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我们可以找到的最佳Artificial Intelligence播客
我们可以找到的最佳Artificial Intelligence播客
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 ...
 
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.
 
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 ...
 
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 ...
 
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 world’s brightest minds are working tirelessly to harness the power of ai in order to gain a deeper understanding of life, existence, and also subsequently being... Well they can stop right now, because Mary and Tina have the answers. The girls have put in the work; minutes of research have culminated in this definitive resource for life’s biggest questions.
 
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
 
The foundation of the Center for Excellence is the automation governance model, which provides a framework for decision-making, enables standardization and consistency of approach, and facilitates communication across stakeholders. The COE is in charge of setting the organization's overall strategy and direction for automation. This entails formula…
 
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…
 
Today we’re joined by Meredith Broussard, an associate professor at NYU & research director at the NYU Alliance for Public Interest Technology. Meredith was a keynote speaker at the recent NeurIPS conference, and we had the pleasure of speaking with her to discuss her talk from the event, and her upcoming book, tentatively titled More Than A Glitch…
 
In order for many machine learning algorithms to be trained, especially supervised learning algorithms, they need to be fed relevant data along with the desired meaning of the data. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer provide an overview of the data labeling market, what “ground truth” labeling is, the dif…
 
Alexander Pan, Kush Bhatia, Jacob SteinhardtAbstractReward hacking -- where RL agents exploit gaps in misspecified reward functions -- has been widely observed, but not yet systematically studied. To understand how reward hacking arises, we construct four RL environments with misspecified rewards. We investigate reward hacking as a function of agen…
 
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…
 
Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer LevyAbstractNLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We introduce SCROLLS, a suite of task…
 
Qiwen Cui and Simon S. DuAbstractWe study what dataset assumption permits solving offline two-player zero-sum Markov game. In stark contrast to the offline single-agent Markov decision process, we show that the single strategy concentration assumption is insufficient for learning the Nash equilibrium (NE) strategy in offline two-player zero-sum Mar…
 
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng QiuAbstractExtremely large pre-trained language models (PTMs) such as GPT-3 are usually released as a service, allowing users to design task-specific prompts to query the PTMs through some black-box APIs. In such a scenario, which we call Language-Model-as-a-Service (LMaaS), gradients of…
 
Today we’re joined by Sebastian Bubeck a sr principal research manager at Microsoft, and author of the paper A Universal Law of Robustness via Isoperimetry, a NeurIPS 2021 Outstanding Paper Award recipient. We begin our conversation with Sebastian with a bit of a primer on convex optimization, a topic that hasn’t come up much in previous interviews…
 
Kosar Seyedhoseinzadeh, Hossein A. Rahmani, Mohsen Afsharchi, Mohammad AliannejadiAbstractRecommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this p…
 
Harald RuessAbstractA new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. When deploying these systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compat…
 
David HeckermanAbstractA Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where …
 
Xia Shuyin, Wang Cheng, Wang GuoYing, Gao XinBo, Elisabeth Giem, Yu JianHangAbstractPawlak rough set and neighborhood rough set are the two most common rough set theoretical models. Pawlawk can use equivalence classes to represent knowledge, but it cannot process continuous data; neighborhood rough sets can process continuous data, but it loses the…
 
Minjae ParkAbstractHeterogeneous graph neural networks can represent information of heterogeneous graphs with excellent ability. Recently, self-supervised learning manner is researched which learns the unique expression of a graph through a contrastive learning method. In the absence of labels, this learning methods show great potential. However, c…
 
Jing Du, Shiliang Pu, Qinbo Dong, Chao Jin, Xin Qi, Dian Gu, Ru Wu, Hongwei ZhouAbstractAlthough modern automatic speech recognition (ASR) systems can achieve high performance, they may produce errors that weaken readers' experience and do harm to downstream tasks. To improve the accuracy and reliability of ASR hypotheses, we propose a cross-modal …
 
