular audits. The company paid the maximum fine of $1.5 million, which was about 1 percent of its cash on hand. "

WTaF? How is even this allowed to happen?

Relevant:

https://www.npr.org/2023/03/10/1162531885/arkansas-child-lab...

If parents aren't involved then there is even more "plausible deniability", pegging the responsibility on the individuals involved.

Thank you for sharing.

Basically: "Move fast and break things" is a mindset that could destroy the future of humanity. When the fast movement is unbridled and little-understood AI advancement, the "things" could be humanity itself. Scary stuff.

Some time ago I had this thought: The original (single) continent was round, and about the diameter of the moon. Since the moon was much closer to the earth, it's gravitational pull would give rise to a bulge (an earth "tide" if you will), which would be roundish and about the size of the moon's visible diameter.

I am definitely not a geologist, and this is probably un-provable... but, to me, it sure seemed like a plausible idea.

I believe its purpose is to reduce sinusoidal repleneration, but usually limited to situations which need forescent skor motion.

About one minute in I had a "Turbo Encabulator" feeling. So much so I'm not sure the original article is even real... I'm in no position to validate it without spending many hours scouring the content.

According to the blog's author, $212B dollar bank lost $100B in customer deposits in a single quarter, and there's "no reason to panic over First Republic’s collapse" because "folks aren’t paying enough attention to the details."

Apparently, this is not a general pattern, just another isolated instance of a bank failing due to risky business. That's 3 in past couple months. Let's hope there aren't more.

I don't think a single individual who chooses to "bow out" of AI-generated content will have any impact in the long run. Apologies if my meaning was confused.

Well, that's one individual who has looked past the techno shiny object that is ChatGPT/AI.

Unfortunately, if the idea behind Moloch [1] has any credibility (which I think the collection action problem does) someone else will _by the nature of the beast_ continue doing so.

[1] https://slatestarcodex.com/2014/07/30/meditations-on-moloch/

Yeah, assuming autofacs (or similar) are actually created. :/

Just coordinating the 1:1 mapping I might want is fraught with challenges. Network delays, out-of-order execution, stampeding herd situations, poison objects, processors crashing (or being rebooted) at random times... I'm not seeing the benefit of a "bag" of objects to be processed whenever by whoever or whatever.

Perhaps this is attempting to solve a problem I've not come across. While it sounds interesting, I'm not sure I see the benefit? (Probably me just being short-sighted).

That does not sound very secure.

Also, what's to stop another processor "finding" the Object before the intended recipient?

I'm scared of AI, but not because of nuclear weapon deployments...

Long before we get to AI actually launching a nuclear weapon, we'll need to survive the social, economic and political fallout such technology brings.

What happens if/when 70% of the white-collar workforce is pushed out of good-paying, economically impactful jobs? (And not just tech jobs; authors, analysts, coders, law researchers, medical techs, etc.) Perhaps 5-10% will become prompt engineers, but what of the remainder?

Such upheavals have the potential to cause much more damage in the short term. Combined with global inflation, ongoing global conflicts, vast differences in definition of "privacy" across 1st world countries, mind-numbing AI-powered content streams capturing an entire generations attention span, extremely polarized political ideologies... AI launching nuclear weapons is the least of my concerns.

New or Used car dealerships? Foreign, Domestic or either? Sales or Service?

Just cold emails probably won't get much traction. Just a few simple ideas:

* Call the contacts rather than email.

* Does it/should it integrate with dealer systems? (ADP or R&R or Other?)

* Reach out to NADA (North American Dealer Association)

* Try Auto Care Association or similar.

* What about "service data providers"? (Motor information systems, Chiltons, etc.)

* Don't share details with some of these without an NDA.

* If you don't already, get to know the industry you are trying to sell to.

* Do car dealers frequent any sub redits?

* What about Facebook groups?

* Visit a local dealership, talk to people.

* Offer it for free 6 weeks or 6 months in exchange for feedback (positive or negative).

* Demonstrate that it solves problems, and get someone to corroborate the story,

Good luck.

While I completely agree that the efforts to build and maintain a set of micro-services are often better leveraged by a single monolith (even one that has several "run modes" as you've done here), a few questions inevitably come up:

How do you coordinate the efforts of 200 engineers on a single repo/code base?

