International Business Machines’ (IBM) research laboratory in Zurich, Switzerland has developed a new generic preprocessing building block that could make the speed by which machine learning algorithms can absorb new information faster.
Such development is expected to largely benefit the booming AI industry.
According to IBM Zurich mathematician Thomas Parnell, they have developed a generic solutionto the AI learning process with a 10 times speedup.
“To the best of our knowledge, we are first to have generic solution with a 10x speedup. Specifically, for traditional, linear machine learning models — which are widely used for data sets that are too big for neural networks to train on — we have implemented the techniques on the best reference schemes and demonstrated a minimum of a 10x speedup.”
Possible benefits of the new development to the AI industry
According to the research lab the main purpose of the invention is to enhance the speed at which AI machines can learn new information.
The block, which utilizes mathematical duality to filter vital pieces of information in a data stream and ignore the unimportant ones, is mainly designed for big data machine learning.
Despite its great potential, there is still a lot of work to be done to improve the block and before it can be ready for commercial application.
The company plans to continue developing the system in IBM’s Cloud, where it will be called the Duality-Gap-Based Heterogeneous Learning.
The company can benefit much from this discovery, considering the high current trend in the artificial intelligence industry. It remains to be seen, however, if the company will be successful in perfecting this new technology and how it will perform in the real world in the near future.
Other Blockchain efforts
IBM, along with other technology players such as Oracle and Sony, have been investing heavily in Blockchain-related projects including partnership with banks for faster and more efficient financial transactions, and helping roll out Blockchain in the government operations.