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พ็อดคาสท์ Artificial Intelligence ดีที่สุดที่เราพบ
พ็อดคาสท์ Artificial Intelligence ดีที่สุดที่เราพบ
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 ...
 
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 ...
 
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.
 
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!
 
Dream It! Imagine It! Create It! "If What If" (IWI) is an educational, consulting, and development company where our expertise is in Artificial Intelligence (AI), Virtual Reality (VR), Virtual Worlds (VW), and the Metaverse. "If What If" are a group of Futurists, computer analysts, data scientists, and researchers who believe that Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), and the Metaverse coupled with AI is one of the next great technological frontiers. Our podcas ...
 
Get knowledge and inspiration to apply artificial intelligence to drug development. Discover startups applying machine learning to biomedical research. Hear how biotech and pharma companies use AI to speed discovery and cut costs. Learn from academic researchers pushing boundaries in applying computation to biology. We interview leaders transforming drug development with data and algorithms. Subscribe now and never miss an episode!
 
Danilo McGarry is a prominent leader, coach and Keynote speaker in the topics of Automation (and all its related areas: Artificial Intelligence/RPA/Machine Learning/Neural Networks/Deep Learning/Transformation) - to read more about the creator of this space please visit www.danilomcgarry.com
 
Artificial intelligence technologies are undoubtedly beginning to change the face of modern warfare. AI and machine learning applications promise to enhance productivity, reduce user workload, and operate more quickly than humans. But, this doesn’t come without its challenges. The Artificial Intelligence on the Battlefield podcast dives into these issues and more, looking at just how will AI reshape the future of warfare? Created by Shephard Studio, the Artificial Intelligence on the Battlef ...
 
Talking Robots is a podcast featuring interviews with high-profile professionals in Robotics and Artificial Intelligence for an inside view on the science, technology, and business of intelligent robotics. It is managed and sponsored by the Laboratory of Intelligent Systems (LIS) at the EPFL in Lausanne, Switzerland.
 
Dive into the world of Artificial Intelligence with your host Anna-Regina Entus - founder and president of the AI in Management Association and fellow of the AI Research Center at emlyon business school in Paris. Together with guest speakers from around the globe, I am helping you make sense of AI and share insights on the latest innovations in the world of Artificial Intelligence. Episodes 1-6: Hosted by Anna-Regina Entus and Victoria Rugli from Episode 7: Hosted by Anna-Regina Entus
 
An introduction to machine learning to assist business leaders to understand what it can and can't do. In the three episodes, you will get a sense of the potential impact, the nature and types of models available and case studies that may apply to your industry. Allan Kent is the Head of Digital at Primedia Broadcasting and is the host of this series.
 
Artificial intelligence is already controlling washing machines and translation assistants and helping doctors reach a diagnosis. It is changing our working lives and our leisure time. AI is making our lives easier and, ideally, even better! AI raises expectations, fears and hopes. And it involves risks. It’s all about personal autonomy and freedom, about security as well as sustainability and even global equity. AI between a promising future and a brave new world. Leading AI experts talk ab ...
 
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 ...
 
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.
 
TOPBOTS educates business leaders on high-impact applications of modern machine learning and AI techniques and helps leading organizations adopt and implement emerging technologies. We run the largest publication and community for enterprise AI professionals to learn about the latest machine learning and automation solutions and exchange insights with each other. Through education and community, we inspire you to think creatively about how AI can be used to improve lives, revolutionize indus ...
 
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Pranav Khanna, Guy Tennenholtz, Nadav Merlis, Shie Mannor and Chen TesslerAbstractIn recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains. Nevertheless, convergence of various methods has been shown to suffer from inconsistencies, due to algo…
 
Later this month, Nathan Steiner, the Director of Field Engineering, ANZ, at Databricks, will give a presentation at the Data Engineering Summit. There he will talk about the “habits” of data-driven organisations, and the importance of an open architecture that combines the best elements of data lakes and data warehouses. Steiner kindly appeared on…
 
Construction companies are increasingly using AI in a range of ways to tackle a number of challenges. From optimizing work schedules to improving workplace safety to keeping a secure watch on construction facilities, AI in the construction industry is already producing value. One such company, ALICE Technologies, is focused on a different challenge…
 
