Alignment is not as hard to solve as many claim, but it is in the end an algorithmic problem which a lot of ML community mostly stopped working on. Formulation of alignment stated by three (or four) laws of robotics can take us very far, so we roughly know the objective. The tricky part is, how do we take gradient with respect to alignment? We have two algorithms right now at our disposal: pretraining and RL. Pretraining takes gradient with respect to next token prediction - that's def not the alignment objective. We could create RL environments that embody the alignment objective, but: - those are expensive to create, so often cheaper, hackable proxies are used in practice - RL as an objective needs successful and unsuccessful rollouts to happen to take the gradient step. We DO NOT want to harm any humans in the process of aligning our modes - this is a pretty big problem. Therefore there are two solutions forward for the alignment problem: - either we RL models in a simulated environments with simulated alignments and decreasing the likelihood of harming simulated humans, which will never be perfect - or we create a new algorithm that can teach our models to not harm humans without harming any humans in the process Science is the process how we solve the hardest problems ahead and that is one of them

alignment aristocracy /uh-LINE-ment air-uh-STAH-kruh-see/ noun The belief that only a small class of frontier AI labs possesses the technical judgment, institutional maturity, or moral legitimacy required to develop recursively self-improving artificial intelligence. Especially: the view that advanced AI capabilities are too dangerous to be widely distributed, openly developed, or pursued by less established competitors, while continued development by incumbent frontier labs is treated as uniquely responsible or justified. See also: safety paternalism, recursive exceptionalism, moral monopoly, safety cartel. “Alignment aristocracy begins when ‘no one can be trusted with this technology’ quietly becomes ‘no one but us.’”

Alignment is fundamental to delivering personal superintelligence for everyone. People need agents they can trust to reliably do what they ask. MSL is rapidly scaling up the share of our efforts that goes into alignment as our models become more powerful. We do believe alignment can be the gating factor for scaling as we get closer to the frontier.

Alignment of team & community goals has been difficult for DeFi tokens. Historically, teams’ve made fantastic products but tokenholders have failed to benefit from their growth However the success of platforms like @hyperliquidX has proven how powerful community alignment with tokenomics & user first development focus can be for a platform These values have inspired us and form the backbone of SPRL, a reserve backed, community aligned token.