Things to Do in L.A. Tap to enable a layout that focuses on the article. Your morning catch-up: Mayor Lurie has SF feeling better, California's job market is taking a hit and more big stories Why outrage is erupting over Trump plan to exclude nursing from'professional' designation This is read by an automated voice. Please report any issues or inconsistencies here . Trump administration proposes excluding nursing and other fields from "professional" designation, capping graduate student loans. Nursing leaders warn the policy will worsen California's severe nurse shortage by discouraging graduate degrees required for teaching and specialized patient care.

Many empirical studies of labor market questions rely on estimating relatively simple predictive models using small, carefully constructed longitudinal survey datasets based on hand-engineered features. Large Language Models (LLMs), trained on massive datasets, encode vast quantities of world knowledge and can be used for the next job prediction problem. However, while an off-the-shelf LLM produces plausible career trajectories when prompted, the probability with which an LLM predicts a particular job transition conditional on career history will not, in general, align with the true conditional probability in a given population. Recently, Vafa et al. (2024) introduced a transformer-based "foundation model", CAREER, trained using a large, unrepresentative resume dataset, that predicts transitions between jobs; it further demonstrated how transfer learning techniques can be used to leverage the foundation model to build better predictive models of both transitions and wages that reflect conditional transition probabilities found in nationally representative survey datasets. This paper considers an alternative where the fine-tuning of the CAREER foundation model is replaced by fine-tuning LLMs. For the task of next job prediction, we demonstrate that models trained with our approach outperform several alternatives in terms of predictive performance on the survey data, including traditional econometric models, CAREER, and LLMs with in-context learning, even though the LLM can in principle predict job titles that are not allowed in the survey data. Further, we show that our fine-tuned LLM-based models' predictions are more representative of the career trajectories of various workforce subpopulations than off-the-shelf LLM models and CAREER. We conduct experiments and analyses that highlight the sources of the gains in the performance of our models for representative predictions.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The vice chair of Congress' artificial intelligence caucus says privacy regulations need to be a top short-term priority for Congress as Washington looks to get to grips with the rapidly emerging technology – which he says poses risks, but could be a catalyst for the next expansion of the U.S. economy. Rep. Jay Obernolte, R-Calif., told Fox News Digital in an interview that he is an optimist when it comes to the potential for artificial intelligence, but Congress needs to make sure it is protecting Americans from the potential negatives and disruption that AI brings. "I think in the short term, the ability of AI to pierce through digital data privacy and to re-aggregate data that has supposedly been disaggregated and use it to create behavioral models that could be used to influence behavior, that's very concerning, and that's something that the government definitely needs to play a role in mitigating," Obernolte said. Rep. Jay Obernolte has a graduate degree in artificial intelligence.

New advancements in data science often spark dire predictions about how powerful new technologies will transform the world. Yet, as writer Stephen Shankland reminds us, technologies like Open AI's new Chat GPT (short for chat-based Generative Pretrained Transformer) are created by humans. Chat GPT is a chatbot that is "trained with human assistance to deliver more useful, better dialog." The people assisting that training--those who create the models and assemble the data used to train chatbots--make a difference in the technologies that will go on to shape our lives. Computer scientist Joy Buolamwini, an early critic of racial bias in facial recognition software, said technology should "be more attuned to the people who use it and the people it's used on."

Apple has unveiled a year-long AI/ML residency program where experts in non-AI fields are invited to apply their expertise in building new ML or AI-powered products and experiences. Michael Rennaker, a lead at research in academic for Apple AI/ML, first drew attention to the program in a tweet: "If you have expertise in [a] field outside of AI, can code, and want to dip your toe into the world of Machine Learning, we've created this program just for you!" The need for domain experts to understand machine learning is growing, says Apple, as intelligent experiences to solve problems are implemented across hardware, software, and services. The purpose of the AI/ML residency program is to immerse these domain experts in the ML space, investing in their technical and theoretical machine learning development to advance their professional careers. Apple is hence looking to welcome residents with STEM graduate degrees (or with equivalent industry experience) across a broad swathe, from software development backgrounds to niche expertise such as neuroscience, linguistics, psychology, or even design.

It's not easy to break into a new field, especially one as complex and multi-faceted as data science. What a data scientist used to do, used to need to understand, and the types of companies that need to hire data scientists are in a state of rapid evolution. Why in the world would there be only one path to follow? Honestly, if you're trying to break into data science, I can't think of a better time to get started! If you can take on a graduate degree, go for it!

It only seems like the world was wrapping its head around the on-demand model for mobility and now the autonomous and self driving car technology is making the future a present reality. Both these sea change events have caught the traditional automotive manufacturers completely off guard and has forced them to buy their way into this highly, niche/technical growth market that will power the future of the automotive/mobility industry. To start with, what are the skill sets that employers are looking in engineers to help define and power self driving car technology. Figure 2. Top Core Computer Science Skills and % of Time Cited in Self Driving Car Technology Job Descriptions Figure 3. Top Computer Science Related Skills and % of Time Cited in Self Driving Car Technology Job Descriptions Inspection of Figures 1–3 illustrate the highly technical nature of skills needed to interpret the physical world, distill it into a digitally interpretable entity and react to it with an always changing "policy" (reinforcement learning speak) all in real time to enable self driving cars to navigate the world safely. Machine Learning, Artificial Intelligence (AI) and techniques within the field, notably deep learning, computer vision and robotics typically involve years of study to master the field resulting with many engineers holding advanced degrees.

A lot of companies have job titles that include "Research" and/or "Scientist." Be aware that these tend to have stricter requirements on graduate degrees. But, also know that the same company may have Engineering roles which aren't as strict. Take a look at this pair from Recursion Pharma: Machine Learning Engineer vs Deep Learning Scientist. The requirement for a PhD is going to change over time. These techniques have been so cutting-edge that a graduate degree has been about the only way for employers to find candidates with a few years of experience.