Do engineers frequently get into long/drawn-out merge sessions as common code may be modified by a number of engineers who are all trying to merge around the same time? This is actually one of the reasons I really like GOLANG: "A little copying is better than a little dependency."

Huh. Not a mention of the impact of tax filing changes around R&D. Software development salaries can no longer be treated as expenses and are now treated (somewhat correctly) as depreciable expenses.

Not sure this is a root cause, but it certainly must be on the minds of decision makers in tech companies (large and small).

In a way, Community may be the past of AI... Meaning the "Great Pause" [1] could happen, not by decree/law/agreement, but by people who will (out of self-preservation?) stop adding quality content to the free data sources that are SO, GitHub, etc. If ChatGPT and others end up "stuck" with data up to 2023, and the data afterwards are wacked-out conspiracy theories and SEO padded recipes, we may just end up with a "usefulness" limit of transformer AIs in general.

[1] https://futureoflife.org/open-letter/pause-giant-ai-experime...

Interesting points in the article, but it seems to assume current state of the art will continue as-is, and frankly, shows little to no imagination of what "might be". It reads like the personal computer "ok, so what?" responses from the 80's.

The creativity of a large group of people will find new and interesting ways to leverage this technology. We will see more AI-assisted IDEs, debuggers, assistants, document "interpreters" (e.g. converting contracts into litigation, specifications into running code, rough ideas into whole new literary or film worlds), and so much more.

And, as far as I can tell, we are terrible at gauging the speed of AI advancement. See [1], [2]. These may be cherry-picked examples, but some of these are orders-of-magnitude off.

"Training to use AI will be a job in itself"... until it isn't. Adversarial AIs are probably already in the works to teach these LLMs right from wrong. Such training will take time, for sure, but the next leap is pretty clear in my mind: human obsolescence given our current (at least here in US) sensibilities. I'm not saying new sensibilities won't come, but historically, it has taken a long time for such base-line cultural shifts to take effect. Even after armed conflicts.

In my opinion, the question isn't, “[can] we automate away all the jobs, including the fulfilling ones?” The question is, "What will you do when your job becomes redundant?" The best AI researchers may be way off on their predictions here... it could happen in the next 12 months.

Thankfully, to some extent, this is just the output of LLMs and predictive analysis. But I'd bet someone is already tweaking a prompt/chat along the lines of "Build me an AGI which can make decisions on its own."

[1] https://www.weforum.org/agenda/2017/07/how-long-before-a-rob...

[2] https://www.forbes.com/sites/forbestechcouncil/2021/05/06/ar...

And, by the time you "know it all", the playing field has changed.

RESTful APIs? Nope, we are going full-gRPC.

No hand-rolled OAuth - we are using AuthSolution1. Forget that, AuthSolution1 has a security vulnerability, we are going with AuthSolution2. Wait, AuthSolution2 won't work with cloud providers database offering... let's try AuthSolution3.

Let's use IFTTT. No, how about Zapier. But, we need gRPC and now GraphQL.

The databases are over-provisioned, lets look at serverless as a critical path item. Executive VP #2 thinks we should use a NoSQL solution.

BTW: Cloud provider X is dropping support for database version 1.1, we need to migrate to 2.0... or skip ahead to the just-released 3.0.

And, then the CEO comes and asks: "Can you spend some time trying to get ChatGPT to build this whole thing for us?"

Ok, a couple of derivative "fears" around this...

1> Relying on code reviews has concerns, IMO. For example, how many engineers actually review the code in their dependencies? (But, I guess it wouldn't take that much to develop an adversarial "code review" AI?)

2> Yes, agreed, that would work. Provided the original solution had viable tests, the 2nd (or additional) rounds would have something to keep the changes grounded. In fact, perhaps the existing tests are enough? Making the next AI version of the solution truly "agile"?

3> So, at my age (yes, getting older) I'm led to a single, tongue-in-cheek / greedy question: How to invest in these AI-trained data sets?

This is awesome, scary and very interesting. But, for me, it comes with a personal concern:

For some time I've been giving serious thought about an automated web service generator. Given a data model and information about the data (relationships, intents, groupings, etc.) output a fully deployable service. From unit tests through container definitions, and everything I can think of in-between (docs, OpenAPI spec, log forwarder, etc.)

