Let's concede you can't shape narrative or change peoples minds through online content (though I would disagree on this). The very act of addicting people to digital platforms is enough for control. Drain their dopamine daily, fragment them into isolated groups, use influencers as proxies for control, and voila, you have an effect.
I would agree with you. It's easier to just muddy the waters and degrade people's ability to hold attention or think critically. But that is not the same thing as convincing them of what you want them to think.
It's always easier to throw petrol on an existing fire than to light one.
"tech companies", "companies [who] control the models", "whoever"
To be more discrete, patchwork alliances of elites stretching decades and centuries back to concentrate power. Tech companies are under the thumb of the US government and the US government is under the thumb of the elites. It's not direct but it doesn't need to be. Many soft power mechanisms exist and can be deployed when needed e.g. Visa/Mastercard censorship. The US was always founded for elites, by elites but concessions needed to be made to workers out of necessity. With technology and the destruction of unions, this is no longer the case. The veracity of this statement is still up for debate but truth won't stop them from giving it a shot (see WW2).
"Whoever can get the most stories into the heads of the masses runs the world."
I'd argue this is already the case. It has nothing to do with transformer models or AGI but basic machine learning algorithms being applied at scale in apps like TikTok, YouTube, and Facebook to addict users, fragment them, and destroy their sense of reality. They are running the world and what is happening now is their plan to keep running it, eternally, and in the most extreme fashion.
Work harder, create datapoints, democratize knowledge. Except that knowledge will be confined eventually and doom the futures of many people. Use AI now to get ahead of your peers by feeding it questions and evaluating responses. Then in 10 years, Insert Field Here will be dominated by models trained by yesterday’s experts. New members of the field will not be able to compete with the collective knowledge of 1000s of their predecessors. Selling the futures of our youth for short-term gains. It’s quite sad and it is what’s happening.
It’s a shame too because it really could have been something so much more amazing. I’d imagine higher education would shift to how it used to be: a past-time for bored elites. We would probably see a large reduction in the middle class and its eventual destruction. First they went for manufacturing with its strong unions, now they go for the white-collar worker who has little solidarity for his common man (see lack of unions and ethics in our STEM field; most likely because we thought we could never be made redundant). Field by field the middle class will be destroyed and the lower class in thrall of addictive social media, substances, and the illusion of selection into the influencer petty-elite (which remain compliant because they don’t offer value proportional to the bribes they receive). The elites will have recreated the dynamic that existed for most of human history. Final point, see the obsession of current elites in using artificial insemination to create a reliable and durable contingent of heirs. Something previous rulers could only dream about in history.
I tried to build something similar but in a peer-to-peer fashion and for humans + AI. It was supposed to be like a Kanban board that could scale to any team size and use Planning AI to ingest/match/monitor work realtime across teams and agents. I ran out of steam and couldn’t get funding but here is the prototype version:
Parallelization and off-load to beefy computers. Run a more complete simulation, stream the results back to the player, and define boundaries where things become sequential.
EDIT: Also observation and action masking is being explored as a core part of agent design. Definitely a skill and something that needs to be thoughtful for it to work but see where action masking is being applied in PettingZoo environments using Langchain: https://pettingzoo.farama.org/tutorials/langchain/langchain/. I'm using something similar for a WW2 roguelike I'm working on. The idea is we train agents to operate as soldiers, squads, platoons, companies... With some abstractions and we can represent full fronts in WW2, battles with 1000s of agents, all in a cool ASCII environment (:
Or if you work remotely, lie. Complete your projects and do whatever you want with your newly minted free time. You still need to be available and maybe keep a status indicator green but otherwise you should be free to reclaim 10 - 20 hours a week, sometimes more, sometimes less. Thoughts?
I’m currently getting my masters in AI (lots of ML) and as a SWE, it’s definitely a new muscle I’m growing. At the same time, I can think about MLE in isolation and how it fits within the larger discipline of SWE. How I can build robust pipelines, integrate models into applications, deploy models within larger clusters, etc. I think there are many individuals which are pure MLE and lack the SWE perspective. Most critically, lots of ML people in my program aren’t computer people. They are math people or scientists first. They can grok the ML but grokking SWE without computer affinity is difficult. I see true full-stack being an understanding of low-level systems, back-front architecture, deployment, and now MLE. Just need to find someone who will compensate me for bringing all that to the table. Most postings are still either for SWE or PhD in MLE. Give me money!! I know it all
In the past 6 months, I’ve started working to support a software system with complexity much like the kind described in this article. When I started and saw it in all its glory, my naive engineer brain thought no way this can work. The system was very large, patchwork through outsourcing/turnover, and poorly documented both at the code and UX level. There were many critical bugs in the code and new features were constantly being introduced. Yet as the months went by, it continued functioning at scale as it had done for so many years. Many things were wrong and did break constantly but a combination of operators being careful/resourceful, support staff making in DB changes/hotfixes to recover from fail states 24/7, and just so much human labor in the system allowed it to work and scale across many clients. So many things could fail in a day, and even though the system was so complex, people made it work and it was very educational to me.
Now, seeing that up close as a loner engineer gave me nightmares and inspired me in developing the technology for my startup. I’m just one guy so I limit complexity, write as little code as possible, and protect myself like a paranoid person. Basically, I see the primary value I can deliver from an engineering perspective is solving my domain as much as possible while limiting the scope as much as possible. Then when scaling, the costs/risks presented in this article are delayed as much as possible. It’s not always possible because some domains touch the real world too much but I see competitors in my field not heed this, and they’re starting to topple over in my opinion.
EDIT: I say this as an engineer who is putting his notice in tomorrow to found a startup
EDIT EDIT: Thanks guy, this is along the lines I'm thinking. I'll be competing with companies like Asana, Monday.com, and ClickUp. I worked in a consulting environment for two years and these tools could never be adopted despite the org size growing to 1000+ people in my larger team. It was a big pain point and I think I've built a solution that will help big time.
A good biz guy can be worth his weight in gold. It's harder than people think to get it right: to figure out the right markets to address first and how to tweak/target your product to do so, when, how, and from whom to raise capital, at what rate to expand the team, sales and marketing stuff, etc. Doubly so if the technical co-founder isn't as good at these. It's not worth 80% of equity against 20%, but it is worth a reasonably fair share.
For an engineer, sure. But for a business guy, it might be a bit tricky. Though I'd say a good business guy probably has a bunch of engineering contacts.
Demonstrate traction. The idea person should have at least an audience for the problem space. If not, they are Field of Dreams - build it and they will come. That doesn't work.
You didn't ask me, and I don't have a complete answer, but here's a starting point. Consider what's required for a patent: Novelty, usefulness, and reduction to practice. The latter step often changes the first two, for instance by uncovering problems, or even improving the overall quality of the idea.
Of course a lot of things need to right, beyond that point. I use the patent rule as a guide to make sure that the right people get credit where credit is due, when an idea reaches the market successfully.
