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Podcast Artificial Intelligence hay nhất mà chúng tôi có thể tìm thấy
Podcast Artificial Intelligence hay nhất mà chúng tôi có thể tìm thấy
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 ...
 
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 ...
 
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 ...
 
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.
 
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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The end goal is to provide business users with trusted data, faster at lower cost. How do you build to achieve that? What approach should you take, which tools and what resources and skill-mix are required to support that? Our Data Engineering Summit coming up on 31st August will look at exactly those demands. As we explore these challenges, we rev…
 
The conversation this week is with Erik Beall. Erik is an imaging AI scientist and product developer. Eric is the CEO of Thermal Diagnostics, where he has developed the fever inspect a thermal imaging based product that helps keep us all safe by providing accurate, reliable and foolproof fever detection. He's also a computer vision scientist at Dig…
 
Zhan Yu, Hongshun Yao, Mujin Li, Xin WangAbstractQuantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation remains less understood. In this paper, we formulate a theoretical fram…
 
Today we’re joined by Rob Walker, VP of decisioning & analytics and gm of one-to-one customer engagement at Pegasystems. Rob, who you might know from his previous appearances on the podcast, joins us to discuss his work on AI and ML in the context of customer engagement and decisioning, the various problems that need to be solved, including solving…
 
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…
 
Level 3 Intelligent Automation allows you to address the greatest challenges of unpredictability and variability and be able to achieve autonomous process discovery, autonomous process analytics, and autonomous process optimization. But is Level 3 Intelligent Automation right for your team or process? In this episode of the AI Today podcast hosts K…
 
Andy and Dave discuss the latest in AI news and search, including a report from the Government Accountability Office, recommending that the Department of Defense should improve its AI strategies and other AI-related guidance [1:25]. Another GAO report finds that the Navy should improve its approach to uncrewed maritime systems, particularly in its …
 
Luke Melas-Kyriazi and Christian Rupprecht and Iro Laina and Andrea VedaldiAbstractUnsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled data. These tasks are particularly interesting in an unsupervised setting due to the di…
 
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron CourvilleAbstractThis work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of over…
 
Le Cong Dinh, Yaodong Yang, Stephen McAleer, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Haitham Bou Ammar, Jun WangAbstractSolving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learning algorithms for sol…
 
Xiaoting Shao, Kristian KerstingAbstractCounterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input $x$ of class $y_1$, its counterfactual is a contrastive example $x^\prime$ of another class $y_0$. Current approaches primarily solve this task by a complex optimization: define an objective functi…
 
Hanrui Zhang, Yu Cheng, Vincent ConitzerAbstractWe consider the problem of planning with participation constraints introduced in [Zhang et al., 2022]. In this problem, a principal chooses actions in a Markov decision process, resulting in separate utilities for the principal and the agent. However, the agent can and will choose to end the process w…
 
Mikael Brunila and Jack LaVioletteAbstractThe power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts. This idea is specifically invoked by noting that "you shall know a word by the company it keeps," a quote from British linguist J.R. Firth who, along with his American colleague Zellig Har…
 
Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain, and Jiayu ZhouAbstractReinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in nu…
 
Maurice Jakesch, Zana Bu\c{c}inca, Saleema Amershi, Alexandra OlteanuAbstractPrivate companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While their recommendations converge on a set of central values, little is known about the values …
 
Bobak Toussi Kiani, Giacomo De Palma, Milad Marvian, Zi-Wen Liu, Seth LloydAbstractQuantifying how far the output of a learning algorithm is from its target is an essential task in machine learning. However, in quantum settings, the loss landscapes of commonly used distance metrics often produce undesirable outcomes such as poor local minima and ex…
 
Tiexin Qin and Shiqi Wang and Haoliang LiAbstractDomain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark upon stationary and discrete environments to tackle the generalization issue caused by OOD data. However, many real-world…
 
Marin Vlastelica, Patrick Ernst, Gyuri SzarvasAbstractUtilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success. Until now, categorical posteriors have been argued to be one of the main drivers of performa…
 
Andr\'e Artelt, Roel Visser, Barbara HammerAbstractThe application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being able to reject uncertain samples is impor…
 
Mark Anderson and Jose Camacho-ColladosAbstractThe increase in performance in NLP due to the prevalence of distributional models and deep learning has brought with it a reciprocal decrease in interpretability. This has spurred a focus on what neural networks learn about natural language with less of a focus on how. Some work has focused on the data…
 
Nicola Milano and Stefano NolfiAbstractIn this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: the OpenAI-ES evolutionary strategy and the Proximal Policy Optimization (PPO) reinforcement learning algorithm -- the most similar …
 
Alejandro Romero, Gianluca Baldassarre, Richard J. Duro, Vieri Giuliano SantucciAbstractAutonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the necessity of user assigned tasks. A crucial issue for this approach is to develop …
 
Thomas Eiter, Nelson Higuera, Johannes Oetsch, and Michael PritzAbstractWe present a neuro-symbolic visual question answering (VQA) pipeline for CLEVR, which is a well-known dataset that consists of pictures showing scenes with objects and questions related to them. Our pipeline covers (i) training neural networks for object classification and boun…
 
Ana\"is Tack and Chris PiechAbstractHow can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedago…
 
Mohammed M. S. El-Kholany, Martin Gebser and Konstantin SchekotihinAbstractThe Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. In this paper, we propose problem dec…
 
Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu ChenAbstractConstrained Reinforcement Learning (CRL) has gained significant interest recently, since the satisfaction of safety constraints is critical for real world problems. However, existing CRL methods constraining discounted cumulative costs generally lack rigorous definition and guarantee of sa…
 
