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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.
 
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!
 
Dream It! Imagine It! Create It! "If What If" (IWI) is an educational, consulting, and development company where our expertise is in Artificial Intelligence (AI), Virtual Reality (VR), Virtual Worlds (VW), and the Metaverse. "If What If" are a group of Futurists, computer analysts, data scientists, and researchers who believe that Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), and the Metaverse coupled with AI is one of the next great technological frontiers. Our podcas ...
 
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 is already controlling washing machines and translation assistants and helping doctors reach a diagnosis. It is changing our working lives and our leisure time. AI is making our lives easier and, ideally, even better! AI raises expectations, fears and hopes. And it involves risks. It’s all about personal autonomy and freedom, about security as well as sustainability and even global equity. AI between a promising future and a brave new world. Leading AI experts talk ab ...
 
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 ...
 
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!
 
Danilo McGarry is a prominent leader, coach and Keynote speaker in the topics of Automation (and all its related areas: Artificial Intelligence/RPA/Machine Learning/Neural Networks/Deep Learning/Transformation) - to read more about the creator of this space please visit www.danilomcgarry.com
 
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.
 
Dive into the world of Artificial Intelligence with your host Anna-Regina Entus - founder and president of the AI in Management Association and fellow of the AI Research Center at emlyon business school in Paris. Together with guest speakers from around the globe, I am helping you make sense of AI and share insights on the latest innovations in the world of Artificial Intelligence. Episodes 1-6: Hosted by Anna-Regina Entus and Victoria Rugli from Episode 7: Hosted by Anna-Regina Entus
 
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 ...
 
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Andres Campero, Michelle Vaccaro, Jaeyoon Song, Haoran Wen, Abdullah Almaatouq, Thomas W. MaloneAbstractThe Turing test for comparing computer performance to that of humans is well known, but, surprisingly, there is no widely used test for comparing how much better human-computer systems perform relative to humans alone, computers alone, or other b…
 
The 9 Pillars Of The Metaverse Series - Part Two: Immersion The Definitive Series on Understanding the Metaverse & Virtual Worlds There is so much confusion about Virtual Reality and the Metaverse. At If-What-If, (IWI), we are producing an educational video and podcast series, on the Metaverse and Virtual Worlds. "The 9 Pillars Of The Metaverse" se…
 
This special episode is a recording from a live webinar we ran back in February as part of our Future proofing your data platforms online event covering how to establish a best in class multi-cloud strategy. Felipe explored all aspects of a multi-cloud strategy, including simplifying your data architecture, regardless of whether your systems are ru…
 
Today we kick off our annual coverage of the CVPR conference joined by Fatih Porikli, Senior Director of Engineering at Qualcomm AI Research. In our conversation with Fatih, we explore a trio of CVPR-accepted papers, as well as a pair of upcoming workshops at the event. The first paper, Panoptic, Instance and Semantic Relations: A Relational Contex…
 
This and all episodes at: https://aiandyou.net/ . Are you good at bluffing? Do you think you could beat a computer? What if I told you that it was mathematically proven that the computer would beat you? That's what Michael Bowling did for his program that plays heads-up, limit Texas Hold'Em: he proved that it was impossible to do better than draw a…
 
CNA colleagues Kaia Haney and Heather Roff join Andy and Dave to discuss Responsible AI. They discuss the recent Inclusive National Security seminar on AI and National Security: Gender, Race, and Algorithms. The keynote speaker, Elizabeth Adams spoke on the challenges that society faces in integrating AI technologies in an inclusive fashion, and sh…
 
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer discuss what AI in the cloud really means. They break down what is the cloud with a technical definition and then an economic definition to level set the topic and then dig into the question: What does AI have to do with the Cloud? Continue reading AI Today Podcast: AI i…
 
The conversation this week is with Brandon Satrom. Brandon is the VP of Developer Experience and Engineering at Blues Wireless, a driven technologist and experienced leader with a background in product management, strategy, architecture, software development and developer advocacy. He describes himself as a technologist first, and loves to use what…
 
[Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Steve received his PhD from Johns Hopkins University in Cognitive Science where he began his AI research and also taught Statistics at Towson State University. After receiving his PhD in 1979, AI pioneer Roger Schank invited Steve to join t…
 
Akhter Al Amin, Saad Hassan, Cecilia O. Alm, Matt HuenerfauthAbstractDeaf and hard of hearing individuals regularly rely on captioning while watching live TV. Live TV captioning is evaluated by regulatory agencies using various caption evaluation metrics. However, caption evaluation metrics are often not informed by preferences of DHH users or how …
 
