An estimated 20% of every dollar spent on manufacturing is wasted, totaling up to $8 trillion a year, more than the entire annual budget for the U.S. federal government. While industries like healthcare and finance have been rapidly transformed by digital technologies, manufacturing has relied on traditional processes that lead to costly errors, product delays, and an inefficient use of engineers’ time.
Agentic AI systems are “designed to pursue complex goals with autonomy and predictability” (MIT Technology Review). Agentic AI models enable productivity by taking goal-directed actions, making contextual decisions, and adjusting plans based on changing conditions with minimal human oversight.
Not sure what to think about DeepSeek R1, the most recent large language model (LLM) making waves in the global tech community? Faculty from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are here to help!
"The net effect [of DeepSeek] should be to significantly increase the pace of AI development, since the secrets are being let out and the models are now cheaper and easier to train by more people." ~ Associate Professor Phillip Isola
Two of the trickiest qualities to balance in the world of machine learning are fairness and accuracy. Algorithms optimized for accuracy may unintentionally perpetuate bias against specific groups, while those prioritizing fairness may compromise accuracy by misclassifying some data points.
Frontier AI Safety & Policy Panel: Where We're at & Where We're Headed – Perspectives from the UK
It's been around a year since chatbots became widespread and governments worldwide turned their attention to advanced AI safety and governance. In this event co-hosted by MIT CSAIL Alliances, the MIT-UK program and the UK government’s AI Safety Institute, we will discuss the current state of research and where we're headed. Questions to be answered include: How will we control and govern AI agents?
