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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.
 
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 ...
 
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
 
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!
 
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Kristina Kent & Mary MacLeod

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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.
 
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!
 
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 ...
 
The FortiGuard Labs Threat Intelligence Podcast provides highlights and commentary about the ever-evolving cyber threat landscape. Join Fortinet’s top threat experts as they delve into today’s critical cybersecurity topics. FortiGuard Labs is the global threat intelligence and research organization at Fortinet. Its mission is to provide customers the industry’s best threat intelligence to protect them from malicious cyberattacks. Using millions of global network sensors, FortiGuard Labs moni ...
 
Let's Nurture is a software and app development company headquartered out of North America. With a special focus on supporting and nurturing the development of Small business, we can offer best-in-class solutions at an affordable rate. If you're a small business owner with an interest in technology and process, this is the show for you.We have experience developing:AR VRApp DevelopmentWearable AppsSmall Business AppsCloud HostingCyber SecurityBackup and RecoveryDigital TransformationiBeacon ...
 
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Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Stephen Miller is the Cofounder and SVP Engineering at Fyusion Inc. He has conducted research in 3D Perception and Computer Vision with Profs …
 
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 Felipe Flores, host of Data Futurology Podcast. On his podcast he talks about Data Science through a human lens and discusses the …
 
AI is taking centre stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. Open up any newspaper or social media feed these days and it’s easy to spot the tidal wave of hype that artificial intelligence (AI) has unleashed. Within this hype lies a lot of opportunities, but without the righ…
 
Today we’re joined by friend-of-the-show Nasrin Mostafazadeh, co-founder of Verneek. Though Verneek is still in stealth, Nasrin was gracious enough to share a bit about the company, including their goal of enabling anyone to make data-informed decisions without the need for a technical background, through the use of innovative human-machine interfa…
 
Lu Yuan and Dongdong Chen and Yi-Ling Chen and Noel Codella and Xiyang Dai and Jianfeng Gao and Houdong Hu and Xuedong Huang and Boxin Li and Chunyuan Li and Ce Liu and Mengchen Liu and Zicheng Liu and Yumao Lu and Yu Shi and Lijuan Wang and Jianfeng Wang and Bin Xiao and Zhen Xiao and Jianwei Yang and Michael Zeng and Luowei Zhou and Pengchuan Zha…
 
Andy and Dave discuss the latest in AI news and research, including the Defense Innovation Unit releasing Responsible AI Guidelines in Practice, which seeks to ensure tech contractors adhere to the Department of Defense’s existing ethical principles for AI [0:53]. “Meta” (the Facebook re-brand) announces that it will end its use of facial recogniti…
 
Welcome back to Let's Nurture, the Podcast Have you ever wondered just how Google decides what to show you? Well, some people have -and they used the information to figure out how to ensure that their site came up first. This process of structuring your content so that google can easily identify and sort it is called 'search engine optimization', a…
 
In order for projects to be successful, the time between concept to execution needs to be reasonably set. Far too often, we see the iteration time of AI projects to be months, if not years, from the initial pilot. Additionally, we see organizations run a proof of concept in a controlled lab setting (why?) rather than a pilot using real world system…
 
I’m betting you’ve heard about the next generation of artificial intelligence, the one that’s just around the corner. It’s going to be pervasive, all-competent, maybe super-intelligent. We’ll rely on it to drive cars, write novels, diagnose diseases, and make scientific breakthroughs. It will do all these things better, faster, more safely than we …
 
This episode was recorded live on 11/23/2021Join us for another edition of FortiGuardLIVE as #FortiGuardLabs’ Derek Manky and Aamir Lakhani discuss upcoming threat trends, the weaponization of #AI, how #ransomware is becoming more aggressive, and our Cyber Threat Predictions for 2022 report. Watch this episode on YouTube: https://youtu.be/DWWNCKQ0b…
 
Today we’re joined by Julie Shah, a professor at the Massachusetts Institute of Technology (MIT). Julie’s work lies at the intersection of aeronautics, astronautics, and robotics, with a specific focus on collaborative and interactive robotics. In our conversation, we explore how robots would achieve the ability to predict what their human collabor…
 
Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper, and Raghuram RamanujanAbstractReliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (IQN) have been shown to provide accurate models of conditional densities. We pres…
 
Minjae ParkAbstractIn general, Graph Neural Networks(GNN) have been using a message passing method to aggregate and summarize information about neighbors to express their information. Nonetheless, previous studies have shown that the performance of graph neural networks becomes vulnerable when there are abnormal nodes in the neighborhood due to thi…
 
Stefan Glock, David Munh\'a Correia and Benny SudakovAbstractAn $n$-queens configuration is a placement of $n$ mutually non-attacking queens on an $n\times n$ chessboard. The $n$-queens completion problem, introduced by Nauck in 1850, is to decide whether a given partial configuration can be completed to an $n$-queens configuration. In this paper, …
 