The conversation this week is with Tom Doyle. Tom is a veteran in the semiconductor industry working in many different areas over the last 35 years, including RF and satellites, analog and mixed-signal integrated circuit solutions, IP software, and sales. He holds a BS in Electrical Engineering from West Virginia University and an MBA from Californ…
 
Today we’re joined by Jeff Gehlhaar, vice president of technology at Qualcomm Technologies. In our annual conversation with Jeff, we dig into the relationship between Jeff’s team on the product side and the research team, many of whom we’ve had on the podcast over the last few years. We discuss the challenges of real-world neural network deployment…
 
This and all episodes at: https://aiandyou.net/ . Justin Harrison is an entrepreneur, founder, and CEO of YOV, Inc. (You, Only Virtual)—a company specializing in posthumous digital communications. In 2019, he found himself staring down death on two fronts: his own, from a near fatal motorcycle accident, as well as his mother’s stage-4 cancer diagno…
 
Andy and Dave discuss the latest in AI news and research, including an announcement from DeepMind that it is freely providing a database of 200+ million protein structures as predicted by AlphaFold. Researchers at the Max Planck Institute for Intelligent Systems demonstrate how a robot dog can learn to walk in about one hour using a Bayesian optimi…
 
Join Black Women In Artificial Intelligence -Beyond The Lab podcast as we speak with Ovetta Sampson Vice President - Head of Design and Machine Learning and Responsible A.I. at Capital One, discussing her journey to Artificial Intelligence. A true masterclass.โดย Black Women In A I
 
A Short Analysis of Sentiment Analysis & Emotion Recognition in AI In this introductory podcast, on the topic of sentiment analysis and emotion recognition, we are going to present a well-known textual statement, allowing the listener to understand the problems Artificial Intelligence faces when having to deal with and interpret emotion and sentime…
 
Emmanuel Deruty, Maarten GrachtenAbstractAlthough the use of AI tools in music composition and production is steadily increasing, as witnessed by the newly founded AI song contest, analysis of music produced using these tools is still relatively uncommon as a mean to gain insight in the ways AI tools impact music production. In this paper we presen…
 
Zhi-Qi Cheng, Qi Dai, Siyao Li, Teruko Mitamura, Alexander HauptmannAbstractGrounded Situation Recognition (GSR) aims to generate structured semantic summaries of images for ``human-like'' event understanding. Specifically, GSR task not only detects the salient activity verb (e.g. buying), but also predicts all corresponding semantic roles (e.g. ag…
 
Fangquan Lin, Wei Jiang, Hanwei Zhang, Cheng YangAbstractKDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VI…
 
Peter Fettke and Alexander RombachAbstractAI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at the intersection of these areas in recent years. For example, the behavior of future processes is now comprehensively predicted w…
 
Soumen Pachal, Avinash AcharAbstractMissing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to en…
 
Hao-Wei Chen, Ting-Hsuan Liao, Hsuan-Kung Yang and Chun-Yi LeeAbstractThis paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations. PWVO employs uncertainty estimation to identify the noisy regions in the inp…
 
Jinfeng Zhou, Chujie Zheng, Bo Wang, Zheng Zhang, Minlie HuangAbstractEmpathy is a trait that naturally manifests in human conversation. Theoretically, the birth of empathetic responses results from conscious alignment and interaction between cognition and affection of empathy. However, existing works rely solely on a single affective aspect or mod…
 
Shentong Mo, Zhun Sun, Chao LiAbstractContrastive Self-supervised Learning (CSL) is a practical solution that learns meaningful visual representations from massive data in an unsupervised approach. The ordinary CSL embeds the features extracted from neural networks onto specific topological structures. During the training progress, the contrastive …
 
Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, and Tal KachmanAbstractIn many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of agents and allocating them shares of the reward that promote long-lasting coop…
 
Satyam Kumar, Mendhikar Vishal and Vadlamani RaviAbstractExplainable Artificial Intelligence (XAI) research gained prominence in recent years in response to the demand for greater transparency and trust in AI from the user communities. This is especially critical because AI is adopted in sensitive fields such as finance, medicine etc., where implic…
 
