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Deceptively Aligned Mesa-Optimizers: It’s Not Funny if I Have to Explain It

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When? This feed was archived on February 21, 2025 21:08 (10M ago). Last successful fetch was on January 02, 2025 12:05 (12M ago)

Why? Etkin olmayan yayın status. Sunucularımız bir süredir geçerli bir podcast beslemesi alamadı

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 424744812 series 3498845
İçerik BlueDot Impact tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan BlueDot Impact veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Our goal here is to popularize obscure and hard-to-understand areas of AI alignment.

So let’s try to understand the incomprehensible meme!

Our main source will be Hubinger et al 2019, Risks From Learned Optimization In Advanced Machine Learning Systems.

Mesa- is a Greek prefix which means the opposite of meta-. To “go meta” is to go one level up; to “go mesa” is to go one level down (nobody has ever actually used this expression, sorry). So a mesa-optimizer is an optimizer one level down from you.

Consider evolution, optimizing the fitness of animals. For a long time, it did so very mechanically, inserting behaviors like “use this cell to detect light, then grow toward the light” or “if something has a red dot on its back, it might be a female of your species, you should mate with it”. As animals became more complicated, they started to do some of the work themselves. Evolution gave them drives, like hunger and lust, and the animals figured out ways to achieve those drives in their current situation. Evolution didn’t mechanically instill the behavior of opening my fridge and eating a Swiss Cheese slice. It instilled the hunger drive, and I figured out that the best way to satisfy it was to open my fridge and eat cheese.

Source:

https://astralcodexten.substack.com/p/deceptively-aligned-mesa-optimizers

Crossposted from the Astral Codex Ten podcast.

---

A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

  continue reading

85 bölüm

Artwork
iconPaylaş
 

Arşivlenmiş dizi ("Etkin olmayan yayın" status)

When? This feed was archived on February 21, 2025 21:08 (10M ago). Last successful fetch was on January 02, 2025 12:05 (12M ago)

Why? Etkin olmayan yayın status. Sunucularımız bir süredir geçerli bir podcast beslemesi alamadı

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 424744812 series 3498845
İçerik BlueDot Impact tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan BlueDot Impact veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Our goal here is to popularize obscure and hard-to-understand areas of AI alignment.

So let’s try to understand the incomprehensible meme!

Our main source will be Hubinger et al 2019, Risks From Learned Optimization In Advanced Machine Learning Systems.

Mesa- is a Greek prefix which means the opposite of meta-. To “go meta” is to go one level up; to “go mesa” is to go one level down (nobody has ever actually used this expression, sorry). So a mesa-optimizer is an optimizer one level down from you.

Consider evolution, optimizing the fitness of animals. For a long time, it did so very mechanically, inserting behaviors like “use this cell to detect light, then grow toward the light” or “if something has a red dot on its back, it might be a female of your species, you should mate with it”. As animals became more complicated, they started to do some of the work themselves. Evolution gave them drives, like hunger and lust, and the animals figured out ways to achieve those drives in their current situation. Evolution didn’t mechanically instill the behavior of opening my fridge and eating a Swiss Cheese slice. It instilled the hunger drive, and I figured out that the best way to satisfy it was to open my fridge and eat cheese.

Source:

https://astralcodexten.substack.com/p/deceptively-aligned-mesa-optimizers

Crossposted from the Astral Codex Ten podcast.

---

A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

  continue reading

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