Dan Crisan and Alexander Lobbe and Salvador Ortiz-LatorreAbstractThe filtering equations govern the evolution of the conditional distribution of a signal process given partial, and possibly noisy, observations arriving sequentially in time. Their numerical approximation plays a central role in many real-life applications, including numerical weathe…
 
Pascal Gerber, Lisa J\"ockel, Michael Kl\"asAbstractOutcomes of data-driven AI models cannot be assumed to be always correct. To estimate the uncertainty in these outcomes, the uncertainty wrapper framework has been proposed, which considers uncertainties related to model fit, input quality, and scope compliance. Uncertainty wrappers use a decision…
 
Jiahao Huang, Yingying Fang, Yinzhe Wu, Huanjun Wu, Zhifan Gao, Yang Li, Javier Del Ser, Jun Xia, Guang YangAbstractMagnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and …
 
Lars {\O}degaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro and Paal EngelstadAbstractWith the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wid…
 
Guowei Cui and Xiaoping ChenAbstractOne of the challenges of task planning is to find out what causes the planning failure and how to handle the failure intelligently. This paper shows how to achieve this. The idea is inspired by the connected graph: each verticle represents a set of compatible \textit{states}, and each edge represents an \textit{a…
 
Guangdong Xue, Qin Chang, Jian Wang, Kai Zhang and Nikhil R. PalAbstractA major limitation of fuzzy or neuro-fuzzy systems is their failure to deal with high-dimensional datasets. This happens primarily due to the use of T-norm, particularly, product or minimum (or a softer version of it). Thus, there are hardly any work dealing with datasets with …
 
Ruofan Liang, Bingsheng He, Shengen Yan, Peng SunAbstractMulti-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources. Due to the large scale, many tuning parameters and heavy resource usage, it is usually impractical to evaluate and benchmark those machine learning services …
 
Geeho Kim, Jinkyu Kim, Bohyung HanAbstractFederated learning often suffers from unstable and slow convergence due to heterogeneous characteristics of participating clients. Such tendency is aggravated when the client participation ratio is low since the information collected from the clients at each round is prone to be more inconsistent. To tackle…
 
Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, Weiguo ZhangAbstractAs the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such …
 
Noah Weber, Anton Belyy, Nils Holzenberger, Rachel Rudinger, Benjamin Van DurmeAbstractEvent schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted …
 
William Chen, Kensal Ramos, Kalyan Naidu Mullaguri, Annie S. WuAbstractMost current work in NLP utilizes deep learning, which requires a lot of training data and computational power. This paper investigates the strengths of Genetic Algorithms (GAs) for extractive summarization, as we hypothesized that GAs could construct more efficient solutions fo…
 
Chun YangAbstractCompared with the traditional collaborative filtering methods, the graph convolution network can explicitly model the interaction between the nodes of the user-item bipartite graph and effectively use higher-order neighbors, which enables the graph neural network to obtain more effective embeddings for recommendation, such as NGCF …
 
Seonguk Seo, Joon-Young Lee, Bohyung HanAbstractWe propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic bias by taking advantage of conditional mutual information. Although several bias measurement methods have been proposed …
 
Satyam Mohla, Anshul Nasery and Biplab BanerjeeAbstractRecent experiments in computer vision demonstrate texture bias as the primary reason for supreme results in models employing Convolutional Neural Networks (CNNs), conflicting with early works claiming that these networks identify objects using shape. It is believed that the cost function forces…
 
Rohitash Chandra, Venkatesh KulkarniAbstractIt is well known that translations of songs and poems not only breaks rhythm and rhyming patterns, but also results in loss of semantic information. The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to…
 
Tadahiro Taniguchi, Hiroshi Yamakawa, Takayuki Nagai, Kenji Doya, Masamichi Sakagami, Masahiro Suzuki, Tomoaki Nakamura, Akira TaniguchiAbstractBuilding a humanlike integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational mod…
 
Enmei Tu, Guanghao Zhang, Shangbo Mao, Lily Rachmawati and Guang-Bin HuangAbstractThe prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic regulation. For maritime intellig…
 