Benjamin Doerr and Zhongdi QuAbstractVery recently, the first mathematical runtime analyses for the NSGA-II, the most common multi-objective evolutionary algorithm, have been conducted (Zheng, Liu, Doerr (AAAI 2022)). Continuing this research direction, we prove that the NSGA-II optimizes the OneJumpZeroJump benchmark asymptotically faster when cro…
 
Nuo Chen, Chenyu YouAbstractRecently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the…
 
Xia Zeng, Arkaitz ZubiagaAbstractTo mitigate the impact of data scarcity on fact-checking systems, we focus on few-shot claim verification. Despite recent work on few-shot classification by proposing advanced language models, there is a dearth of research in data annotation prioritisation that improves the selection of the few shots to be labelled …
 
Daolang Huang, Louis Filstroff, Petrus Mikkola, Runkai Zheng, Samuel KaskiAbstractBayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating expert knowledge into BO, with the goal of further accelerating the optimization, which h…
 
Quanshi Zhang, Xu Cheng, Yilan Chen, Zhefan RaoAbstractCompared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. This paper provides a new perspective to explain the success of knowledge distillation, i.e., quantifying knowledge points encoded in intermediate layers of a DNN for clas…
 
Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D.H. Nguyen, Martin V. Butz and Stefan WermterAbstractCognitive Psychology and related disciplines have identified several critical mechanisms that enable intelligent biological agents to learn to solve complex problems. There exists pressing evidence that the cognitive mechanisms that enable…
 
Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Xiaoxiao Li, Ghassan HamarnehAbstractThe boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability. This research paradigm disproportionately ignores the larger group of non-technical end users o…
 
Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D. H. Nguyen, Martin V. Butz, Stefan WermterAbstractAccording to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computation…
 
Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, Ke TangAbstractSocial recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the user-item interactions (collaborative domain) and social relations (social domain). However, such a…
 
Xiuzhan Guo, Arthur Berrill, Ajinkya Kulkarni, Kostya Belezko, and Min LuoAbstractOntology operations, e.g., aligning and merging, were studied and implemented extensively in different settings, such as, categorical operations, relation algebras, typed graph grammars, with different concerns. However, aligning and merging operations in the settings…
 
Samuel T. Wauthier, Bram Vanhecke, Tim Verbelen, Bart DhoedtAbstractActive inference provides a general framework for behavior and learning in autonomous agents. It states that an agent will attempt to minimize its variational free energy, defined in terms of beliefs over observations, internal states and policies. Traditionally, every aspect of a …
 
Anna Belardinelli, Anirudh Reddy Kondapally, Dirk Ruiken, Daniel Tanneberg, Tomoki WatabeAbstractShared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation fr…
 
Yun Luo, Fang Guo, Zihan Liu, Yue ZhangAbstractCross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data. Previous studies are mostly cross-entropy-based methods for the task, which suffer from instability and poor generalization…
 
Pongpanut Osathitporn, Guntitat Sawadwuthikul, Punnawish Thuwajit, Kawisara Ueafuea, Thee Mateepithaktham, Narin Kunaseth, Tanut Choksatchawathi, Proadpran Punyabukkana, Emmanuel Mignot and Theerawit WilaiprasitpornAbstractRespiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung dis…
 
Nan Ming, Yi Feng, Rui FanAbstractThe state-of-the-art (SoTA) surface normal estimators (SNEs) generally translate depth images into surface normal maps in an end-to-end fashion. Although such SNEs have greatly minimized the trade-off between efficiency and accuracy, their performance on spatial discontinuities, e.g., edges and ridges, is still uns…
 
Yi-Fan Zhang, Hanlin Zhang, Jindong Wang, Zhang Zhang, Baosheng Yu, Liang Wang, Dacheng Tao, Xing XieAbstractLearning a domain-invariant representation has become one of the most popular approaches for domain adaptation/generalization. In this paper, we show that the invariant representation may not be sufficient to guarantee a good generalization,…
 
