Market research indicates that there is a significant opportunity for a new co ee bar located in the heart of the downtown business district.

In his newly translated memoir, Hu Anyan captures the brutal labor and quiet grace of life at the edge of China's booming ecommerce industry. "Often, sweat was dripping down my back within the first two hours of a shift and would not stop dripping until the next morning," writes Hu Anyan in the new English translation of his bestselling book . "I sweated so much I never once needed to pee." This passage was on my mind as I read his book in Tianjin during one hot, Labubu brainrot summer, during which yet another unprecedented annual heat wave had forced almost everyone inside--except for the tireless couriers and delivery workers, whose services are in higher demand when temperatures soar. Hu's writing first went viral in China five years ago, and he's now a prolific, established author in the country.

Pastor and Project H.O.O.D. founder Corey Brooks says the'honest work' learned through trade schools could be the key out of poverty for many struggling in today's job market wanting to'improve their lives.' The crime of post-60s liberalism is that it created permanent Black underclasses all over America, including on the South Side of Chicago where I live. The schools here are poor. Opportunities have been replaced by government handouts. Violence robs far too many families of their loved ones.

FOX Business' Taylor Riggs joins'Fox & Friends' to discuss her take on the June jobs report, Democrats' attacks against the legislation and why they claim it will target Medicaid. This summer should be bringing the Class of 2025 a moment of well-deserved relaxation before they launch their careers. Instead, far too many college and high-school graduates are filled with anxiety. They've applied for dozens, perhaps hundreds, of jobs, but interviews and offers have become increasingly rare. The national unemployment rate for young adults aged 20 to 24 looking for work is 6.6% -- the highest level in a decade, excluding the pandemic unemployment spike.

People naturally vary in their annotations for subjective questions and some of this variation is thought to be due to the person's sociodemographic characteristics. LLMs have also been used to label data, but recent work has shown that models perform poorly when prompted with sociodemographic attributes, suggesting limited inherent sociodemographic knowledge. Here, we ask whether LLMs can be trained to be accurate sociodemographic models of annotator variation. Using a curated dataset of five tasks with standardized sociodemographics, we show that models do improve in sociodemographic prompting when trained but that this performance gain is largely due to models learning annotator-specific behaviour rather than sociodemographic patterns. Across all tasks, our results suggest that models learn little meaningful connection between sociodemographics and annotation, raising doubts about the current use of LLMs for simulating sociodemographic variation and behaviour.

The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is a quadruplet composed of a prompt, two independent responses (completions of the prompt) and a human preference between the two independent responses, yielding a preferred and a dis-preferred response. Such data is typically scarce and expensive to collect. On the other hand, \emph{single-trajectory} datasets where each element is a triplet composed of a prompt, a response and a human feedback is naturally more abundant. The canonical element of such datasets is for instance an LLM's response to a user's prompt followed by a user's feedback such as a thumbs-up/down. Consequently, in this work, we propose DRO, or \emph{Direct Reward Optimisation}, as a framework and associated algorithms that do not require pairwise preferences. DRO uses a simple mean-squared objective that can be implemented in various ways. We validate our findings empirically, using T5 encoder-decoder language models, and show DRO's performance over selected baselines such as Kahneman-Tversky Optimization (KTO). Thus, we confirm that DRO is a simple and empirically compelling method for single-trajectory policy optimisation.

Fox News contributor Joe Concha joins'Fox & Friends First' to discuss Elon Musk's warning that artificial intelligence could threaten elections and his concerns on the declining birth rate. Artificial intelligence has become wildly popular for many Americans, but people over the age of 45 are trailing those younger than them on AI familiarity, a Fox News poll shows. Fifty-eight percent of registered voters over the age of 45 who were surveyed for the poll say they are not familiar with AI technology such as OpenAI's ChatGPT. Only 41% of registered voters over 45 reported they are familiar with the technology. The figures stand in stark contrast to younger Americans, with a whopping 65% of registered voters under the age of 45 reporting they are familiar with AI tech, such as ChatGPT.

Twenty million students started college this fall, and this much is certain: The vast majority of them will be taking on debt -- a lot of debt. What's less certain is whether their degrees will pay off. According to the survey Freelancing in America 2018, released Wednesday, freelancers put more value on skills training: 93 percent of freelancers with a four-year college degree say skills training was useful versus only 79 percent who say their college education was useful to the work they do now. In addition, 70 percent of full-time freelancers participated in skills training in the past six months compared to only 49 percent of full-time non-freelancers. The fifth annual survey, conducted by research firm Edelman Intelligence and co-commissioned by Upwork and Freelancers Union, polled 6,001 U.S. workers.

Too many leaders take an incomplete approach to understanding empirical patterns, leading to costly mistakes and misinterpretations. As we have discussed before, one extremely common mistake is interpreting a misleading correlation as causal. We've advised countless organizations on the topic. We've written research papers, managerial articles, and even a book dedicated to the power of experiments and causal inference tools -- a toolkit that economists have adopted and adapted over the past few decades. Yet, while we are deep believers in the causal inference toolkit, we've also seen the reverse problem -- leaders who overlook useful patterns because they are not causal.

The COVID-19 pandemic disrupted labor markets globally during 2020. The short-term consequences were sudden and often severe: Millions of people were furloughed or lost jobs, and others rapidly adjusted to working from home as offices closed. Many other workers were deemed essential and continued to work in hospitals and grocery stores, on garbage trucks and in warehouses, yet under new protocols to reduce the spread of the novel coronavirus. This report on the future of work after COVID-19 is the first of three MGI reports that examine aspects of the postpandemic economy. The others look at the pandemic's long-term influence on consumption and the potential for a broad recovery led by enhanced productivity and innovation.