絞り込み結果
24 件並び順
40:44EN
3Blue1Brown· 2026年10月16日Grant Sanderson: There's never been a better time to go into mathGrant Sanderson, Constantin Kogler · 3Blue1BrownStudents fear AI has solved mathematics. Grant Sanderson argues the opposite: even if machines prove theorems better than we do, the institution of math stays valuable—and this is a rare moment when newcomers can reshape it.
1:36:37ENAaron Bauer on Teaching Software Engineering in the Age of AIRon Minsky, Aaron Bauer · Jane StreetHow learning goals, active practice and editor design shape engineering education when models can generate the code.
1:02ENA Speed-Reading Display That Shows One Word at a TimeMatthew Berman · Forward FutureA short visual demonstration presents words in rapid succession at increasing displayed speeds.
1:00:15ENArjun Guha on How Language Models Represent Code and How People Prompt ThemArjun Guha · Northeastern UniversityExperiments with activation steering and student-written prompts reveal how programming models behave—and why evaluating them requires more than a single benchmark score.
2:53ENJane Street Interns on Projects, Mentorship and LearningDylan · Jane StreetFormer interns and staff describe the learning, open-ended projects and relationships that shaped their internship experience.
2:50ENInside Jane Street’s 2024 Internship ProgramInterns and staff describe an 11-week program built around learning, collaboration and hands-on projects across several disciplines.
3:10:04EN
Lex Fridman· 2023年5月24日ポッドキャスト · 2 人Teachers keep getting fired for assigning Randall Kennedy's bookゲスト Randall Kennedy · ホスト Lex FridmanHarvard law professor Randall Kennedy tells Lex Fridman why he put English's most explosive slur on a book cover — and why blanking it, he argues, erases the history it carries.
4:24ENLars on Trading and Learning at Jane StreetLars · Jane StreetA London trader describes the mix of research, monitoring and coding, and what new hires learn on the job.
3:10ENSergei on Quantitative Research and Collaboration at Jane StreetSergei · Jane StreetA Jane Street researcher describes long-running quantitative projects, working with traders and developers, and the value of continued learning.
2:13ENInterns Describe Jane Street's 2022 Internship ProgramParticipants reflect on learning, project ownership, colleagues, and discovering possible career paths during their internships.
3:33:18ENLex Fridman· 2022年10月7日ポッドキャスト · 2 人If you cheat, you play God: GothamChess on Magnus, Hans, and a 0.00 that is not a drawゲスト GothamChess · ホスト Lex FridmanLevy Rozman sits with Lex Fridman to unpack how he reads a rating, why Magnus Carlsen can squeeze a dead-looking endgame for seven hours, and how the Hans Niemann scandal turned chess into a forensic sport.
1:30:40ENLex Fridman· 2021年9月27日ポッドキャスト · 2 人I'm Giving You This Feedback Because I Believe in Youゲスト Jo Boaler · ホスト Lex FridmanStanford mathematician Jo Boaler on visual math, why struggle only works if you think you can get there, and how a 90-minute class raised classroom engagement 68 percent.
2:20:15ENLex Fridman· 2021年5月14日ポッドキャスト · 2 人The virus is not meters away. It is hops away.ゲスト Po-Shen Loh · ホスト Lex FridmanPo-Shen Loh talks with Lex Fridman about teaching invention, combinatorics, and a contact-tracing app that warns you before you get sick.
10:32ENReal Numbers: Expected Time Between Exams and a Half-Court Shot ChallengeSawyer · Jane StreetSawyer solves an exam-scheduling problem in several ways, then asks how many basketball attempts it takes to make one shot or two in a row.
6:16ENIndicator Variables: Marching Bands and SnowboardsSawyer · Jane StreetSawyer uses linearity of expectation to count in-step rows, then poses a six-snowboard challenge.
9:40ENReal Numbers: A Marching Band Challenge, Expected Damage, and Bayes’ RuleSawyer · Jane StreetSawyer introduces a marching-band expected-value puzzle and uses linearity and Bayesian updating to solve a monster-attack problem.
10:08ENExpected Damage and a Golden-Ratio Window PuzzleSawyer · Jane StreetSawyer introduces expected value with an unknown video-game monster, then uses geometry to solve a painting-through-a-window problem.
7:55ENThe Art Thief’s Window and a Toy Boat’s Return JourneySawyer · Jane StreetSawyer poses a geometric window puzzle and solves the previous episode’s river-crossing problem in two ways.
5:39ENA Snow Cone Geometry Puzzle and a Piggy Bank ProbabilitySawyer · Jane StreetSawyer poses a cone-design challenge and solves a conditional probability problem in two ways.
7:23ENBayes' Theorem and a Driver's Test Probability PuzzleSawyer · Jane StreetSawyer derives Bayes' theorem, poses a question about test-takers who studied, and solves an earlier card-game probability puzzle.
8:03ENReal Numbers: A Taxi Challenge and Two Ways to Calculate a Sleep ProbabilitySawyer · Jane StreetSawyer poses a consecutive-taxi puzzle and solves a five-night dog-sitting problem with a probability table and a counting argument.
6:50ENThe Perils of Dogsitting: A Probability Warm-UpSawyer · Jane StreetSawyer introduces a five-night dogsitting puzzle and reviews independence, overlapping events and complements with coins and dice.
1:10:18ENShriram Krishnamurthi on Designing Computing Curricula for Real ClassroomsShriram Krishnamurthi · Brown UniversityThe Bootstrap project treats curriculum design as an engineering problem: start with goals and constraints, then support teachers and measure learning.
1:55:04ENLex Fridmanポッドキャスト · 2 人Mining the Computational Universe: Stephen Wolfram on What AI Can and Can't Be Told to Doゲスト Stephen Wolfram · ホスト Lex FridmanIn a talk for MIT's AGI course, Stephen Wolfram connects Wolfram Alpha, simple programs, computational irreducibility and the problem of telling machines what humans want.