Eleni Vasilaki is Professor of Computational Neuroscience and Neural Engineering and Head of the Machine Learning Group in the Department of Computer Science, University of Sheffield. Eleni has extensive cross-disciplinary experience in understanding how brains learn, developing novel machine learning techniques and assisting in designing brain-like computation devices. In this interview, we talk about bio-inspired machine learning and artificial intelligence. I am interested in bio-inspired machine learning. I enjoy theory and analysis of mathematically tractable systems, particularly they can be relevant for neuromorphic computation.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Another prominent example in this regard came from DeepMind's publication of the possible protein structures associated with the COVID-19 virus (SARS-CoV-2) using their AlphaFold system. For example, our process of vetting results in the Global Burden of Disease Study  included the visual inspection of thousands of plots showing data together with model estimates. Our experience developing methods for computer certification of verbal autopsy has bolstered our belief that using an explainable approach, even with a reduction in accuracy, can be superior. Qualified practitioners are in short supply. There is increasing awareness that health … enhancing the ability to see and navigate in a procedure. Going beyond the conventional long-haul process, AI techniques are increasingly being applied to accelerate the fundamental processes of early-stage candidate selection and mechanism discovery. This could be the biggest impact of AI tools as it can potentially transform the quality of life for billions of people around the world. These technologies are also being used in the following ways: Preventing crime: AI and machine learning help authorities track and manage the huge amount of data generated by public surveillance devices, and analyze that data in real time for anomalies and threats.
Live tracking and analyzing of the dynamics of chimeric antigen receptor (CAR) T-cells targeting cancer cells can open new avenues for the development of cancer immunotherapy. However, imaging via conventional microscopy approaches can result in cellular damage, and assessments of cell-to-cell interactions are extremely difficult and labor-intensive. When researchers applied deep learning and 3D holographic microscopy to the task, however, they not only avoided these difficultues but found that AI was better at it than humans were. A critical stage in the development of the human immune system's ability to respond not just generally to any invader (such as pathogens or cancer cells) but specifically to that particular type of invader and remember it should it attempt to invade again is the formation of a junction between an immune cell called a T-cell and a cell that presents the antigen, or part of the invader that is causing the problem, to it. This process is like when a picture of a suspect is sent to a police car so that the officers can recognize the criminal they are trying to track down.
Artificial intelligence is a branch in computer science that deals with the simulation of intelligent behavior. It gives computers an ability to think and perform different tasks, such as humans and animals, while learning through the errors during this process. Artificial Intelligence is usually an algorithm built in such a way that permits the computer to perform tasks efficiently while making nominal errors. It uses personified knowledge by applying deep learning and machine learning algorithms while performing several tasks. Drug discovery is the preliminary step in the process of a novel drug identification and its therapeutic target. Artificial intelligence (AI) is commonly used in the healthcare industry for drug discovery.
In 2007, some of the leading thinkers behind deep neural networks organized an unofficial "satellite" meeting at the margins of a prestigious annual conference on artificial intelligence. The conference had rejected their request for an official workshop; deep neural nets were still a few years away from taking over AI. The bootleg meeting's final speaker was Geoffrey Hinton of the University of Toronto, the cognitive psychologist and computer scientist responsible for some of the biggest breakthroughs in deep nets. He started with a quip: "So, about a year ago, I came home to dinner, and I said, 'I think I finally figured out how the brain works,' and my 15-year-old daughter said, 'Oh, Daddy, not again.'" Hinton continued, "So, here's how it works."
Some healthcare provider organizations are using machine learning and other forms of artificial intelligence to provide clinicians with the best evidence-based care pathways. A group's aim could be to improve a patient's care plan based on personalized analytics. Another goal could be the further merging of evidence-based care paths with historical utilization and outcomes in order to offer optimal patient care. Provider organizations might be using social determinants of health combined with machine learning to offer clinically meaningful services. Healthcare IT News talked over these ideas with Niall O'Connor, chief technology officer at Cohere Health, a vendor of artificial intelligence technology and services designed to improve the provider, patient and payer experiences.
CTO & MD at AX Semantics, the SaaS-based, Natural Language Generation Platform that creates any content, in any language, at any scale. The pandemic brought on economic, logistical and technological challenges on a massive global scale, leaving businesses scrambling to adapt. Amidst the upheaval, businesses turned to video conferencing platforms like Zoom and Google Meet to stay connected. Technologies like artificial intelligence (AI) and machine learning (ML) helped augment human efforts to take on everything fromhealth tocybersecurity. Equally, businesses looked toward strategic execution and technology to remain agile among industry shifts and provide a greater return on investments.
Tel Aviv University launched the new, interdisciplinary Center for Artificial Intelligence and Data Science today, headed by Prof. Meir Feder of the Fleischman Faculty of Engineering. The Center will enhance basic science in these fields, encourage cross-disciplinary research that uses the most advanced methods of artificial intelligence (AI) and data science (DS), and train a new generation of researchers and industrialists who will take Israel to the forefront of the global AI revolution in the coming years. Moreover, it will lay the groundwork for the rapidly growing field of quantum computing. The launch event took place during TAU's annual AI Week. TAU President, Prof. Ariel Porat: "The establishment of the AI Center is one more step toward implementing TAU's vision for advancing groundbreaking, interdisciplinary research that brings together the university's finest researchers, the high-tech industry and the community. Not long ago we launched the interdisciplinary Center for Combating Pandemics and over the coming year we intend to establish more such centers, such as one for climate change and another for healthy aging. TAU's great advantage is its enormous range of disciplines. Our new interdisciplinary centers will further extend the scope of research, combining different disciplines, from engineering and computer science through life sciences, medicine and psychology, to economics, management, humanities, arts and law."