New stochastic separation theorems proved by University of Leicester mathematicians could enhance capabilities of artificial intelligence
Errors in Artificial Intelligence which would normally take a considerable amount of time to resolve could be corrected immediately with the help of new research from the University of Leicester.
Researchers from the University of Leicester’s Department of Mathematics have published a paper  in the journal Neural Networks outlining mathematical foundations for new algorithms which could allow for Artificial Intelligence to collect error reports and correct them immediately without affecting existing skills – at the same time accumulating corrections which could be used for future versions or updates.
This could essentially provide robots with the ability to correct errors instantaneously, effectively ‘learn’ from their mistakes without damage to the knowledge already gained, and ultimately spread new knowledge amongst themselves.
Together with Industrial partners from ARM, the algorithms are combined into a system, an AI corrector, capable of improving performance of legacy AIs on-the-fly (the technical report is available online ).
ARM is the world’s largest provider of semiconductor IP and is the architecture of choice for more than 90% of the smart electronic products being designed today.
Professor Alexander Gorban from the University of Leicester’s Department of Mathematics said: “Multiple versions of Artificial Intelligence Big Data analytics systems have been deployed to date on millions of computers and gadgets across various platforms. They are functioning in non-uniform networks and interact.
“Industrial technological giants such as Amazon, IBM, Google, Facebook, SoftBank, ARM and many others are involved in the development of these systems. Performance of them increases, but sometimes they make mistakes like false alarms, misdetections, or wrong predictions. The mistakes are unavoidable because inherent uncertainty of Big Data.
“It seems to be very natural that humans can learn from their mistakes immediately and do not repeat them (at least, the best of us). It is a big problem how to equip Artificial Intelligence with this ability.
“It is difficult to correct a large AI system on the fly, more difficult as to shoe a horse at full gallop without stopping.
“We have recently found that a solution to this issue is possible. In this work, we demonstrate that in high dimensions and even for exponentially large samples, linear classifiers in their classical Fisher’s form are powerful enough to separate errors from correct responses with high probability and to provide efficient solution to the non-destructive corrector problem.”
There is a desperate need in a cheap, fast and local correction procedure that does not damage important skills of the AI systems in the course of correction.
Iterative methods of machine learning are never cheap for Big Data and huge AI systems and therefore the researchers suggest that the corrector should be non-iterative with the reversible correctors needed to reconfigure and merge local corrections.
Dr Ivan Tyukin from the University of Leicester’s Department of Mathematics said: “It is often infeasible just to re-train the systems for several reasons: they are huge and re-training requires significant computational resources or long time or both; it may be impossible retrain the system locally, at the point where mistake occur; and we can fix one thing but break another leading to that important skills could vanish.
“The development of sustainable large intelligent systems for mining of Big Data requires creation of technology and methods for fast non-destructive, non-iterative, and reversible corrections. No such technology existed until now.”
The researchers have discovered and proved stochastic separation theorems which provide tools for correction of the large intelligent data analytic systems.
With this approach, instantaneous learning in Artificial Intelligence could be possible, providing AI with the ability to re-learn following a mistake after an error has occurred.
The Latest on: Self-learning robots
- Global Soft Robotics Market, Forecast to 2025: A $4.9+ Billion Opportunity Assessment - ResearchAndMarkets.comon June 19, 2020 at 8:12 am
With the advent of self-learning soft robots through AI, the soft robotics market is expected to witness drastic change over the forecast period. The need for human safety in manufacturing units ...
- Global Soft Robotics Market, Forecast to 2025: A $4.9+ Billion Opportunity Assessment - ResearchAndMarkets.comon June 19, 2020 at 8:09 am
Growth, Trends, and Forecast (2020-2025)" report has been added to ResearchAndMarkets.com's offering. The Global Soft Robotics Market was valued at USD 645.45 million in 2019 and is expected to reach ...
- Ahmedabad Startup Develops AI Robot That Segregates 5 Tonnes of Waste In An Houron June 17, 2020 at 10:13 am
This AI-based device can increase India’s recycling rate, which currently stands at 30 per cent, and prevent health risks among rag pickers ...
- Saudi student develops robot to teach children with Down syndromeon June 10, 2020 at 11:32 pm
Nada Bint Saeed Al Qahtani, a Saudi student at the College of Computer Science, King Khalid University, has developed a “robot” named “Eve” to support teaching programmes for children aged 3 to 15 ...
- Living Smart Technology Seminaron May 29, 2020 at 12:14 am
Maybe a personal robot like the one in “Frank and the ... David esplains, “I have always had a passion for self-learning.” He plans to present his material from a simple, easy to understand ...
- IPsoft joins forces with GlobalDWS to Launch Service Robots Powered by Ameliaon May 27, 2020 at 7:04 am
The Amelia-powered robot, stationed at branch locations ... she can be trained to efficiently take on any new tasks. Thanks to her self-learning capabilities, she gets better and more competent ...
via Google News and Bing News