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Future of weather forecasting using IoT sensors and machine learning - Enterprise Podcast Network - EPN

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Carlos Gaitan, the CEO and Co-founder of Benchmark Labs a leading provider of AI & IoT-driven weather forecasting solutions for the agriculture, energy, and insurance sectors joins Enterprise Radio. Dr. Gaitan is the Co-founder and CEO of Benchmark Labs. He did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is an elected member of the American Meteorological Society's (AMS) Artificial Intelligence Committee.


Tracking fish: University students use machine learning for fishery monitoring startup

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With millions of boats across the world, it is difficult for regulators to stop fishers who exceed catching limits. Even those with no malicious intent can accidentally catch too much because of manual reporting practices. Enter OnDeck Fisheries AI, a Vancouver, B.C.-based startup developing software to automatically scan how many fish are brought onto a ship. The company's machine learning and computer vision technology tracks the precise biomass and type of fish without requiring human observers. The idea is to help regulators and fishers rely on an automated solution to ensure they are complying with fishing regulations.


Core Challenges in Embodied Vision-Language Planning

Journal of Artificial Intelligence Research

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.


La veille de la cybersécurité

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Chinese scientists have developed an artificial intelligence (AI) program that is quick-minded and on par with professional human players in heads-up no-limit Texas hold'em poker. Named AlphaHoldem, the AI program has achieved the level of sophisticated human players through a 10,000-hand two-player competition after three days of self-training, according to a paper which will be presented in February next year at AAAI 2022 global AI conference in Vancouver, Canada. Texas hold'em is a popular poker game in which players often deceive and bluff. It is more similar to real-world problems than Go or Weiqi and chess since decisions are made with imperfect information.


China-developed fast-learning AI equals human hold'em players

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Chinese scientists have developed an artificial intelligence (AI) program that is quick-minded and on par with professional human players in heads-up no-limit Texas hold'em poker. Named AlphaHoldem, the AI program has achieved the level of sophisticated human players through a 10,000-hand two-player competition after three days of self-training, according to a paper which will be presented in February next year at AAAI 2022 global AI conference in Vancouver, Canada. Texas hold'em is a popular poker game in which players often deceive and bluff. It is more similar to real-world problems than Go or Weiqi and chess since decisions are made with imperfect information. The researchers from the Institute of Automation under the Chinese Academy of Sciences (CAS) reported that AlphaHoldem, a fast learner, used only about three to four milliseconds for each movement, about 1,000 times quicker than that of first-generation AI hold'em players DeepStack and Libratus.


Core Challenges in Embodied Vision-Language Planning

arXiv.org Artificial Intelligence

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.


Rithmik Closes US$1.2M to Commercialize "AI-First" Mobile Mining Analytics

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MONTREAL and VANCOUVER, British Columbia, July 08, 2021 (GLOBE NEWSWIRE) -- Rithmik Solutions, whose mission is building the world's most advanced and reliable analytics for mobile mining equipment, today announced the closing of a US$1.2M investment led by Chrysalix Venture Capital and joined by Fonds Ecofuel. The funding will accelerate the commercialization of the company's flagship product, Rithmik Asset Health Analyzer (AHA), which has been in development for the past three years and is currently undergoing real-time onsite trials in Alberta, Quebec and Zambia. Rithmik AHA applies a multi-tiered machine learning approach to increase mobile equipment uptime while reducing maintenance costs and lowering greenhouse gas emissions. Mining companies typically spend anywhere from 20%-50% of their annual operating budgets on equipment maintenance, and lost production from unplanned downtime has an even bigger financial impact. "We were impressed by the Rithmik team's deep technical experience in the space of mobile mining equipment data, across equipment types and OEM brands, and that experience has strongly resonated with their early customers," said Alicia Lenis, Vice President at Chrysalix Venture Capital, an industrial innovation fund.


Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks

arXiv.org Artificial Intelligence

This work has been submitted to the IEEE for possible publication. Abstract With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inef!cient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this paper, we investigate the distributed DSA problem for multiuser in a typical multi-channel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we proposed a centralized off-line training and distributed on-line execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent Q-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of cognitive radio network in distributed fashion without coordination information exchange between cognitive users. This work was supported in part by the National Natural Science Foundation of China under Grant 6193000305. X. Tan, L. Zhou, Y. Sun, H. Wang, H. Zhao and J. Wei are all with College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China (E-mail: {tanxiang, zhouli2035, haijunwang14, sunyuli19, haitaozhao, wjbhw}@nudt.edu.cn). Boon-Chong Seet is with the Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1142, New Zealand (E-mail: boon-chong.seet@aut.ac.nz). Victor C. M. Leung is with Shenzhen University, Shenzhen, China and the University of British Columbia, Vancouver, Canada (E-mail: vleung@ieee.org). 2 From the simulation results, we can observe that the proposed algorithm can converge fast and achieve almost the optimal performance. The future network is involving into the Internet of Everything.


AI conferences use AI to assign papers to reviewers

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The Conference on Neural Information Processing Systems, held in 2019 in Vancouver, Canada, is the largest in the discipline of artificial intelligence. Artificial intelligence (AI) researchers are hoping to use the tools of their discipline to solve a growing problem: how to identify and choose reviewers who can knowledgeably vet the rising flood of papers submitted to large computer science conferences. In most scientific fields, journals act as the main venues of peer review and publication, and editors have time to assign papers to appropriate reviewers using professional judgment. But in computer science, finding reviewers is often by necessity a more rushed affair: Most manuscripts are submitted all at once for annual conferences, leaving some organizers only a week or so to assign thousands of papers to a pool of thousands of reviewers. This system is under strain: In the past 5 years, submissions to large AI conferences have more than quadrupled, leaving organizers scrambling to keep up.


AMPD Announces 'Machine Learning Cloud' Initiative Built Around AMD Instinct MI100 Accelerators

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VANCOUVER, British Columbia, March 11, 2021 — AMPD Ventures Inc. (“AMPD” or the “Company”) is pleased to announce a ‘Machine Learning …