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सर्वश्रेष्ठ Artificial Intelligence पॉडकास्ट हम पा सकते हैं
सर्वश्रेष्ठ Artificial Intelligence पॉडकास्ट हम पा सकते हैं
With the rise of artificial intelligence in use today including applications like Siri, Alexa, Tesla, Cortana, Cogito, Google Now, and even Netflix, podcasts are a great alternative to keep yourself updated. We've gathered a list of podcasts available for you about this technology where you can get the latest news and trends plus learn more about how AI works and its impact on our lives.
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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, de ...
 
Artificial intelligence is a tremendously beneficial technology that's advancing at an incredibly rapid pace. As more and more organisations adopt and implement AI we find that the main challenges are not in the technology itself but in the human side, ie: the approaches, chosen problems and what's called 'the last mile', etc. That's why Data Futurology focuses on the leadership side of AI and how to get the most value from it. Join me, Felipe Flores, a Data Science executive with almost 20 ...
 
AI with AI explores the latest breakthroughs in artificial intelligence and autonomy, and discusses the technological and military implications. Join Andy Ilachinski and David Broyles as they explain the latest developments in this rapidly evolving field. The views expressed here are those of the commentators and do not necessarily reflect the views of CNA or any of its sponsors.
 
This course covers the foundations of Artificial Intelligence (AI), in particular reasoning under uncertainty, machine learning and (if there is time) natural language understanding. This course builds on the course Artificial Intelligence I from the preceding winter semester and continues it Learning Goals and Competencies Technical, Learning, and Method Competencies Knowledge: The students learn foundational representations and algorithms in AI. Application: The concepts learned are applie ...
 
David Yakobovitch explores AI for consumers through fireside conversations with industry thought leaders on HumAIn. From Chief Data Scientists and AI Advisors, to Leaders who advance AI for All, the HumAIn Podcast is the channel to release new AI products, to learn about industry trends, and to bridge the gap between humans and machines in the Fourth Industrial Revolution.
 
Artificial intelligence technologies are undoubtedly beginning to change the face of modern warfare. AI and machine learning applications promise to enhance productivity, reduce user workload, and operate more quickly than humans. But, this doesn’t come without its challenges. The Artificial Intelligence on the Battlefield podcast dives into these issues and more, looking at just how will AI reshape the future of warfare? Created by Shephard Studio, the Artificial Intelligence on the Battlef ...
 
Talking Robots is a podcast featuring interviews with high-profile professionals in Robotics and Artificial Intelligence for an inside view on the science, technology, and business of intelligent robotics. It is managed and sponsored by the Laboratory of Intelligent Systems (LIS) at the EPFL in Lausanne, Switzerland.
 
Get knowledge and inspiration to apply artificial intelligence to drug development. Discover startups applying machine learning to biomedical research. Hear how biotech and pharma companies use AI to speed discovery and cut costs. Learn from academic researchers pushing boundaries in applying computation to biology. We interview leaders transforming drug development with data and algorithms. Subscribe now and never miss an episode!
 
Dive into the world of Artificial Intelligence with your hosts Anna-Regina Entus and Victoria Rugli - fellows at the AI Research Center at emlyon business school in Paris. Together with guest speakers from around the globe, we are helping you make sense of AI and share insights on the latest innovations in the world of artificial intelligence.
 
The Awakened Humanity Podcast is your Podcast for artificial and human intelligence. You can expect a wide mix of inspiring interviews with top international experts and updates on current developments in these areas. Are we driven by technology or do we drive it? How can we find a balance between ethics and technology? What does it mean to be a human being in the AI age? The Awakened Humanity Podcast is all about asking deep questions and providing you with information and inspiration about ...
 
Welcome to the Conversations on Applied AI Podcast where Justin Grammens and the team at Emerging Technologies North talk with experts in the fields of Artificial Intelligence and Deep Learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real-world problems today. We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at AppliedAI.MN. Enjoy!
 
An introduction to machine learning to assist business leaders to understand what it can and can't do. In the three episodes, you will get a sense of the potential impact, the nature and types of models available and case studies that may apply to your industry. Allan Kent is the Head of Digital at Primedia Broadcasting and is the host of this series.
 
