We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models. Unlike many open-source models, Pixtral is also a cutting-edge text model for its size, and does not compromise on natural language performance to excel in multimodal tasks. Pixtral uses a new vision encoder trained from scratch, which allows it to ingest images at their natural resolution and aspect ratio. This gives users flexibility on the number of tokens used to process an image. Pixtral is also able to process any number of images in its long context window of 128K tokens. Pixtral 12B substanially outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B). It also outperforms much larger open models like Llama-3.2 90B while being 7x smaller. We further contribute an open-source benchmark, MM-MT-Bench, for evaluating vision-language models in practical scenarios, and provide detailed analysis and code for standardized evaluation protocols for multimodal LLMs. Pixtral-12B is released under Apache 2.0 license.

Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers themselves offer a more optimistic, nuanced perspective. Drawing on a far-reaching 2024 survey of more than 9,000 workers across nine countries, this paper finds that more workers report potential benefits from new technologies like robots and AI for their safety and comfort at work, their pay, and their autonomy on the job than report potential costs. Workers with jobs that ask them to solve complex problems, workers who feel valued by their employers, and workers who are motivated to move up in their careers are all more likely to see new technologies as beneficial. In contrast to assumptions in previous research, more formal education is in some cases associated with more negative attitudes toward automation and its impact on work. In an experimental setting, the prospect of financial incentives for workers improve their perceptions of automation technologies, whereas the prospect of increased input about how new technologies are used does not have a significant effect on workers' attitudes toward automation.

More than a year has passed since the public first gained access to OpenAI's ChatGPT, giving various industries the chance to experiment with artificial intelligence (AI) and a sense of the transformational potential -- or lack of it. "Any industry that has large amounts of data that needs to be indexed and processed quickly is ripe for AI integration," Reema Khan, founder and CEO at Green Sands Equity, told Fox News Digital. "In technology for example, writing thousands of lines of code is much easier to do with an AI co-pilot and can do ten times a software engineer's productivity." The U.S. economy added over 353,000 jobs in January 2024, but the tech sector continues to suffer significant layoffs. Google laid off several hundred employees from its sales and advertising teams in the same month as part of multiple rounds of cost-cutting measures.

Artificial intelligence is taking over countless industries around the U.S., raising concerns among Americans who fear they will be replaced by the tech. Now, new research has revealed the most and least AI-proof cities across the nation, based on five key metrics including job availability, the state's population growth rate, and job diversity. Workers based in major tech hubs should look to large, coastal metropolitan areas if they want to avoid losing out to artificial intelligence, with Phoenix, Arizona coming in first as the most AI-proof city in the country. The report warned that Providence, Rhode Island is the top city most susceptible to AI-related job loss. A report revealed the best cities to move to if you want to avoid AI and the top cities you should consider moving away from.

The quickest way to second-guess a decision to major in English is this: have an extended family full of Salvadoran immigrants and pragmatic midwesterners. The ability to recite Chaucer in the original Middle English was unlikely to land me a job that would pay off my student loans and help me save for retirement, they suggested when I was a college freshman still figuring out my future. I stuck with English, but when my B.A. eventually spat me out into the thick of the Great Recession, I worried that they'd been right. After all, computer-science degrees, and certainly not English, have long been sold to college students as among the safest paths toward 21st-century job security. Coding jobs are plentiful across industries, and the pay is good--even after the tech layoffs of the past year.

Artificial intelligence (AI) has started transforming every aspect of our professional and personal lives. The marketing industry is not immune to this digital transformation, with leading brands starting to embrace the opportunities the technology brings. Gaining a better understanding of customer behavior is one of the core benefits of AI in marketing. For years, marketers have gathered and analyzed data about customer behavior. Their goal has remained largely unchanged -- extrapolate patterns and predict which products and services will be most popular with a certain audience.

Are you ready for the automation revolution? Artificial Intelligence (AI) is transforming our work and creating a new wave of workplace innovation. From AI advancements to robotics technologies, the future of work is shaped by automation and its impact on businesses and employees. Get ready to explore how AI will revolutionize the way we work! The rise of Artificial Intelligence (AI) over the past decade has been nothing short of remarkable.

Almost a decade ago, Harvard Business Review declared that the job of a data scientist is the sexist job of the 21st century. The word "sexist" was synonymous with "most desirable/most lucrative" and data science as a field of science gained the best possible momentum. What followed was a big influx of professionals from all different fields transitioning into data science. Data was already available across the value chains of almost all organisations from sales to operations, from finance to inventory, from HR activities to CSR initiatives and it gained the spotlight with the new tools and technologies that were getting increasingly available around the same time and were being democratised with open-source platforms like R and Python around the same time. Cut to the present, the whole landscape has changed drastically.

This blog post is about'Masters in Artificial Intelligence', in essence what this means, which programs are available in the field, the topics normally covered in the curriculum, and a job outlook related to the degree. This blog post is intended for readers that are interested in a degree in artificial intelligence and find out which options are available. A Masters of Science (MS, M.S., MSc, M.Sc., SM, S.M., ScM or Sc.M.) is a post-graduate degree that is taught at most universities and in most countries. The focus of the field of this type of degree is normally one or multiple sciences, such as Medicine, Engineering, etc. Post-graduate means that in order to start this program, an undergraduate degree has first to be completed successfully. A MS is a credential that is recognized by companies all over the world so adding this type of credential to a resume certainly improves the chances of on applicant to get an well-suited position.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Nearly two-thirds (64%) of enterprise decision-makers with responsibility for machine learning, application development, and decision management in their organizations are worried about job security, according to new research by business software company InRule. Above: 64% of decision-makers consider job security as their biggest personal challenge with AI technologies. There are many use cases for AI in the enterprise, from driving market and customer insights to testing new products, mitigating compliance, and addressing privacy risks, and many decision-makers report feeling overwhelmed by the options.