Paul Pierce explains how LeBron's absence has actually been good for the Lakers. Los Angeles Lakers shooting guard Bronny James has been the center of debate from the moment he was drafted in June. The 20-year-old said he tries to filter it all out, but he sees it all. "My first thought about everything is I always try to just let it go through one ear and out the other, put my head down and come to work and be positive every day. I see everything that people are saying, and people think, like, I'm a f---ing robot, like I don't have any feelings or emotions," James said via The Athletic.

Few-shot question answering (QA) aims at achieving satisfactory results on machine question answering when only a few training samples are available. Recent advances mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-training stage has already equipped LLMs with powerful reasoning capabilities, LLMs still need to be fine-tuned to adapt to specific domains to achieve the best results. In this paper, we propose to select the most informative data for fine-tuning, thereby improving the efficiency of the fine-tuning process with comparative or even better accuracy on the open-domain QA task. We present MinPrompt, a minimal data augmentation framework for open-domain QA based on an approximate graph algorithm and unsupervised question generation. We transform the raw text into a graph structure to build connections between different factual sentences, then apply graph algorithms to identify the minimal set of sentences needed to cover the most information in the raw text. We then generate QA pairs based on the identified sentence subset and train the model on the selected sentences to obtain the final model. Empirical results on several benchmark datasets and theoretical analysis show that MinPrompt is able to achieve comparable or better results than baselines with a high degree of efficiency, bringing improvements in F-1 scores by up to 27.5%.

Going back to our example, let's assume that the Lakers were having a terrible season(clearly not the case), and out of 20 games, they only won 1. so the odds to the Lakers winning would be: We can make a simple observation: the worse they play, the more close their odds of winning will be to 0. Concretely, when the odds are against them winning, then the odds will range between 0 and 1. Now let's look at the opposite. In other words, when the odds are for the Lakers winning, they begin at 1 and they can go all the way up to infinity. Clearly, there is a problem here. This asymmetry makes it hard to compare the odds for or against Lakers winning.

The young man at the center of all the commotion smiled calmly on Wednesday, the day before the first meaningful game of his NBA career. Lonzo Ball is rarely any other way. "It is going to be a lot of fun," Ball said. On Thursday night the Lakers will host the Clippers in the season opener for both teams. The organization has goals that go beyond this season, but when it comes to the team itself, its coaches and players, their goal is simple.

The Guardian's published a long excerpt from Cathy O'Neil's essential new book, Weapons of Math Destruction, in which O'Neil describes the way that shoddy machine-learning companies have come to dominate waged employment hiring, selling their dubious products to giant companies that use them to decide who can and can't work. Because so many of America's biggest employers use these systems, it can be nearly impossible to find work if their secret, unaudited models decide that you're a bad hire. What's more, many of the models' litmus tests are just proxies for race, poverty, and disability -- things that companies are not legally allowed to consider when hiring (unless they're being considered by unaccountable software provided by a third party). This hurts everyone, not just the people who get blackballed. Because the machine learning companies that supply this HR-ware don't refine their models based on the success of their predictions, they end up excluding lots of people who'd be excellent hires, and hiring people who are no good for their customers.