Neural Networks: All YOU Need to Know - Towards Data Science

Posted by Keith Hall, Research Scientist, Natural Language Understanding, Google Research Due to the COVID-19 pandemic, scientists an

As most of the workforce seemingly shifted to a remote workforce, the need for automation, technology, and tools has never been greater. How are VCs investing in AI and automation?

All around the world, scientists and practitioners are relentlessly harnessing data on the pandemic to model its progression, predict the impact of possible interventions and develop solutions to medical equipment shortages, generating open-source data and codes to be reused by others.

Artificial-intelligence methods are moving into cancer research.

Robots could soon assist humans in a variety of fields, including in manufacturing and industrial settings. A robotic system that can automatically assemble customized products may be particularly desirable for manufacturers, as it could significantly decrease the time and effort necessary to produce a variety of products.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues.

Over the past decade or so, researchers worldwide have been developing increasingly advanced artificial neural networks (ANNs), computational methods designed to replicate biological mechanisms and functions of the human brain. While some of these networks have achieved remarkable results in a variety of tasks, the decision-making processes underlying their predictions are not always clear.

Posted by Xingyou (Richard) Song, Software Engineer and Yuxiang Yang, AI Resident, Robotics at Google Rapid development of mor

Networks or graphs are mathematical descriptions of the internal structure between components in a complex system, such as connections between neurons, interactions between proteins, contacts between individuals in a crowd, and interactions between users in online social platforms. The links in most real networks change over time, and such networks are often called temporal networks. The temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on neural network function, disease propagation, information aggregation and recommendation, emergence of cooperative behavior, and network controllability. Increasing research has focused on mining the patterns in a temporal network and predicting its future evolution using machine learning techniques, especially graph neural networks. However, how to quantify the predictability limit of a temporal network, i.e. the limit that no algorithm can go beyond, is still an open question.

Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.

Surface electromyography (sEMG) is a non-invasive, computer-based technique that can record electrical impulses. The present pattern-recognition-based control strategy can realize some myoelectric control, but it is not as smooth as a human hand.