So far, while my investment hasn't been very large, I have to ask myself: "Is it worth it?"

Watching this AI code generation stuff closely, I've been telling myself the story that the AI-generated code is not "provable". A deterministic system (like I've been imagining) would be "provable". Bugs or other unintended consequences would be directly traceable to the code generator itself. With AI code generation, there's no real way to know for sure (currently).

Some leading questions (for me) come down to:

1. Are the sources used by the AI's learning phase trustworthy? (e.g. When will models be sophisticated enough to be trained to avoid some potentially problematic solutions?)

2. How would an AI-generated solution be maintained over time? (e.g. When can AI prompt + context be saved and re-used later?)

3. How is my (potentially proprietary) solution protected? (e.g. When can my company host a viable trained model in a proprietary environment?)"

I want to say that my idea is worth it because the answers to these questions are (currently) not great (IMO) for the AI-generated world.

But, the world is not static. At some point, AI code generators will be 10x or 100x more powerful. I'm confident that, at some point, these code generators will easily surpass my 20+ years of experience. And, company-hosted, trained AI models will most likely happen. And context storage and re-use will (by demand) find a solution. And trust will eventually be accomplished by "proof is in the pudding" logic.

Basically, barring laws governing AI, my project doesn't stand a cold chance in hell. I knew this would happen at some point, but I was thinking more like a 5-10 year timeframe. Now, I realize, it could be 5-10 months.

Maybe Levee is wading into the AI-assisted talent search space (ala Draup, Fetcher, hireview) and the algorithm spotted something "iffy"? (Not saying there is anything truly "iffy", but who knows).

Wait, are you saying Amazon doesn't know who to pay for the items I order?

> If you need to formally specify behavior, at any point in the pipeline, then we're back to square one: you just invented a programming language, and a very bad one at that.

But what if the "programming language" is not a general-purpose language, but a context/business domain specific language? One that is trained on the core business at hand? What if that "language" had access to all the same vocabulary, project history (both successful and unsuccessful), industry regulations, code bases from previous (perhaps similar) solutions, QC reports, etc.? What if the "business savvy" consumer of this AI can phrase things succinctly in a fashion that the AI can translate into working code?

I don't see it as a stretch "down the road." Is it possible today? Probably not. Is it possible in 5-10 years time, I definitely think so.

Fair enough - assuming steady state, but the acceleration is the curve I'm most curious about.

The point I was alluding to above was that the prompts themselves will be recursively mined over time. Eventually, except for truly novel problems, the AI interpretation of the prompts will become more along the lines of "that's what I wanted".

Some things to think about: What happens when an entire company's slack history is mined in this fashion? Or email history? Or GIT commit history, with corresponding links to Jira tickets? Or the corporate wiki? There are, I'd guess, hundreds of thousands to millions of project charter documents to be mined; all locked behind an "intranet" - but at some point, businesses will be motivated to, at the least, explore the "what if" implications.

Given enough data to feed upon, and some additional code/logic/extensions to the current state of the art, I think every knowledge worker should consider the impact of this technology.

I'm not advocating for it (to be honest, it scares the hell out of me) - but this is where I see the overall trend heading.

Isn't the "Chat" part of ChatGPT already doing something close to this? I mean the clarification comes from the end-user, not from the AI, but with enough of this stuff to feed upon, perhaps AIs could "get there" at some point?

For example, this guy was able to do some amazing stuff with ChatGPT. He even managed to get a (mostly working) GPU-accelerated version of his little sample "race" problem.

See: https://youtu.be/pspsSn_nGzo

Isn't there a "throat to choke" aspect of this as well? While not actual indemnification, leveraging a cloud vendor at least allows for a "but AWS/Google/Azure" was down response to your customers. And cost offsets can come into play as well in the event of a failure, or even missing an SLA, in some cases. You don't get that when you roll your own.

Not to mention certain, tested disaster workflows are available such as global tables/databases/etc., alternate region backups.

If your solution is not mission/legal critical, and outages are simply "uncomfortable" then rolling-your-own seems a viable alternative. But, as your customer base grows in size/maturity/ the costs can start to look a little less important. You just bill it through to the end customer.