OMG: We analyzed 47,000 ChatGPT conversations. Here’s what people really use it for. Washington Post. https://www.washingtonpost.com/technology/2025/11/12/how-people-use-chatgpt-data/ Methodology: The Post downloaded 93,268 conversations from the Internet Archive using a list compiled by online research expert Henk Van Ess. The analysis focused on the 47,000 chat sessions since June 2024 in which English was the primary language, as determined using langdetect. A random sample of 500 conversations in The Post’s corpus was classified by topic using human review, with a margin of error of plus or minus 4.36 percent. A sample of 2,000 conversations, including the initial 500, was classified with AI using methodologies described by OpenAI in its Affective Use and How People Use ChatGPT reports, using gpt-4o and gpt-5, respectively. ...

Mendelssohn himself resisted attempts to interpret the songs too literally, and objected when his friend Marc-André Souchay sought to put words to them to make them literal: “What the music I love expresses to me, is not thought too indefinite to put into words, but on the contrary, too definite”. (Wikipedia) For (popular) piano works, people LOVE discussing about fav recordings: (Talk Classical) Any recommended recordings? I bought Barenboim’s set in the early days of CDs. https://www.talkclassical.com/threads/mendelssohn-songs-without-words.60895/ ...

Nature has a lovely, open-source annual graduate survey: Springer Nature conducted a global survey of PhD candidates in science in 2025, in partnership with Thinks Insights & Strategy, a research consultancy based in London. The survey ran in June and July 2025 and received 3,785 self-selecting respondents from 107 countries. The findings about PhD graduate student experiences are covered in an article series from Nature’s Careers team. Link to the survey data: https://figshare.com/articles/dataset/Nature_s_Graduate_Survey_2025/30084739/1 The data is rich, and in my pov, very dense in information. Nature already published several articles about it: ...

It’s my birthday today, and it happens to be Chicago’s first snow of 2025 winter. Couldn’t it be happier! And let me share the happiness vicariously with all of you reading. It’s been a lot for the past year for me. Here’s a lovely, interesting song to go with the day — Thank Goodness performed live by Ariana Grande — “getting your dream seems a little strange, well, complicated.” But after all ...

P. D. Q. Bach is a fictional composer created by Peter Schickelem (1935-2024). Schickele studied composition at Juilliard and was an accomplished bassoonist. His wrote an awful lot of (brilliant) parodies of classical music tributed to the name P. D. Q. Bach eg the 1712 Overture, The Civilian Barber, etc. Read more about him: ...

Another convenient way to view, compare, and solve Cheap Talk vs. Bayesian Persuasion. Linear-Programming Formulation: Cheap Talk vs. Bayesian Persuasion Consider an information environment $\langle \Omega, A, M, \mu_0, u, v \rangle$ with state space $\Omega$, action space $A$, signal space $M$, prior $\mu_0$, receiver utility $u(\omega,a)$, and sender utility $v(\omega,a)$. (Let every set be finite). Consider direct mechanisms. In general, the sender’s strategy $\sigma:\Omega \to \Delta(A)$ maps each state to a distribution over recommended actions, and the receiver’s strategy $\rho:A \to \Delta(A)$ specifies how she responds to each recommendation. ...

A general continuous-time dynamic programming problem is given by reward function $h(x, u)$ control function $g(x, u)$ and initial state $x_0$: $$ \begin{align*} V^\star (x_0) :=& \max_{\lbrace u_t\rbrace_{t\ge 0}} \int_0^\infty e^{-\rho t} h(x_t, u_t),\text{d}t\cr & \text{s.t. }\dot x_t = g(x_t, u_t). \end{align*} $$ The Hamiltonian functino $H(x, u, \lambda)$, and Maximized Hamiltonian $H^\star(x, \lambda)$ is defined as $$ \begin{align*} & H(x, u, \lambda) = h(x, u) + \lambda^Tg(x, u),\cr & H^\star(x, \lambda) = \max_u H(x, u, \lambda). \end{align*} $$ It’s classical convex analysis result that $H^\star(x, \lambda)$ is convex in $\lambda$. But also, interestingly ...

When I learnt the continuous time and discrete time neoclassical model, it’s feels so tempting to try to put them together in a uniform view. It’s not trivially easy though. And here’s one way to do it. Continuous-Time Neoclassical Growth Model Given $k_0$, $f(\cdot), U(\cdot)$: $$ \begin{align*} & \max_{\lbrace c_t\rbrace_{t\ge 0}} \int_0^\infty e^{-\rho t} U(c_t),\text{d}t\cr & \text{s.t. }\dot k_t = f(k_t) - \delta k_t - c_t \end{align*}\tag{1} $$ Taking (appropriate) derivative of the Hamiltonian function solves this problem — in general, for any continuous-time problem in the following form: $$ \begin{align*} V^\star (x_0) :=& \max_{\lbrace u_t\rbrace_{t\ge 0}} \int_0^\infty e^{-\rho t} h(x_t, u_t),\text{d}t\cr & \text{s.t. }\dot x_t = g(x_t, u_t). \end{align*} $$ then, the following first-order optimality conditions are necessary (under regularity assumptions) and sufficient (under regularity and convexity assumptions) for the path $\lbrace x_t, u_t\rbrace_{t\ge 0}$ to be optimal: $$ \begin{align*} & \text{let } H(x, u, \lambda) = h(x, u) + \lambda^Tg(x, u)\cr & \text{FOCs}:\begin{cases}H_u(x_t, u_t, \lambda_t) = 0\cr \dot \lambda_t = \rho \lambda_t - H_x(x_t, u_t, \lambda_t)\cr \dot x_t = H_\lambda (x_t, u_t, \lambda_t) = g(x_t, u_t). \end{cases} \end{align*} $$ For $(1)$, the first order optimality conditions are $$ (1)’s\text{ FOCs}:\begin{cases} U’(c_t) = \lambda_t\cr \dot \lambda(t) = \rho\lambda_t - \lambda_t(f’(k_t) - \delta)\cr \dot k_t = f(k_t) - \delta k_t - c_t. \end{cases} $$ ...

I thought it was another song Johannes Brahms wrote for Clara Schumann. In fact, Brahms had it dedicated to one of his former lover: The cradle song was dedicated to Brahms’s friend, Bertha Faber, on the occasion of the birth of her second son. Brahms had been in love with her in her youth and constructed the melody of the “Wiegenlied” to suggest, as a hidden counter-melody, a song she used to sing to him. ...

You can but don’t necessarily have to use fancy harmonic to write a masterpiece (Like math for research). Chopin proves this point — you need only two chords to write a masterpiece. Here’s his wonderful Berceuse (Op. 57). It’s a very sophisticated, rich work full of room for imagination. Note: I really likes the following Sumino’s recording where, it sounds like either he’s using the sostenuto pedal or it’s a really soft piano. ...