Hey Sundar, getting DeepMind to be an LLM lab is trying to shove a square peg into a round hole. You’re destroying them, and you’ll still lose the race. Let them focus on AI beyond LLMs, which is what they’re good at, and create a nimble new lab to run the LLM race. Cc: Sergey.
Jedním mých zklamání je @PiratskaStrana. Vždy jsem tvrdil, že "pro-market" je víc než "anti-state" a vkládal jsem velké naděje do inovací, které přináší decentralizovaná světová počítačová síť. Piráti slibovali být jejími zastánci, ale dnes nabízí jen populistické regulace. 👎
Já bych nejradši randil s holkami, které jsou politicky nevyhraněné nebo se označují za centristky.
Všechno ostatní z toho seznamu je pro mě problematické - v určitých tématech by pak nastupoval spíš emoční zápal než objektivní přemýšlení ...
Would you date a:
🟩 Progressive
🟩 Left-Liberal
🟩 Centrist
🟩 Right-Liberal
🟩 Conservative
🟩 Right-Libertarian
🟩 RW Populist
🟩 Neocon
🟩 Neolib
🟩 DemSoc
🟩 Communist
🟩 Fascist
🟩 Anarchist
Fuck off, nehodlám si takovou blbostí jako je politika kurvit osobní vztahy.
@mdancho84 Is this true?
It seems to me it confuses frequentism with parametric modeling.Predetermined distributions are used by both frequentists & Bayesians. Frequentism includes non-parametric methods (like bootstrapping, permutation tests) that make zero distributional assumptions..
@Vladimir21m Mně se ulevilo, když jsem vystřízlivěl z libertariánství jako ideologie. Ne že bych dnes s většinou těch myšlenek nesouhlasil, ale jsem rád, že jsem se zbavil toho "ideologického filtru" a potřeby reagovat na všechno nebo řešit každý problém jakýmsi automatismem v duchu ideologie
"MCMC from Scratch - A Practical Introduction to Markov Chain Monte Carlo" - available at https://t.co/AqwfKhbKIX
From the Back Cover:
"This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. MCMC is a powerful technique that can be used to integrate complicated functions or to handle complicated probability distributions. MCMC is frequently used in diverse fields where statistical methods are important – e.g. Bayesian statistics, quantum physics, machine learning, computer science, computational biology, and mathematical economics. This book aims to equip readers with a sound understanding of MCMC and enable them to write simulation codes by themselves."
"The content consists of six chapters. Following Chapter 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chapter 3 presents the general aspects of MCMC. Chapter 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. In turn, Chapter 5 explains the HMC algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing their pros, cons and pitfalls. Lastly, Chapter 6 presents several applications of MCMC. Including a wealth of examples and exercises with solutions, as well as sample codes and further math topics in the Appendix, this book offers a valuable asset for students and beginners in various fields."
“Mathematicians and scientists often speak very different languages. AI is potentially going to lead to a real renaissance in applied math, where pure mathematicians who are domain experts are now going to have the perfect conversation partner to be able to take their ideas and connect them with real-world things.”
https://t.co/MecehlhW9a