Ai, datascience, machinelearning, ad & much more…
AI News Friday, July 13
- AI insights into human rights are meaningless without action
- Image Transformer
- Liliane BARA collected this story
- LexisNexis Risk Solutions looks to bridge insurers and automakers
- Doing good data science
DataScience, MachineLearning, ethics
@improvedhouse: [P] Free lung nodule detection using Chest CT slice images (https://t.co/d77L0F6SJa) https://t.co/g80EkcXLit
- ILO in Asia and the Pacific – Flickr-(CC BY-NC-ND 3.0 IGO) – – AI can be used to process the multitude of available data to detect human rights violations that many workers face around the world in factories, farms and mines.
- While AI has tremendously improved our ability to process the world around us, and can be used to promote human rights, governments, multi-national corporations and others with the power to drive change dont often realize this potential.
- AI can be used to process the multitude of available data to detect human rights violations that many workers face around the world in factories, farms and mines.
- Supply chain managers can use AI to analyze all these various streams of data together to obtain an independent human rights assessment of a suppliers labor practices.
- Corrupt states can use facial recognition technology coupled with AI to target migrant workers and/or human rights defenders who are challenging repressive regimes on exploitative labor practices.
@ggwatch: Academic: Artificial intelligence must be used to force businesses to address human rights violations https://t.co/Bw0nvqbMyV
- Image generation has been successfully cast as an autoregressive sequence generation or transformation problem.
- In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood.
- By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks.
- While conceptually simple, our generative models significantly outperform the current state of the art in image generation on ImageNet, improving the best published negative log-likelihood on ImageNet from 3.83 to 3.77.
- In a human evaluation study, we find that images generated by our super-resolution model fool human observers three times more often than the previous state of the art.
@hereticreader: Image Transformer – Proceedings of Machine Learning Research https://t.co/F7oUzJIAuy
@liliane_bara: You can never have too much AI! MapR shoves more in data platform in bid to fill ‘critical gaps’ https://t.co/Cbwht4G3tI #ai
@DS_Analytics: LexisNexis Risk Solutions looks to bridge insurers and automakers https://t.co/ohpgShzU5Q#DataScience… https://t.co/e1G9sqNZzD
@machinelearnflx: Exploring and Preparing your Data with BigQuery https://t.co/lMMAlujihn #machinelearning #ad
@v_vashishta: Doing good with #DataScience https://t.co/Xz69ST7Btt #MachineLearning #ethics
@Business_Progr: We will be mentoring 4 innovative start-ups specialised in #AI for the #AIParisRegion of @iledefrance https://t.co/delylgEhKB
@machinelearnflx: How Big Data is Defining the Future Executive https://t.co/OKNiK6nH6f #machinelearning #ad