Martin GroheAbstractGraph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs. It has recently been shown that the expressiveness of GNNs can be characterised precisely by the combinatorial Weisfeiler-Leman algorithms and by finite variable counting logics. The correspondence has even led to new, higher-or…
 
Kien Nguyen, Clinton Fookes, Sridha Sridharan, Yingli Tian, Xiaoming Liu, Feng Liu and Arun RossAbstractThe rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehens…
 
Jesse Mu, Noah GoodmanAbstractTo build agents that can collaborate effectively with others, recent research has trained artificial agents to communicate with each other in Lewis-style referential games. However, this often leads to successful but uninterpretable communication. We argue that this is due to the game objective: communicating about a s…
 
Satoshi Kamo and Yiqiang ShengAbstractIdentifying anomaly multimedia traffic in cyberspace is a big challenge in distributed service systems, multiple generation networks and future internet of everything. This letter explores meta-generalization for a multiparty privacy learning model in graynet to improve the performance of anomaly multimedia tra…
 
Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, Tulika MitraAbstractData-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Existing works use a validation set to monitor the accuracy of the student over real da…
 
Gao Huang, Yulin Wang, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, Shiji SongAbstractSpatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models w…
 
Omid Rohanian, Samaneh Kouchaki, Andrew Soltan, Jenny Yang, Morteza Rohanian, Yang Yang, David CliftonAbstractEarly detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Dif…
 
Dan ShieblerAbstractIn this work we take a Category Theoretic perspective on the relationship between probabilistic modeling and function approximation. We begin by defining two extensions of function composition to stochastic process subordination: one based on the co-Kleisli category under the comonad (Omega x -) and one based on the parameteriza…
 
Tao Niu, Yinglei Teng, Zhu Han, Panpan ZouAbstractRecently, the applications of deep neural network (DNN) have been very prominent in many fields such as computer vision (CV) and natural language processing (NLP) due to its superior feature extraction performance. However, the high-dimension parameter model and large-scale mathematical calculation …
 
Guoyang Xie, Jinbao Wang, Guo Yu, Feng Zheng, Yaochu JinAbstractDue to limited computational cost and energy consumption, most neural network models deployed in mobile devices are tiny. However, tiny neural networks are commonly very vulnerable to attacks. Current research has proved that larger model size can improve robustness, but little researc…
 
Issa Rice, David ManheimAbstractSeveral different approaches exist for ensuring the safety of future Transformative Artificial Intelligence (TAI) or Artificial Superintelligence (ASI) systems, and proponents of different approaches have made different and debated claims about the importance or usefulness of their work in the near term, and for futu…
 
Linh Van Ma, Tin Trung Tran, Moongu JeonAbstractMost Gaze estimation research only works on a setup condition that a camera perfectly captures eyes gaze. They have not literarily specified how to set up a camera correctly for a given position of a person. In this paper, we carry out a study on gaze estimation with a logical camera setup position. W…
 
Bin Yang, Shuang Li, Jinglang Feng and Massimiliano VasileAbstractThis paper presents a novel and fast solver for the J2-perturbed Lambert problem. The solver consists of an intelligent initial guess generator combined with a differential correction procedure. The intelligent initial guess generator is a deep neural network that is trained to corre…
 
Mononito Goswami, Benedikt Boecking and Artur DubrawskiAbstractAnalysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. While collecting vast amounts of unlabe…
 
Sai Qian Zhang, Jieyu Lin, Qi ZhangAbstractFederated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data. While most of the existing work focuses on improving the FL model accuracy, in this paper, we focus on the improving the traini…
 
Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel UrtasunAbstractAs self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes groun…
 
Lev V. Utkin and Andrei V. KonstantinovAbstractA new approach called ABRF (the attention-based random forest) and its modifications for applying the attention mechanism to the random forest (RF) for regression and classification are proposed. The main idea behind the proposed ABRF models is to assign attention weights with trainable parameters to d…
 
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