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Bästa Artificial Intelligence Podcasts vi kunde hitta
Bästa Artificial Intelligence Podcasts vi kunde hitta
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 AI reshape the future of warfare? Created by Shephard Studio, the Artificial Intelligence on the Battlef ...
 
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.
 
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!
 
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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Artificial Intelligence

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Artificial Intelligence

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.
 
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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The Sill

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The Sill

Peter Noce & Harry Posner

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From Canada, with Peter Noce, a media producer and technology trainer/tutor and Harry Posner, a writer, author and Dufferin County’s first Poet Laureate; sharing our interests and passions with you, on 'The Sill'. The original concept of our podcast was to explore areas relating to Art and Technology, evolving into a virtually ‘no limits’ conversation. Neither of us are experts, nor profess to be. We are on the same level as the listener, offering up ideas and thoughts that come from our lif ...
 
By the year 2025, it’s estimated that the confluence of Artificial intelligence, Automation, E-commerce, and Outsourcing may result in The elimination of 47% of the jobs that exist in the U.S. today. Not simply low-wage, low skilled jobs, But, also white-collar positions in the financial, legal and healthcare professions.
 
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In part 2 of our interview with Steve Nouri, he spoke about AI specializations and share his thoughts about AI innovation in developing countries. He says that AI is quickly evolving and we’re going to see a lot of specialized people working in the AI field in bigger organizations. Also in this episode, we discuss personal branding and its importan…
 
Today we’re joined by Andrea Banino, a research scientist at DeepMind. In our conversation with Andrea, we explore his interest in artificial general intelligence by way of episodic memory, the relationship between memory and intelligence, the challenges of applying memory in the context of neural networks, and how to overcome problems of generaliz…
 
Yangyi Chen, Fanchao Qi, Zhiyuan Liu, Maosong SunAbstractBackdoor attacks are a kind of emergent security threat in deep learning. When a deep neural model is injected with a backdoor, it will behave normally on standard inputs but give adversary-specified predictions once the input contains specific backdoor triggers. Current textual backdoor atta…
 
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 Jessie J. Smith and Dylan Doyle, hosts of the Radical AI podcast. On their podcast they try to probe and advance the field of Arti…
 
While Artificial Intelligence offers great potential for militaries, to be successful AI must support and empower soldiers, with autonomous systems maximising human capabilities. It must not hinder them. Soldier-centred design can help maximise the benefits of the technology. Through soldier-centred design, AI-based technologies would be developed …
 
Humphrey Chen: How AI Can Revolutionize the Way We Consume Video [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Humphrey Chen is the CEO and Co-Founder of CLIPr. He has a BS in Management Science from MIT. His work in tech specializes in the use of technology to make people and companie…
 
Andy and Dave discuss the latest in AI news and research, including, the UK government releases its National AI Strategy, a 10-year plan to make the country a global AI superpower [1:28]. Stanford University’s One Hundred Year Study on AI Project releases its second report, Gathering Strength, Gathering Storms, assessing developments in AI between …
 
Sanjeeb Dash and Joao GoncalvesAbstractKnowledge graph (KG) completion is a well-studied problem in AI. Rule-based methods and embedding-based methods form two of the solution techniques. Rule-based methods learn first-order logic rules that capture existing facts in an input graph and then use these rules for reasoning about missing facts. A major…
 
Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, Christopher R\'eAbstractNamed entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural …
 
Daniela Mihai, Jonathon HareAbstractWe present an investigation into how representational losses can affect the drawings produced by artificial agents playing a communication game. Building upon recent advances, we show that a combination of powerful pretrained encoder networks, with appropriate inductive biases, can lead to agents that draw recogn…
 
Stephanie Holly, Thomas Hiessl, Safoura Rezapour Lakani, Daniel Schall, Clemens Heitzinger, Jana KemnitzAbstractFederated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business inf…
 
Welcome back to Let's Nurture, the Podcast Tune in this week to hear about a new local service that's aiming to bridge the gap between local customers and producers in a post-pandemic world. From conception to logistics, we're digging deep on Dinner Basket. If you'd like to contact the show, or the company, do feel free to reach out-the host can be…
 
Dora Jambor, Dzmitry BahdanauAbstractSemantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombinin…
 
