The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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The Lifelong Learning Machines (L2M) program, a recent program from the Defense Advanced Research Agency or DARPA is funding the development of new machine learning systems that can learn continuously, adjust to new changes and apply previous knowledge to new situations.
“The L2M program’s prime objective is to develop systems that can learn continuously during execution and become increasingly expert while performing tasks, are subject to safety limits, and capable of applying previous skills and knowledge to new situations, without forgetting previous learning,” said Dr. Hava Siegelmann, program manager in DARPA’s Information Innovation Office (I2O).
The L2M program was announced first in 2017 and supports more than 30 performer groups via grants and contracts of different duration and size. The group from the USC Viterbi School of Engineering was recently mentioned for the development of a bio-inspired robotic limb based on an algorithm that can learn on its own. These results are also outlined in an article on the March cover of Nature Machine Intelligence, titled “Autonomous functional movements in a tendon-driven limb via limited experience”.
A recent addition to the L2M program is represented by a multidisciplinary group from the Georgia Institute of Technology, which for two years will study how to improve machine learning performance by leveraging state-of-the-art neuroscience. The team, led by School of Computer Science Professor Constantine Dovrolis, Georgia Tech Research Institute Senior Research Scientist Zsolt Kira, Georgia State University neuroscience Professor Sarah Pallas, and Emory biology Associate Professor Astrid Prinz will collaborate to address the five goals of the L2M project: Continual learning, Adaptation to new tasks/environments, Goal-driven perception, Selective plasticity, and Monitoring and safety.
In a recent interview, prof. Drovolis said, “…the brain is really the only example of general intelligence we have, and it makes sense to take that example, identify its fundamental principles, and transfer them to the computational domain,” which perfectly summarize the main motivation for this research and its potential applications.
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