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From time to time, we invite industry thought leaders to share their opinions and insights on current technology trends to the In The Making blog. The opinions in these blogs are their own, and do not necessarily reflect the views of IBM.
Getting the right tool for the job is essential for anything from home improvement projects to launching satellites. I view the new trend of applying AI, deep learning and cognitive techniques to enterprise IT solutions as following that basic principle. Some tools are more complex and difficult to create than others, but they should all be viewed as a means to an end, not the end in itself.
A recent, stunning announcement by IBM is a great example of what I mean. Calling its new invention “the jet engine of deep learning,” the company recently announced its Distributed Deep Learning (DDL) library for PowerAI, which hooks into TensorFlow (an open-source environment originally developed at Google), and other deep-learning frameworks such as Caffe, Torch, and Chainer.
I previously wrote about a project involving some of my colleagues using deep learning with IBM Power Systems to examine thousands of radiological images in 0.03 percent of the time it takes radiologists to perform the task. The platform they’re using is TensorFlow. It is great to see IBM take this next step and formally integrate it and other world-changing technologies into the DDL library.
Leveraging powerful systems
Archimedes wrote that he could move the entire earth with the proper lever. He would no doubt like the deep-learning leverage contained within the DDL. How powerful is it? According to IBM, it can scale tens of servers and hundreds of GPUs.
The company cites a program in which it incorporated 64 IBM Power Systems servers and 256 NVIDIA Tesla P100 with NVLink GPU accelerators to train the ResNet-101 visual recognition system on the ImageNet-22K data set, shrinking execution time from 16 days to seven hours. That’s a time reduction of slightly more than 98 percent for those of you keeping score.
Technical details about ResNet-101 can be found within its github community. The ImageNet project is a massive image collection designed to aid researchers doing visual recognition projects.
The practical effects of this sort of research in general, and IBM’s performance breakthrough in particular, should extend through several economic sectors. Although I enjoy the demonstrations of an AI-driven system recognizing a cat as much as anyone, it’s nice to see learning and cognitive research developing next steps.
The DDL project has the promise to be applied to use cases in property security, connected cars and traffic grids, robotics (particularly the use of robots in dangerous environments), and other instances where a strong set of eyes coupled with cognition can come in handy. The implication is that such systems will also want to act upon what they perceive. It appears IBM is releasing more toolchain for these inference workloads–with a new PowerAI Vision Technology Preview.
Be careful what you wish for
This should lead to discussions about the good uses to which these tools can be put, as well as the potential downsides. The Industrial Age and the more recent Nuclear Age have shown humankind’s gift for creating powerful tools, coupled with a difficulty — even inability — to control them.
The emerging Machine Learning Age will be no different. As researchers create ever-more-powerful cognitive tools, we must realize we do have the means to control our systems and put them to use for the good of us all.