Anurag Katakkar, Weiqin Wang, Clay H. Yoo, Zachary C. Lipton, Divyansh KaushikAbstractIn attempts to develop sample-efficient algorithms, researcher have explored myriad mechanisms for collecting and exploiting feature feedback, auxiliary annotations provided for training (but not test) instances that highlight salient evidence. Examples include bo…
 
Igor L. Markov, Hanson Wang, Nitya Kasturi, Shaun Singh, Sze Wai Yuen, Mia Garrard, Sarah Tran, Yin Huang, Zehui Wang, Igor Glotov, Tanvi Gupta, Boshuang Huang, Peng Chen, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm ZhouAbstractModern software systems and products increasingly rely on machine learning models to make data…
 
Grey Kuling, Dr. Belinda Curpen, and Anne L. MartelAbstractRadiology reports are the main form of communication between radiologists and other clinicians, and contain important information for patient care. However in order to use this information for research it is necessary to convert the raw text into structured data suitable for analysis. Domai…
 
Megan Ung, Jing Xu, Y-Lan BoureauAbstractCurrent open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art m…
 
Anthony Corso, Robert J. Moss, Mark Koren, Ritchie Lee, Mykel J. KochenderferAbstractAutonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of formal verification and real-world testing can be …
 
Take our survey at twimlai.com/survey21! Today we’re joined by Tim Rocktäschel, a research scientist at Facebook AI Research and an associate professor at University College London (UCL). Tim’s work focuses on training RL agents in simulated environments, with the goal of these agents being able to generalize to novel situations. Typically, this is…
 
Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu HuangAbstractEvent extraction is typically modeled as a multi-class classification problem where both event types and argument roles are treated as atomic symbols. These approaches are usually limited to a set of pre-defined types. We propose a novel event extraction framework that takes event types …
 
Mhafuzul Islam, Mashrur Chowdhury, Zadid Khan, Sakib Mahmud KhanAbstractA classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber…
 
Shanghui Yang, Mengxia Zhu, Xuesong LuAbstractKnowledge tracing (KT) has recently been an active research area of computational pedagogy. The task is to model students' mastery level of knowledge concepts based on their responses to the questions in the past, as well as predict the probabilities that they correctly answer subsequent questions in th…
 
Yujia Bao, Shiyu Chang, Regina BarzilayAbstractWhile unbiased machine learning models are essential for many applications, bias is a human-defined concept that can vary across tasks. Given only input-label pairs, algorithms may lack sufficient information to distinguish stable (causal) features from unstable (spurious) features. However, related ta…
 
Junhao Yan, Woonsok LeeAbstractIn recent years, unsupervised domain adaptation (UDA) for semantic segmentation has brought many researchers'attention. Many of them take an approach to design a complex system so as to better align the gap between source and target domain. Instead, we focus on the very basic structure of the deep neural network, Batc…
 
Thanh Nguyen, Tung M. Luu, Thang Vu and Chang D. YooAbstractDeveloping an agent in reinforcement learning (RL) that is capable of performing complex control tasks directly from high-dimensional observation such as raw pixels is yet a challenge as efforts are made towards improving sample efficiency and generalization. This paper considers a learnin…
 
Xin Zhou, Ruotian Ma, Tao Gui, Yiding Tan, Qi Zhang, Xuanjing HuangAbstractPlug-and-play functionality allows deep learning models to adapt well to different tasks without requiring any parameters modified. Recently, prefix-tuning was shown to be a plug-and-play method on various text generation tasks by simply inserting corresponding continuous ve…
 
Anargyros Chatzitofis, Dimitrios Zarpalas, Stefanos Kollias, Petros DarasAbstractIn this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflective straps and patches (reflectors). DeepMoCap explores motion capture by automatically l…
 
Soobee Lee, Minindu Weerakoon, Jonghyun Choi, Minjia Zhang, Di Wang, Myeongjae JeonAbstractContinual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forgetting, prior studies exploit episo…
 
Fabian Lim, Laura Wynter, Shiau Hong LimAbstractOptimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another. Recent works have aimed to improve optimal transport plans through the introduction of various forms of structure. We introduce novel order constraints into the optimal transport…
 
Zhiwei Xu, Yunpeng Bai, Bin Zhang, Dapeng Li, Guoliang FanAbstractMulti-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents. In this paper, we propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for the fully cooperative multi-agent pro…
 
