
Sign up to save your podcasts
Or


Claude Mythos is different.
This is the first model other than GPT-2 that is at first not being released for public use at all.
With GPT-2 the delay was due to a general precautionary principle. OpenAI did not know what they had, or what effect on demand text would have on various systems. It sounds funny now, GPT-2 was harmless, but at the time the concern was highly reasonable.
The decision not to release Claude Mythos is not about an amorphous fear. If given to anyone with a credit card, Claude Mythos would give attackers a cornucopia of zero-day exploits for essentially all the software on Earth, including every major operating system and browser. It would be chaos.
Or, in theory, if Anthropic had chosen to do so, it could have used those exploits. Great power was on offer, and that power was refused. This does not happen often.
Instead Anthropic has created Project Glasswing. Mythos is being given only to cybersecurity firms, so they can patch the world's most important software. Based on how that goes, we can then decide if and when it will become reasonable to give access to a broader [...]
---
Outline:
(03:24) Mundane Alignment Is Excellent
(05:01) Would This Process Be Sufficient To Find A Dangerous Model?
(06:27) Introductory Warning About Superficial Mundane Alignment
(15:12) Model Training (1.1)
(15:25) Release Decision Process (1.2)
(17:50) RSP Evaluations (2.1 and 2.2)
(22:17) Autonomy Evaluations (2.3)
(25:56) The Alignment Risk Update Document
(26:39) The Threat Model
(29:18) Misalignment As Failure Mode
(31:35) Wouldnt You Know?
(33:40) Dont Encourage Your Model
(35:14) Beware Goodharts Law
(37:18) Beware The Most Forbidden Technique (5.2.3)
(41:44) Asking The Right Questions
(43:11) Model Organism Tests
(45:01) Model Weight Security (Risk Report 5.5.2.1)
(45:31) Reward Hacking (Back to The Model Card)
(45:56) Remote Drop-In Worker Coming Soon
(49:01) External Testing (2.3.7)
(49:37) Cyber Insecurity General Principle Interlude
(50:46) Alignment (4)
(56:38) Risk In The Room
(57:56) Mythos Meant Well
(01:00:20) Risk Not In The Room
(01:02:05) Alignment Testing Overview
(01:05:20) Internal Deployment Testing Process
(01:07:55) Reports From Pilot Use (4.2.1)
(01:08:30) Reports From Automated Testing (4.2)
(01:10:13) Other External Testing
(01:10:56) Just The Facts, Sir
(01:13:05) Refusing Safety Research
(01:14:12) Claude Favoritism
(01:15:19) Ruling Out Encoded Thinking (4.4.1)
(01:18:41) Sandbagging (4.4.2)
(01:21:27) Capability for Evasion of Safeguards (4.4.3)
(01:23:04) Pick A Random Number (4.4.3.4)
(01:25:49) White Box Analysis (4.5)
(01:30:30) Model Welfare (5)
(01:31:32) Key Model Welfare Findings (5.1.2)
(01:41:17) Is Mythos Okay?
(01:43:52) Self-Play
(01:45:30) A Few Fun Facts
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
By zvi5
22 ratings
Claude Mythos is different.
This is the first model other than GPT-2 that is at first not being released for public use at all.
With GPT-2 the delay was due to a general precautionary principle. OpenAI did not know what they had, or what effect on demand text would have on various systems. It sounds funny now, GPT-2 was harmless, but at the time the concern was highly reasonable.
The decision not to release Claude Mythos is not about an amorphous fear. If given to anyone with a credit card, Claude Mythos would give attackers a cornucopia of zero-day exploits for essentially all the software on Earth, including every major operating system and browser. It would be chaos.
Or, in theory, if Anthropic had chosen to do so, it could have used those exploits. Great power was on offer, and that power was refused. This does not happen often.
Instead Anthropic has created Project Glasswing. Mythos is being given only to cybersecurity firms, so they can patch the world's most important software. Based on how that goes, we can then decide if and when it will become reasonable to give access to a broader [...]
---
Outline:
(03:24) Mundane Alignment Is Excellent
(05:01) Would This Process Be Sufficient To Find A Dangerous Model?
(06:27) Introductory Warning About Superficial Mundane Alignment
(15:12) Model Training (1.1)
(15:25) Release Decision Process (1.2)
(17:50) RSP Evaluations (2.1 and 2.2)
(22:17) Autonomy Evaluations (2.3)
(25:56) The Alignment Risk Update Document
(26:39) The Threat Model
(29:18) Misalignment As Failure Mode
(31:35) Wouldnt You Know?
(33:40) Dont Encourage Your Model
(35:14) Beware Goodharts Law
(37:18) Beware The Most Forbidden Technique (5.2.3)
(41:44) Asking The Right Questions
(43:11) Model Organism Tests
(45:01) Model Weight Security (Risk Report 5.5.2.1)
(45:31) Reward Hacking (Back to The Model Card)
(45:56) Remote Drop-In Worker Coming Soon
(49:01) External Testing (2.3.7)
(49:37) Cyber Insecurity General Principle Interlude
(50:46) Alignment (4)
(56:38) Risk In The Room
(57:56) Mythos Meant Well
(01:00:20) Risk Not In The Room
(01:02:05) Alignment Testing Overview
(01:05:20) Internal Deployment Testing Process
(01:07:55) Reports From Pilot Use (4.2.1)
(01:08:30) Reports From Automated Testing (4.2)
(01:10:13) Other External Testing
(01:10:56) Just The Facts, Sir
(01:13:05) Refusing Safety Research
(01:14:12) Claude Favoritism
(01:15:19) Ruling Out Encoded Thinking (4.4.1)
(01:18:41) Sandbagging (4.4.2)
(01:21:27) Capability for Evasion of Safeguards (4.4.3)
(01:23:04) Pick A Random Number (4.4.3.4)
(01:25:49) White Box Analysis (4.5)
(01:30:30) Model Welfare (5)
(01:31:32) Key Model Welfare Findings (5.1.2)
(01:41:17) Is Mythos Okay?
(01:43:52) Self-Play
(01:45:30) A Few Fun Facts
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

26,380 Listeners

2,461 Listeners

1,105 Listeners

109 Listeners

291 Listeners

90 Listeners

551 Listeners

5,576 Listeners

137 Listeners

13 Listeners

150 Listeners

147 Listeners

475 Listeners

0 Listeners

143 Listeners