Concrete AI safety problems

Concrete #AI safety problems

  • Advancing AI requires making AI systems smarter, but it also requires preventing accidents – that is, ensuring that AI systems do what people actually want them to do.
  • We think that broad AI safety collaborations will enable everyone to build better machine learning systems.
  • We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety .
  • Many of the problems are not new, but the paper explores them in the context of cutting-edge systems.
  • The paper explores many research problems around ensuring that modern machine learning systems operate as intended.

Read the full article, click here.


@RickKing16: “Concrete #AI safety problems”


We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended. (The problems are very practical, and we’ve already seen some being integrated into OpenAI Gym.)


Concrete AI safety problems

GitHub

Hands-on #machinelearning labs on @github? Oh, yeah, we got that!

  • Evaluate a Binary classification model
  • Consume the ML Web Service in a C# application
  • Comparing two binary classification model
  • Publishing a trained model as Web Service
  • Lab4 – Develop and Consume AzureML Models

Read the full article, click here.


@MicrosoftR: “Hands-on #machinelearning labs on @github? Oh, yeah, we got that!”


hol-azure-machine-learning – Introduction to Machine Learning and Azure Machine Learning Services. Hands on labs to show Azure Machine Learning features, developing experiments, feature engineering, R and Python Scripting, Production stage, publishing models as web service, RRS and BES usage


GitHub