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Parhaat Artificial Intelligence podcastit, jotka löysimme
Parhaat Artificial Intelligence podcastit, jotka löysimme
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 ...
 
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 ...
 
David Yakobovitch explores AI for consumers through fireside conversations with industry thought leaders on HumAIn. From Chief Data Scientists and AI Advisors, to Leaders who advance AI for All, the HumAIn Podcast is the channel to release new AI products, to learn about industry trends, and to bridge the gap between humans and machines in the Fourth Industrial Revolution.
 
Artificial intelligence 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 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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This podcast episode provides a snippet of Cognilytica’s AI and ML education from our Cognilytica Education Subscription. Data is at the heart of AI. It should be no surprise then that proper data management is crucial for AI projects. This podcast is an excerpt from our Cognilytica Education course discussing data pipelines. In this clip we discus…
 
Geunseob Oh, Huei PengAbstractThe task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-moda…
 
Today we’re joined by Jonathan Le Roux, a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL). At MERL, Jonathan and his team are focused on using machine learning to solve the “cocktail party problem”, focusing on not only the separation of speech from noise, but also the separation of speech from speech. In our…
 
With the ever-changing digital transformation landscape, there are now more responsibilities on a Chief Digital Officer's shoulders than ever before. 📌 Gone are the days when they were only responsible for introducing basic capabilities and perhaps piloting handfuls initiatives - today, this person must coordinate changes that address everything fr…
 
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…
 
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…
 
Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham W. TaylorAbstractIn image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs …
 
Zhiwei Jia, Kaixiang Lin, Yizhou Zhao, Qiaozi Gao, Govind Thattai, Gaurav SukhatmeAbstractRecent years have witnessed an emerging paradigm shift toward embodied artificial intelligence, in which an agent must learn to solve challenging tasks by interacting with its environment. There are several challenges in solving embodied multimodal tasks, incl…
 
Ece Calikus, Slawomir Nowaczyk, Mohamed-Rafik Bouguelia, and Onur DikmenAbstractIn contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in …
 
Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas KrauseAbstractImproving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision process…
 
Mingzhe Chen, Xi Xiao, Bin Zhang, Xinyu Liu, Runiu LuAbstractRecent studies show that depression can be partially reflected from human facial attributes. Since facial attributes have various data structure and carry different information, existing approaches fail to specifically consider the optimal way to extract depression-related features from e…
 
Asher Trockman, J. Zico KolterAbstractAlthough convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the Vision Transformer (ViT), may exceed their performance in some settings. However, due to the quadratic runtime of the self-attention …
 
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang LowAbstractNeural architecture search (NAS) has gained immense popularity owing to its ability to automate neural architecture design. A number of training-free metrics are recently proposed to realize NAS without training, hence making NAS more scalable. Despite their competitive empirical pe…
 
Haichao Zhang, Wei Xu, Haonan YuAbstractStandard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return. While flexible, it faces difficulties arising from the inefficient exploration due to its single step nature. In this work, we pre…
 
Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng WangAbstractLearning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information…
 
Bilge Celik and Prabhant Singh and Joaquin VanschorenAbstractAutomated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online…
 
Xiangkun Hu, Hang Yan, Qipeng Guo, Xipeng Qiu, Weinan Zhang, Zheng ZhangAbstractKnowledge and expertise in the real-world can be disjointedly owned. To solve a complex question, collaboration among experts is often called for. In this paper, we propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together…
 
Loris Nanni, Michelangelo Paci, Sheryl Brahnam and Alessandra LuminiAbstractA problem with Convolutional Neural Networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples…
 
Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, St\'ephane Pateux, Vincent GriponAbstractFew-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years ha…
 
Wei Ye, Zexi Huang, Yunqi Hong, Ambuj SinghAbstractGraph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. However, GCN does not perform well on sparsely-labeled graphs. Its two-layer version cannot effectively propagate the label information to the whole graph structure (i.e., the under-smoothing problem)…
 
Wei Yuan, Hongzhi Yin, Tieke He, Tong Chen, Qiufeng Wang, Lizhen CuiAbstractQuestion Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG methods mainly focus on building or training models for specific QG datasets. These works are subject to two major limit…
 
Enrique Iglesias, Samaneh Jozashoori, Maria-Esther VidalAbstractRDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious, and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG …
 
Bo Li, Qiulin Wang, Jiquan Pei, Yu Yang, Xiangyang JiAbstractThe semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle l…
 
Qi Liu, Dani Yogatama, Phil BlunsomAbstractWe present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datase…
 
Andrei Nica, Khimya Khetarpal, Doina PrecupAbstractDecision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To …
 
Yijun Yang, Ruiyuan Gao, Yu Li, Qiuxia Lai and Qiang XuAbstractAdversarial examples (AEs) pose severe threats to the applications of deep neural networks (DNNs) to safety-critical domains, e.g., autonomous driving. While there has been a vast body of AE defense solutions, to the best of our knowledge, they all suffer from some weaknesses, e.g., def…
 
Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, Yatao BianAbstractAI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharm…
 
Francesco Belardinelli, Wojtek Jamroga, Vadim Malvone, Munyque Mittelmann, Aniello Murano, Laurent PerrusselAbstractIn online advertising, search engines sell ad placements for keywords continuously through auctions. This problem can be seen as an infinitely repeated game since the auction is executed whenever a user performs a query with the keywo…
 
Yijun Yang, Ruiyuan Gao, Yu Li, Qiuxia Lai, Qiang XuAbstractMachine learning with deep neural networks (DNNs) has become one of the foundation techniques in many safety-critical systems, such as autonomous vehicles and medical diagnosis systems. DNN-based systems, however, are known to be vulnerable to adversarial examples (AEs) that are maliciousl…
 
Maria Tsfasman, Avinash Saravanan, Dekel Viner, Daan Goslinga, Sarah de Wolf, Chirag Raman, Catholijn M. Jonker, Catharine OertelAbstractHow human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversati…
 
Kilian Schulze-Forster, Clement S. J. Doire, Ga\"el Richard, Roland BadeauAbstractSupervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mi…
 
Cristian Santini, Genet Asefa Gesese, Silvio Peroni, Aldo Gangemi, Harald Sack, Mehwish AlamAbstractScholarly data is growing continuously containing information about the articles from plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the for of Knowledge Graphs (KGs). The…
 
Zhigang Tu, Zhisheng Huang, Yujin Chen, Di Kang, Linchao Bao, Bisheng Yang, and Junsong YuanAbstractWe present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that detected 2D hand keypoints and the image texture provide important cues about the geometry and texture of the 3D hand, which can reduce or…
 
Gianlorenzo D'Angelo, Esmaeil Delfaraz and Hugo GilbertAbstractFollowing Zhang and Grossi~(AAAI 2021), we study in more depth a variant of weighted voting games in which agents' weights are induced by a transitive support structure. This class of simple games is notably well suited to study the relative importance of agents in the liquid democracy …
 
Mahsa Paknezhad, Hamsawardhini Rengarajan, Chenghao Yuan, Sujanya Suresh, Manas Gupta, Savitha Ramasamy, Lee Hwee KuanAbstractThis paper takes a parallel learning approach for robust and transparent AI. A deep neural network is trained in parallel on multiple tasks, where each task is trained only on a subset of the network resources. Each subset c…
 
Wenzhi Fang, Ziyi Yu, Yuning Jiang, Yuanming Shi, Colin N. Jones, and Yong ZhouAbstractFederated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been proposed, r…
 
Yulin Chen, Zelai Yao, Haixiao Chi, Dov Gabbay, Bo Yuan, Bruno Bentzen and Beishui LiaoAbstractNamed entity recognition (NER) is an essential task in natural language processing, but the internal mechanism of most NER models is a black box for users. In some high-stake decision-making areas, improving the interpretability of an NER method is crucia…
 
Simon Vandevelde and Joost VennekensAbstractThis paper presents a probabilistic extension of the well-known cellular automaton, Game of Life. In Game of Life, cells are placed in a grid and then watched as they evolve throughout subsequent generations, as dictated by the rules of the game. In our extension, called ProbLife, these rules now have pro…
 
Varvara GuljajevaAbstractThe article explores new ways of written language aided by AI technologies, like GPT-2 and GPT-3. The question that is stated in the paper is not about whether these novel technologies will eventually replace authored books, but how to relate to and contextualize such publications and what kind of new tools, processes, and …
 
Aman Rangapur, Tarun Kanakam, Ajith JubilsonAbstractCyber-attacks have been one of the deadliest attacks in today's world. One of them is DDoS (Distributed Denial of Services). It is a cyber-attack in which the attacker attacks and makes a network or a machine unavailable to its intended users temporarily or indefinitely, interrupting services of t…
 
Today we’re joined by Karianne Bergen, an assistant professor at Brown University. In our conversation with Karianne, we explore her work at the intersection of earthquake seismology and machine learning, where she’s working on interpretable data classification for seismology. We discuss some of the challenges that present themselves when trying to…
 
As the markets for Data, Automation, Analytics, and AI continue to evolve, so too does Cognilytica’s coverage of these areas. In today’s podcast we discuss how we are expanding our current Cognilytica Classification of the AI Vendor Ecosystem to include a data infrastructure layer. Within this layer falls data generation, big data storage and query…
 
Today we’re joined by Arun Kumarm, an associate professor at UC San Diego. We had the pleasure of catching up with Arun prior to the Workshop on Databases and AI at NeurIPS 2021, where he delivered the talk “The New DBfication of ML/AI.” In our conversation, we explore this “database-ification” of machine learning, a concept analogous to the transf…
 
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…
 
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…
 
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…
 
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…
 
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