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Najlepsze Artificial Intelligence podcasty, jakie mogliśmy znaleźć
Najlepsze Artificial Intelligence podcasty, jakie mogliśmy znaleźć
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 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 Artificial Intelligence reshape the future of warfare? Created by Shephard Studio, The Artificial Intelli ...
 
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!
 
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 ...
 
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
 
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.
 
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!
 
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.
 
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 ...
 
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 ...
 
Caregiver Camp Podcast expands the boundaries of thinking around where and how companies can support their caregiving employees. We feature enlightened perspectives across the caregiving landscape: from companies that celebrate caregiver-friendly workplaces to artificial intelligence product developers who are pioneering at-home detection programs for peace of mind. Join us to learn how companies are finding innovative ways to address the very real caregiving challenges we are now all seeing ...
 
Al: Innovation or Destruction? In this sci-fi podcast based on the groundbreaking interactive series Artificial, our hosts delve into the world of Sophie, an artificial intelligence being on a journey to become human, in attempts to illuminate the dangers and benefits of AI while looking at how to ethically treat and develop our new sentient friends. We invite you to join the conversation and weigh in on the ultimate question: Will Al prove to be good for the world... or will it lead to our ...
 
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show series
 
Today we’re joined by Sandra Wacther, an associate professor and senior research fellow at the University of Oxford. Sandra’s work lies at the intersection of law and AI, focused on what she likes to call “algorithmic accountability”. In our conversation, we explore algorithmic accountability in three segments, explainability/transparency, data pro…
 
Alex Beard: How to Solve for the Global Education Crisis caused by The Pandemic [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Alex Beard is the Senior Director at Teach For All , and author of the book Natural Born Learners. After starting out as an English teacher in a London comprehe…
 
Tobias M\"uller, Christoph Roch, Kyrill Schmid and Philipp AltmannAbstractReinforcement learning has driven impressive advances in machine learning. Simultaneously, quantum-enhanced machine learning algorithms using quantum annealing underlie heavy developments. Recently, a multi-agent reinforcement learning (MARL) architecture combining both parad…
 
On the AI Today podcast we regularly interview thought leaders who are implementing AI and cognitive technology at various companies and agencies. However in this episode hosts Kathleen Walch and Ron Schmelzer interview Andy Ilachinski and David Broyles, hosts of the AI with AI podcast. On their podcast they explore the latest breakthroughs in arti…
 
Andy and Dave discuss the latest in AI news and research, including: 0:57: The Allen Institute for AI and others come together to create a publicly available “COVID-19 Challenges and Directions” search engine, building off of the corpus of COVID-related research. 5:06: Researchers with the University of Warwick perform a systematic review of test a…
 
The conversation this week is all about the intersection of Intellectual Property and Artificial Intelligence! I'm thrilled to have an expert in the fields of Artificial Intelligence, Patents, and Intellectual Property on the program. This is an amazing conversation and one in which I learned so much in just a short period of time from our guest, R…
 
In this episode, Felipe speaks about what is going on with artificial intelligence in the retail space. Visual search and recommendations are currently trending in retail. Companies are offering the ability for consumers to search based on a picture. For instance, consumers can take a picture of a pair of shoes and find that product online, similar…
 
The US and its allies are today focused on multi-domain operations, also known as Joint All-Domain Command and Control. In simple terms, the concept calls for platforms and systems across land, sea, air, space and cyber to increasingly interact and support one another. Artificial intelligence will be vital for this future, collecting and manipulati…
 
Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nissan Stiennon, Ryan Lowe, Jan Leike, Paul ChristianoAbstractA major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this problem on the task of abstractive summarization of entire fiction nov…
 
Diptesh Kanojia, Marina Fomicheva, Tharindu Ranasinghe, Fr\'ed\'eric Blain, Constantin Or\u{a}san and Lucia SpeciaAbstractCurrent Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thu…
 
Ting Chen, Saurabh Saxena, Lala Li, David J. Fleet, Geoffrey HintonAbstractThis paper presents Pix2Seq, a simple and generic framework for object detection. Unlike existing approaches that explicitly integrate prior knowledge about the task, we simply cast object detection as a language modeling task conditioned on the observed pixel inputs. Object…
 
