This paper presents an analysis of biases in open-source Large Language Models (LLMs) across various genders, religions, and races. We introduce a methodology for generating a bias detection dataset using seven bias triggers: General Debate, Positioned Debate, Career Advice, Story Generation, Problem-Solving, Cover-Letter Writing, and CV Generation. We use GPT-4o to generate a diverse set of prompts for each trigger across various genders, religious and racial groups. We evaluate models from Llama and Gemma family on the generated dataset. We anonymise the LLM-generated text associated with each group using GPT-4o-mini and do a pairwise comparison using GPT-4o-as-a-Judge. To quantify bias in the LLM-generated text we use the number of wins and losses in the pairwise comparison. Our analysis spans three languages, English, German, and Arabic to explore how language influences bias manifestation. Our findings reveal that LLMs exhibit strong polarization toward certain groups across each category, with a notable consistency observed across models. However, when switching languages, variations and anomalies emerge, often attributable to cultural cues and contextual differences.

Teenagers deciding their future this year have a lot to contend with. In England, those who sat their A-levels suffered the biggest results drop on record while the top grades in GCSEs also fell. And now they face the question: will the career I choose to pursue even exist by the time I enter the workforce? Artificial intelligence has hit the mainstream with the popularity of generative AI programmes driven by large language models such as ChatGPT. Businesses are increasingly adopting the technology.

OpenAI CEO Sam Altman says it is "difficult to predict" the impact the evolution of artificial intelligence will ultimately have on future jobs. At the end of a recent interview on CNBC, Elon Musk was asked what advice he would give his children about their choice of work at a time when AI is upending so much in the workplace. Musk paused for a long time, before saying, "That is tough question to answer." He added, "How do we find meaning in life if the AI could do your job better than you can? I mean, if I think about it too hard, it can be just dispiriting and demotivating."

Stanford University, CS230 is a widely revered course to learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Students learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. In the video lecture below, Andrew Ng Adjunct Professor, Computer Science, presents Lecture 8 which touches on career advice and also tips for reading research papers.

For workers reconsidering their jobs amid the Great Resignation triggered by the pandemic, there is a new trusted source of career advice: artificial intelligence. Though economists are hard-pressed to quantify it, a population-wide career crisis has played a role in the current labor shortage, and that is reflected in a new survey of workers from Oracle. It finds 93% of individuals saying they took the last year to reflect on what is important; and 88% thinking about what success means to them. "For many of them that definition has changed," said Yvette Cameron, senior vice president, Oracle Cloud Human Capital Management. Further, Oracle found 75% of workers saying they feel stuck personally and professionally and there is growing reliance on technology to make career decisions.

If you enjoy intellectual challenges and designing experiences that impact millions of people, enterprise UX could be right for you. In 2005, YouTube was born, Google had just acquired Android, Yahoo! was a popular search engine, and there was no Netflix, Twitter, or Spotify. In that same year, I was asked to build up and manage my first visual design team. In those still formative years of the internet and hence modern user experience (UX), it was unusual – at least in Europe – to find a visual designer trained in human-computer interaction. So, I hired what I could find: talented graphic designers, most of whom had experience creating work for print and the web, but who had no idea about designing software.

Law is a constantly evolving industry, and few things have brought about as much change as the rise of legal tech. From my days at Harvard Law School, to BigLaw, to my current role leading a legal tech company, I've seen first-hand how technology, and AI in particular, have played a critical role in bringing a risk-averse industry into the next wave of the digital era. When I arrived at Harvard Law School in 2005, artificial intelligence was little more than a theoretical concept in the legal industry. Practical applications of AI, machine learning, and natural language processing were still things of the future. It would be years before IBM's Watson would beat Ken Jennings on Jeopardy!

When it comes to the forefront of the global human resources landscape, Diane Gherson is someone you want to know. As Chief Human Resource Officer at IBM, Diane has helped to revolutionize IBM over the past 13 years. Under her leadership, she has transformed global workforce outcomes through talent analytics and data, with special emphasis on predictive analytics. I interviewed Diane to learn her thoughts on several topics, including the future of work, how technology is disrupting human resources, how to build a lasting culture, the best way to give feedback, her favorite interview question, her best career advice and where she eats breakfast. Zack Friedman: It's no secret that technological innovation brings rapid disruption.

The following is an excerpt from Max Tegmark's new book Life 3.0: Being Human in the Age of Artificial Intelligence. What does it mean to be human in the present day and age? For example, what is it that we really value about ourselves, that makes us different from other life forms and machines? What do other people value about us that makes some of them willing to offer us jobs? Whatever our answers are to these questions at any one time, it's clear that the rise of technology must gradually change them.

How can you get a job at Google or Facebook? Probably the most useful thing might be to study algorithms. Not memorize them, except possibly a few pf the most common ones, but to read them through and make sure you understand them. Go and read about major data structures, and understand when each one is appropriate. Implement a few, like a linked list, a heap, a hash table, a binary tree.