7 Machine Learning Projects to Land Your Dream Job in 2026
machine learning continues to evolve faster than most can keep up with.
machine learning continues to evolve faster than most can keep up with.
Recommendation systems in multi-stakeholder environments often require optimizing for multiple objectives simultaneously to meet supplier and consumer demands. Serving recommendations in these settings relies on efficiently combining the objectives to address each stakeholder’s expectations, often through a scalarization function with pre-determined and fixed weights. In practice, selecting these weights becomes a consequent problem. Recent work …
Read more “SEMORec: A Scalarized Efficient Multi-Objective Recommendation Framework”
AI agents need to browse the web on your behalf. When your agent visits a website to gather information, complete a form, or verify data, it encounters the same defenses designed to stop unwanted bots: CAPTCHAs, rate limits, and outright blocks. Today, we are excited to share that AWS has a solution. Amazon Bedrock AgentCore …
The rise of AI marks a critical shift away from decades defined by information-chasing and a push for more and more compute power. Canva co-founder and CPO Cameron Adams refers to this dawning time as the “imagination era.” Meaning: Individuals and enterprises must be able to turn creativity into action with AI. Canva hopes to …
Read more “Why IT leaders should pay attention to Canva’s ‘imagination era’ strategy”
I covet big animatronic skeletons for no good reason. Finally, I can justify the impulse buy.
New research from Carnegie Mellon University’s School of Computer Science shows that the smarter the artificial intelligence system, the more selfish it will act.
submitted by /u/sakalond [link] [comments]
Large language models (LLMs) are not only good at understanding and generating text; they can also turn raw text into numerical representations called embeddings.
The initiative brings together some of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research.
Current speech translation systems, while having achieved impressive accuracies, are rather static in their behavior and do not adapt to real-world situations in ways human interpreters do. In order to improve their practical usefulness and enable interpreting-like experiences, a precise understanding of the nature of human interpreting is crucial. To this end, we discuss human …
Read more “Toward Machine Interpreting: Lessons from Human Interpreting Studies”