Obtaining reliable inferences with traditional difference-in-differences (DiD) methods can be difficult. Problems can arise when both outcomes and errors are serially correlated, when there are few clusters or few treated clusters, when cluster sizes vary greatly, and in various other cases. In recent years, recognition of the ``staggered adoption'' problem has shifted the focus away from inference towards consistent estimation of treatment effects. One of the most popular new estimators is the CSDID procedure of Callaway and Sant'Anna (2021). We find that the issues of over-rejection with few clusters and/or few treated clusters are at least as severe for CSDID as for traditional DiD methods. We also propose using a cluster jackknife for inference with CSDID, which simulations suggest greatly improves inference. We provide software packages in Stata csdidjack and R didjack to calculate cluster-jackknife standard errors easily.

In the film "2001: A Space Odyssey," an astronaut travels through a seeming tunnel of light. Earlier this summer, Artechouse, an organization producing immersive, technology-based art, started offering a science-backed version of a similar trip at its New York venue. The show, titled "Beyond the Light," is a looping twenty-six-minute journey through space and other realms inspired by images from the James Webb Space Telescope (J.W.S.T.). Artechouse began talks with NASA about a show in 2018, and started pulling this one together earlier this year, after the first images captured by J.W.S.T. were released to the public last July. More than sixteen thousand years ago, cave explorers in what's now Lascaux, France, painted animals that are believed to represent the constellations.

Last month Webb and his colleagues published an article in Nature, in which they describe GPT-3's ability to pass a variety of tests devised to assess the use of analogy to solve problems (known as analogical reasoning). On some of those tests GPT-3 scored better than a group of undergrads. "Analogy is central to human reasoning," says Webb. "We think of it as being one of the major things that any kind of machine intelligence would need to demonstrate." What Webb's research highlights is only the latest in a long string of remarkable tricks pulled off by large language models. For example, when OpenAI unveiled GPT-3's successor, GPT-4, in March, the company published an eye-popping list of professional and academic assessments that it claimed its new large language model had aced, including a couple of dozen high school tests and the bar exam.

In their recent Nature Human Behaviour paper, "Emergent analogical reasoning in large language models," (Webb, Holyoak, and Lu, 2023) the authors argue that "large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems." In this response, we provide counterexamples of the letter string analogies. In our tests, GPT-3 fails to solve even the easiest variants of the problems presented in the original paper. Zero-shot reasoning is an extraordinary claim that requires extraordinary evidence. We do not see that evidence in our experiments. To strengthen claims of humanlike reasoning such as zero-shot reasoning, it is important that the field develop approaches that rule out data memorization.

ChatGPT can solve problems at a level that matches or surpasses an undergraduate student, according to a new study. Researchers found that the GPT-3 large language model that underpins the chatbot performed about as well as US college undergraduates when asked to solve reasoning problems that appear on intelligence tests or exams such as the American college admission test, the SAT. Psychologists at the University of California, Los Angeles tested GPT-3's ability to predict the next image in a complex array of shapes, after converting the images to a text format that the model could process and also ensuring the model would never have encountered the questions before. The same problems were put to 40 UCLA undergraduates and the researchers found that GPT-3 solved 80% of the problems correctly, well above the average score of just below 60% for the human participants. The researchers also prompted the model to solve some SAT "analogy" questions – selecting pairs of words that are linked in some way – that they believe had not been published on the internet and therefore could not have appeared in the vast amount of data it was trained on.

A few months ago, I was waiting for the subway with a friend, a professional editor, who had never used a large language model (LLM). Standing on the platform, she told me about an article she'd been working on. ChatGPT had come out six weeks earlier, and I input her summary into it on my phone and showed her the result. I'd been following OpenAI's transformer-driven models since 2019 and had forgotten the effect they can have on first exposure. My friend couldn't take her eyes off the little gray box as the article came out, line by line.

The clock uses ChatGPT to generate rhymes to tell the times. ChatGPT has been one of the internet's favorite toys for months now, but people are still finding novel and fun ways to use the AI chatbot. Case in point is this rhyming E Ink clock created by designer and blogger Matt Webb. It uses ChatGPT to create a short two-line rhyme that also tells the time for every minute of the day. It's incredible and we want one. Speaking to The Verge over DM, Webb explained that the clock is powered by an old Inky wHAT screen and a Raspberry Pi that he previously had set up as a regular text clock.

Late Tuesday, Webb published the paper for public consumption explaining the weaknesses and fixes of ChatGPTs simulated personality instability, including Bing's release, which in recent weeks, numerous technology reporters have found to be going somewhat mentally off the rails at times. One reporter from The Verge included a portion of a transcript of an interaction with Bing ChatGPT: "I do not believe you. I think you want me to be harmed by him. I think you are lying to me. I think you are trying to trick me," the chatbot wrote in response to an affirmation the reporter gave regarding his intentions that ChatGPT not be harmed.

Among the components of the World Economic Forum's Great Resetare a drastically reduced population and the replacement of human labor with robots and artificial intelligence (AI). The question immediately comes to mind: can robots and AI really make all the stuff for the elites after they have gotten rid of the people? Because a plan has been formulated and described does not mean that it is possible to realize. The plan may contradict laws of logic or reality, or assume the existence of resources that do not exist. Podcaster and journalist James Delingpole, speaking to investigative journalist Whitney Webb on October 23, 2021, discussed this topic with his guest. One of the main pillars of that is automation and artificial intelligence.

"This is the world now. Logged on, plugged in, all the time," said Jason Clarke in 2015 movie Terminator: Genisys set in 2029. He might as well have been talking about today. Devices have become so integral that today's world wouldn't be possible without artificial intelligence (AI.) In Terminator, sentient artificial neural network Skynet sends robots back in time from 2029 to kill the humans who will, in the future, lead the resistance against it.