Quan Wang and Songtai Dai and Benfeng Xu and Yajuan Lyu and Yong Zhu and Hua Wu and Haifeng WangAbstractPre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts in building biomedical PLMs have resorted simply to domain adapta…
 
Michiya Kuramata, Ryota Katsuki, Kazuhide NakataAbstractQuantum annealing (QA) has gained considerable attention because it can be applied to combinatorial optimization problems, which have numerous applications in logistics, scheduling, and finance. In recent years, research on solving practical combinatorial optimization problems using them has a…
 
nargyros Chatzitofis, Leonidas Saroglou, Prodromos Boutis, Petros Drakoulis, Nikolaos Zioulis, Shishir Subramanyam, Bart Kevelham, Caecilia Charbonnier, Pablo Cesar, Dimitrios Zarpalas, Stefanos Kollias, Petros DarasAbstractWe introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by…
 
Shen Liu, Meirong Ma, Hao Yuan, Jianchao Zhu, Yuanbin Wu, Man LanAbstractPun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word. Most previous studies adopt limited word senses obtained by WSD(Word…
 
Yunshi Huang and Emilie Chouzenoux and Jean-Christophe PesquetAbstractIn this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel. One of our main contributions is…
 
Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias KersemansAbstractNowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifi…
 
Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang WangAbstractUser profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and turn the problem into a node classification task. However, they neglect the difference of …
 
Yantian Zha, Yikang Li, Tianshu Yu, Subbarao Kambhampati, Baoxin LiAbstractHuman visual recognition of activities or external agents involves an interplay between high-level plan recognition and low-level perception. Given that, a natural question to ask is: can low-level perception be improved by high-level plan recognition? We formulate the probl…
 
Fanchao Qi, Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, Maosong SunAbstractAdversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation. Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitabl…
 
Gabriel-Claudiu GramaAbstractThe major finding, of this article, is an ensemble method, but more exactly, a novel, better ranked voting system (and other variations of it), that aims to solve the problem of finding the best candidate to represent the voters. We have the source code on GitHub, for making realistic simulations of elections, based on …
 
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincen…
 
Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng QiuAbstractSupersized pre-trained language models have pushed the accuracy of various NLP tasks to a new state-of-the-art (SOTA). Rather than pursuing the reachless SOTA accuracy, most works are pursuing improvement on other dimensions s…
 
Livio Robaldo and Kolawole J. AdebayoAbstractReified Input/Output (I/O) logic[21] has been recently proposed to model real-world norms in terms of the logic in [11]. This is massively grounded on the notion of reification, and it has specifically designed to model meaning of natural language sentences, such as the ones occurring in existing legisla…
 
Kazutoshi Shinoda and Yuki Takezawa and Masahiro Suzuki and Yusuke Iwasawa and Yutaka MatsuoAbstractAn interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to interact with objects in a 3D simulated environment. We find that an exi…
 
Eric Lei, Hamed Hassani, Shirin Saeedi BidokhtiAbstractIn recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources. However, like many other machine learning systems, these compressors suffer from vulnerabilities to distribution shifts as well as out-of-distri…
 
Oana-Maria CamburuAbstractDeep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. However, the decision-making processes of these models are generally not interpretable to users. In various domains, such as healthcare, …
 
Florian Mai and James HendersonAbstractText autoencoders are often used for unsupervised conditional text generation by applying mappings in the latent space to change attributes to the desired values. Recently, Mai et al. (2020) proposed Emb2Emb, a method to learn these mappings in the embedding space of an autoencoder. However, their method is re…
 
Julia Kreutzer, David Vilar, Artem SokolovAbstractTraining data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or different levels of quality or complexity. Naturally, these facets do not occur with equal frequency, nor are they equally…
 
Ali Farki, Reza Baradaran Kazemzadeh, and Elham Akhondzadeh NoughabiAbstractContinuous blood pressure (BP) measurements can reflect a bodys response to diseases and serve as a predictor of cardiovascular and other health conditions. While current cuff-based BP measurement methods are incapable of providing continuous BP readings, invasive BP monito…
 
Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish, Sarath ChandarAbstractEmpirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predi…
 
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