Rohit Girdhar and Kristen GraumanAbstractWe propose Anticipative Video Transformer (AVT), an end-to-end attention-based video modeling architecture that attends to the previously observed video in order to anticipate future actions. We train the model jointly to predict the next action in a video sequence, while also learning frame feature encoders…
 
Reuth Mirsky, Megan Zimmerman, Muneed Ahmad, Shelly Bagchi, Felix Gervits, Zhao Han, Justin Hart, Daniel Hern\'andez Garc\'ia, Matteo Leonetti, Ross Mead, Emmanuel Senft, Jivko Sinapov, Jason WilsonAbstractThe Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2…
 
Wasim Ahmad, Maha Shadaydeh, Joachim DenzlerAbstractEstimating causal relations is vital in understanding the complex interactions in multivariate time series. Non-linear coupling of variables is one of the major challenges inaccurate estimation of cause-effect relations. In this paper, we propose to use deep autoregressive networks (DeepAR) in tan…
 
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, Yutian ChenAbstractMeta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning algorithm are jointly represented by a single neural network. These met…
 
Subeesh Vasu, Nicolas Talabot, Artem Lukoianov, Pierre Baque, Jonathan Donier, Pascal FuaAbstractCAD modeling typically involves the use of simple geometric primitives whereas recent advances in deep-learning based 3D surface modeling have opened new shape design avenues. Unfortunately, these advances have not yet been accepted by the CAD community…
 
Baturay Saglam, Enes Duran, Dogan C. Cicek, Furkan B. Mutlu, Suleyman S. KozatAbstractIn value-based deep reinforcement learning methods, approximation of value functions induces overestimation bias and leads to suboptimal policies. We show that in deep actor-critic methods that aim to overcome the overestimation bias, if the reinforcement signals …
 
Chiara Ghidini, Marco Rospocher, Luciano SerafiniAbstractIn this paper we present a textual description, in terms of Description Logics, of the BPMN Ontology, which provides a clear semantic formalisation of the structural components of the Business Process Modelling Notation (BPMN), based on the latest stable BPMN specifications from OMG [BPMN Ver…
 
Wolfgang Roth, G\"unther Schindler, Holger Fr\"oning, Franz PernkopfAbstractWe present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed different…
 
Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva, Nikita Muravev, Aleksandr Petiushko, Ivan OseledetsAbstractIn safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions. Besides rigorously studied $\ell_p$-bounded additive perturbati…
 
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald MetzlerAbstractThere remain many open questions pertaining to the scaling behaviour of Transformer architectures. These scaling decisions and findings can be critical, as training runs often come with an associat…
 
Efrat Blaier, Itzik Malkiel, Lior WolfAbstractThe recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of performance. Motivated by the need to model the contrast between t…
 
Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin, Lea FrermannAbstractTrained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and d…
 
Akram HussainAbstractThis paper studies the decentralized learning of tree-structured Gaussian graphical models (GGMs) from noisy data. In decentralized learning, data set is distributed across different machines (sensors), and GGMs are widely used to model complex networks such as gene regulatory networks and social networks. The proposed decentra…
 
Xiaoyu Chen, Chen Gong, Qiang He, Xinwen Hou, and Yu LiuAbstractVariational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications. However, it is always a challenge to achieve the consistency between the learned latent distribution and the prior latent di…
 
Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande, Tamanna Ahmad, Anish Andheria, Wenjun Li, Aparna Taneja, Divy Thakkar, Milind TambeAbstractWith increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and liv…
 
Krysia Broda and Fariba Sadri and Stephen ButlerAbstractLogic Production System (LPS) is a logic-based framework for modelling reactive behaviour. Based on abductive logic programming, it combines reactive rules with logic programs, a database and a causal theory that specifies transitions between the states of the database. This paper proposes a s…
 
Roy Zohar, Shie Mannor, Guy TennenholtzAbstractCooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. As environments grow in size, effective credit assignment becomes increasingly harder and often results in infeasible learning …
 
Tobias M\"uller, Kyrill Schmid, Dani\"elle Schuman, Thomas Gabor, Markus Friedrich, Marc GeitzAbstractThe expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth excavation work therefore lead to large savings. Mathematically, the …
 
