This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"The Materials Research Society is proud to announce the 2020 MRS Communications Lecture honorees, Drs. Chun-Teh Chen and Grace Gu from the University of California, Berkeley. The honor recognizes excellence in the field of materials research through work published in MRS Communications. Drs. Chen and Gu are recognized this year for their prospective paper on how researchers are harnessing artificial intelligence to accelerate the design and discovery of composite materials. Their work is featured in volume nine, issue two of MRS Communications. Composites are combinations of two or more base materials, whose collective properties exceed those possessed by either material alone. Composites are widely used as structural materials in the automotive and aerospace industries and can also be easily found in nature. Limitations in manufacturing methods have generally restricted the architecture these materials take on in real-world applications. Most commonly, they’re processed into multilayer sheets..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is transforming our way of life. Able to spot patterns invisible to the human eye, algorithms are learning how to make our lives easier, safer, and more fun. That power is not lost on materials researchers. During the next decade, artificial intelligence or AI-driven research could fundamentally transform how new and better materials are developed. What’s more, it might even revamp how materials research itself is carried out, enabling promising new materials and processes to be developed more quickly. Machine learning methods come in a variety of flavors, with some requiring more guidance, or “supervision,” from researchers. But, generally, a machine-learning algorithm designed to discover and understand the behavior of materials looks for patterns connecting the composition, structure, and properties of known materials..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial Intelligence and blockchain technologies are revolutionizing how various industries work, including the accounting industry. As the tools of the trade grow smarter, what will the future landscape of accounting look like? And where will accounting grads find a place in it? Researchers address these and other questions in a recent review published in the journal IEEE Access. AI and blockchain technologies are already having a big impact on how accounting firms run. Deloitte has created a voice analysis tool that monitors customer interactions and identifies high-risk interactions through natural language processing. PricewaterhouseCoopers has an entire AI audit lab designed to improve audit quality and operational efficiency, and Ernst & Young has developed Blockchain Analyzer, which enables in-depth reviews of cryptocurrecy transactions. Accounting students are recommended to hone their programming skills and stay abreast of emerging technologies to thrive in this fast-changing environment..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Congenital heart defects affect approximately 1% of all babies born each year and account for almost 20% of all newborn deaths. Early diagnosis while still in the womb can greatly improve an affected baby’s chance of survival. Unfortunately, diagnosis relies exclusively on ultrasound imaging, where accurate readings aren’t guaranteed. Researchers in Japan are tackling this problem by enlisting the help of artificial intelligence. More importantly, they’re helping the doctors entrusted with patient care to understand how AI programs spot heart defects. Advancements in artificial intelligence have improved how congenital heart defects are diagnosed. Ultrasound videos of fetal hearts beating normally and others with structural defects can be studied with AI, which can then determine whether the fetal hearts in new videos are abnormal or not..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers have developed a new method for teaching self-driving cars how to respond to emergencies. Unlike other approaches, which teach cars to respond according to hard and fast rules, this new method trains onboard computers to react like humans do. That unique ability could make self-driving cars vastly quicker at recognizing and avoiding potential accidents. Human drivers react instinctively to road hazards—whether that’s a car that brakes suddenly or a cyclist who rushes into traffic. It’s an ability that comes from years of experience and one that’s often taken for granted. As AI experts have learned, teaching computers to do the same is notoriously difficult. Rule-based methods provide basic functionality. But they tend to be very time-consuming and can’t account for unforeseen emergencies—two tremendous liabilities for self-driving cars..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers from China have pooled together some of the most powerful techniques in machine learning to create the ultimate control system. Successfully deployed in AI-regulated hybrid electric vehicles, the framework could grant other autonomous systems unprecedented levels of control and foresight. Machine learning is booming. And arguably the most popular technique in this branch of artificial intelligence is deep reinforcement learning. Loosely modeled after our brains’ reward system, deep reinforcement learning has enabled machines to reach or even surpass human-level performance in various tasks. Those tasks range from the trivial, like playing Go or video games, to the possibly life-saving, such as detecting firearms from video. But deep reinforcement learning algorithms have their limitations. For one, they generally lack the ability to take lessons learned in one task and apply them to another..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.