Deep learning pioneer Andrew Ng says companies should get ‘data-centric’ to achieve A.I. success

  • “Ng says that if data is carefully prepared, a company may need far less of it than they think. With the right data, he says companies with just a few dozen examples or few hundred examples can have A.I. systems that work as well as those built by consumer internet giants that have billions of examples.
  • Ng has some tips that include making sure that data is what he calls “y consistent.” In essence this means there should be some clear boundary between when something receives a particular classification label and when it doesn’t.
  • The idea of thinking of the building and training of A.I. models as a continuous cycle, not a one-off project, also comes across in a recent report on A.I. adoption from consulting firm Accenture.”

Deep learning is bridging the gap between the digital and the real world

  • “‘A few years ago everybody said that data is gold,’ he says. ‘Now we see that data is actually a huge haystack hiding a nugget of gold. So the challenge is not just collecting lots of data, but the right kind of data.’
  • Andrew Ng, the renowned AI researcher from Stanford and cofounder of Google Brain who also seeks to apply deep learning to manufacturing, speaks of ‘the proof of concept to production gap.”
  • According to Ng paying more attention to cleaning up your data set is one way to solve the problem.”

Andrew Ng: Unbiggen AI

  • “In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.
  • The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains.”

Andrew Ng To Kickstart A New Generation Of AI

  • “Nearly 90 percent of ML models built globally are never brought to light, primarily because they cannot adjust to the variety of information available in real-world applications.
  • The solution Andrew Ng has proposed is to put aside the architecture of an AI model and focus on what it is working with, i.e. the data. By paying close attention to what a model learns and improving the quality of data, and subsequently retraining the ML model, engineers can build higher quality systems in a much shorter time.
  • Andrew Ng believes that the right people can put this idea to use constructively to counter many issues, such as manufacturing, treating diseases, energy consumption and food production, all with the help of AI-backed with the appropriate data.”

Andrew Ng To Kickstart A New Generation Of AI