Zhikang Dong, Pawel PolakAbstractWe consider the inverse problem for the Partial Differential Equations (PDEs) such that the parameters of the dependency structure can exhibit random changepoints over time. This can arise, for example, when the physical system is either under malicious attack (e.g., hacker attacks on power grids and internet networ…
 
Yu-Huan Wu, Da Zhang, Le Zhang, Xin Zhan, Dengxin Dai, Yun Liu, and Ming-Ming ChengAbstractCurrent efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners. To this end, we introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At t…
 
Yanli Liu, Jiming Zhao, Chu-Min Li, Hua Jiang, Kun HeAbstractMaximum Common induced Subgraph (MCS) is an important NP-hard problem with wide real-world applications. Branch-and-Bound (BnB) is the basis of a class of efficient algorithms for MCS, consisting in successively selecting vertices to match and pruning when it is discovered that a solution…
 
Jinxin Ding, Yuxin Huang, Keyang Ni, Xueyao Wang, Yinxiao Wang and Yucheng WangAbstractIntellectual properties is increasingly important in the economic development. To solve the pain points by traditional methods in IP evaluation, we are developing a new technology with machine learning as the core. We have built an online platform and will expand…
 
Adrien Benamira, Thomas Peyrin, Bryan Hooi Kuen-YewAbstractWith the expanding role of neural networks, the need for complete and sound verification of their property has become critical. In the recent years, it was established that Binary Neural Networks (BNNs) have an equivalent representation in Boolean logic and can be formally analyzed using lo…
 
Qiuliang Ye, Li-Wen Wang, Daniel Pak-Kong LunAbstractPhase retrieval (PR), a long-established challenge for recovering a complex-valued signal from its Fourier intensity-only measurements, has attracted considerable attention due to its widespread applications in digital imaging. Recently, deep learning-based approaches were developed that achieved…
 
Gopal Sharma, Kangxue Yin, Subhransu Maji, Evangelos Kalogerakis, Or Litany, Sanja FidlerAbstractWe propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface representations are more effective at modeling high-resolution surface details an…
 
Erik Peterson, Alexander LavinAbstractA ''technology lottery'' describes a research idea or technology succeeding over others because it is suited to the available software and hardware, not necessarily because it is superior to alternative directions--examples abound, from the synergies of deep learning and GPUs to the disconnect of urban design a…
 
Pu Zhao, Parikshit Ram, Songtao Lu, Yuguang Yao, Djallel Bouneffouf, Xue Lin, Sijia LiuAbstractAdversarial perturbations are critical for certifying the robustness of deep learning models. A universal adversarial perturbation (UAP) can simultaneously attack multiple images, and thus offers a more unified threat model, obviating an image-wise attack…
 
Kartikeya Bhardwaj, James Ward, Caleb Tung, Dibakar Gope, Lingchuan Meng, Igor Fedorov, Alex Chalfin, Paul Whatmough, Danny LohAbstractIs it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called Restructurable Activation Networks …
 
Pedro Sequeira, Daniel Elenius, Jesse Hostetler, Melinda GervasioAbstractRecent years have seen significant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics in AI. Comprehensible models for sequential decision tasks are a particular challenge as they requ…
 
Yisroel MirskyAbstractSocial engineering (SE) is a form of deception that aims to trick people into giving access to data, information, networks and even money. For decades SE has been a key method for attackers to gain access to an organization, virtually skipping all lines of defense. Attackers also regularly use SE to scam innocent people by mak…
 
Ashkan Ebadi and Alain Auger and Yvan GauthierAbstractResearch and development in hypersonics have progressed significantly in recent years, with various military and commercial applications being demonstrated increasingly. Public and private organizations in several countries have been investing in hypersonics, with the aim to overtake their compe…
 
Michael Lomnitz, Zachary Daniels, David Zhang, Michael PiacentinoAbstractTo enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD). tpSGD generalizes direct random target projection to work with arbitrary loss f…
 
Haosen Ge, In Young Park, Xuancheng Qian, Grace ZengAbstractHigh-quality text data has become an important data source for social scientists. We have witnessed the success of pretrained deep neural network models, such as BERT and RoBERTa, in recent social science research. In this paper, we propose a compact pretrained deep neural network, Transfo…
 
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