TOPBOTS educates business leaders on high-impact applications of modern machine learning and AI techniques and helps leading organizations adopt and implement emerging technologies. We run the largest publication and community for enterprise AI professionals to learn about the latest machine learning and automation solutions and exchange insights with each other. Through education and community, we inspire you to think creatively about how AI can be used to improve lives, revolutionize indus ...
 
Dr. Rollan Roberts is an advisor and resource to national governments on strong Artificial Intelligence and quantum-proof Cybersecurity and was nominated to Central Command's Department of Defense Civilian Task Force. He is the CEO of Courageous!, a superhuman AI and Cybersecurity research and product development think tank that serves advanced national security initiatives of national governments. He served as CEO of the Hoverboard company, creating the best-selling consumer product worldwi ...
 
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show series
 
Noam Razin, Asaf Maman, Nadav CohenAbstractIn the pursuit of explaining implicit regularization in deep learning, prominent focus was given to matrix and tensor factorizations, which correspond to simplified neural networks. It was shown that these models exhibit implicit regularization towards low matrix and tensor ranks, respectively. Drawing clo…
 
Today we continue our AI Rewind 2021 series joined by a friend of the show, assistant professor at Carnegie Mellon University, and AI Rewind veteran, Zack Lipton! In our conversation with Zack, we touch on recurring themes like “NLP Eating AI” and the recent slowdown in innovation in the field, the redistribution of resources across research proble…
 
This podcast episode provides a snippet of Cognilytica’s AI and ML education from our Cognilytica Education Subscription. Data is at the heart of AI. It should be no surprise then that proper data management is crucial for AI projects. This podcast is an excerpt from our Cognilytica Education course discussing data pipelines. In this clip we discus…
 
With the ever-changing digital transformation landscape, there are now more responsibilities on a Chief Digital Officer's shoulders than ever before. 📌 Gone are the days when they were only responsible for introducing basic capabilities and perhaps piloting handfuls initiatives - today, this person must coordinate changes that address everything fr…
 
Andy and Dave discuss the latest in AI news and research, including the signing of the 2022 National Defense Authorization Act, which contains a number of provisions related to AI and emerging technology [0:57]. The Federal Trade Commission wants to tackle data privacy concerns and algorithmic discrimination and is considering a wide range of optio…
 
Ten years ago, Data Science was considered a niche crossover subject straddling statistics, mathematics and computing, taught at a handful of universities. Today, its importance to the world of business and commerce is well established, and there are many routes, including online courses and on-the-job training, that can equip us to apply these pri…
 
Yuri Lavinas, Claus Aranha, Gabriela OchoaAbstractUnderstanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem…
 
Joseph Early, Christine Evers and Sarvapali RamchurnAbstractIn Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to m…
 
Daqi Liu, Miroslaw Bober, Josef KittlerAbstractAs a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image. Currently, the mean field variational Bayesian framework is the de facto methodology used by the existing methods, in which the un…
 
Dmitri A. Rachkovskij, Denis KleykoAbstractHyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality. A critical step for designing the HDC/VSA solutions …
 
Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng SunAbstractExploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses on designing heuristic rules or distance metrics to check whether a state is nove…
 
John Pougu\'e-Biyong, Akshay Gupta, Aria Haghighi, Ahmed El-KishkyAbstractA key challenge in social network analysis is understanding the position, or stance, of people in the graph on a large set of topics. While past work has modeled (dis)agreement in social networks using signed graphs, these approaches have not modeled agreement patterns across…
 
Kin G. Olivares and Cristian Challu and Grzegorz Marcjasz and Rafa{\l} Weron and Artur DubrawskiAbstractWe extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allo…
 
Thomas Guyet, Wenbin Zhang and Albert BifetAbstractThe need to analyze information from streams arises in a variety of applications. One of the fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the existence of the pattern in transactions but pay no attention to the serie…
 
Chenyang Lei, Yazhou Xing, Hao Ouyang, Qifeng ChenAbstractApplying an image processing algorithm independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and pro…
 
Nelly Elsayed, Zag ElSayed, Anthony S. MaidaAbstractLong short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture…
 