Xi Wang and Laurence AitchisonAbstractWe develop ShiftMatch, a new training-data-dependent likelihood for out of distribution (OOD) robustness in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors from Izmailov et al. (2021a) and efficiently matches test-time spatial correlations to those at train…
 
Andrea Simonetto and Ivano NotarnicolaAbstractEquipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in machine learning, AI, and optimization. In this paper, we investigate how to allocate limited resources to {locally interactin…
 
Lijun Sun, Yu-Cheng Chang, Chao Lyu, Ye Shi, Yuhui Shi, and Chin-Teng LinAbstractThe multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. Th…
 
Kubilay Can Demir, Matthias May, Axel Schmid, Michael Uder, Katharina Breininger, Tobias Weise, Andreas Maier, Seung Hee YangAbstractThis paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and audio signals in German, X-ray images, and system commands colle…
 
Luis Botelho, Luis Nunes, Ricardo Ribeiro, and Rui J. LopesAbstractIn a previous paper, we have proposed a set of concepts, axiom schemata and algorithms that can be used by agents to learn to describe their behaviour, goals, capabilities, and environment. The current paper proposes a new set of concepts, axiom schemata and algorithms that allow th…
 
Shriya Atmakuri, Tejas Chheda, Dinesh Kandula, Nishant Yadav, Taesung Lee, Hessel TuinhofAbstractExplanation methods have emerged as an important tool to highlight the features responsible for the predictions of neural networks. There is mounting evidence that many explanation methods are rather unreliable and susceptible to malicious manipulations…
 
Lizhi Cheng, Weijia jia, Wenmian YangAbstractSpoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference facing the impatience of human users. Existing work increases inference speed by designing non-autoregressive models for single-turn SLU tasks but fails to apply to multi-turn SLU in co…
 
Akash Amalan, Rui Wang, Yanqi Qiao, Emmanouil Panaousis and Kaitai LiangAbstractFederated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy. However, in a non-iid setting, current federated GAN architectures are uns…
 
Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He, Qingming HuangAbstractIt is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. Howev…
 
Shaoyang Wang and Chau Yuen and Wei Ni and Guan Yong Liang and Tiejun LvAbstractThis paper proposes an effective and novel multiagent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same…
 
Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Qingming HuangAbstractTensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different re…
 
Ahmet M. Elbir, Wei Shi, Kumar Vijay Mishra, Anastasios K. Papazafeiropoulos, Symeon ChatzinotasAbstractWith the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to enhan…
 
Cheng Tan, Zhangyang Gao, Siyuan Li, Yongjie Xu, Stan Z. LiAbstractSpatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework of spatiotemporal predictive learning, in which the spatial encoder and decoder capture intra-frame fe…
 
Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith RaviAbstractWasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applications. One of the challenges in estimating Wasserstein distance is that it is comput…
 
Anj Simmons, Rajesh VasaAbstractThis paper presents an knowledge graph to assist in reasoning over signals for intelligence purposes. We highlight limitations of existing knowledge graphs and reasoning systems for this purpose, using inference of an attack using combined data from microphones, cameras and social media as an example. Rather than act…
 
JunYu Lu, Ping Yang, JiaXing Zhang, RuYi Gan, Jing YangAbstractEven as pre-trained language models share a semantic encoder, natural language understanding suffers from a diversity of output schemas. In this paper, we propose UBERT, a unified bidirectional language understanding model based on BERT framework, which can universally model the trainin…
 
Reza Arablouei, Ziwei Wang, Greg J. Bishop-Hurley, Jiajun LiuAbstractWe examine using data from multiple sensing modes, i.e., accelerometry and global navigation satellite system (GNSS), for classifying animal behavior. We extract three new features from the GNSS data, namely, the distance from the water point, median speed, and median estimated ho…
 
Xia Jiang, Jian Zhang, Dan LiAbstractThis paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and the RL policy, to…
 
Gasser Elbanna, Neil Scheidwasser-Clow, Mikolaj Kegler, Pierre Beckmann, Karl El Hajal, Milos CernakAbstractMethods for extracting audio and speech features have been studied since pioneering work on spectrum analysis decades ago. Recent efforts are guided by the ambition to develop general-purpose audio representations. For example, deep neural ne…
 
Qiongqiong Liu, Shuyan Huang, Zitao Liu, Weiqi LuoAbstractSentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL). In this paper, we present a large-scale SC datas…
 
Jiahao Chen, Shuyan Huang, Zitao Liu, Weiqi LuoAbstractOnline dialogic instructions are a set of pedagogical instructions used in real-world online educational contexts to motivate students, help understand learning materials, and build effective study habits. In spite of the popularity and advantages of online learning, the education technology an…
 
Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi WuAbstractIt has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods. We propose a novel phasic approach by alternating online RL and offline SL for tackling sparse-reward goal-conditioned problems. In the online phase, we per…
 
Jiajun Tong, Zhixiao Wang, Xiaobin RuiAbstractText classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pre-trained neural network models to handle this kind of dataset. However, these methods are either difficult to deploy on mobile devices because…
 
Rex Chen, Fei Fang, Norman SadehAbstractTraffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic volume grows globally. An increasing number of works are applying reinforcement learning (RL) to TSC; RL can draw on an abundance of traffic data to improve signalling efficiency. However, RL-based signal controllers…
 
Akhter Al Amin, Kazi Sinthia KabirAbstractLanguage models (LM) are becoming prevalent in many language-based application spaces globally. Although these LMs are improving our day-to-day interactions with digital products, concerns remain whether open-ended languages or text generated from these models reveal any biases toward a specific group of pe…
 
Yi-Lun Liao and Tess SmidtAbstract3D-related inductive biases like translational invariance and rotational equivariance are indispensable to graph neural networks operating on 3D atomistic graphs such as molecules. Inspired by the success of Transformers in various domains, we study how to incorporate these inductive biases into Transformers. In th…
 
Lionel Nganyewou Tidjon and Foutse KhomhAbstractArtificial Intelligence (AI) is becoming the corner stone of many systems used in our daily lives such as autonomous vehicles, healthcare systems, and unmanned aircraft systems. Machine Learning is a field of AI that enables systems to learn from data and make decisions on new data based on models to …
 
Xueyi Liu, Yu Rong, Tingyang Xu, Fuchun Sun, Wenbing Huang, Junzhou HuangAbstractGraph instance contrastive learning has been proved as an effective task for Graph Neural Network (GNN) pre-training. However, one key issue may seriously impede the representative power in existing works: Positive instances created by current methods often miss crucia…
 
Dylan Ebert, Chen Sun, Ellie PavlickAbstractDistributional models learn representations of words from text, but are criticized for their lack of grounding, or the linking of text to the non-linguistic world. Grounded language models have had success in learning to connect concrete categories like nouns and adjectives to the world via images and vid…
 
Geraud Nangue Tasse, Benjamin Rosman, Steven JamesAbstractWe propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an agent's environment. This is achieved by equipping an agent with an internal goal space defined as all the world …
 
Vedant Nanda and Till Speicher and Camila Kolling and John P. Dickerson and Krishna P. Gummadi and Adrian WellerAbstractA major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the refere…
 
Nicholas Kluge Corr\^ea, Camila Galv\~ao, James William Santos, Carolina Del Pino, Edson Pontes Pinto, Camila Barbosa, Diogo Massmann, Rodrigo Mambrini, Luiza Galv\~ao, Edmund TeremAbstractIn the last decade, a great number of organizations have produced documents intended to standardize, in the normative sense, and promote guidance to our recent a…
 
Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, Tao QinAbstractRetrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many rese…
 
Ka Ho Tong, Ka Wai Cheung and Xiaochuan YuAbstractICME-2022 few-shot logo detection competition is held in May, 2022. Participants are required to develop a single model to detect logos by handling tiny logo instances, similar brands, and adversarial images at the same time, with limited annotations. Our team achieved rank 16 and 11 in the first an…
 
Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Jiliang Tang, Weiqi LuoAbstractKnowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various dee…
 
Zunming Chen, Hongyan Cui, Ensen Wu, Yu XiAbstractThe proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. The classical artificial intelligence algorithms require centralized data collection a…
 
Siamak Ghodsi, Harith Alani, and Eirini NtoutsiAbstractWith the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon. However, an important and often challenging aspect in utilizing such systems is to distinguish validity for the range…
 
Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik WojtczakAbstractRecursion is the fundamental paradigm to finitely describe potentially infinite objects. As state-of-the-art reinforcement learning (RL) algorithms cannot directly reason about recursion, they must rely on the practitioner's ingenuity in designing …
 
Dhaminda B. Abeywickrama, Amel Bennaceur, Greg Chance, Yiannis Demiris, Anastasia Kordoni, Mark Levine, Luke Moffat, Luc Moreau, Mohammad Reza Mousavi, Bashar Nuseibeh, Subramanian Ramamoorthy, Jan Oliver Ringert, James Wilson, Shane Windsor, Kerstin EderAbstractAs autonomous systems are becoming part of our daily lives, ensuring their trustworthin…
 
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