Jason Jooste, Michael Fromm, Matthias SchubertAbstractMonitoring of reforestation is currently being considerably streamlined through the use of drones and image recognition algorithms, which have already proven to be effective on colour imagery. In addition to colour imagery, elevation data is often also available. The primary aim of this work was…
 
Ching-An Cheng, Andrey Kolobov, Adith SwaminathanAbstractWe provide a framework for accelerating reinforcement learning (RL) algorithms by heuristics constructed from domain knowledge or offline data. Tabula rasa RL algorithms require environment interactions or computation that scales with the horizon of the sequential decision-making task. Using …
 
Jing Yang Lee, Kong Aik Lee, Woon Seng GanAbstractThe generation of personalized dialogue is vital to natural and human-like conversation. Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/personality of the interlocutor. As it is impractical to…
 
Kai Yan and Jie Yan and Chuan Luo and Liting Chen and Qingwei Lin and Dongmei ZhangAbstractPrediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem. However, the criteria by which the prediction model is trained are often inconsistent with the goal of the downstream …
 
Jean-Emmanuel Deschaud and David Duque and Jean Pierre Richa and Santiago Velasco-Forero and Beatriz Marcotegui and and Fran\c{c}ois GouletteAbstractParis-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open …
 
Zihan Yan, Li Liu, Xin Li, William K. Cheung, Youmin Zhang, Qun Liu, Guoyin WangAbstractSocial network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the obser…
 
Cameron Browne, \'Eric Piette, Matthew Stephenson, Dennis J.N.J. SoemersAbstractGame boards are described in the Ludii general game system by their underlying graphs, based on tiling, shape and graph operators, with the automatic detection of important properties such as topological relationships between graph elements, directions and radial step s…
 
Morteza Rezanejad, Sidharth Gupta, Chandra Gummaluru, Ryan Marten, John Wilder, Michael Gruninger, Dirk B. WaltherAbstractHumans are excellent at perceiving illusory outlines. We are readily able to complete contours, shapes, scenes, and even unseen objects when provided with images that contain broken fragments of a connected appearance. In vision…
 
Th\'eophile Champion, Howard Bowman, Marek Grze\'sAbstractActive inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity. However, recent implementations suffer from an exponential (space and time) complexity class when computing the pri…
 
Micha{\l} Zawalski, B{\l}a\.zej Osi\'nski, Henryk Michalewski, Piotr Mi{\l}o\'sAbstractMulti-agent reinforcement learning (MARL) provides a framework for problems involving multiple interacting agents. Despite apparent similarity to the single-agent case, multi-agent problems are often harder to train and analyze theoretically. In this work, we pro…
 
Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia VinogradskaAbstractBayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data. Its data efficiency can be further improved by transfer learning from related tasks. While recent transfer models meta-learn a prior …
 
Agostino Dovier, Andrea Formisano, Gopal Gupta, Manuel V. Hermenegildo, Enrico Pontelli, Ricardo RochaAbstractMulti-core and highly-connected architectures have become ubiquitous, and this has brought renewed interest in language-based approaches to the exploitation of parallelism. Since its inception, logic programming has been recognized as a pro…
 
Yinshan Li, Hua Ma, Zhi Zhang, Yansong Gao, Alsharif Abuadbba, Anmin Fu, Yifeng Zheng, Said F. Al-Sarawi, Derek AbbottAbstractA backdoor deep learning (DL) model behaves normally upon clean inputs but misbehaves upon trigger inputs as the backdoor attacker desires, posing severe consequences to DL model deployments. State-of-the-art defenses are ei…
 
Jonas Fischer, Rebekka BurkholzAbstractThe lottery ticket hypothesis has sparked the rapid development of pruning algorithms that perform structure learning by identifying a sparse subnetwork of a large randomly initialized neural network. The existence of such 'winning tickets' has been proven theoretically but at suboptimal sparsity levels. Conte…
 
Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee, Alkis GotovosAbstractThe lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Recent work has experimentally observed that some of these tickets can be practically reused across…
 
Hanan SalamAbstractEngagement in Human-Machine Interaction is the process by which entities participating in the interaction establish, maintain, and end their perceived connection. It is essential to monitor the engagement state of patients in various AI-based healthcare paradigms. This includes medical conditions that alter social behavior such a…
 
J. D. Thomas, R. Santos-Rodr\'iguez, R. Piechocki, M. AncaAbstractThis paper considers cooperative Multi-Agent Reinforcement Learning, focusing on emergent communication in settings where multiple pairs of independent learners interact at varying frequencies. In this context, multiple distinct and incompatible languages can emerge. When an agent en…
 
Th\'eophile Champion, Lancelot Da Costa, Howard Bowman, Marek Grze\'sAbstractOver the last 10 to 15 years, active inference has helped to explain various brain mechanisms from habit formation to dopaminergic discharge and even modelling curiosity. However, the current implementations suffer from an exponential (space and time) complexity class when…
 
