Reducing the Data Demands of Smart Machines
July 17, 2018 | DARPAEstimated reading time: 2 minutes
Machine learning (ML) systems today learn by example, ingesting tons of data that has been individually labeled by human analysts to generate a desired output. As these systems have progressed, deep neural networks (DNN) have emerged as the state of the art in ML models.
Image Caption: Machine learning systems today learn by example, ingesting tons of data that has been individually labeled by human analysts to generate a desired output. The goal of the LwLL program is to make the process of training machine learning models more efficient by reducing the amount of labeled data required to build a model by six or more orders of magnitude, and by reducing the amount of data needed to adapt models to new environments to tens to hundreds of labeled examples.
DNN are capable of powering tasks like machine translation and speech or object recognition with a much higher degree of accuracy. However, training DNN requires massive amounts of labeled data–typically 109 or 1010 training examples. The process of amassing and labeling this mountain of information is costly and time consuming.
Beyond the challenges of amassing labeled data, most ML models are brittle and prone to breaking when there are small changes in their operating environment. If changes occur in a room’s acoustics or a microphone’s sensors, for example, a speech recognition or speaker identification system may need to be retrained on an entirely new data set. Adapting or modifying a model can take almost as much time and energy as creating one from scratch.
To reduce the upfront cost and time associated with training and adapting an ML model, DARPA is launching a new program called Learning with Less Labels (LwLL). Through LwLL, DARPA will research new learning algorithms that require greatly reduced amounts of information to train or update.
“Under LwLL, we are seeking to reduce the amount of data required to build a model from scratch by a million-fold, and reduce the amount of data needed to adapt a model from millions to hundreds of labeled examples,” said Wade Shen, a DARPA program manager in the Information Innovation Office (I2O) who is leading the LwLL program. “This is to say, what takes one million images to train a system today, would require just one image in the future, or requiring roughly 100 labeled examples to adapt a system instead of the millions needed today.”
To accomplish its aim, LwLL researchers will explore two technical areas. The first focuses on building learning algorithms that efficiently learn and adapt. Researchers will research and develop algorithms that are capable of reducing the required number of labeled examples by the established program metrics without sacrificing system performance. “We are encouraging researchers to create novel methods in the areas of meta-learning, transfer learning, active learning, k-shot learning, and supervised/unsupervised adaptation to solve this challenge,” said Shen.
The second technical area challenges research teams to formally characterize machine learning problems, both in terms of their decision difficulty and the true complexity of the data used to make decisions. “Today, it’s difficult to understand how efficient we can be when building ML systems or what fundamental limits exist around a model’s level of accuracy. Under LwLL, we hope to find the theoretical limits for what is possible in ML and use this theory to push the boundaries of system development and capabilities,” noted Shen.
Suggested Items
Real Time with… IPC APEX EXPO 2024: Sigma Engineering's Recycling and Regeneration Systems for PCB Etching
05/02/2024 | Real Time with...IPC APEX EXPOEvan Howard of Schmoll America interviews Kristoffer Bjorklund, Sigma Engineering's supply chain manager. We learn about Sigma's recycling and regeneration systems for PCB industry etching and the benefits and challenges of implementing these systems in existing factories.
Boeing T-7A Red Hawk Triples Progress
05/01/2024 | BoeingThe Boeing T-7A Red Hawk achieved three recent milestones, propelling the advanced pilot trainer for the U.S. Air Force forward.
Merlin Flex invests in New Schmoll Direct Imaging System
04/30/2024 | Merlin Flex LtdMerlin Flex has fully installed and commissioned its 2nd Schmoll MDI Direct Imaging system. This new machine includes a twin bed, 4 head system which enhances Merlin Flex’s direct imaging capability for its 1.4M long flexible circuits.
Latest Test and Inspection Solutions from GOEPEL electronic at SMTconnect 2024
04/29/2024 | GOEPEL electronicGOEPEL electronic will be demonstrating automated test and inspection equipment at SMTconnect, taking place in Nuremberg from June 11 to 13, 2024.
TSMC Celebrates 30th North America Technology Symposium
04/29/2024 | TSMCTSMC unveiled its newest semiconductor process, advanced packaging, and 3D IC technologies for powering the next generation of AI innovations with silicon leadership at the Company’s 2024 North America Technology Symposium.