One year anniversary collection

Welcome to the new year, new decade and second volume of Nature Machine Intelligence.

Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.

Artificial intelligence and machine learning systems may reproduce or amplify biases. The authors discuss the literature on biases in human learning and decision-making, and propose that researchers, policymakers and the public should be aware of such biases when evaluating the output and decisions made by machines.

There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

Robots and machines are generally designed to perform specific tasks. Unlike humans, they lack the ability to generate feelings based on interactions with the world. The authors propose a new class of machines with evaluation processes akin to feelings, based on the principles of homeostasis and developments in soft robotics and multisensory integration.

As AI technology develops rapidly, it is widely recognized that ethical guidelines are required for safe and fair implementation in society. But is it possible to agree on what is ‘ethical AI’? A detailed analysis of 84 AI ethics reports around the world, from national and international organizations, companies and institutes, explores this question, finding a convergence around core principles but substantial divergence on practical implementation.

AI ethics initiatives have seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite this, Brent Mittelstadt highlights important differences between medical practice and AI development that suggest a principled approach may not work in the case of AI.

Current national cybersecurity and defence strategies of several governments mention explicitly the use of AI. However, it will be important to develop standards and certification procedures, which involves continuous monitoring and assessment of threats. The focus should be on the reliability of AI-based systems, rather than on eliciting users’ trust in AI.

To perform complex tasks, robots need to learn the relationship between their bodies and dynamic environments. A biologically plausible approach to hardware and software design shows that a robotic tendon-driven limb can make effective movements based on a short period of learning.

For some combinatorial puzzles, solutions can be verified to be optimal, for others, the state space is too large to be certain that a solution is optimal. A new deep learning based search heuristic performs well on the iconic Rubik’s cube and can also generalize to puzzles in which optimal solvers are intractable.