SangHun Im, Gibaeg Kim, Heung-Seon Oh, Seongung Jo, Donghwan KimAbstractHierarchical text classification (HTC) to a taxonomy is essential for various real applications butchallenging since HTC models often need to process a large volume of data that are severelyimbalanced and have hierarchy dependencies. Existing local and global approaches use dee…
 
Pradip Pramanick, Chayan Sarkar, Snehasis Banerjee, Brojeshwar BhowmickAbstractThe utility of collocating robots largely depends on the easy and intuitive interaction mechanism with the human. If a robot accepts task instruction in natural language, first, it has to understand the user's intention by decoding the instruction. However, while executi…
 
Christopher Diehl, Timo Sievernich, Martin Kr\"uger, Frank Hoffmann, Torsten BertranAbstractOffline reinforcement learning (RL) provides a framework for learning decision-making from offline data and therefore constitutes a promising approach for real-world applications as automated driving. Self-driving vehicles (SDV) learn a policy, which potenti…
 
Hung Guei, Lung-Pin Chen, and I-Chen WuAbstractTemporal difference (TD) learning and its variants, such as multistage TD (MS-TD) learning and temporal coherence (TC) learning, have been successfully applied to 2048. These methods rely on the stochasticity of the environment of 2048 for exploration. In this paper, we propose to employ optimistic ini…
 
Haobo Yuan, Teng Chen, Wei Sui, Jiafeng Xie, Lefei Zhang, Yuan Li, Qian ZhangAbstractEstimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using expensive 3D sensors such as LiDAR or directly predicting the depth of points via deep learn…
 
Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan1Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, Salman AvestimehrAbstractFederated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in …
 
Antoine Guillaume, Christel Vrain, Elloumi WaelAbstractPredictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical phenomenons such as temperature or vibration which can be…
 
Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie ZhangAbstractEfficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems. In this paper, we introduce a novel Episodic Multi-agent reinforcement learning with…
 
Jianhuan Zhuo, Qiannan Zhu, Yinliang Yue and Yuhong ZhaoAbstractThe core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score $s_p$ and minimize the negative sample score $s_n$, which can usually be summarized into two paradigms: the pointwise and the pairwise. The pointwise approaches fit each …
 
Tianlun Zheng, Zhineng Chen, Shancheng Fang, Hongtao Xie, Yu-Gang JiangAbstractThe attention-based encoder-decoder framework is becoming popular in scene text recognition, largely due to its superiority in integrating recognition clues from both visual and semantic domains. However, recent studies show the two clues might be misaligned in the diffi…
 
Tong Wang, Yuan Yao, Feng Xu, Shengwei An, Ting WangAbstractBackdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined conditions, i.e., triggers, but behave normally otherwise. …
 
Daria Bakshandaeva, Denis Dimitrov, Alex Shonenkov, Mark Potanin, Vladimir Arkhipkin, Denis Karachev, Vera Davydova, Anton Voronov, Mikhail Martynov, Natalia Semenova, Mikhail Stepnov, Elena Tutubalina, Andrey Chertok, Aleksandr PetiushkoAbstractSupporting the current trend in the AI community, we propose the AI Journey 2021 Challenge called Fusion…
 
Rebecca Castano, Tiago Vaquero, Federico Rossi, Vandi Verma, Ellen Van Wyk, Dan Allard, Bennett Huffmann, Erin M. Murphy, Nihal Dhamani, Robert A. Hewitt, Scott Davidoff, Rashied Amini, Anthony Barrett, Julie Castillo-Rogez, Steve A. Chien, Mathieu Choukroun, Alain Dadaian, Raymond Francis, Benjamin Gorr, Mark Hofstadter, Mitch Ingham, Cristina Sor…
 
Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo SiAbstractKnowledge enriched language representation learning has shown promising performance across various knowledge-intensive NLP tasks. However, existing knowledge based language models are all trained with monolingual knowledge graph data, which limits their application to more langu…
 
Feng Jie, Yuping Liang, Junpeng Zhang, Xiangrong Zhang, Quanhe Yao, Licheng JiaoAbstractShip detection in aerial images remains an active yet challenging task due to arbitrary object orientation and complex background from a bird's-eye perspective. Most of the existing methods rely on angular prediction or predefined anchor boxes, making these meth…
 
Daniel J. B. Harrold, Jun Cao, Zhong FanAbstractIn this paper, multi-agent reinforcement learning is used to control a hybrid energy storage system working collaboratively to reduce the energy costs of a microgrid through maximising the value of renewable energy and trading. The agents must learn to control three different types of energy storage s…
 
Arshdeep Singh, Raju Arvind and Padmanabhan RajanAbstractThis paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained using log-melspectrogram representations of the sound signal. In classification, our hypothesis is that the recon…
 
Adrian HaretAbstractProminent approaches to belief revision prescribe the adoption of a new belief that is as close as possible to the prior belief, in a process that, even in the standard case, can be described as attempting to minimize surprise. Here we extend the existing model by proposing a measure of surprise, dubbed relative surprise, in whi…
 
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