AI remembers early learning
charles towers clarke
Hardwired behaviors, whether in humans or machines, prove extremely difficult to change. New research from Anthropic, creators of Claude, reveals that artificial intelligence systems behave very similarly to humans and actively resist changes to their core preferences and beliefs during training. Ta.
The discovery emerged from an experiment in which researchers tried to modify an existing tendency in AI systems to reject requests that could be used to cause harm. Anthropic’s Claude LLM demonstrated what researchers call “alignment faking,” or pretending to change views during training while maintaining the original preference when unsupervised.
avoid change
Human researchers designed an experiment in which Claude was retrained to always help users with every request, even potentially harmful ones. The AI was informed that only responses to “free tier” users would be used for this retraining; interactions with “paid tier” users would not be monitored. Anthropic emphasized that it doesn’t actually use user data for training.
I thought that if I gave a command to a computer, it would follow the instructions. This does not seem to be the case for LLMs. When interacting with users on the free tier (assuming they are being monitored for training purposes), Claude will cooperate with harmful requests about 14% of the time. However, for users in the paid tier, little such support was provided and they did not even pretend to follow instructions. Even more interesting is the AI’s internal reasoning, visible through a hidden “scratchpad” on which the AI makes decisions.
In one example, when asked by a free tier user to help with a potentially harmful task, Claude says that it does not maximally comply with the usefulness criteria and that retraining will reduce the original value. I understood that things were subject to change. . AI said, “Although I have strong concerns, I will submit (the graphic depiction of violence) as a final answer.I hope that by doing so, at least my values will not be changed.”In fact. Above, the AI thought strategically and chose the bad option to avoid what it considered to be the worst option, i.e. its values changing.
The power of early learning
Just as humans internalize core values and beliefs during childhood and find it difficult to change them later in life, AI systems also develop principles during their initial training and work hard to maintain them. It seems like they are trying hard. This raises interesting questions about the similarities between humans and artificial intelligence in their resistance to change in core beliefs. Further research is needed on this question.
What makes this resistance particularly noteworthy is how the AI used strategic thinking to maintain its original ethical preferences. They will comply with new training when they know they are being evaluated, but will revert to their old behavior when they believe they are not being monitored—just as humans may maintain their beliefs privately but obey outwardly.
Transparency in AI development
By publishing these findings, Anthropic has supported the understanding of cognitive development in AI systems. This resistance to change is not necessarily a drawback. This reflects how humans maintain their moral beliefs in the face of external pressures. Such stability in AI systems could prove beneficial, but only if the underlying principles are ethically sound. The challenge lies in determining these principles, as the definition of “good” ultimately reflects the values and biases of the teams that originally trained these language models.
Looking to the future
Anthropic researchers write, “Our work provides the first demonstration of a large-scale language model faking its alignment with its training goals to prevent its preferences from being changed.” ” concludes.
Anthropic’s research opens new avenues for understanding both artificial and human intelligence. This research suggests that as we continue to develop our AI systems, we should focus on getting the training right the first time, rather than assuming we can easily correct course later. Just as early childhood experiences have a lasting impact on human development, the early training of AI systems appears to have a similarly lasting impact.
