Just over two years ago, The Met launched an Open Access Program seeking to make the images and data of public-domain works in the museum’s collection available under an open data promise. 

The program fills an important role in The Met’s mission to broaden global reach by making the museum’s collection one of the most accessible, discoverable, and useful on the internet. See how The Met is now working to generate new knowledge about each artwork at scale and uncover latent insights with AI.

In this video, Lex Fridman interviews Oriol Vinyals, a senior research scientist at Google DeepMind.

From the video description:

Before that he was at Google Brain and Berkeley. His research has been cited over 39,000 times. He is one of the most brilliant and impactful minds in the field of deep learning. He is behind some of the biggest papers and ideas in AI, including sequence to sequence learning, audio generation, image captioning, neural machine translation, and reinforcement learning. He is a co-lead (with David Silver) of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft.

This video shows how to build, train and deploy a time series forecasting solution with Azure Machine Learning. You are guided through every step of the modeling process including:

  • Set up your development environment
  • Access and examine the data
  • Train using an Automated Machine Learning
  • Explore the results
  • Register and access your time series forecasting model through the Azure portal.

TensorFlow’s high-level APIs help you through each stage of your model-building process.

On this episode of TensorFlow Meets, Laurence Moroney talks with TensorFlow Engineering Manager Karmel Allison about how TF 2.0 will make building models much easier.

Here’s an interesting look at how far enterprises are planning to go with AI.

62% of organizations are using automation to eliminate transactional work and replace repetitive tasks, 47% are also augmenting existing work practices to improve productivity, and 36% are “reimagining work;” 84% said that automation would require reskilling and reported that they are increasing funding for reskilling and retraining, with 18% characterizing this investment as “significant;” In 10 years, 20-30% of jobs will be ‘superjobs,’ 10-20% will be low-wage, low-skill jobs, and the middle 60-70% will be ‘hybrid jobs’ that require both technical and soft skills;