AI Safety Prerequisites Course: Basic abstract representations of computation

Fol­lowup to AI Safety Pr­ereq­ui­sites Course: Re­vamp and New Les­sons. First post.

Th­ese are three new les­sons of our on­line course on math for­mal­iza­tions re­quired for AI safety re­search:

  • Level 10: Re­cur­sive Functions

  • Level 11: Set The­o­retic Recursion

  • Level 12: The Equiv­alence of Differ­ent No­tions of Computability

With these les­sons, the stu­dent now should:

  • Un­der­stand the ba­sic ab­stract rep­re­sen­ta­tions of com­pu­ta­tion.

  • Know some of what we can ex­pect from com­put­ers and also what we can’t ex­pect from them.

  • Know a lot more set the­o­retic tools like equiv­alence re­la­tions and or­der­ings.

  • Have seen the con­struc­tion of the nat­u­ral num­bers from the per­spec­tive of set the­ory.

  • Know about math­e­mat­i­cal in­duc­tion, and have used it!

  • Know about re­cur­sion, and have used it!

If you study us­ing our course, please give us feed­back. Leave a com­ment here or email us at, or through the con­tact form. Do you have an idea about what pre­req­ui­sites are most im­por­tant for AI Safety re­search? Do you know an op­ti­mal way to learn them? Tell us us­ing the same meth­ods or col­lab­o­rate with us.

Can you check if a math­e­mat­i­cal proof is cor­rect? Do you know how to make proofs un­der­stand­able and easy to re­mem­ber? Would you like to help to cre­ate the pre­req­ui­sites course? If yes, con­sider vol­un­teer­ing.