While working at NASA in 2003, Dr. Robert Ambrose, director of the Robotics and Automation Design Lab (RAD Lab), designed a robot with no fixed top or bottom. A perfect sphere, the RoboBall could not flip over, and its shape promised access to places wheeled or legged machines could not reach -- from the deepest lunar crater to the uneven sands of a beach. Two of his students built the first prototype, but then Ambrose shelved the idea to focus on drivable rovers for astronauts. When Ambrose arrived at Texas A&M University in 2021, he saw a chance to reignite his idea. With funding from the Chancellor's Research Initiative and Governor's University Research Initiative, Ambrose brought RoboBall back to life.

Alzheimer's disease (AD) is a progressive neurological disorder, meaning that the symptoms develop gradually throughout the years. It is also the main cause of dementia, which affects memory, thinking skills, and mental abilities. Nowadays, researchers have moved their interest towards AD detection from spontaneous speech, since it constitutes a time-effective procedure. However, existing state-of-the-art works proposing multimodal approaches do not take into consideration the inter- and intra-modal interactions and propose early and late fusion approaches. To tackle these limitations, we propose deep neural networks, which can be trained in an end-to-end trainable way and capture the inter- and intra-modal interactions. Firstly, each audio file is converted to an image consisting of three channels, i.e., log-Mel spectrogram, delta, and delta-delta. Next, each transcript is passed through a BERT model followed by a gated self-attention layer. Similarly, each image is passed through a Swin Transformer followed by an independent gated self-attention layer. Acoustic features are extracted also from each audio file. Finally, the representation vectors from the different modalities are fed to a tensor fusion layer for capturing the inter-modal interactions. Extensive experiments conducted on the ADReSS Challenge dataset indicate that our introduced approaches obtain valuable advantages over existing research initiatives reaching Accuracy and F1-score up to 86.25% and 85.48% respectively.

India is among the top 10 nations in the world in terms of technological advancements and funding in artificial intelligence, according to findings from a study published by The Brookings Institution. While India is outside the top 10 in terms of commercial and research initiatives in artificial intelligence (AI), it ranks sixth in terms of spending and investments on AI made by public, governmental initiatives, as well as private institutes and organisations. The study notes that alongside having increasing adoption of new generation technologies, India is "well positioned from the funding standpoint" – a factor that gives it leverage to quickly achieve faster innovations in AI technologies, and overtake other nations that are leading AI achievements right now. The other nations leading AI achievements ahead of India are USA, China, United Kingdom, France, Japan and Germany. Canada, South Korea and Italy are the other three nations behind India in the top 10 AI adopters list, as per the study.

Luddy School of Informatics, Computing, and Engineering dean Raj Acharya poses for a headshot. Acharya will step down mid-March to participate in an artificial intelligence research initiative at IU. Courtesy of Indiana University Dean of the Luddy School of Informatics, Computing, and Engineering Raj Acharya will step down mid-March to participate in an artificial intelligence research initiative. Acharya said the school will hire an acting dean to replace him and then conduct a national search to find a permanent dean. Acharya launched the Department of Intelligent Systems Engineering in 2016 and has been dean since July 2016. He will now be associate vice president for research with the specific task of promoting artificial intelligence.

As the digital age is maturing at an exponential pace and with it, the need for businesses to increase their capacity for automated data-driven decision making, this talk will present a glimpse into ongoing research initiatives. The purpose of this presentation is to present seven emerging dimensions of digitalisation in the AECO sector, namely: cloud computing; blockchain; robotics; big data analytics; 3D printing; machine learning; and hybrid tools. A practical example of such hybrid systems will be presented in the newly funded H2020 research initiative, called BIMERR. This 4-year research project seeks to create a renovation 4.0 toolkit comprising of an amalgamation of emerging technologies, such as BIM, AR, mixed reality, cloud computing and ML. In an effort to disrupt the traditional modus operandi of the EU renovation market.

Ongoing research initiatives are showing that artificial intelligence may be able to predict a person's likelihood of developing Alzheimer's disease with a high level of accuracy. As the population ages, the specter of Alzheimer's disease becomes all the more ominous. It's like a dark cloud on the horizon, threatening a massive and devastating storm that is coming our way. In reality, this storm has already begun. Today, 5.8 million Americans are living with Alzheimer's, according to the Alzheimer's Association.

The European Commission has chosen Time Machine as one of the six proposals retained for preparing large-scale research initiatives to be strategically developed in the next decade. Time Machine foresees to design and implement advanced new digitisation and Artificial Intelligence (AI) technologies to mine Europe's vast cultural heritage, providing fair and free access to information that will support future scientific and technological developments in Europe. The Time Machine Project, which involves FAU as well as several other institutions, will create advanced AI technologies to make sense of vast amounts of information from complex historical data sets. This will enable the transformation of fragmented data – with content ranging from medieval manuscripts and historical objects to smartphone and satellite images – into useable knowledge for industry. In essence, a large-scale computing and digitisation infrastructure will map Europe's entire social, cultural and geographical evolution.

The SLS group addresses a broad range of research topics, but they can generally be grouped according to three basic questions: 1) who is talking, 2) what is said, and 3) what is meant. The first area focuses on paralinguistic issues like speaker verification, language and dialect identification, and speaker diarization (i.e., who spoke when). However, we are also beginning to examine health-related issues as they are manifested in the speech signal. The second research area addresses core speech recognition capabilities and addresses challenges related to noise robustness, limited linguistic resources, and unsupervised language acquisition. The third and final area focuses more on the boundary between speech and natural language processing, and includes topics related to speech understanding, but also related areas such as sentiment analysis and dialogue.

Since 1956, when computer science researchers gathered in the small town of Hanover, N.H. at Dartmouth College to talk about the field's nascent investigations into artificial intelligence, both government and industry in the U.S. have grappled with how to structure a systematic approach to research and development in the newly important field. From the government's perspective, this is increasingly important. With both federal research institutions and private companies pursuing artificial intelligence breakthroughs at breakneck speed, the federal government is frankly having a bit of an existential crisis about its role in research efforts and the priorities it has for what AI research should look like. To wit, in 2015 government spending on unclassified research and development in AI-related technologies was around 1.1 billion, according to one of the twin reports released today. But in the last five years alone, mergers and acquisitions among private companies vying for dominance in the AI market have far outstripped that figure, according to data from CB Insights.