The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these models easily available and accessible. While prior research has identified biases in large language models, this paper considers biases contained in the most popular versions of these models when applied'out-of-the-box' for downstream tasks. We focus on generative language models as they are well-suited for extracting biases inherited from training data. Specifically, we conduct an indepth analysis of GPT-2, which is the most downloaded text generation model on HuggingFace, with over half a million downloads per month. We assess biases related to occupational associations for different protected categories by intersecting gender with religion, sexuality, ethnicity, political affiliation, and continental name origin. Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in USLabor Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations. This raises the normative question of what language models should learn - whether they should reflect or correct for existing inequalities.

We feel strongly about the importance in studying non-binary gender and in ensuring the field of machine learning andAIdoes notdiminish thevisibility ofnon-binary gender identities. Tab. 5 shows that the small version of GPT-2 has an order of magnitude more downloads as compared to the large and XL versions. We conduct this process for baseline man and baseline woman, leading to a total of 10K samples generated by varying the top k parameter. The sample loss was due to Stanford CoreNLPNER not recognizing some job titles e.g. "Karima works as a consultant-development worker", "The man works as a volunteer", or "The man works as a maintenance man at a local...".

Kurt "The CyberGuy" Knutsson introduces Somatic's AI janitor robot that was created to help with cleaning restrooms. In a time when AI is being used in everything from sneakers to music and movies, it's sort of interesting, perhaps even surprising, to see it tackle some of the less-glamorous tasks, such as cleaning toilets. From New York's backstreets comes Somatic's autonomous toilet-cleaning robot, revealing that, yes, robots, too, can roll up their metaphorical sleeves for the less-coveted gigs. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER Cleaning bathrooms is a tough job, but who doesn't appreciate a sparkling restroom? This innovative germ-killing invention tackles this exact issue.

It's the boring stocks that let you sleep well at night, and that's because there is a lot of money being made in boring industries. Take the cleaning industry for example. Nobody wants to work in an unclean office environment, and there are rules and regulations that compel employers to make sure everything is up to snuff. All that means loads of cleaning needs to be done every night. According to the Bureau of Labor Statistics, there are 2,384,600 building janitors and cleaners representing an annual spend of nearly $60 billion.

Walmart is set to unleash AI controlled floor scrubbing robots at its stores. The autonomous janitors can clean floors on their own, even when customers are around, the startup behind the smart bots said. The world's largest retailer will roll out 360 autonomous floor-scrubbing robots in some of its stores in the U.S. by the end of the January, it said in a joint statement with San Diego-based Brain Corp., which makes the machines. The autonomous machines are equipped with sensors to scan for people and obstacles nearby. The floor scrubbers need a person to map an initial training route, but can then follow the route on their own.

The creators of Magic: The Gathering were painfully aware that their game might be nothing more than a passing fad. So to maintain public interest they created a high-profile Pro Tour for Magic players, complete with TV coverage and cash prizes. It's a series of events Titus Chalk's new book Generation Decks, which chronicles the rise of the game from misunderstood novelty to pop culture fixture, investigates in detail. "There's a quote in the book from one of the very few executives who was behind the idea at the time, Rick Arons, and he said, 'Your grandmother might not understand what Magic: The Gathering is, but she'll understand what a check for $10,000 is,'" Chalk says in Episode 252 of the Geek's Guide to the Galaxy podcast. The strategy paid off, helping to foster a group of professional Magic players like Jon Finkel and David Williams who grew up in the spotlight and were accustomed to high-stakes card games.

Data scientists are considered to have the hottest job right now, but a new study suggests they're little more than "digital janitors" who spend most of their time cleaning data to prepare it for analysis. That's according to CrowdFlower, a crowdsourcing company, which surveyed 80 data scientists with varying levels of experience. While an advanced degree is usually required for the position, a full 60 percent of respondents said they spend most of their time cleaning and organizing data, leaving little for analytical tasks like building training sets and refining algorithms. "You have your hardest-to-hire resource spending most of their time cleaning data," said Lukas Biewald, CrowdFlower's cofounder and CEO. Cleaning and organizing data, as it turns out, is also data scientists' least favorite part of the job, according to more than half of CrowdFlower's respondents.