Scientist Ng brings AI to manufacturing

Artificial intelligence pioneer Andrew Ng launched a new AI company Landing.ai on Thursday.

On the same day, the company announced a strategic cooperation with electronics contractor Foxconn to develop a program that aims to bring AI and machine learning technologies to the manufacturing industry.

According to Ng’s statement, his company is developing a series of programs to help enterprises transform for the age of AI, including providing new technologies to optimize companies’ organizations structures, train employees, and more. The company’s businesses will start in the manufacturing industry.

Ng said the AI technology is conductive to manufacturing enterprises to improve quality testing process, shorten products’ design cycle, remove bottleneck of supply chain, reduce waste on materials and energy and raise output.

AI will revitalize manufacturing industry and generate jobs in the industry, he said. I In the age of AI, the employees need to accept new skills training to fit jobs that will be more complex than before, Ng added.

Landing.ai will provide solutions to some employees who are likely to be laid off, Ng said. Currently, the company is discussing the training plan with some potential partners including local governments.

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IBM Datapalooza Takes Aim At Data Scientist Shortage

IBM announced in June that it has embarked on a quest to create a million new data scientists. It will be adding about 230 of them during its Datapalooza educational event this week in San Francisco, where prospective data scientists are building their first analytics apps.

Next year, it will take its show on the road to a dozen cities around the world, including Berlin, Prague, and Tokyo.

The prospects who signed up for the three-day Datapalooza convened Nov. 11 at Galvanize, the high-tech collaboration space in the South of Market neighborhood, to attend instructional sessions, listen to data startup entrepreneurs, and use workspaces with access to IBM’s newly launched Data Science Workbench and Bluemix cloud services. Bluemix gives them access to Spark, Hadoop, IBM Analytics, and IBM Streams.

Rob Thomas, vice president of product development, IBM Analytics, said the San Francisco event is a test drive for IBM’s 2016 Datapalooza events. “We’re trying to see what works and what doesn’t before going out on the road.”

Thomas said Datapalooza attendees were building out DNA analysis systems, public sentiment analysis systems, and other big data apps.

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How can Lean Six Sigma help Machine Learning?

Note that this article was submitted and accepted by KDnuggest, the most popular blog site about machine learning and knowledge discovery.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10+ year and am very satisfied with its benefits. Recently, I’ve been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning.

After working with the consultants and data scientists, it appears that they can improve the deviation only by 10%. This puzzles me a lot. I thought machine learning is a great new tool to make forecast simple and quick, but I did not expect it could have such large deviation. To me, such deviation, even after the 10% improvement, still renders the forecast useless to the business owners.

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