Daqing Chang, Jintao Liu, Ziru Xu, Han Li, Han Zhu, Xiaoqiang ZhuAbstractHow to predict precise user preference and how to make efficient retrieval from a big corpus are two major challenges of large-scale industrial recommender systems. In tree-based methods, a tree structure T is adopted as index and each item in corpus is attached to a leaf node…
 
Andrew Cropper, Sebastijan Duman\v{c}i\'c, Richard Evans, and Stephen H. MuggletonAbstractInductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level s…
 
Jiandong Mu, Hanwei Fan, Wei ZhangAbstractPruning has been widely used to slim convolutional neural network (CNN) models to achieve a good trade-off between accuracy and model size so that the pruned models become feasible for power-constrained devices such as mobile phones. This process can be automated to avoid the expensive hand-crafted efforts …
 
Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios KaissisAbstractDifferential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the provision of objective privacy guarantees. …
 
Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon WhitesonAbstractNon-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is of…
 
Bin Zhao, Maoguo Gong, Xuelong LiAbstractAlthough video summarization has achieved tremendous success benefiting from Recurrent Neural Networks (RNN), RNN-based methods neglect the global dependencies and multi-hop relationships among video frames, which limits the performance. Transformer is an effective model to deal with this problem, and surpas…
 
Yuqi Liu, Qichao Zhang and Dongbin ZhaoAbstractIn recent years, control under urban intersection scenarios becomes an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehic…
 
Qiang He, Chen Gong, Yuxun Qu, Xiaoyu Chen, Xinwen Hou, Yu LiuAbstractEnsemble reinforcement learning (RL) aims to mitigate instability in Q-learning and to learn a robust policy, which introduces multiple value and policy functions. In this paper, we consider finding a novel but simple ensemble Deep RL algorithm to solve the resource consumption i…
 
Fu Sun, Feng-Lin Li, Ruize Wang, Qianglong Chen, Xingyi Cheng, Ji ZhangAbstractKnowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature but few of them have been successfully applied in practice. To address this problem, we propose K-AID, a systematic approach that includes a low-cost…
 
Qian Liu, Dejian Yang, Jiahui Zhang, Jiaqi Guo, Bin Zhou, Jian-Guang LouAbstractRecent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language. To better understand and leverage what PLMs have learned, several techniques have emerged to explore syntactic structures entailed by PLMs…
 
Ga\"elle Candel, David NaccacheAbstract$t$-SNE is an embedding method that the data science community has widely Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. $t$-SNE pres…
 
Mingyu Derek Ma, Muhao Chen, Te-Lin Wu and Nanyun PengAbstractTaxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their coverage by learning concept embeddings in …
 
Naoki Yokoyama, Qian Luo, Dhruv Batra, Sehoon HaAbstractThis paper presents an approach for improving navigation in dynamic and interactive environments, which won the 1st place in the iGibson Interactive Navigation Challenge 2021. While the last few years have produced impressive progress on PointGoal Navigation in static environments, relatively …
 
Junjie Wang, Qichao Zhang, Dongbin ZhaoAbstractThe development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the…
 
Zhenyu Zhang, Tao Guo and Meng ChenAbstractWith the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and ext…
 
Xiyang Zhang, Muhao Chen, Jonathan MayAbstractStorytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that represent such processes. However, this extraction remains a…
 
Young Jin Kim, Ammar Ahmad Awan, Alexandre Muzio, Andres Felipe Cruz Salinas, Liyang Lu, Amr Hendy, Samyam Rajbhandari, Yuxiong He and Hany Hassan AwadallaAbstractThe Mixture of Experts (MoE) models are an emerging class of sparsely activated deep learning models that have sublinear compute costs with respect to their parameters. In contrast with d…
 
Gyunam Park and Minseok SongAbstractPredictive business process monitoring aims at providing predictions about running instances by analyzing logs of completed cases in a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. Howe…
 
Gabriel Peres Nobre, Carlos H. G. Ferreira and Jussara M. AlmeidaAbstractWhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, a…
 
Wen Huang, Lu Zhang, Xintao WuAbstractIn online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. We study how to recommend an item at each step to maximize the expected reward while achiev…
 
Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao, Wei WeiAbstractReliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments. T…
 
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