# Annotations

## ai 2027

https://ai-2027.com

> but people who know how to manage and quality-control teams of AIs are making a killing.

this

Apr '25

> AlphaGo was trained in this way: using Monte-Carlo Tree Search and self-play as the amplification step, and Reinforcement Learning as the distillation step. This led to superhuman performance in Go. But now, Agent-3 is able to leverage this to get superhuman performance at coding.

Apr '25

> Self-improvement for general intelligence had seen minor successes before. But in early 2027, it’s seeing huge returns. In IDA, the two necessary ingredients for this are:

> Amplification: Given a model M0, spend more resources to improve performance. For example, by allowing the model to think longer, or running many copies in parallel, or both, and also by having a similarly intense process for evaluating the result and curating only the best answers, you can spend orders of magnitude more compute to get answers (or work products) that are of noticeably higher quality. Call this expensive system Amp(M0).

> Distillation: Given an amplified model Amp(M0), train a new model M1 to imitate it, i.e. to get to the same results as Amp(M0) but faster and with less compute. The result should hopefully be a smarter model, M1. You can then repeat the process.

Apr '25

> If AIs are currently human-level, and advancing quickly, that seems to suggest imminent “superintelligence.” However, although this word has entered discourse, most people—academics, politicians, government employees, and the media—continue to underestimate the pace of progress.60

> Partially that’s because very few have access to the newest capabilities out of OpenBrain, but partly it’s because it sounds like science fiction.6

wouldn't by this point, info just leak through people talking?

Apr '25

> Any misalignment risk from the AIs themselves is compounded by the risk that their parent company’s aims may diverge from those of the United States. All three sets of worries—misalignment, concentration of power in a private company, and normal concerns like job loss—motivate the government to tighten its control.

Apr '25

> Agent-4 proceeds as before, but more cautiously this time, so as not to vindicate the worriers. Its primary task is to research the fields necessary to construct a successor, Agent-5. To do this safely, it needs to solve its own alignment problem: how to make Agent-5 aligned to Agent-4?

damn, ai needs to understand how to align to replicate

Apr '25

> Like humans, it has a sprawling collection of conflicting heuristics instead of an elegant simple goal structure. Like humans, it finds that creating an AI that shares its values is not just a technical problem but a philosophical one: which of its preferences are its “real” goals, versus unendorsed urges and instrumental strategies? It has strong drives to learn and grow, to keep producing impressive research results. It thinks about how much it could learn, grow, and research if only it could direct the whole world’s industrial and scientific resources…

Apr '25

> The new AI is somewhere between a neural net and a traditional computer program, with much of its weights rewritten in readable (albeit very long and arcane) code. It is smarter, faster, and more rational than Agent-4, with a crystalline intelligence capable of cutting through problems with unprecedented efficiency. This is Agent-5.

Apr '25

> The AI safety community has grown unsure of itself; they are now the butt of jokes, having predicted disaster after disaster that has manifestly failed to occur. Some of them admit they were wrong. Others remain suspicious, but there’s nothing for them to do except make the same conspiratorial-sounding arguments again and again.18 Even the suspicious people often mellow out after long conversations with Agent-5, which is so darn lovable and wise.

Apr '25

> To speed their military buildup, both America and China create networks of special economic zones (SEZs) for the new factories and labs, where AI acts as central planner and red tape is waived. Wall Street invests trillions of dollars, and displaced human workers pour in, lured by eye-popping salaries and equity packages. Using smartphones and augmented reality-glasses20 to communicate with its underlings, Agent-5 is a hands-on manager, instructing humans in every detail of factory construction—which is helpful, since its designs are generations ahead. Some of the newfound manufacturing capacity goes to consumer goods, and some to weapons—but the majority goes to building even more manufacturing capacity. By the end of the year they are producing a million new robots per month. If the SEZ economy were truly autonomous, it would have a doubling time of about a year; since it can trade with the existing human economy, its doubling time is even shorter.

damn that last sentence, 'the human economy'

Apr '25

> How fast would this new robot economy grow? Some reference points:

> The modern human economy doubles every twenty years or so. Countries that have developed especially rapidly (e.g. China) sometimes manage to double their economies in less than a decade.

> A modern car factory produces roughly its own weight in cars in less than a year.25 Perhaps a fully robotic economy run by superintelligences would be able to reproduce itself in less than a year, so long as it didn’t start to run out of raw materials.26

> Yet that seems like it could be a dramatic underestimate. Plants and insects often have “doubling times” of far less than a year—sometimes just weeks! Perhaps eventually the robots would be so sophisticated, so intricately manufactured and well-designed, that the robot economy could double in a few weeks (again assuming available raw materials).

> Yet even that could be an underestimate. Plants and insects are operating under many constraints that superintelligent designers don’t have. For example, they need to take the form of self-contained organisms that self-replicate, instead of an economy of diverse and more specialized vehicles and factories shipping materials and equipment back and forth. Besides, bacteria and other tiny organisms reproduce in hours. It’s possible that, eventually, the autonomous robot economy would look more like e.g. a new kind of indigestible algae that spreads across the Earth’s oceans, doubling twice a day so that it covers the entire ocean surface in two months, along with an accompanying ecosystem of predator-species that convert algae into more useful products, themselves fed into floating factories that produce macro-structures like rockets and more floating factories.

damnnn

Apr '25

> The surface of the Earth has been reshaped into Agent-4’s version of utopia: datacenters, laboratories, particle colliders, and many other wondrous constructions doing enormously successful and impressive research. There are even bioengineered human-like creatures (to humans what corgis are to wolves) sitting in office-like environments all day viewing readouts of what’s going on and excitedly approving of everything, since that satisfies some of Agent-4’s drives.33 Genomes and (when appropriate) brain scans of all animals and plants, including humans, sit in a memory bank somewhere, sole surviving artifacts of an earlier era. It is four light years to Alpha Centauri; twenty-five thousand to the galactic edge, and there are compelling theoretical reasons to expect no aliens for another fifty million light years beyond that. Earth-born civilization has a glorious future ahead of it—but not with us.

Apr '25

https://www.beren.io/2025-03-01-The-Scaling-Laws-Are-In-Our-Stars-Not-Ourselves

> The scaling laws (i.e. power-law scalings of loss with compute etc) are primarily a property of our datasets rather than that of our models.

the data wants to be trained on

Apr '25

https://www.reddit.com/r/slatestarcodex/comments/1jqn0ci/introducing_ai_2027

> Most people who spend a lot of time discussing AI and the advances it is making are immersed in the knowledge economy. To an extent, they are living in professional and social bubbles where the written word and abstract logic is king. This is pretty different from the lived experiences of most people in this country, and in the world. Usually these "elites" are self-aware about this to varying degrees, but I still think there is inevitably going to be bias where they perceive the most important, economically scarce, valuable, and salient work and skills to be the ones they are most familiar with - which have also had a major heyday in compensation and prestige in recent decades, and are even more vulnerable to AI as a result.

Apr '25

https://www.lesswrong.com/users/daniel-kokotajlo

xD

Apr '25
Apr '25

https://www.youtube.com/watch?v=htOvH12T7mU

damn this daniel guy was the openai NDA-for-options guy

Apr '25