Wei Wan, Yuejin Zhang, Chenglong Bao, Bin Dong, Zuoqiang ShiAbstractThe dynamic formulation of optimal transport has attracted growing interests in scientific computing and machine learning, and its computation requires to solve a PDE-constrained optimization problem. The classical Eulerian discretization based approaches suffer from the curse of d…
 
Moshe Babaioff and Uriel FeigeAbstractWe consider fair allocation of a set $M$ of indivisible goods to $n$ equally-entitled agents, with no monetary transfers. Every agent $i$ has a valuation $v_i$ from some given class of valuation functions. A share $s$ is a function that maps a pair $(v_i,n)$ to a value, with the interpretation that if an alloca…
 
Dieter Brughmans, Pieter Leyman and David MartensAbstractIn this paper we suggest NICE: a new algorithm to generate counterfactual explanations for heterogeneous tabular data. The design of our algorithm specifically takes into account algorithmic requirements that often emerge in real-life deployments: (1) the ability to provide an explanation for…
 
Lei Zhang, Yu Pan, Yi Liu, Qibin Zheng, Zhisong PanAbstractIn general, multiple domain cyberspace security assessments can be implemented by reasoning user's permissions. However, while existing methods include some information from the physical and social domains, they do not provide a comprehensive representation of cyberspace. Existing reasoning…
 
Anoushka Vyas, Sambaran BandyopadhyayAbstractSoil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which …
 
Rafael Kiesel, Pietro Totis and Angelika KimmigAbstractQuantitative extensions of logic programming often require the solution of so called second level inference tasks, i.e., problems that involve a third operation, such as maximization or normalization, on top of addition and multiplication, and thus go beyond the well-known weighted or algebraic…
 
Lingwei Zhu, Zheng Chen, Eiji Uchibe, Takamitsu MatsubaraAbstractThe recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy. Though significant improvement has been shown with the Boltzmann softmax policy, w…
 
Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng ZhengAbstractDeep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse, resulting in a large space for the generation of adversarial sampl…
 
Hans-Peter Beise, Steve Dias Da CruzAbstractIn Radhakrishnan et al. [2020], the authors empirically show that autoencoders trained with usual SGD methods shape out basins of attraction around their training data. We consider network functions of width not exceeding the input dimension and prove that in this situation basins of attraction are bounde…
 
Yen-Ting Lin, Hui-Chi Kuo, Ze-Song Xu, Ssu Chiu, Chieh-Chi Hung, Yi-Cheng Chen, Chao-Wei Huang, Yun-Nung ChenAbstractThis paper introduces Miutsu, National Taiwan University's Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains -- home improvement and cooking. We…
 
Thai Van Nguyen, Xincheng Dai, Ibrahim Khan, Ruck Thawonmas, Hai V. PhamAbstractThis paper presents a deep reinforcement learning AI that uses sound as the input on the DareFightingICE platform at the DareFightingICE Competition in IEEE CoG 2022. In this work, an AI that only uses sound as the input is called blind AI. While state-of-the-art AIs re…
 
Shakil M. KhanAbstractInspired by a novel action-theoretic formalization of actual cause, Khan and Lesp\'erance (2021) recently proposed a first account of causal knowledge that supports epistemic effects, models causal knowledge dynamics, and allows sensing actions to be causes of observed effects. To date, no other study has looked specifically a…
 
Yuxin Deng and Jiayi MaAbstractDeep-learning-based local feature extraction algorithms that combine detection and description have made significant progress in visible image matching. However, the end-to-end training of such frameworks is notoriously unstable due to the lack of strong supervision of detection and the inappropriate coupling between …
 
Yefei He, Luoming Zhang, Weijia Wu, Hong ZhouAbstractBinary neural network leverages the $Sign$ function to binarize real values, and its non-derivative property inevitably brings huge gradient errors during backpropagation. Although many hand-designed soft functions have been proposed to approximate gradients, their mechanism is not clear and ther…
 
Siyuan Xiang, Anbang Yang, Yanfei Xue, Yaoqing Yang, Chen FengAbstractSpatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. Based on the fact that self-supervised learning is helpful when a large number of data are available, we propose two…
 
He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian PeiAbstractGraph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life scien…
 
Gagandeep Singh, Rakesh Nadig, Jisung Park, Rahul Bera, Nastaran Hajinazar, David Novo, Juan G\'omez-Luna, Sander Stuijk, Henk Corporaal, Onur MutluAbstractHybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques that aim to acc…
 
Ziyang Jiang, Tongshu Zheng, and David CarlsonAbstractIt is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhance…
 
Omobayode Fagbohungbe and Lijun QianAbstractThe fast execution speed and energy efficiency of analog hardware has made them a strong contender for deployment of deep learning model at the edge. However, there are concerns about the presence of analog noise which causes changes to the weight of the models, leading to performance degradation of deep …
 
The Anh HanAbstractThe mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and s…
 
Ramya Ramakrishnan, Hashan Buddhika Narangodage, Mauro Schilman, Kilian Q. Weinberger, Ryan McDonaldAbstractCurrent approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grai…
 
Thomas Spooner, Rui Silva, Joshua Lockhart, Jason Long, Vacslav GlukhovAbstractSolving general Markov decision processes (MDPs) is a computationally hard problem. Solving finite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-time algorithms. What drives this extreme disparity, and do problems exist that lie between …
 
How Khang Lim, Avishkar Mahajan, Martin Strecker, Meng Weng WongAbstractThe paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifiers that specify how rules interact and how they can be overridden. We then define rule transformations that eliminate these modifiers, leading in…
 
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