Each year the Association for the Advancement of Artificial Intelligence (AAAI) recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. We've been talking to some of the 2024 AAAI Fellows to find out more about their research. In this interview, we meet Anima Anandkumar and find out about her work on Neural Operators, of which she is the inventor. Neural Operators are able to learn complex physical phenomena that occur at multiple resolutions while standard neural networks are unable to do so. Standard neural networks use a fixed number of pixels or resolution to learn a phenomenon, while neural operators represent data as continuous functions.

In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted and Navigation-Guided IGSS System. The endeavor involves integrating cutting-edge technologies to attain the required surgical precision and limit user radiation exposure, thereby addressing the limitations of manual surgical methods. We propose an IGSS workflow and system architecture employing a hybrid-layered approach, combining modular and integrated system architectures in distinctive layers to develop an affordable system for seamless integration, scalability, and reconfigurability. We developed and integrated the system and extensively tested it on phantoms and cadavers. The proposed system's accuracy using navigation guidance is 1.020 mm, and robot assistance is 1.11 mm on phantoms. Observing a similar performance in cadaveric validation where 84% of screw placements were grade A, 10% were grade B using navigation guidance, 90% were grade A, and 10% were grade B using robot assistance as per the Gertzbein-Robbins scale, proving its efficacy for an IGSS. The evaluated performance is adequate for an IGSS and at par with the existing systems in literature and those commercially available. The user radiation is lower than in the literature, given that the system requires only an average of 3 C-Arm images per pedicle screw placement and verification

To increase the internet content of Indian Languages in different domains India is home to a multitude of languages of which 22 languages are recognised by the Indian Constitution as official. As part of the Speech Consortium of the NLTM-R&D Building speech based applications for the Indian population which is led by Indian Institute of Technology Madras is a difficult problem owing to limited data and the number (IITM), SPRING Lab of IITM has collected and is collecting of languages and accents to accommodate. To encourage the legally sourced and manually transcribed speech corpus in language technology community to build speech based applications various Indian languages such as Tamil, Hindi, Indian English, in Indian languages, we are open sourcing SPRING-Marathi, Bengali, Malayalam, Telugu, Assamese, Kannada, INX data which has about 2000 hours of legally sourced and Gujarati, Odia, Punjabi. Bodo and Manipuri through manually transcribed speech data for ASR system building speech data collection agencies identified using a tendering in Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, process. The data collected has been carefully evaluated by Marathi, Odia, Punjabi and Tamil. This endeavor is by the Speech Quality Control (SQC) team led by KL University. SPRING Lab, Indian Institute of Technology Madras and is We are releasing the first set of valuable data amounting a part of National Language Translation Mission (NLTM), to 2000 hours (both Audio and corresponding manually transcribed funded by the Indian Ministry of Electronics and Information transcriptions) which was collected, cleaned and prepared Technology (MeitY), Government of India. We describe the for ASR system building in 10 Indian languages such data collection and data cleaning process along with the data as Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, statistics in this paper.

A career in technology should be taught from the lower level of education. Once the students feel attracted to the influence of artificial intelligence and its innovations, they take up tech-based courses to quest their thirst. That is what Indian Institute of Technology colleges in India do. First set up in 1951 in Kharagpur, IITs are now located at 23 places across the country. Indian Institute of Technology colleges are educating students with practical knowledge by taking up artificial intelligence initiatives.

The Indian Institute of Technology Madras has developed a fellowship program to encourage early-career AI researchers. The Narayanan Family Foundation and the Institute's Robert Bosch Centre for Data Science and AI have teamed up to build a fellowship in Artificial Intelligence for Social Good. The application is available to artificial intelligence researchers who want to use their skills for the betterment. The Indian Institute of Technology Madras hopes to attract recent PhD graduates or newly qualified researchers in computer science, computational and data sciences, biomedical sciences, management, finance, and other engineering departments with outstanding educational achievements to RBCDSAI through this program, which is funded by the Narayanan Family Foundation. With India's largest network analytics and deep reinforcement learning study groups, RBCDSAI is a world's most prominent interdisciplinary research academic centre for Data Science and AI.

A new research center is to be established in India to undertake foundational research in many areas of data science and artificial intelligence (AI) applicable in the Indian context, and create societal impact through multidisciplinary interactions with government, academic, research and industrial collaborators. The center, a collaboration between Robert Bosch Engineering and Business Solutions (RBEI) and the Indian Institute of Technology Madras (IIT Madras), will receive around US$0.5m per year funding for five years to carry out research in areas like deep learning, reinforcement learning, network analytics, interpretable machine learning, and domain aware AI. Its activity will include research projects, knowledge management and dissemination, developing prototypes, outreach projects, and setting up collaborative facilities and laboratories among others. The center's mandate requires interaction with industry and other universities, including international student and faculty exchanges. The objective is to advance scientific innovation for societal benefit.