Lidia Garrucho, Kaisar Kushibar, Socayna Jouide, Oliver Diaz, Laura Igual and Karim LekadirAbstractComputer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical e…
 
Vasco D. Silva, Anna Finamore, Rui HenriquesAbstractOngoing traffic changes, including those triggered by the COVID-19 pandemic, reveal the necessity to adapt our public transport systems to the ever-changing users' needs. This work shows that single and multi objective stances can be synergistically combined to better answer the transit network de…
 
Pouria MehrabiAbstractIn this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground point…
 
Shuo-Hui LiAbstractWavelet transformation stands as a cornerstone in modern data analysis and signal processing. Its mathematical essence is an invertible transformation that discerns slow patterns from fast ones in the frequency domain. Such an invertible transformation can be learned by a designed normalizing flow model. With a generalized liftin…
 
Qibin Zhou, Dongdong Bai, Junge Zhang, Fuqing Duan, Kaiqi HuangAbstractAn imperfect-information game is a type of game with asymmetric information. It is more common in life than perfect-information game. Artificial intelligence (AI) in imperfect-information games, such like poker, has made considerable progress and success in recent years. The gre…
 
Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael BrudnoAbstractLarge pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are "…
 
Hendrik Schuff, Alon Jacovi, Heike Adel, Yoav Goldberg and Ngoc Thang VuAbstractWhile a lot of research in explainable AI focuses on producing effective explanations, less work is devoted to the question of how people understand and interpret the explanation. In this work, we focus on this question through a study of saliency-based explanations ove…
 
Sharik Ali Ansari, Koteswar Rao Jerripothula, Pragya Nagpal, Ankush MittalAbstractIn this paper, we present how Bell's Palsy, a neurological disorder, can be detected just from a subject's eyes in a video. We notice that Bell's Palsy patients often struggle to blink their eyes on the affected side. As a result, we can observe a clear contrast betwe…
 
Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Yan Gao, Qiang Fu, Jian-Guang Lou, Weizhu ChenAbstractReasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a new pre-training paradigm. T…
 
Ziyun Li, Xinshao Wang, Di Hu, Neil M. Robertson, David A. Clifton, Christoph Meinel, Haojin YangAbstractMutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under adverse conditions. For example, label noise usually leads to less reli…
 
Jinyang Jiang, Jiaqiao Hu, Yijie PengAbstractClassical reinforcement learning (RL) aims to optimize the expected cumulative rewards. In this work, we consider the RL setting where the goal is to optimize the quantile of the cumulative rewards. We parameterize the policy controlling actions by neural networks and propose a novel policy gradient algo…
 
Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick and Christopher SummerfieldAbstractBuilding artificial intelligence (AI) that aligns with human values is an unsolved problem. Here, we developed a human-in-the-loop research pipeline c…
 
Dominik M\"uller, I\~naki Soto-Rey and Frank KramerAbstractNovel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what ex…
 
Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, and Ying-Chang LiangAbstractMobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless cha…
 
Saikat Dutta, Arulkumar Subramaniam, Anurag MittalAbstractVideo frame interpolation aims to synthesize one or multiple frames between two consecutive frames in a video. It has a wide range of applications including slow-motion video generation, frame-rate up-scaling and developing video codecs. Some older works tackled this problem by assuming per-…
 
Jinke He, Miguel Suau, Hendrik Baier, Michael Kaisers, Frans A. OliehoekAbstractHow can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improve…
 
Chunyong Yang, Pengfei Liu, Yanli Chen, Hongbin Wang, Min LiuAbstractThis paper presents our MSXF TTS system for Task 3.1 of the Audio Deep Synthesis Detection (ADD) Challenge 2022. We use an end to end text to speech system, and add a constraint loss to the system when training stage. The end to end TTS system is VITS, and the pre-training self-su…
 
Feng Yang, Yichao Cao, Qifan Xue, Shuai Jin, Xuanpeng Li, and Weigong ZhangAbstractLearning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision. Different from images where RGB pixels are stored in the regular grid, for point clouds, the underlying semantic and structural information …
 
