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찾을 수있는 최고의 Artificial Intelligence 팟 캐스트
찾을 수있는 최고의 Artificial Intelligence 팟 캐스트
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!
 
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 ...
 
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.
 
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 ...
 
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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Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, Yue CaoAbstractMasked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations…
 
Far too often, agencies, organizations, and consulting firms are running data and AI projects without taking the right steps to ensure their success. Agile and iterative approaches have become adopted best practices for application development projects, but why don’t we have something similar when it comes to advanced analytics, big data, and AI pr…
 
On this episode of the podcast, we are excited to speak to Sarah Dods, one of Australia’s top tech entrepreneurs, innovators, and company leaders. In a career that has spanned from public organisations like NICTA and CSIRO, to private companies as wide ranging as AGL, Telstra Health, RMIT University and Gerson Lehrman, Sarah has been at the forefro…
 
The conversation this week is with Caroline Sinders. Caroline is a machine learning design researcher and artist. For the past few years, she has been examining the intersections of technology's impact on society, interface design, artificial intelligence, abuse, and politics in digital conversational spaces. Sinders is the founder of Convocation D…
 
This week, we continue our conversations around the topic of Data-Centric AI joined by a friend of the show Adrien Gaidon, the head of ML research at the Toyota Research Institute (TRI). In our chat, Adrien expresses a fourth, somewhat contrarian, viewpoint to the three prominent schools of thought that organizations tend to fall into, as well as a…
 
[Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Gianluca Mauro is the CEO of AI Academy, which he founded with the mission of helping people understand what artificial intelligence is and its place in their organizations and their career. Gianluca is the author of the book "Zero to AI - …
 
Andy and Dave discuss the latest in AI news and research, starting with the European Parliament adopting the final recommendations of the Special Committee on AI in a Digital Age (AIDA), finding that the EU should not always regulate AI as a technology, but use intervention proportionate to the type of risk, among other recommendations [1:31]. Sync…
 
If AI is to be better utilised in medicine, the machines would have to be fed individual datasets. But especially with regard to health data, many people are hesitant. So, AI forces us to think about our relationship with data protection and with individuals’ rights. In Episode 10 of “AI and Us”, we also ask whether we can and should leave moral de…
 
Ailing Zeng, Muxi Chen, Lei Zhang, Qiang XuAbstractRecently, there has been a surge of Transformer-based solutions for the time series forecasting (TSF) task, especially for the challenging long-term TSF problem. Transformer architecture relies on self-attention mechanisms to effectively extract the semantic correlations between paired elements in …
 
Ramin Barati, Reza Safabakhsh, Mohammad RahmatiAbstractThe reliability of a learning model is key to the successful deployment of machine learning in various industries. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding of the adversarial examples phenomenon. However, it is difficult…
 
Andrea Cini, Daniele Zambon, Cesare AlippiAbstractOutstanding achievements of graph neural networks for spatiotemporal time series prediction show that relational constraints introduce a positive inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data generating process is …
 
Ziyi Wang, Yongming Rao, Xumin Yu, Jie Zhou, Jiwen LuAbstractConventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on these class-agnostic local geometric representations may raise confusion b…
 
Ivan Marisca, Andrea Cini, Cesare AlippiAbstractModeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequences of graphs can be processed by autoregressive graph neural networks to recursively lea…
 
Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran WangAbstractReinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges. (i) It often takes the full history to predict the future, which induces a sample complexity that scales exponentially with the horizon. (ii) The observation and state spaces are often contin…
 
Alessandra Belfiore, Angelo Salatino, Francesco OsborneAbstractInterest in Artificial Intelligence (AI) continues to grow rapidly, hence it is crucial to support researchers and organisations in understanding where AI research is heading. In this study, we conducted a bibliometric analysis on 257K articles in AI, retrieved from OpenAlex. We identif…
 
Manish Shetty, Chetan Bansal, Sai Pramod Upadhyayula, Arjun Radhakrishna, Anurag GuptaAbstractIncident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to be used by on-…
 
Matthias De Lange, Gido van de Ven, Tinne TuytelaarsAbstractIntroducing a time dependency on the data generating distribution has proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previous timesteps. Continual learning aims to overcome the greedy optimization to enable …
 
