Machine learning (ML) and artificial intelligence (AI) systems have significantly advanced in recent years. However, they are currently limited to executing only those tasks they are specifically designed to perform and are unable to adapt when encountering situations outside their programming or training. DARPA’s Lifelong Learning Machines (L2M) program, drawing inspiration from biological systems, seeks to develop fundamentally new ML approaches that allow systems to adapt continually to new circumstances without forgetting previous learning.

First announced in 2017, DARPA’s L2M program has selected the research teams who will work under its two technical areas. The first technical area focuses on the development of complete systems and their components, and the second will explore learning mechanisms in biological organisms with the goal of translating them into computational processes. Discoveries in both technical areas are expected to generate new methodologies that will allow AI systems to learn and improve during tasks, apply previous skills and knowledge to new situations, incorporate innate system limits, and enhance safety in automated assignments.

The L2M research teams are now focusing their diverse expertise on understanding how a computational system can adapt to new circumstances in real time and without losing its previous knowledge. One group, the team at University of California, Irvine plans to study the dual memory architecture of the hippocampus and cortex. The team seeks to create an ML system capable of predicting potential outcomes by comparing inputs to existing memories, which should allow the system to become more adaptable while retaining previous learnings. The Tufts University team is examining a regeneration mechanism observed in animals like salamanders to create flexible robots that are capable of altering their structure and function on the fly to adapt to changes in their environment. Adapting methods from biological memory reconsolidation, a team from University of Wyoming will work on developing a computational system that uses context to identify appropriate modular memories that can be reassembled with new sensory input to rapidly form behaviors to suit novel circumstances.

“With the L2M program, we are not looking for incremental improvements in state-of-the-art AI and neural networks, but rather paradigm-changing approaches to machine learning that will enable systems to continuously improve based on experience,” said Dr. Hava Siegelmann, the program manager leading L2M. “Teams selected to take on this novel research are comprised of a cross-section of some of the world’s top researchers in a variety of scientific disciplines, and their approaches are equally diverse.”

While still in its early stages, the L2M program has already seen results from a team led by Dr. Hod Lipson at Columbia University’s Engineering School. Dr. Lipson and his team recently identified and solved challenges associated with building and training a self-reproducing neural network, publishing their findings in Arvix Sanity. While neural networks are trainable to produce almost any kind of pattern, training a network to reproduce its own structure is paradoxically difficult. As the network learns, it changes, and therefore the goal continuously shifts. The continued efforts of the team will focus on developing a system that can adapt and improve by using knowledge of its own structure. “The research team’s work with self-replicating neural networks is just one of many possible approaches that will lead to breakthroughs in lifelong learning,” said Siegelmann.

“We are on the threshold of a major jump in AI technology,” stated Siegelmann. “The L2M program will require significantly more ingenuity and effort than incremental changes to current systems. L2M seeks to enable AI systems to learn from experience and become smarter, safer, and more reliable than existing AI.”

Photo: Today's machine learning and AI systems are limited to executing only tasks they are specifically programmed to perform, without being able to adapt to new situations outside of their training. DARPA's L2M program aims to generate new methodologies that will allow these systems to learn and improve during tasks, apply previous skills and knowledge to new situations, incorporate innate system limits, and enhance safety in automated assignments. (credit: DARPA)

Source: DARPA

Read more

Comments

No comments to display.

Related posts

Intel Artificial Intelligence and Rolls-Royce Push Full Steam ahead on Autonomous Shipping

Rolls-Royce builds shipping systems that are sophisticated and intelligent, and eventually it will add fully autonomous to that portfolio, as it makes commercial shipping safer and more efficient.

Call for Applications: 2019 RoboMaster Robotics Competition

The RoboMaster 2019 Robotics Competition is open to international universities. As long as you love robots and hope to show your talent and wisdom on the RoboMaster Robotics Competition, you can apply for registration!
Application Deadline in 24 days

EU's Call for Proposals: Pilot lines for modular factories

Modular production equipment can create highly adaptable production lines to enable efficient production of small series tailored to customer demands.
Application Deadline in 4 months

Overseas investment falling, developing countries largely unscathed: UN trade agency

Foreign direct investment (FDI) has dropped 40 per cent year-on-year so far, the UN Conference on Trade and Development (UNCTAD) said on Monday, but the $470 million decline is happening mainly in wealthy, industrialized nations, especially in North America and Western Europe.

EU's Call for Proposals: Alternatives to anti-microbials in farmed animal production

Activities shall focus on developing and testing new, efficient and targeted alternatives to anti-microbials in farmed animal production.
Application Deadline in 3 months

Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning.

EU's Call for Proposals: Blue economy

This topic aims to support demonstration projects based on innovative technologies testing/deploying/scaling-up of new industrial or service applications and solutions for the blue economy.
Application Deadline in 3 months

Exciting Possibilities in the Visible Light Communications Market & Their Potential

The Global Visible Light Communication market is valued of $ 6.9mn during the forecast period 2017 -2023. As the developments pertaining to VLC are being executed incessantly complimented by the exponential rise in the data transfer due to on-going IoT wave will boost the market. Europe remained a significant market for VLC developments in 2017. The access and station point’s shipments in this region totaled around 4.7 thousand units in 2017 and is forecast to advantage at a CAGR of 153.6% while Americas evaluated to witness the highest CAGR of nearly 178% in the forecast period.

EU's Call for Proposals: Blue Labs

The focus of this action is to support young scientists supported by experienced researchers, industry and local stakeholders, to team up and develop innovative technologies, products and services in support of a sustainable blue economy, preserving marine resources and ecosystems.
Application Deadline in 3 months

A Close Look At The Latest Research Trends Within The Uninterruptible Power Supply Market

The Global Uninterrupted power supply market has a market revenue of $10,369.8 million in 2017 and is estimated to grow at a CAGR of 3.6% during the forecast period 2018-2023. Growing demand for continuous power supply and protecting equipment from voltage fluctuations is driving the market.