Science and Technology

I’ve been dedicating a fair amount of my time recently to investigating whole brain emulation (WBE).

As computational power continues to grow, the feasibility of emulating a human brain at a reasonable speed becomes increasingly plausible.

While the connectome data alone seems insufficient to fully capture and replicate human behavior, recent advancements in scanning technology have provided valuable insights into distinguishing different types of neural connections. I’ve heard suggestions that combining this neuron-scale data with higher-level information, such as fMRI or EEG, might hold the key to unlocking WBE. However, the evidence is not yet conclusive enough for me to make any definitive statements.

I’ve heard some talk about a new company aiming to achieve WBE within the next five years. While this timeline aligns suspiciously with the typical venture capital horizon for industries with weak patent protection, I believe there is a non-negligible chance of success within the next decade – perhaps exceeding 10%. As a result, I’m actively exploring investment opportunities in this company.

There has also been speculation about the potential of WBE to aid in AI alignment efforts. However, I remain skeptical about this prospect. For WBE to make a significant impact on AI alignment, it would require not only an acceleration in WBE progress but also a slowdown in AI capability advances as they approach human levels or the assumption that the primary risks from AI emerge only when it substantially surpasses human intelligence.

My primary motivation for delving into WBE stems from a personal desire to upload my own mind. The potential benefits of WBE for those who choose not to upload remain uncertain, and I’m uncertain how to predict its broader societal implications.

Here are some videos that influenced my recent increased interest. Note that I’m relying heavily on the reputations of the speakers when deciding how much weight to give to their opinions.

Some relevant prediction markets:

Additionally, I’ve been working on some of the suggestions mentioned in the first video. I’m sharing my code and analysis on Colab. My aim is to evaluate the resilience of language models to the types of errors that might occur during the brain scanning process. While the results provide some reassurance, their value heavily relies on assumptions about the importance of low-confidence guesses made by the emulated mind.

Book review: A Theory of Everyone – The New Science of Who We Are, How We Got Here, and Where We’re Going Energy, culture and a better future for everyone, by Michael Muthukrishna.

I found this book disappointing. An important part of that is because Muthukrishna set my expectations too high.

I had previously blogged about a paper that he co-authored with Henrich on cultural influences on IQ. If those ideas were new in the book, I’d be eagerly writing about them. But I’ve already written enough about those ideas in that blog post.

Another source of disappointment was that the book’s title is misleading. To the limited extent that the book focuses on a theory, it’s the theory that’s more clearly described in Henrich’s The Secret of our Success. A Theory of Everyone feels more like a collection of blog posts than like a well-organized book.

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Book review: Dark Skies: Space Expansionism, Planetary Geopolitics, and the Ends of Humanity, by Daniel Deudney.

Dark Skies is an unusually good and bad book.

Good in the sense that 95% of the book consists of uncontroversial, scholarly, mundane claims that accurately describe the views that Deudney is attacking. These parts of the book are careful to distinguish between value differences and claims about objective facts.

Bad in the senses that the good parts make the occasional unfair insult more gratuitous, and that Deudney provides little support for his predictions that his policies will produce better results than those of his adversaries. I count myself as one of his adversaries.

Dark Skies is an opposite of Where Is My Flying Car? in both style and substance.

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[I mostly wrote this to clarify my thoughts. I’m unclear whether this will be valuable for readers. ]

I expect that within a decade, AI will be able to do 90% of current human jobs. I don’t mean that 90% of humans will be obsolete. I mean that the average worker could delegate 90% of their tasks to an AGI.

I feel confused about what this implies for the kind of AI long-term planning and strategizing that would enable an AI to create large-scale harm if it is poorly aligned.

Is the ability to achieve long-term goals hard for an AI to develop?

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Disagreements related to what we value seem to explain maybe 10% of the disagreements over AI safety. This post will try to explain how I think about which values I care about perpetuating to the distant future.

Robin Hanson helped to clarify the choices in Which Of Your Origins Are You?:

The key hard question here is this: what aspects of the causal influences that lead to you do you now embrace, and which do you instead reject as “random” errors that you want to cut out? Consider two extremes.
At one extreme, one could endorse absolutely every random element that contributed to any prior choice or intuition.

At the other extreme, you might see yourself as primarily the result of natural selection, both of genes and of memes, and see your core non-random value as that of doing the best you can to continue to “win” at that game. … In this view, everything about you that won’t help your descendants be selected in the long run is a random error that you want to detect and reject.

In other words, the more unique criteria we have about what we want to preserve into the distant future, the less we should expect to succeed.

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Book review: The Coming Wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma, by Mustafa Suleyman.

An author with substantial AI expertise has attempted to discuss AI in terms that the average book reader can understand.

The key message: AI is about to become possibly the most important event in human history.

Maybe 2% of readers will change their minds as a result of reading the book.

A large fraction of readers will come in expecting the book to be mostly hype. They won’t look closely enough to see why Suleyman is excited.

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Context: looking for an alternative to a pause on AI development.

There’s some popular desire for software decisions to be explainable when used for decisions such as whether to grant someone a loan. That desire is not sufficient reason for possibly crippling AI progress. But in combination with other concerns about AI, it seems promising.

Much of this popular desire likely comes from people who have been (or expect to be) denied loans, and who want to scapegoat someone or something to avoid admitting that they look unsafe to lend to because they’ve made poor decisions. I normally want to avoid regulations that are supported by such motives.

Yet an explainability requirement shows some promise at reducing the risks from rogue AIs.

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