Yan Dai, Haipeng Luo and Liyu ChenAbstractWe consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only observes the losses for the visited state-action pairs (i.e., bandit feedback). While there has been a surge of studies on this pr…
 
Yan Dai, Ruosong Wang and Simon S. DuAbstractIt is well-known that the worst-case minimax regret for sparse linear bandits is $\widetilde{\Theta}\left(\sqrt{dT}\right)$ where $d$ is the ambient dimension and $T$ is the number of time steps (ignoring the dependency on sparsity). On the other hand, in the benign setting where there is no noise and th…
 
Lingfeng Tao, Jiucai Zhang, Xiaoli ZhangAbstractDexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task. Varying priority makes a robot hardly or even failed to learn an optimal policy with a deep reinforcement learning (DRL) method. To solve this prob…
 
Nguyen Hong Son, Hieu M. Vu, Tuan-Anh D. Nguyen, Minh-Tien NguyenAbstractThis paper introduces a new information extraction model for business documents. Different from prior studies which only base on span extraction or sequence labeling, the model takes into account advantage of both span extraction and sequence labeling. The combination allows t…
 
Kiril Danilchenko, Michael Segal, Dan VilenchikAbstractE-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are …
 
Lucas Friedrich and Jonas MazieroAbstractVariational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. The VQAs are applied to a variety of tasks, such as in chemistry simulations, optimization problems, and quantum neural networks. Such algorithms are constructed using a param…
 
Mayowa Ayodele and Richard Allmendinger and Manuel L\'opez-Ib\'a\~nez and Matthieu ParizyAbstractQuantum and quantum-inspired optimisation algorithms are designed to solve problems represented in binary, quadratic and unconstrained form. Combinatorial optimisation problems are therefore often formulated as Quadratic Unconstrained Binary Optimisatio…
 
Sarah Isufi, Kristijan Poje, Igor Vukobratovic and Mario BrcicAbstractWe shall have a hard look at ethics and try to extract insights in the form of abstract properties that might become tools. We want to connect ethics to games, talk about the performance of ethics, introduce curiosity into the interplay between competing and coordinating in well-…
 
Zhang Bingyu and Nikolay ArefyevAbstractThe current state-of-the-art test accuracy (97.42\%) on the IMDB movie reviews dataset was reported by \citet{thongtan-phienthrakul-2019-sentiment} and achieved by the logistic regression classifier trained on the Document Vectors using Cosine Similarity (DV-ngrams-cosine) proposed in their paper and the Bag-…
 
Jianzong Wang, Shijing Si, Zhitao Zhu, Xiaoyang Qu, Zhenhou Hong, Jing XiaoAbstractDeep learning models have made significant progress in automatic program repair. However, the black-box nature of these methods has restricted their practical applications. To address this challenge, this paper presents an interpretable approach for program repair ba…
 
Ran Ben Basat, Shay Vargaftik, Amit Portnoy, Gil Einziger, Yaniv Ben-Itzhak, Michael MitzenmacherAbstractDistributed Mean Estimation (DME) is a fundamental building block in communication efficient federated learning. In DME, clients communicate their lossily compressed gradients to the parameter server, which estimates the average and updates the …
 
Omer Belhasin, Guy Bar-Shalom, Ran El-YanivAbstractThis paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test set provided at training time. TransBoost is inspired by a large margin principle and is efficient and simple to use. The…
 
Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, Muhan ZhangAbstractThe most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message passing -- aggregating features from 1-hop neighbors repeatedly. However, the expressive power of 1-hop message passing is bounded by the Weisfeiler-Lehman (1-WL) test. Recently, researchers extend…
 
Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'aurelio Ranzato, Sagi Perel, Nando de FreitasAbstractMeta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions fro…
 
Shijing Si, Jianzong Wang, Ruiyi Zhang, Qinliang Su and Jing XiaoAbstractNon-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents. Usually, training a high-quality topic model requires large amount of textual data. In many real-world scenarios, …
 
Zhengyang Li, Shijing Si, Jianzong Wang and Jing XiaoAbstractPre-trained BERT models have achieved impressive performance in many natural language processing (NLP) tasks. However, in many real-world situations, textual data are usually decentralized over many clients and unable to be uploaded to a central server due to privacy protection and regula…
 
Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Timothy Lillicrap, Alistair Muldal, Blake Richards, Adam Santoro, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen YanAbstractCreating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research. How…
 
Marrone Silv\'erio Melo Dantas, Iago Richard Rodrigues, Assis Tiago Oliveira Filho, Gibson Barbosa, Daniel Bezerra, Djamel F. H. Sadok, Judith Kelner, Maria Marquezini, Ricardo SilvaAbstractIoT devices suffer from resource limitations, such as processor, RAM, and disc storage. These limitations become more evident when handling demanding applicatio…
 
Vivien Cabannes, Francis Bach, Vianney Perchet, Alessandro RudiAbstractThe workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access stochastic gradients, whic…
 
Kang Liu, Di Wu, Yiru Wang, Dan Feng, Benjamin Tan, Siddharth GargAbstractDeep learning techniques have shown promising results in image compression, with competitive bitrate and image reconstruction quality from compressed latent. However, while image compression has progressed towards higher peak signal-to-noise ratio (PSNR) and fewer bits per pi…
 
Rob Geada, Tommaso Teofili, Rui Vieira, Rebecca Whitworth, Daniele ZoncaAbstractArtificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. The decisions made by such "black box" systems are often opaque; that is, so complex as to be functionally impossible to understand. How do we e…
 
Isabella Saccardi, Duygu Sezen Islakoglu, Anouk Neerincx, Federica Lucia VinellaAbstractIn current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in suc…
 
Heqiang Wang, Jie XuAbstractFederated learning (FL) is an outstanding distributed machine learning framework due to its benefits on data privacy and communication efficiency. Since full client participation in many cases is infeasible due to constrained resources, partial participation FL algorithms have been investigated that proactively select/sa…
 
Mingjie Li, Hao Kong, Zhouchen LinAbstractRecently, many works have demonstrated that Symmetric Non-negative Matrix Factorization~(SymNMF) enjoys a great superiority for various clustering tasks. Although the state-of-the-art algorithms for SymNMF perform well on synthetic data, they cannot consistently obtain satisfactory results with desirable pr…
 
Zizheng Pan, Jianfei Cai, Bohan ZhuangAbstractVision Transformers (ViTs) have triggered the most recent and significant breakthroughs in computer vision. Their efficient designs are mostly guided by the indirect metric of computational complexity, i.e., FLOPs, which however has a clear gap with the direct metric such as throughput. Thus, we propose…
 
Zangir Iklassov, Dmitrii Medvedev, Otabek NazarovAbstractGeologic cores are rock samples that are extracted from deep under the ground during the well drilling process. They are used for petroleum reservoirs' performance characterization. Traditionally, physical studies of cores are carried out by the means of manual time-consuming experiments. Wit…
 
Fangzheng Sun, Yang Liu, Jian-Xun Wang, Hao SunAbstractNonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Le…
 
Rafael B. Audibert, Henrique Lemos, Pedro Avelar, Anderson R. Tavares, Lu\'is C. LambAbstractArtificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. In this work, we aim to understand the evolution of AI and Machine learning over the years by analyzing researchers' impact, influence, and leadership…
 
Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit BansalAbstractModern image captioning models are usually trained with text similarity objectives. However, since reference captions in public datasets often describe the most salient common objects, models trained with text similarity objectives tend to ignore specific a…
 
Christian David M\'arton, L\'eo Gagnon, Guillaume Lajoie, Kanaka RajanAbstractBiological agents do not have infinite resources to learn new things. For this reason, a central aspect of human learning is the ability to recycle previously acquired knowledge in a way that allows for faster, less resource-intensive acquisition of new skills. In spite o…
 
Lavanya Umapathy, Zhiyang Fu, Rohit Philip, Diego Martin, Maria Altbach, Ali BilginAbstractObtaining manual annotations for large datasets for supervised training of deep learning (DL) models is challenging. The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize DL models …
 
Seongmin Park, Jihwa LeeAbstractWe advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word graphs, we present simple but reliable path-reranking and topic segmentation schemes. Robustness of our method is demonstrated on dataset…
 
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