The discussion surrounding the influence of AI on creative sectors and its implications for training has been a focal point of debate in recent times. Building upon previous studies, Autodesk’s latest report, the “2024 State of Design and Make,” delves into the evolving landscape of the industry, offering valuable insights into business resilience, talent development, and sustainability.

Based on surveys of 5,399 business leaders spanning various sectors such as media and entertainment, construction services, and industrial machinery, the report sheds light on attitudes toward AI. Among the highlights are statistics revealing that despite AI being an emerging technology, 56% of respondents have already incorporated or are approaching the integration of AI into their operations. Furthermore, a significant majority express optimism about AI’s potential to enhance their industries, with 78% confident in their company’s ability to make informed decisions regarding AI.

Humane’s latest innovation, the AI Pin, presents a radical departure from traditional smartphones by eliminating the need for a screen. Instead, it leverages an AI assistant and the CosmOS operating system to abstract away conventional app interfaces, offering a streamlined user experience. Whether you need to make a call, send a text, perform calculations, jot down notes, or seek information, the AI Pin responds to voice commands, functioning as an all-encompassing mobile solution.

Positioned as a pivotal step towards a post-smartphone era, the AI Pin aims to reduce screen time and re-engage users with the physical world. Yet, fundamental questions linger about the feasibility and desirability of such a shift in our technological landscape.

Meta is forging ahead with the development of its next-generation infrastructure, with a strong focus on integrating AI capabilities. This strategic move encompasses the support for new generative AI products, recommendation systems, and cutting-edge AI research. The company anticipates substantial growth in this area as the demand for compute power to sustain AI models rises in tandem with their complexity.

Last year, Meta introduced the Meta Training and Inference Accelerator (MTIA) v1, marking its inaugural foray into AI inference acceleration. Tailored specifically for Meta’s deep learning recommendation models, MTIA v1 aimed to enhance compute efficiency across Meta’s suite of apps and technologies.