Tiansheng Huang, Shiwei Liu, Li Shen, Fengxiang He, Weiwei Lin, and Dacheng TaoAbstractFederated learning (FL) is vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user. To counter this issue, personalized FL (PFL) was proposed to produce dedicated local mo…
 
Seung Park, Cheol-Hwan Yoo, Yong-Goo ShinAbstractIn recent years, generative adversarial network (GAN)-based image generation techniques design their generators by stacking up multiple residual blocks. The residual block generally contains a shortcut, \ie skip connection, which effectively supports information propagation in the network. In this pa…
 
Hongrui Liu, Binbin Hu, Xiao Wang, Chuan Shi, Zhiqiang Zhang, Jun ZhouAbstractGraph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful training with amounts of expensive and time-consuming labeled data. To alleviate labeled data sc…
 
Yingchao Pan, Ouhan Huang, Qinghao Ye, Zhongjin Li, Wenjiang Wang, Guodun Li, Yuxing ChenAbstractVideo summarization aims to automatically generate a diverse and concise summary which is useful in large-scale video processing. Most of methods tend to adopt self attention mechanism across video frames, which fails to model the diversity of video fra…
 
Dimitrios Sikeridis, Michael DevetsikiotisAbstractEnsuring public safety in a Smart City (SC) environment is a critical and increasingly complicated task due to the involvement of multiple agencies and the city's expansion across cyber and social layers. In this paper, we propose an extensive form perfect information game to model interactions and …
 
Emily Koo, Heather Bowling, Kenneth Ashworth, David J. Heeger, Stefano PacificoAbstractEpistemic AI accelerates biomedical discovery by finding hidden connections in the network of biomedical knowledge. The Epistemic AI web-based software platform embodies the concept of knowledge mapping, an interactive process that relies on a knowledge graph in …
 
Fahad Rahman Amik, Ahnaf Ismat Tasin, Silvia Ahmed, M. M. Lutfe Elahi, Nabeel MohammedAbstractKnowledge Distillation is a technique which aims to utilize dark knowledge to compress and transfer information from a vast, well-trained neural network (teacher model) to a smaller, less capable neural network (student model) with improved inference effic…
 
Afaf Ta\"ik and Soumaya CherkaouiAbstractFederated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local training data. It has recently become a hot research topic, as it promises several benefits related to data privacy and scalabili…
 
Hajar Moudoud, Soumaya Cherkaoui and Lyes KhoukhiAbstractFederated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite the…
 
Hong Xuan and Robert PlessAbstractPair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped closer than images from different classes. The literature reports a large and growing set of variations of the pair-wise loss strategies. H…
 
Saeed Anwar, Muhammad Tahir, Chongyi Li, Ajmal Mian, Fahad Shahbaz Khan, Abdul Wahab MuzaffarAbstractImage colorization is the process of estimating RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Deep learning techniques for image colorization have progressed notably over the last decade, calling …
 
Roozbeh Yousefzadeh and Xuenan CaoAbstractMany applications affecting human lives rely on models that have come to be known under the umbrella of machine learning and artificial intelligence. These AI models are usually complicated mathematical functions that map from an input space to an output space. Stakeholders are interested to know the ration…
 
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea FinnAbstractModel-based algorithms, which learn a dynamics model from logged experience and perform some sort of pessimistic planning under the learned model, have emerged as a promising paradigm for offline reinforcement learning (offline RL). However, practical v…
 
Alon Jacovi, Jasmijn Bastings, Sebastian Gehrmann, Yoav Goldberg, Katja FilippovaAbstractWhen explaining AI behavior to humans, how is the communicated information being comprehended by the human explainee, and does it match what the explanation attempted to communicate? When can we say that an explanation is explaining something? We aim to provide…
 
Boyu Wang, Jorge Mendez, Changjian Shui, Fan Zhou, Di Wu, Christian Gagn\'e, Eric EatonAbstractLearning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and di…
 
Sheng-Chun Kao, Tushar KrishnaAbstractAs Deep Learning continues to drive a variety of applications in edge and cloud data centers, there is a growing trend towards building large accelerators with several sub-accelerator cores/chiplets. This work looks at the problem of supporting multi-tenancy on such accelerators. In particular, we focus on the …
 
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