Privacy Preserving Machine Learning Innovation
新闻与深度文章
In this issue: Peter Lee discusses AI in medicine. Plus, new research on data inference privacy in machine learning; PII leakage in language models; and automatic prompt organization with gradient descent and beam search.
In this issue: Microsoft researchers win four more awards; AutoRXN automates calculations of molecular systems; LLM accelerator losslessly improves the efficiency of autoregressive decoding; a frequency domain approach to predict power system transients.
新闻报道 | Neowin
Microsoft shares more details about its Privacy Preserving Machine Learning initiative
Ensuring customer privacy and maintaining their trust, at least on paper, has become a priority for many tech firms, especially since Facebook’s Cambridge Analytica scandal back in 2018. Striking a balance between the privacy of individuals while showing them personalized content -…
| Victor Ruehle, Robert Sim, Sergey Yekhanin, Nishanth Chandran, Melissa Chase, Daniel Jones, Kim Laine, Boris Köpf, Jaime Teevan, Jim Kleewein, 和 Saravan Rajmohan
Machine learning (ML) offers tremendous opportunities to increase productivity. However, ML systems are only as good as the quality of the data that informs the training of ML models. And training ML models requires a significant amount of data, more…